April 19, 2022

10 min read

How to Judge COVID Risks and When to Wear a Mask

Scientific American asks experts in medicine, risk assessment and other fields how to balance the risks of COVID with the benefits of visiting public indoor spaces

By Devabhaktuni Srikrishna

Patrons at a bar, some with face masks

Jeff J Mitchell/Getty Images

As COVID cases declined across the U.S. in recent months and mask mandates were lifted, more people returned to restaurants, concert halls and offices maskless. But the novel coronavirus’s Omicron subvariant BA.2 —which caused another wave in Europe and China—and related variants threaten to reverse that progress here. Earlier this month dozens of attendees (including high-ranking government officials) tested positive for COVID after  attending a dinner  in Washington, D.C.   The safest option, of course, is to continue avoiding crowded indoor activities. But there remains a lot of interest in safely enjoying bars, cafes and other higher-risk venues that offer the benefits of social interaction.

Scientific American asked experts in epidemiology, medicine, risk assessment and aerosol transmission for advice on how to decide which risks we are willing to take. These decisions are based on assessments of personal risk, community risk and exposure risk—and the steps one can take to take to mitigate them. Personal risk refers to the danger of contracting COVID faced by an individual and the members of their household. Community risk is the current likelihood of encountering COVID among members of one’s community. And exposure risk accounts for the increased chances of catching COVID at a particular venue based on airflow characteristics of the space itself and other people’s behavior.

Here is what experts say about managing these risks while maintaining some of the benefits of public life.

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How should a person factor personal risk for severe COVID into their decisions?

The number-one predictor of having a severe case of the disease is age, followed by the presence of comorbidities and immunocompromised status, according to Katelyn Jetelina, an epidemiologist who studies COVID risks at the University of Texas Health Science Center at Houston. Using data from the U.S. Centers for Disease Control and Prevention, she estimates that even vaccine-boosted people ages 50 to 64 are more than 10 times more likely to die from a severe breakthrough case than 18- to 49-year-olds with the same vaccination status. Donald Milton, a physician and clinical researcher who studies respiratory viruses at the University of Maryland, highlights recent research showing that, in households with a person who was infected with the Omicron variant (B.1.1.529) of the COVID-causing virus SARS-CoV-2, 43 to 64 percent of people became infected as well , depending on whether the initially infected person was boosted, fully vaccinated or unvaccinated. Jetelina cautions that we also need to account for the personal risks of the people with whom we live in our own risk assessments.

In general, people should discuss personal COVID risk with their doctor; it depends, in part, on which medications they take. Ethan Craig, a rheumatologist at the University of Pennsylvania, cares for patients who are immunosuppressed because of disease or medication and studies COVID risks in that population. One such immunosuppressive drug, rituximab, “knocks out your ability to make antibodies against new viral exposures and impairs your ability to make a response to a vaccine,” he says. Craig adds that such patients usually take precautions of their own accord, such as wearing high-filtration N95 masks , and “if anything, I end up having to talk people down sometimes and be like ‘Look, it’s okay to go to the grocery store.’” For some people, however, even this amount of exposure could be considered an unacceptable risk.

How does the risk of dying from COVID compare to the risk of dying from other causes linked to common activities?

Jetelina estimates that, for people between the ages of 18 and 49 who are boosted, the risk of dying from COVID is roughly equal to the risk of dying when someone drives about 10,000 miles. COVID risk goes up substantially with age and with being unboosted or unvaccinated. Thanks to vaccines, infection-induced immunity, therapeutics, better care and other factors, the relative risk of dying from COVID if you catch it is now, broadly speaking, comparable to that of seasonal flu, Jetelina says—but importantly, because you are more likely to catch COVID than flu, the absolute risk remains much greater. Jetelina recommends COViD-Taser’s Relative Risk Tool , a resource funded by the National Science Foundation, that she helped to develop. It compares one’s risk of death from the disease to such risk posed by other activities, including driving. Although it is a research tool, Jetelina says she can “really trust the science and mathematics behind it.”

But Baruch Fischhoff, a professor of engineering and public policy at Carnegie Mellon University and an authority on how to communicate health risks, cautions against using risk-risk comparisons to make choices without fully considering benefits or unquantified risks. Employers may also misuse such comparisons to compel employees to accept certain risks on the job, which is not exactly a choice. Currently, risk calculators provide estimates based on retrospective data and may be unable to reliably weigh long-term complications of COVID.

How should one assess community risk?

There is no perfect way to measure community risk because it would take repeated random testing, so experts use other estimates: daily cases per 100,000 residents, test positivity rates and growth rates. Jetelina recommends using the New York Times ’ tracker to look up community transmission for your county. She considers community risk high when there are more than 50 weekly cases per 100,000 residents. When the risk is lower than that, Jetelina—a healthy, young boosted person—feels comfortable taking off her mask indoors. “I will say it’s taken a lot of time for me to be comfortable with that,” she says. “Once transmission rates of those indicators start increasing a bit, I’m putting my mask back on.” Others suggest a slightly higher risk threshold of 10 daily (or 70 weekly) cases per 100,000 residents.*

Daily city or county case counts are often an undercount because not everyone is getting tested and home test results are not always reported. As a work-around, health authorities use the “test positivity rate,” or “ percent positive ”—the percentage of COVID tests reported to public health authorities that were positive. If that number exceeds 5 percent, it is widely considered high risk for community transmission (provided the amount of testing in that area is adequate). But the community sample used to measure test positivity likely includes many people who seek out testing because they are currently experiencing COVID symptoms. So test positivity is typically higher than the infection rates among the people you might encounter in a cafe or grocery store, most of whom do not have any symptoms but could still be infectious.

Still, Robert M. Wachter, a professor and chair of the department of medicine at the University of California, San Francisco, says there is no test positivity threshold that separates “safe” from “not safe” because it also depends on other factors , such as whether the benefit outweighs the risk to you, personally, the number of people you will be exposed to, and the closeness and duration of exposure.

Because of these large uncertainties in test coverage, Gerardo Chowell, a professor of mathematical epidemiology at Georgia State University, prefers to look at the general trend in daily COVID cases, hospitalizations and deaths, or percent positive . “When the trend is going up, you’re seeing the transmission chains expand,” Chowell says. “That means that the reproduction number”—the expected number of secondary infections from each infected person—“must be greater than one. If it is increasing, that’s probably the time when [one has the] highest risk of acquiring COVID in a social setting without a mask,” he says.” Wachter points out that, where available, wastewater surveillance may also give an early indication of COVID trends.

What is known about exposure risk in different settings, such as bars or movie theaters?

Linsey Marr, a professor of civil and environmental engineering at Virginia Tech and one of the world’s leading experts on airborne transmission of viruses, says COVID risk in indoor spaces exists on a continuum. It is believed that reducing the amount of virus inhaled (i.e., the inhalation dose) makes infections less likely or illness less likely to be severe . Marr says one of the riskiest settings is an aerobic exercise studio: if somebody is infected, they are going to be exhaling more virus, and everyone else will be inhaling at a faster rate, too. Breathing heavily produces up to 10 times more aerosol particles that carry viruses than breathing normally, according to Richard Corsi, an expert on indoor air quality and dean of the College of Engineering at the University of California, Davis.

Marr says that talking in bars expels a similar number of respiratory particles as coughing, “so it’s like everyone’s in there coughing together.” Craig uses smoking as an analogy for aerosols exhaled during breathing and talking. In other words, “if a person was smoking in this place, would I be able to smell it?” he says. In movie theaters, there is risk of exposure from those seated immediately around you, but because of limited talking and, typically, a high ceiling, there is a lot more dilution of the air. So such a theater may be less risky than other crowded indoor venues. By that reasoning, museums, big-box retailers and grocery stores with high ceilings tend to be relatively safer as well.

Places with rapid rates of ventilation and filtration—such as some subways—are also much lower risk. The Bay Area Rapid Transport (BART) system in San Francisco Bay, for example, filters the air more than 50 times an hour with “virus-trapping MERV-14 air filters ” inside each car. An Italian study of schools found that classrooms with ventilation systems that exchanged air six times per hour reduced infections by more than 80 percent , but many classrooms in the U.S. fail to meet this standard. Corsi characterized current public health recommendations of four to six air exchanges per hour as “a little bit anemic … we can do better.” He recommends owners or managers of crowded indoor spaces, such as classrooms, offices and bars, aim to filter or ventilate with fresh air at rates approaching 12 air exchanges per hour to reduce risks down to the level of an airborne isolation room in a hospital. Not all venues have the resources to do this, but the benefits increase with greater filtration rates, so the closer to this ideal, the better. In places with inadequate ventilation, consider bringing a portable high-efficiency particulate air (HEPA) purifier —or building your own using box fans and high-quality HVAC (heating, ventilating and air-conditioning) filters—to run nearby.

Although the virus is thought to be transmitted primarily through the air, there have been a few documented cases of surface transmission, so it remains a good idea to wash your hands frequently, Marr says.

How can one further reduce the risk of getting COVID from everyday activities?

Getting vaccinated and boosted protects against death, hospitalization and, to a lesser extent, catching and spreading the virus. To avoid infection, Wachter recommends wearing an N95 mask . He has observed that the risk of U.C.S.F. health care workers—himself included—getting infected from their patients while wearing a well-fitting N95 is extraordinarily low. These respirators get close to filtering all of the virus, but they do not filter 100 percent. And if an N95 does not form an airtight seal with your face, it may allow unfiltered air into your lungs. So it is essential to try out and select N95 models that fit and seal to your face without gaps.

What is the risk of taking your mask off in a restaurant or bar to take a sip or bite?

In the 1990s medical researcher Stanley Wiener, then at the University of Illinois College of Medicine, proposed that a person could use respirators to survive aerosolized biological attacks, taking it off briefly to consume food and drink. During the pandemic, many places have allowed masks (or N95 respirators) to be removed while actively eating and drinking. Removing an N95 momentarily for a bite or sip carries “some risk, but I think it’s pretty tiny if you’re exposed for three seconds,” Corsi says, unless an infected person is “right in your face ... and shedding a lot [of virus].” Provided community risk is low or trending downward, Chowell, too, feels comfortable briefly removing his respirator to eat or drink at a party.

What do we know so far about the risk of “long COVID”?

Ranu Dhillon, a physician at Brigham and Women’s Hospital in Boston, who advises governments on infectious disease outbreaks, says he is seeing some patients with “a constellation of different types of symptoms after acute COVID infection,” including young, boosted and relatively healthy people. Wachter cautions that some fraction of vaccinated individuals who get infected—which one study estimates to be around 5 percent and possibly higher—may continue to feel short of breath or fatigued or think less clearly than before. COVID may increase the risks of heart attack, stroke , brain abnormalities or the onset of diabetes . While there have been preliminary studies of the rates of long COVID, including risks of developing cardiovascular complications , Wachter says many of these involved unvaccinated people or infections with variants prior to Omicron. Provisionally, he likens these risks to 20 years of untreated high blood pressure or smoking and points out that one cannot know the risk of long COVID among vaccinated and boosted individuals until long-term studies have concluded, which will take years.

How can we balance these risks with the benefits of socializing and being with others?

According to Wachter, one of the most important factors in overall COVID risk is whether “the person next to me has it.” He acknowledges that if someone is both vaccinated and boosted, it is not irrational for that person to decide that the mental energy and angst of calculating risks and taking precautions is high enough—and the risks of getting sick or dying from COVID are low enough—that they will go back to “living like it’s 2019”—as people in many parts of the country already have. He still worries about the risk of long COVID, though. Milton says that many people “don’t want to wear masks forever” and that we should work to make our built environments better at stopping aerosol transmission . He says people also have to decide whether to wear a high-quality mask when they are around those at higher risk, such as the elderly or immunocompromised, or around other people in general, such as at a party. When community transmission is low, Chowell says he may feel comfortable removing his N95 at parties in some situations, such as to have a drink. “Then you find a way to still interact with people, and they smile back once in a while,” he adds.

* Editor’s Note (4/19/22): This paragraph was edited after posting to correct the description of the threshold of COVID transmission that Katelyn Jetelina considers a high community risk.

Calculating COVID-19 risk

A new COVID-19 risk calculator developed at Harvard T.H. Chan School of Public Health can help people understand the ways that masking, ventilation, filtration, and other factors can mitigate the spread of COVID-19 in indoor environments.

The tool, developed by Joseph Allen , associate professor of exposure assessment science, and his team at the Healthy Buildings program, is based on a model of the COVID-19 outbreak on the Diamond Princess cruise ship in early 2020 as well as other superspreader events.

Allen and Parham Azimi , a research fellow in the exposure, epidemiology and risk program at Harvard Chan School, described how to use the calculator in an April 6, 2021, opinion piece in the Washington Post.

Users can plug in information such as the size of the room they’ll be in, how long they’ll be in the room, whether they’ll be seated or active, whether people are wearing masks and staying six feet apart from each other, and whether the windows are open. Once all the information is filled in, the tool produces a risk estimate. The tool also enables users to switch parameters in order to see how beefing up safety measures can reduce risk.

Read the Washington Post article: Opinion: So you’re unvaccinated and want to see a friend. Here’s how to calculate your risk.

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The COVID-19 pandemic: Virus transmission and risk assessment

N gayathri menon.

1 Centre for Research in Nanotechnology and Science (CRNTS), Indian Institute of Technology Bombay, India

Sanjeeb Mohapatra

2 NUS Environmental Research Institute, National University of Singapore, 1 Create Way, Create Tower, #15-02, Singapore 138602, Singapore

The coronaviruses are the largest known RNA viruses of which SASR-CoV-2 has been spreading continuously due to its repeated mutation triggered by several environmental factors. Multiple human interventions and lessons learned from the SARS 2002 outbreak helped reduce its spread considerably, and thus, the virus was contained but the emerging mutations burdened the medical facility leading to many deaths in the world. As per the world health organization (WHO) droplet mode transmission is the most common mode of SASR-CoV-2 transmission to which environmental factors including temperature and humidity play a major role. This article highlights the responsibility of environmental causes that would affect the distribution and fate of the virus. Recent development in the risk assessment models is also covered in this article.

Introduction

Coronaviruses, in general, were known to be of veterinary impact only until the severe acute respiratory syndrome (SARS) outbreak in 2002 [ 1 ]. Among the various genera of coronaviruses, members of both alpha and beta coronaviruses have been proven to infect humans. Before the SARS outbreak, 19 coronaviruses were identified, including 15 mammalians (2 infecting humans) and 4 avian coronaviruses [ 2 ]. Similarly, 5 other human coronaviruses (HCoV) currently in circulation in the human population include the alpha coronaviruses, HCoV-NL63 and HCoV-229E and betacoronaviruses, HCoV-OC43 and HKU1 [ 3 ]. The enveloped viruses have a similar structure, that is, a lipo-proteinaceous, host-derived envelope covering a nucleocapsid formed of viral protein strings that finally encloses the virus's genetic material with or without specific regulatory proteins ( Figure 1 ) [ 22 , 52 ]. Human immunodeficiency virus (HIV) [ 4 ], Ebola, dengue, Zika, and coronaviruses are examples of some enveloped viruses that have been the cause of major viral outbreaks in the past and are zoonotic [ 5 , 6 , 7 ]. Thus, when the source of SARS coronavirus-2 (SASR-CoV-2) is unclear, the rapid transmission of this virus could be explained with the help of several environmental factors which govern transmission and establishment of the infection. Moreover, the severity of an outbreak depends not only on the lethality of the virus but also upon its mode of transmission and the availability of cure/vaccine [ 8 ]. Vaccination and disease management has been able to control diseases like polio and HIV-acquired immunodeficiency syndrome (AIDS), respectively, but the novel SARS-CoV-2 is continued to be a global pandemic even when mass vaccination is at its peak in several parts of the world. Understanding the factors that work in favour and/or against the SARS CoV-2 will help predict and prepare for future outbreaks. While governments and scientists are pondering over the possibility of reinfection, modes of transport of the virus and nature of the dormant stage, the part of the environment in the spread of the disease and subsequent risk assessment are of paramount importance. The article is divided into four major sections. A virus transmission follows the introduction, and the role of temperature and humidity is discussed in Section Virus transmission . Section Risk assessment highlights the recent developments on risk assessment strategy to combat the transmission. Research gaps and precautionary measures are highlighted in the conclusion section.

Figure 1

The genome organisation of SARS-CoV-2 with specific amino acid insertions present in S protein. ( a ) Gene organisation of SARS-CoV-2. ( b ) Sequence comparison of amino acid residues of RBD of the S protein of closely related CoVs. The residues in red are the conserved residues present in all the sequences compared. The residues highlighted in green are mutations in the current SARS-CoV-2. ( c ) Polybasic furin cleavage site (RRAR) present in SARS-CoV-2 not in other closely related CoVs. The presence of such polybasic cleavage sites in other viruses have been shown to be a determinant of pathogenicity.

Virus transmission

The structural and genetic differences between the viruses allow them to infect various hosts and make them vulnerable to different environmental stress. Several factors, including air temperature [ 9 , 10 ], size of the aerosol droplet [ 6 , 10 ] and relative humidity [ 10 , 11 ], the stage of infection in the index person [ 12 ] would determine the transmissibility of the virus through air. For instance, asymptomatic patients infected with SARS-CoV who travelled in an aircraft did not affect any onboard passengers, although other environmental factors were conducive to disease transmission [ 13 , 14 ]. During the incubation time of the virus within a human host, the host exhibits no symptoms. Hence, chances of coughing or sneezing remain low, and correspondingly, the chances of transmission are also low. However, such conclusions do not hold for the transmission of SARS-CoV-2 as transmission from asymptomatic patients is also well documented [ 15 , 16 ]. The transmissibility and reproduction number (R 0 ) of SARS-CoV-2, that is, 3.28 with a median of 2.79 is greater than that of SARS-CoV infections in Singapore, and Hong Kong where R 0 was estimated to be 2.7 and 2.1, respectively [ 17 , 18 ]. Another article also reported slightly high value for SARS-CoV-2 R 0 , that is, 5.7 [ 19 ]. Such instances indicate alternative transmission routes and may be attributed to genetic and molecular variations between SARS-CoV and SARS-CoV-2. The two extremely crucial elements that can affect viral survival and viability in droplets are relative humidity and temperature.

Role of relative humidity (RH)

At low RH, the droplets evaporate rapidly. The lower the RH, the smaller is the final droplet size [ 20 ]. This leads to the retention and concentration of viral particles in a smaller droplet, travelling longer distances than larger droplets. Some studies report that SARS-CoV-2 can transmit at a higher rate in dry and indoor places of low humidity (< 40%) than that of high humid places (i.e., >90% RH) [ 21 ]. Additionally, under such conditions, along with the virus, several salts and proteins may also be trapped within the droplet [ 20 ]. Evaporation alters the microenvironment of the droplet significantly, and many viruses and other microbes may be inactivated due to the increased salinity (from mucous or saliva) within the droplet [ 20 ]. However, enveloped viruses, which are usually hydrophobic, are reported to be surrounded by hydrophobic moieties and proteins, making their inactivation within the droplet extremely difficult.

Previously, studies with the human coronavirus 229E and SARS-CoV demonstrated that the viruses were stable for up to 6 days at a relative humidity of 50% (Geller et al., 2012). The case study pertaining to transmission of infection from one symptomatic passenger travelling in an aircraft to 18% of the healthy travellers onboard during the SARS-CoV outbreak [ 13 ] established the transmissibility of coronaviruses through the air at low humidity. Low humidity coupled with air re-circulation systems were proposed as the reasons for the spread. A retrospective sketch of the seating pattern in the aircraft indicated that the spread was mediated via small droplets or aerosols. The larger, more visible droplets could only travel ∼36 in, which was less than the distance between the seats in the aircraft. Even seating within two rows from the index person could increase the risk [ 12 ].

Higher viability of SARS-CoV-2 at lower relative humidity may also explain why the pandemic started when winter gave way to spring, that is, when most of China, Europe, and the USA had lower relative humidity than during the summer months. Another interpretation of this transmission mechanism was suggested by Sun et al. (2020) [ 23 ]. During periods of decreased RH, the mucosal layer may not trap pathogens as effectively as during higher RH, enabling increased chances of infections during this time at an infectious dose if present in the air. Contradictorily, Gunthe et al. (2020) described no major correlation between RH and the number of persons infected in 80 locations worldwide [ 24 ]. Similarly, no statistically significant correlation was reported when RH (>70%) values from 127 countries were used to study the variation in daily COVID-19 cases [ 25 ]. Another study reported that droplets could travel much further in a high-humidity environment. But, the generation of aerosol particles increases in low-humidity environments, which remain suspended in the air for several minutes to hours [ 10 ], facilitating virus transmission ( Figure 2 ). Using the chemistry fundamentals, a mechanistic model found that SARS-CoV-2 inactivation followed a U-shaped dependence on RH [ 26 ]. Thus, relative humidity between 40 and 60% is recommended as optimal for human health in indoor places [ 21 ].

Figure 2

RH and temperature effect on COVID-19 transmission through droplet contact and exposure to aerosol particles: (a) Effect on maximum droplet spreading distance, (b) aerosolization rate of respiratory droplets, (c) average aerosol particle diameter and (d) total mass of PM2.5 particles [ 10 ]. (a) “This figure is made available via the ACS COVID-19 subset for unrestricted RESEARCH re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic”.

Role of temperature

When researchers studied the effects of temperature on the transmission of the virus using weather data over the period from 23 rd January to 10 th February 2020 in several countries, including China, Thailand, Singapore, Japan, South Korea, and Taiwan, no decline in transmission of the virus was observed with the increase in temperature and humidity in the Northern Hemisphere [ 27 ]. However, a recent article taking into account temperature and UV index data from 85 different locations at different latitudes from 2 nd February 2020 to 10 th March 2020 indicated that the SARS-CoV-2 virus was most viable within a narrow temperature range of 5–15 °C and the infection rates were much lower both above and below this range [ 24 ]. These results agreed with results presented by J. Liu et al., (2020), who suggested a low temperature (0–10 °C) with mild diurnal temperature variation (a difference of 4–8 °C) favours disease transmission [ 28 ]. Another study on the spread of respiratory droplets and aerosol particles generated by speech under a range of temperatures (0–40 °C) further confirmed that droplets could travel three times farther in low temperature, but more aerosols are generated in high temperature ( Figure 2 ) [ 10 ]. At low temperature, the virus can survive the longest with an estimated half-life > 24 h at 10 °C (40% RH) but ∼1.5 h at 27 °C (65% RH) [ 26 ]. The time required to decrease SARS-CoV-2 infection by 90% range from 4.8 min at 40 °C (20% RH) under high intensity simulated sunlight representative of noon on a clear day in the summer indicated the importance of temperature and RH on the virus survival in aerosols and subsequent transmission [ 29 ].

Recently, many studies considering several environmental factors including RH, temperature, UV radiation, population density, ventilation, wind speed, precipitation, aerosol concentration and air distribution to model the SARS-CoV-2 transmission and subsequent modelling as highlighted in Table 1 .

Table 1

Environmental factors considered to model SARS-CoV-2 transmission.

Risk assessment

Mathematical modelling plays a starring role in emergency and preparedness design, risk assessment, policy, and decision-making during disease outbreaks. The application of mathematical modelling to SARS [ 30 ], influenza [ 31 ], West Nile virus [ 32 ] and Zika virus [ 33 ] is well documented in the literature. By combining genomic and geographical data, Dellicour et al. (2016) studied the mode and tempo of pathogen dispersal during epidemics. Similar attempts were made by Myer et al. (2017) by combining 40 ecological, meteorological, and built-environment covariates, such as precipitation, temperature, septic tanks, sand and soil. Multivariate logistic regression analysis emphasised the role of eight environmental factors (bodies of water, wetlands, transportation routes, migration routes, main cities, precipitation, elevation and poultry density) towards the transmission of avian influenza caused by H5N1 [ 36 ]. Integration of the geographic information system (GIS) and binary response models highlighted the significance of small-scale conventional sewage treatment plants and commercial areas favouring norovirus outbreaks [ 37 ]. The role of zoonotic reservoirs and spatiotemporal climate variability was also documented in some studies [ 38 , 39 ]. A Bayesian phylogeographic study suggested a significant role of high human density, freight transportation, and temperature on avian influenza virus (AIV) [ 31 ]. Hence, by using spatial models which take into account the geographical behaviour of hosts, movement (migration and mixing patterns), and metapopulation models that look at the spread of the diseases at the subpopulation level, risk assessment of the corona outbreak can be made.

A recent review on the factors contributing to the SARS-CoV-2 virus outbreak in Wuhan, China argued that the combination of several factors such as external environment, natural hosts, intermediate hosts and susceptible populations had resulted in the incidence of SARS-CoV in the past, and these could have likewise been responsible for the current incidence of SARS-CoV-2 [ 23 ]. The likelihood of the unnatural origin of SARS-CoV-2 cannot be ignored, as has been reported for MERS-CoV using the Grunow–Finke assessment tool (GFT) [ 40 ]. However, additional studies may be conducted to support such claims. Integration of Bayesian “Quantitative Microbial Risk Assessment (QMRA)” approach and atmospheric dispersion models, which considers historical wind speed, may allow an assessment of the SARS-CoV-2 infection risk and potential spread, as has been assessed for norovirus infection risk at WWTPs [ 41 ]. A detailed review on the pragmatic approach of risk perception to the previously reported spread of diseases, such as SARS and avian influenza, was published in 2009 [ 42 ]. While referring to these studies, computational fluid dynamics (CFD) attempts were further made to study the spatial–temporal aerosol concentrations and quantify exposure risk to SARS-CoV-2 considering separation distance, exposure duration, and environmental conditions such as airflow and ventilation and, most importantly, face-covering [ 43 ]. However, while performing risk assessment, it is essential to consider the uncertainties around SARS-CoV-2 genome copies deposited in the respiratory tract [ 44 ], proximity, that is, interpersonal distance [ 45 ], and ventilation facility and air distribution [ 46 ], as evaluated for the virus elsewhere.

Conclusions

Epidemic outbreaks following SARS in 2002 and MERS in 2012, and the COVID-19 pandemic have revealed the rapid mutation rates, leading to species breaching and/or wide host range infections. These zoonotic viruses bear envelopes derived from the host cell membrane and thus also bear different envelope proteins. These single-stranded RNA viruses can adapt to rapidly changing ecological niches due to their high substitution rate. This enhances their potential to cause a pandemic, as can be seen, today. Although many theories and correlations were reported to evaluate the role of environmental factors or climatic conditions to study the role of geographical locations using weather data over the period in several countries, no decline in transmission of the virus was observed with the increase in temperature and humidity in the North Hemisphere. However, SARS-CoV-2 is most viable in the narrow temperature range of 5–15 °C and most transmissible at low RH (<40%). All these factors should be taken into account while performing SARS-CoV-2 transmission modelling. Professionals from various backgrounds must come together and predict diseases and build risk assessment models in such an alarming situation. The success of a risk assessment model is determined by the variables that are accounted for in building a mathematical model. In this context, attempts must be made to study the role of other environmental variables such as temperature, humidity that vary notably throughout pandemics. Additionally, pathogenic risk groups, host specificity, routes of transmission, infectious dose, communicability, case fatality ratio, and persistence should be considered for risk assessment studies. Moreover, droplet/aerosol concentrations, viral load, infectivity rate, viral viability, lung-deposition probability, and inhalation rate should also be considered in risk assessment studies. Risk mitigation majors, including engineering controls, administrative controls, and personal protective equipment (PPE) should be considered to avoid infections due to SARS-CoV-2.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

Acknowledgement

The authors are thankful to Dr. Aryamav Pattnaik, School of Veterinary Medicine and Biomedical Sciences, University of Nebraska–Lincoln (UNL), USA for extending kind support in preparing the Figure 1 .

This review comes from a themed issue on Occupational Safety and Health 2022: COVID-19 in environment: Treatment, Infectivity, Monitoring, Estimation

Edited by Manish Kumar , Ryo Honda , Prosun Bhattacharya , Dan Snow and Payal Mazumder

COVID-19: Who's at higher risk of serious symptoms?

Other health conditions, such as heart or lung disease, can increase your risk of developing dangerous symptoms if you get coronavirus disease 2019 (COVID-19).

Coronavirus disease 2019 (COVID-19) symptoms can vary widely. Some people have no symptoms at all. But others become so sick that they need to stay in the hospital and may eventually need a machine to breathe.

The risk of developing dangerous symptoms of COVID-19 may be increased in people who are older. The risk may also be increased in people of any age who have other serious health problems — such as heart or lung conditions, weakened immune systems, obesity, or diabetes. This is similar to what is seen with other respiratory illnesses, such as the flu (influenza).

Each of these factors can increase the risk of severe COVID-19 symptoms. But people who have several of these other health problems are at even higher risk.

People of any age can catch COVID-19 . But it most commonly affects middle-aged and older adults. The risk of developing dangerous symptoms increases with age, with those who are age 85 and older are at the highest risk of serious symptoms. In the U.S., about 81% of deaths from the disease have been in people age 65 and older. Risks are even higher for older people when they have other health conditions.

Take all your medications as prescribed. Consider developing a care plan that includes information about your medical conditions, medications, health care professionals' names and emergency contacts.

Nursing home residents are at high risk because they often have multiple health problems, combined with advanced age. And germs can spread very easily between people who live near each other. If you live in a nursing home, follow the guidelines to prevent infection. Ask about protection measures for residents and visitor restrictions. Let staff know if you feel ill.

Older people are also more likely to have Alzheimer's disease. Alzheimer's can make it more difficult for them to remember the precautions recommended to prevent infection.

Lung problems, including asthma

COVID-19 targets the lungs. So, you're more likely to develop severe symptoms if you already have various chronic lung problems, including:

  • Chronic obstructive pulmonary disease (COPD)
  • Lung cancer
  • Cystic fibrosis
  • Pulmonary fibrosis
  • Moderate to severe asthma
  • Pulmonary hypertension
  • Pulmonary embolism

Some medications for these conditions can weaken your immune system. However, it's important to stay on your maintenance medications to keep symptoms as controlled as possible. You may want to talk to your health care professional about getting an emergency supply of prescription medications, such as asthma inhalers.

It may also help to avoid the things that make your asthma worse. These asthma triggers can vary from person to person. Examples include pollen, dust mites, tobacco smoke and cold air. Strong emotions and stress can trigger asthma attacks in some people. Others are bothered by strong odors, so make sure the disinfectant you're using isn't an asthma trigger for you.

Besides being an asthma trigger, smoking or vaping can harm your lungs and inhibit your immune system, which increases the risk of serious complications with COVID-19 .

Heart disease

Many types of heart disease can make you more likely to develop severe COVID-19 symptoms. These include:

  • Cardiomyopathy
  • Congenital heart disease
  • Heart failure
  • Coronary artery disease

Continue to take your medications exactly as prescribed. If you have high blood pressure, your risk may be higher if you don't control your blood pressure and take your medications as directed.

Brain and nervous system conditions

Some conditions that affect the brain or nervous system can increase your risk of developing severe COVID-19 symptoms.

These include:

Diabetes and obesity

Type 1 or type 2 diabetes can increase your risk of serious COVID-19 symptoms. Having a higher body mass index that's considered overweight, obese or severely obese also increases this risk.

Diabetes and obesity both reduce how well a person's immune system works. Diabetes increases the risk of infections in general. This risk can be reduced by keeping blood sugar levels controlled and continuing your diabetes medications and insulin. If you are overweight or obese, aim to lose weight by eating a healthy diet and getting regular physical activity.

Cancer and certain blood disorders

People who currently have cancer are at higher risk of developing more severe illness from COVID-19 . This risk can vary, depending on the type of cancer and the kind of treatment you're receiving.

Sickle cell anemia is another condition that increases the risk of severe COVID-19 symptoms. This inherited disorder causes your red blood cells to become hard, sticky and shaped like the letter "C." These deformed red blood cells die early, so oxygen can't be transported around your body as well. It also causes painful blockages in small blood vessels.

Another inherited blood disorder, called thalassemia, might also make you more likely to have serious COVID-19 symptoms. In thalassemia, the body doesn't produce enough hemoglobin and this affects how well the red blood cells can carry oxygen.

Weakened immune system

A healthy immune system fights the germs that cause disease. But many conditions and treatments can weaken your immune system, including:

  • Organ transplants
  • Cancer treatments
  • Bone marrow transplant
  • Long-term use of prednisone or similar drugs that weaken your immune system

If you have a weakened immune system, you may need to take extra precautions to avoid the virus that causes COVID-19 . Routine health care appointments may be delayed or happen via phone or video conference. You may want to have your medications mailed to you, so you don't have to go to the pharmacy.

Chronic kidney or liver disease

Chronic kidney or liver disease can weaken your immune system, which may increase your risk of being seriously ill with COVID-19 . Also, having serious COVID-19 symptoms and taking medications to treat the disease may have negative effects on the liver.

If you're on dialysis for chronic kidney disease, go to every dialysis appointment. Let your doctor know if you feel ill.

Mental health conditions

People with mental health conditions such as depression and schizophrenia spectrum disorders may be more likely to develop serious COVID-19 symptoms.

Down syndrome

People with Down syndrome are more likely to develop lung infections in general, so they are particularly vulnerable to COVID-19 . They are also at higher risk of already having many of the health problems that have been linked to developing severe COVID-19 symptoms — including heart disease, sleep apnea, obesity and diabetes.

Many adults with Down syndrome live in nursing homes, where it can be harder to avoid exposure to germs from other residents and staff. Down syndrome also often affects intellectual abilities, so it may be more difficult for this population to follow prevention measures.

Protect yourself; prevent unnecessary risk

The CDC recommends a COVID-19 vaccine for everyone age 6 months and older. The COVID-19 vaccine can lower the risk of death or serious illness caused by COVID-19 . It lowers the risk for you and it lowers the risk that you may spread it to people around you.

The COVID-19 vaccines available in the United States are:

  • 2023-2024 Pfizer-BioNTech COVID-19 vaccine. This vaccine is available for people age 6 months and older.

Among people with a typical immune system:

  • Children age 6 months up to age 4 years are up to date after three doses of a Pfizer-BioNTech COVID-19 vaccine.
  • People age 5 and older are up to date after one Pfizer-BioNTech COVID-19 vaccine.
  • For people who have not had a 2023-2024 COVID-19 vaccination, the CDC recommends getting an additional shot of that updated vaccine.
  • 2023-2024 Moderna COVID-19 vaccine. This vaccine is available for people age 6 months and older.
  • Children age 6 months up to age 4 are up to date if they've had two doses of a Moderna COVID-19 vaccine.
  • People age 5 and older are up to date after one Moderna COVID-19 vaccine.
  • 2023-2024 Novavax COVID-19 vaccine. This vaccine is available for people age 12 years and older.
  • People age 12 years and older are up to date if they've had two doses of a Novavax COVID-19 vaccine.

In general, people age 5 and older with typical immune systems can get any vaccine that is approved or authorized for their age. They usually don't need to get the same vaccine each time.

Some people should get all their vaccine doses from the same vaccine maker, including:

  • Children ages 6 months to 4 years.
  • People age 5 years and older with weakened immune systems.
  • People age 12 and older who have had one shot of the Novavax vaccine should get the second Novavax shot in the two-dose series.

Talk to your healthcare professional if you have any questions about the vaccines for you or your child. Your healthcare team can help you if:

  • The vaccine you or your child got earlier isn't available.
  • You don't know which vaccine you or your child received.
  • You or your child started a vaccine series but couldn't finish it due to side effects.

People with weakened immune systems

Your health care team may suggest added doses of COVID-19 vaccine if you have a moderately or severely weakened immune system.

If you have a weakened immune system or have a higher risk of serious illness, wear a mask that provides you with the most protection possible when you're in an area with a high number of people with COVID-19 in the hospital. Check with your healthcare professional to see if you should wear a mask when you're in an area with a lower number of people with COVID-19 in the hospital.

There are many steps you can take to reduce your risk of infection from the COVID-19 virus and reduce the risk of spreading it to others. The World Health Organization (WHO) and the U.S. Centers for Disease Control and Prevention (CDC) recommend following these precautions for avoiding COVID-19 :

  • Get vaccinated. COVID-19 vaccines reduce the risk of getting and spreading COVID-19 .
  • Avoid close contact with others. Avoid anyone who is sick.
  • Keep distance between yourself and others if COVID-19 when you're in indoor public spaces if you're not fully vaccinated. This is especially important if you have a higher risk of serious illness.
  • Wash your hands often with soap and water for at least 20 seconds, or use an alcohol-based hand sanitizer that contains at least 60% alcohol.
  • Wear a face mask in indoor public spaces if you're in an area with a high number of people with COVID-19 in the hospital.
  • Cover your mouth and nose with your elbow or a tissue when you cough or sneeze. Throw away the used tissue. Wash your hands right away.
  • Avoid touching your eyes, nose and mouth.
  • Avoid sharing dishes, glasses, towels, bedding and other household items if you're sick.
  • Clean and disinfect high-touch surfaces, such as doorknobs, light switches, electronics and counters, regularly.
  • Stay home from work, school and public areas if you're sick, unless you're going to get medical care. Avoid taking public transportation, taxis and ride-sharing if you're sick.

In addition to these everyday precautions, if you are at higher risk of infection or of developing serious COVID-19 symptoms, you might also want to:

  • Make sure you have at least a 30-day supply of your regular prescription and over-the-counter medications.
  • Check to see if your vaccinations are up to date, particularly for the flu and pneumonia. These vaccines won't prevent COVID-19 . But becoming ill with the flu or pneumonia may worsen your outcome if you also catch COVID-19 .
  • Plan an alternate way of communicating with your health care provider in case you need to stay at home for a period of time. Some health care providers are doing appointments via phone or video conference.
  • Arrange for delivery or curbside orders of restaurant meals, groceries or medications so you can avoid crowds.
  • Call your health care provider if you have questions about your medical conditions and COVID-19 or if you're ill. If you need emergency care, call your local emergency number or go to your local emergency department.
  • Call your health care provider if you have questions about non-critical medical appointments. You'll be advised whether a virtual visit, in-person visit, delaying the appointment or other options are appropriate.

There is a problem with information submitted for this request. Review/update the information highlighted below and resubmit the form.

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  • People with certain medical conditions. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-with-medical-conditions.html. Accessed June 18, 2023.
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Original research article, risk assessment and prediction of covid-19 based on epidemiological data from spatiotemporal geography.

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  • 1 School of Geography and Planning, Sun Yat-sen University, Guangzhou, China
  • 2 Key Laboratory of the Sustainable Development of Xinjiang’s Historical and Cultural Tourism, Xinjiang University, Urumqi, China
  • 3 School of Architecture and Urban Planning, Yunnan University, Kunming, China

COVID-19 is a highly infectious disease and public health hazard that has been wreaking havoc around the world; thus, assessing and simulating the risk of the current pandemic is crucial to its management and prevention. The severe situation of COVID-19 around the world cannot be ignored, and there are signs of a second outbreak; therefore, the accurate assessment and prediction of COVID-19 risks, as well as the prevention and control of COVID-19, will remain the top priority of major public health agencies for the foreseeable future. In this study, the risk of the epidemic in Guangzhou was first assessed through logistic regression (LR) on the basis of Tencent-migration data and urban point of interest (POI) data, and then the regional distribution of high- and low-risk epidemic outbreaks in Guangzhou in February 2021 was predicted. The main factors affecting the distribution of the epidemic were also analyzed by using geographical detectors. The results show that the number of cases mainly exhibited a declining and then increasing trend in 2020, and the high-risk areas were concentrated in areas with resident populations and floating populations. In addition, in February 2021, the “Spring Festival travel rush” in China was predicted to be the peak period of population movement. The epidemic risk value was also predicted to reach its highest level at external transportation stations, such as Baiyun Airport and Guangzhou South Railway Station. The accuracy verification showed that the prediction accuracy exceeded 99%. Finally, the interaction between the resident population and floating population could explain the risk of COVID-19 to the highest degree, which indicates that the effective control of population agglomeration and interaction is conducive to the prevention and control of COVID-19. This study identifies and predicts high-risk areas of the epidemic, which has important practical value for urban public health prevention and control and containment of the second outbreak of COVID-19.

Introduction

As of October 15, 2020, there were 38,599,508 confirmed cases of COVID-19 and 1,093,548 deaths worldwide ( Fan et al., 2021 ). The World Health Organization has classified the outbreak as a “global pandemic”. The rapid and extensive spread of COVID-19 requires the consideration of as many factors as possible, and quickly responding to this major public health event poses a great challenge to the scientific community. Therefore, at the intersection of medicine, virology, geography, public administration and other disciplines, there is an urgent need to formulate accurate epidemic prevention policies ( Yu et al., 2020 ).

Although China’s COVID-19 epidemic has been effectively controlled with the joint efforts of the Chinese government and the Chinese people ( Zhang et al., 2020a ), the number of COVID-19 patients continues to show an upward trend. As the weather becomes cooler and virus activity increases, there are already signs of a second outbreak of COVID-19 ( Gosavi and Marley, 2020 ). Therefore, assessing the risk of COVID-19 and simulating the areas at high risk of future COVID-19 outbreaks can contribute to early prevention and effective containment of a second outbreak of COVID-19 in advance ( Thomas et al., 2020 ).

Since the outbreak of COVID-19, scholars have conducted numerous studies from the perspectives of pathological diagnosis ( Xie and Zhu, 2020 ), drugs and vaccines ( Liu et al., 2020 ), transmission relationships ( Heidari et al., 2020 ), spatiotemporal models ( Babac and Mornar, 2020 ), epidemic prediction ( Wang et al., 2020a ), transmission simulation ( Werth et al., 2021 ), risk assessment ( Jia et al., 2020 ), and epidemic impact ( Du et al., 2020 ), and all of these studies have played a positive role in the prevention and treatment of COVID-19. In terms of epidemic risk assessment, Jia proposed a risk model of population mobility and conducted risk assessment of an epidemic by analyzing population mobility data ( Jia et al., 2020 ). Du coupled a population mobility accumulation model and an exponential growth model ( Xu et al., 2020a ) to construct an epidemic model and assessed the epidemic risk using Tencent positioning data. Moreover, Pan divided the infection risk of COVID-19 in various states in the United States on the basis of mobile phone positioning data ( Hâncean et al., 2020 ). Other scholars have evaluated the risk of COVID-19 in different countries and regions based on natural and social environmental factors ( Chatterjee et al., 2020 ), and these evaluations based on the vulnerability of the region itself could also play a positive role in assessing the risk of the COVID-19 epidemic ( Xu et al., 2020a ). The abovementioned studies are mainly based on population mobility and assess the risk of the COVID-19 epidemic; however, the risk distribution of COVID-19 is determined by multiple urban spatial factors ( Ribeiro et al., 2020 ).

In terms of epidemic prediction, statistical and dynamic models are often used to estimate future cases and infection trends. Statistical models include methods such as linear regression analysis ( Chatterjee et al., 2020 ; Piovella, 2020 ; Cartenì et al., 2020 ), time series analysis and statistical process control ( Feroze, 2020 ; Zhang et al., 2020b ). Statistical models are generally applied to detect and provide an early warning of COVID-19 outbreaks. Since infectious disease theory is not involved here, only short-term predictive analysis can be performed ( Polo et al., 2020 ). Dynamic models can be divided into several basic types, such as susceptible-infected (SI), susceptible-infected-susceptible (SIS), susceptible-infected-recovered (SIR), and susceptible-exposed-infected-recovered (SEIR) models, based on the characteristics of pathogens, infectious agents, post infection immunity, the source of infection, the route of transmission, and susceptible populations ( Yawney and Gadsden, 2020 ). Moreover, as dynamic models take into account the factors influencing disease transmission and related social factors, they can effectively reveal the trends of the epidemic and change course of the disease ( Li et al., 2020 ). However, these basic dynamic models hardly consider the significant differences among geographical units and dynamic changes in populations, which makes it difficult for these models to support refined risk assessment and simulation by epidemic prevention departments at all levels from single-scale to multiscale coordination ( Liu and Mesch, 2020 ).

In-depth studies have been carried out in different countries and regions on the global spread, modeling and understanding of COVID-19, among which studies from Italy and Romania have demonstrated the necessity to develop new routes between EU countries to contain the spread of the epidemic in the early stages of the outbreak ( Hâncean et al., 2020 ). Studies from Brazil have shown that there are differences in morbidity and mortality between large and small cities and that different age compositions and distributions of health infrastructure all have important effects on COVID-19 ( Ribeiro et al., 2020 ). In Kenya, studies have taken the perspective of household energy and food security during the COVID-19 period, and a sustainable development model during the COVID-19 period has been obtained ( Shupler et al., 2021 ). Norway, on the other hand, has determined national containment strategies depending on the characteristics of a given city during similar crises by analyzing its urban working environment and migration patterns ( Venter et al., 2020 ). India, currently the country with the highest risk of COVID-19, has analyzed the impact of a national lockdown on the urban air quality during COVID-19 ( Navinya et al., 2020 ). Some scholars in the United States have established an early warning and evaluation model based on the responsibility system by using city-related indicators of COVID-19 and performed experimental verification of the epidemic in 17 major cities in the country ( Li et al., 2021 ). Based on the existing models and understanding of the spread of the pandemic in different countries and regions, it can be concluded that developed countries and regions such as the United States and Europe are more concerned about the impact of COVID-19 on the existing urban living environment ( Kan et al., 2021 ), while developing countries and regions such as Southeast Asia and Africa are more concerned about the impact of the pandemic on urban public health resources ( Zvobgo and Do, 2020 ), which illustrates the differences in the level of development among these different countries and regions. Therefore, as China is the largest developing country in the world, studies on the transmission, modeling, and understanding of COVID-19 in China should explore the urban environmental factors that influence the distribution and transmission of COVID-19, taking into account urban public health resources. Such research is likely to be of great regional value ( Hou et al., 2021 ).

In the studies on the early outbreak of COVID-19 and the cross-regional transmission of it, location characteristics of geographical space occupy a large proportion, mainly because there are huge differences in the spatial variability and aggregation degree of COVID-19 infection rate and mortality rate in different countries ( Khavarian-Garmsir et al., 2021 ). However, although some studies have analyzed the heterogeneity of the geospatial distribution of patients with COVID-19, few studies have considered the spatio-temporal variation of confirmed patients with COVID-19 in geospatial space ( DuPre et al., 2021 ). It has been found in previous studies on spatial epidemiology that urban geospatial factors have a strong spatiotemporal effect on the transmission of viruses, including the analysis of the possibility of infectious epidemics from the perspective of the degree of population aggregation in geographic space ( Hasselwander et al., 2021 ). Therefore, the study on the risk distribution of COVID-19 in urban space should carefully consider its spatial and temporal characteristics ( Mansour et al., 2021 ), and analyze the geospatial relationship between communities with different levels of infection and population agglomeration ( Hassan et al., 2021 ), so as to reveal the spatiotemporal changes of COVID-19 in geographical space ( Kwok et al., 2021 ). Spatiotemporal geographic epidemiological data, including cellular signaling data ( Xiao et al., 2019 ) , ( Zhan et al., 2021 ), population flow data ( He et al., 2020 ; Zhang and Yuan, 2021 ), and urban point of interest (POI) data, etc. ( He et al., 2021 ; Mahajan et al., 2021 ). In a word, these spatiotemporal geographic data can represent the characteristics of epidemic risk in urban space, providing a new research perspective and solution to problems related to epidemic risks in relation to urban geography ( Bachir et al., 2019 ; Sharifi and Khavarian-Garmsir, 2020 ). Compared with statistical survey data about the epidemic, spatiotemporal geographic epidemiological data have spatiotemporal continuity, and their strong data volume, analysis and processing mode, display capability and other advantages greatly compensate for the insufficient amount of statistical survey data in research on epidemic analysis ( Silva et al., 2018 ; Alsunaidi et al., 2021 ). Therefore, spatiotemporal geographic epidemiological data can play an important auxiliary role in assessing and simulating COVID-19 risk ( Hu et al., 2021 ).

In recent years, with the development of computer technology, machine learning and deep learning have gradually been applied to relevant research on cities and have achieved good results ( Milojevic-Dupont and Creutzig, 2021 ; Wang et al., 2020b ). The goal of machine learning is to obtain patterns from existing data samples and to then analyze and predict based on the patterns obtained. Logistic regression (LR) models are among the classic models of machine learning ( Cao et al., 2020 ), and they have advantages related to the objective methods and rigorous calculations involved. Compared with linear regression ( Yuchi et al., 2019 ; Sharifi and Khavarian-Garmsir, 2020 ), gradient neural network-convergence analysis (GNN-CA) ( Aarthi and Gnanappazham, 2018 ), cellular automata and other simulation algorithms ( Zhou et al., 2020 ), LR is simpler and more efficient in terms of the variables and normality assumptions, and it provides a new solution path for studies on urban decision-making and simulation ( Siddiqui et al., 2018 ).

Accurately assessing and predicting the distribution of high and low risks of COVID-19 is crucial for epidemic prevention and the control of a second outbreak of the epidemic in Guangzhou, which is one of the cities with the largest permanent population and floating population in China ( Granella et al., 2021 ). Taking Guangzhou as an example, this study assesses and simulates the COVID-19 risk from the perspective of geography using machine learning and spatiotemporal geographic epidemiological data. The mechanism and impact of various spatial factors on COVID-19 are discussed, and the assessment and simulation results are verified ( Yorio and Moore, 2018 ). Compared with the existing studies on the epidemic risk, this study has the advantage of smaller scale by evaluating and simulating the high and low risk distribution of the epidemic in Guangzhou through machine learning, which enables the epidemic risk distribution fed back to geographical units more refined, and the epidemic risk analysis based on urban geospatial factors can be greatly conducive to epidemic prevention and control in urban space. At the same time, using geographic detectors to analyze the primary and secondary factors affecting the distribution of epidemic risk level in urban space has important practical significance for the formulation of epidemic prevention and control policies and urban public security.

Materials and Methods

The research area is Guangzhou, Guangdong Province, China ( Figure 1 ). As one of the most urbanized and modernized cities in China, Guangzhou has 11 districts with a total area of 7,434.4 square kilometers. According to the Statistical Bulletin of The National Economic and Social Development of Guangzhou 2019 released by the Bureau of Statistics of Guangzhou Municipality on March 6, 2020, the permanent resident population of Guangzhou reached 15.3059 million in 2019. Guangzhou is one of the cities with the largest permanent population and floating population in China; thus, the assessment of the risk of COVID-19 conducted in this study can not only help to understand the areas in Guangzhou at high risk of a COVID-19 epidemic but also provide a decision-making basis for COVID-19 prevention and control nationwide.

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FIGURE 1 . Study area (the study area is Guangzhou City, Guangdong Province, China, which is located on the southern coast of China and is one of the cities with the highest level of urbanization and modernization in China).

Data Introduction

Spatiotemporal geographic epidemiological data about the epidemic should be directly or indirectly used to monitor and analyze this disease. According to the “triangle” theoretical model of public security ( Wang et al., 2020c ), public security consists of four parts: the emergency, the disaster carrier, emergency management and disaster elements. The emergency is the disaster itself, the disaster carrier refers to the people and things affected by the emergency when the emergency occurs, and the disaster factor is the factor inducing the occurrence of the emergency. Based on the analysis of the emergency, the disaster carrier and disaster factors, the whole process of an emergency, from occurrence and development to disaster formation and emergency measures, can be controlled.

In this study, the emergency is COVID-19, and the data on COVID-19 come from the National Health Commission of the People’s Republic of China. The data mainly include the number of people infected with COVID-19 in Guangzhou in 2020 and the geographic location of the disease as announced by the committee. The disaster carrier is the entire population of Guangzhou affected by COVID-19, including the floating population and permanent population. Here, the data on the floating population are derived from Guangzhou population heat map data in January, February, and August 2020, combined with the average monthly data from January to August obtained from Tencent-migration data, while the permanent resident data are obtained from the 2019 Statistical Yearbook of Guangdong Province. Disaster factors mainly refer to factors that induce and spread COVID-19, including the main public places where people communicate and gather in cities, such as hospitals, fever clinics, life markets, supermarkets, hotels, restaurants, schools, administrative centers, cultural exchange places, etc. ( Stevens et al., 2021 ), These places play an important role in the flow of urban elements, so they have also become the main places for COVID-19 transmission within cities ( Rousseau and Deschacht, 2020 ). After the outbreak of the COVID-19, Chinese government implements a strict isolation policy by closing schools, administrative units, public services and other places, which restricts the communication and interaction of people in these public places ( Liu et al., 2021 ). In addition, the development of a series of online remote interaction modes such as online teaching and online office has further reduces the level of epidemic risk in these areas ( Wu et al., 2021a ). Therefore, combining the existing literature and China’s current epidemic prevention policy ( Chen et al., 2021 ), the distance from fever clinic, the distance from living market, the distribution density of supermarket, the density of isolated hotel, the distribution density of catering, and the location distance from traffic station are selected by this study as the disaster factors, which were all screened through and obtained from Guangzhou POIs in 2020.

Based on the “triangle” theoretical model of public security, the following spatiotemporal geographic epidemiological data related to COVID-19 are determined in this study: the fever clinic distance, population flow, supermarket distance, COVID-19 distribution, population density, shopping mall density, restaurant density, public transit station density, and hotel density. Since this study analyzes the risk distribution level of the epidemic based on urban geographic space, the spatial resolution of the data in this study is the study scale unit (the spatial resolution of the data used in this study is unified as 25 × 25 m). The high-precision research unit scale also makes the simulated epidemic risk distribution more refined.

Data Preprocessing

1) After cleaning and duplicate checking of the POI data of Guangzhou obtained from the AMap application programming interface (API), it is found that the total numbers of supermarkets, hotels, shopping malls, public transit stations and restaurants in Guangzhou in 2020 are 27,738, 16,134, 24,686, 57,882 and 15,009, respectively. There are 102 fever clinics announced by the government. The Euclidean distances to fever clinics ( Wu et al., 2021b ), public transit stations, and shopping malls and the densities of supermarkets, hotels and restaurants are calculated, and the results are shown in Figure 2 .

2) Population data preprocessing: The population data are divided into resident population data and floating population data. The floating population data comprise Tencent-migration data as population flow change data. Tencent-migration data can be obtained from Tencent’s positioning big data service window ( http://heat.qq.com/index.php ). Based on the analysis of the user location information of the user positioning by Tencent’s multiple app programs, Tencent-migration data with a spatial resolution of 25 m × 25 m are obtained. The average monthly Tencent-migration data for January, February and August 2020 are obtained from the Tencent API ( Figure 3B–D ). The permanent population data come from the 2019 Guangzhou Statistical Yearbook. In 2019, the permanent population of Guangzhou was 15.3059 million, which is consistent with the spatial resolution of the floating population data obtained through resampling ( Figure 3A ).

3) COVID-19 data: The COVID-19 data come from the National Health Commission of the People’s Republic of China ( http://www.nhc.gov.cn/ ). As of the end of February 2020, there were no significant cumulative new COVID-19 infections in Guangzhou. The cumulative number of COVID-19 infections in January and February 2020 was 137 and 209 cases, respectively, and the spatial resolution was found to be consistent with the floating population data through calibration sampling of their incidence locations; Figure 4 illustrates the results.

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FIGURE 2 . POI data preprocessing results [ (A–F ) are Catering density, Market distance, Hotel density, Quotient hyperdensity, Traffic density, and Fever outpatient distance of the spatiotemporal geographic epidemiological data of Guangzhou; the color in the figure ranges from blue to red, indicating density and distance value ranges from low to high].

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FIGURE 3 . Population data preprocessing results [ (A) Population density represents the permanent population density of Guangzhou in 2019; (B–D) represent the monthly average data of Tencent population migration in January, February, and August of 2020 in Guangzhou, respectively, and the color in the figure ranges from blue to red, indicating permanent population density and population migration index value ranges from low to high].

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FIGURE 4 . COVID-19 data preprocessing results [ (A, B) Respectively represent the cumulative number and density distribution of COVID-19 infection in Guangzhou in January 2020, in which COVID-19 represents the incidence location, and the color in the figure ranges from blue to red, indicating the spatial density distribution of COVID-19 patients varies from high to low].

Logistic Regression

As one of the classic methods of machine learning ( Lai et al., 2021 ), LR can build a linear regression based on the sigmoid function, and with the help of an LR model, it is possible to further explore the relation between independent and dependent variables and to quantitatively analyze the probability of disaster events. Compared with models such as support vector machines (SVMs) and neural networks, LR models have great advantages in training and recognition time, with probability results ranging from 0 to 1, which are easier to interpret ( Cheng and Masser, 2003 ). An LR model is meaningful only when the independent variable is significant. Therefore, the relationship between the occurrence probability of COVID-19 and explanatory factors can be expressed as follows:

where P represents the occurrence probability of COVID-19 on a spatiotemporal geographic scale, which is in the range of [0,1]. The closer the value of P is to 1, the higher the probability of COVID-19 occurring in the area; the closer the value of P is to 0, the lower the probability of COVID-19 occurring in the area. Z stands for a linear combination. Therefore, the fitting equation involved in LR is as follows:

where C stands for the intercept of the model and represents the error value of the occurrence probability of COVID-19 in urban space under the selected indicator factors; B_1, B_2, … B_n stand for the LR coefficient X_1, X_2, … X_n for the index factor.

The technical route of LR model evaluation is shown in Figure 5 .

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FIGURE 5 . Technical route of the LR model (modeling process and verification flow chart of LR model).

Geographic Detectors

According to the first law of geography, everything is interrelated, and the degree of correlation changes with the change in distance ( Luo et al., 2019 ). In geographic space, it can be assumed that if an independent variable has a significant influence on the dependent variable, then the spatial distributions of the independent variable and the dependent variable should be similar in geographic space. A geographic detector is a statistical method based on the spatial variance analysis theory proposed by Wang Jinfeng et al ( Fan et al., 2020 ). The detector can be used to detect the degree of spatial differentiation of different impact factors in geographic space and to verify the coupling of the spatial distribution of two variables as well as the possible causal relationship between the variables ( Li et al., 2017 ).

1) Factor detector

The spatial differentiation degree of COVID-19 detection and the extent to which risk factors explain the spatial differentiation of COVID-19 can be represented by q, and the expression of the factor detector can be expressed as follows:

where h = 1 … L stands for the state of risk factors for COVID-19, while N h and N stand for the number of units in layer h   and the whole study area, respectively. σ 2 h and σ 2 represent the variances in layer h and the risk factors in the whole study area, respectively. S S W and S S T represent the within-sum of squares and the total sum of squares, respectively. The value range of q is [0,1], and the larger the value is, the more obvious the spatial differentiation of COVID-19 in geographic space. In addition, the larger the value of q is, the stronger the explanatory power of the risk factor for COVID-19 in geographic space, and vice versa.

A simple change in the q value satisfies the noncentral F distribution:

where ƛ stands for the noncentral parameter and \overline{Y} stands for the mean value of layer h. Eq. 5 can be used to determine whether the q value is significant.

2) Interaction detector

To identify the interactions between different risk factors, X n assesses whether the explanatory power of the spatial distribution of COVID-19 will be strengthened or weakened when the X 1 and X 2 factors work together; that is, it assesses whether the impacts of these risk factors on COVID-19 are independent of each other. After calculating q X 1 , X 2 and then calculating the value of q ( X 1 ∩ ​ X 2 ) of the two and comparing them with q X 1 , X 2 , the relationship between the two risk factors can be divided into the following categories ( Table 1 ).

3) Risk detector

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TABLE 1 . Detection of interaction.

Whether there is a significant difference between the mean value of the attributes of the two subintervals is detected, and the t\ statistic is used for testing:

where Y ¯ h stands for the mean value of the attributes in subregion h , which, here, represents the incidence of COVID-19; n h stands for the number of samples in subregion h ; and V a r stands for the variance. The t statistic approximately obeys Student’s distribution, and the calculation method of the degrees of freedom is as follows:

It is assumed that if Y ¯ h = 1 = Y ¯ h = 2 ,   there is a significant difference between the mean value of the attributes of the two self-fetching parts.

4) Ecological detector

Whether the two impact factors X 1 and X 2 have significant differences in the spatial distribution of attribute Y is compared and measured by the F statistic:

where N X 1 and N X 2 represent the sample sizes of risk factors X 1 and X 2 , respectively; SS W X 1 and SS W X 2 represent the sum of the intralayer variances in the layers formed by X 1 and X 2 , respectively; and L 1   and L 2 represent the number of levels of risk factors for X 1 and X 2 , respectively. If SS W X 1 and SS W X 2 are equal, the spatial distribution effects of risk factors X 1 and X 2 are significantly different.

Logistic Regression Model Training

On the basis of COVID-19 data from January and February 2020 and floating population data from January, February and August 2020, COVID-19 infection areas were divided, and positive and negative sample construction data sets were built. Since the nine spatial factors used in this study may show multicollinearity, which will cause a serious deviation in the operation results of the LR model, collinearity diagnosis of different factors should be carried out first ( Saedi et al., 2020 ). The product of tolerance (TOL) and the variance inflation factor (VIF) is equal to 1, which is also a common indicator that reflects the degree of collinearity of factors. In general, when the VIF is greater than or equal to 10 or the value of TOL is less than or equal to 0.1, there is a high degree of collinearity among factors, which does not satisfy the modeling conditions ( Zhang et al., 2020c ). In this study, multicollinearity analysis of nine factors was carried out based on Python, and the results are shown in Figure 6 . The VIF and TOL of all factors are 1, which meets the modeling conditions. Therefore, the nine spatial factors should be imported for model training.

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FIGURE 6 . Heat map of the collinearity diagnosis of influencing factors (Collinearity diagnosis of different factors is shown in the figure: the TOL of different factors is on the left, the VIF of different factors is on the bottom; the value in the figure is the product value of TOL and VIF between different factors; the color in the figure ranges from blue to red, indicating the corresponding value of TOL and VIF ranges from 0 to 1).

Assessment and Prediction of COVID-19 Risk

Assessment of covid-19 risk.

Based on the model training results, the higher the risk level is, the higher the probability of COVID-19 occurrence. Incorporating actual geographical locations, a distribution map of the risk level of COVID-19 in Guangzhou in January ( Figure 7A ), February ( Figure 7B ), and August ( Figure 7C ) 2020 is obtained.

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FIGURE 7 . Distribution map of the risk level of COVID-19 in (A) January, (B) February, and (C) August 2020 [ (A–C) are the high- and low-risk distributions of COVID-19 in Guangzhou in January, February and August of 2020, respectively. The color in the figure ranges from blue to red, indicating that the risk level of COVID-19 ranges from low to high. In addition, the areas with high risk in January, February and August of 2020 are more concentrated].

The distribution map ( Figure 7A ) shows that the areas at high risk of a COVID-19 epidemic in January 2020 were mainly concentrated in the Yuexiu, Haizhu, Tianhe and Liwan Districts. Comparing Figure 2 and Figure 4 reveals that in January, there was a large number of new COVID-19 patients in these regions. Guangzhou is a city with a high concentration of the floating population and permanent resident population, and Guangzhou is also an area with a relatively high distribution density of other spatial factors, such as hotels, shopping malls, and supermarkets. All of these factors increase the risk of COVID-19 outbreaks in these four regions.

The areas at high risk of a COVID-19 epidemic in February 2020 were mainly concentrated in Yuexiu District and Tianhe District. Figure 2 shows that although the Yuexiu and Tianhe Districts are relatively densely populated with permanent residents, the “home quarantine” policy not only greatly restricted the mobility and interaction of people but also reduced the transmission routes and pathways of COVID-19. The “home quarantine” policy effectively curbed the spread of the virus, bringing the cumulative number of new COVID-19 infections under control.

The areas at high risk of a COVID-19 epidemic in August 2020 were mainly concentrated in the Yuexiu, Haizhu, Liwan, Baiyun and Panyu Districts as well as external transportation hubs, including Baiyun Airport and high-speed railway stations. The COVID-19 epidemic was effectively controlled after February, and population activities and urban interactions began to return to normal starting in May 2020. However, with the large-scale mobility and interaction of the population, the risk areas of the epidemic changed from the previous low-risk areas to high-risk areas.

Comparing the high-risk distribution map of the COVID-19 epidemic in January, February and August 2020 reveals that in general, the level of risk experienced a rapid decline and then a slow rise, with the risk reaching its lowest point in February. In addition, in terms of the cumulative number of new patients, there were basically no new local patients after February, which suggests that the “home quarantine” policy was a positive and effective means of epidemic prevention. Additionally, comparing the distribution of regions with a high risk of an epidemic in the 3 months above shows that in February, the areas with a high risk of an epidemic were mainly concentrated in the areas with a dense permanent population, while in January and August, these areas were mainly concentrated in areas with a dense floating population and a dense permanent population, demonstrating that controlling the flow and interaction of the population is the best means of epidemic prevention.

Prediction of COVID-19 Risk

The COVID-19 risk levels in January, February and August 2020 were reintroduced into the model to simulate and predict the COVID-19 risk distribution in February 2021. As shown in Figure 8 , the distribution of COVID-19 risk in February 2021 is roughly similar to that in August 2020; that is, the high-risk areas are mainly concentrated in the Yuexiu, Haizhu, Tianhe, Liwan, Baiyun and Panyu Districts, but the epidemic risk value is higher than that in August 2020. Since these areas have always been areas where the resident population and the floating population are highly concentrated, without corresponding epidemic prevention measures, the mobility and interaction of the population will continuously promote the spread of COVID-19. Therefore, in the event of a second COVID-19 outbreak, these areas will be more likely to spread the virus.

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FIGURE 8 . Distribution map of the risk level of COVID-19 in February 2021 (the forecast of COVID-19 risk level in February 2021 is shown in the figure, in which the color ranges from black to white, indicating the predicted value of COVID-19 risk level ranging from low to high).

Compared with August 2020, external transportation hubs such as Baiyun Airport and the Guangzhou South Railway Station, which have been important regions for population mobility and interaction, have a significantly higher risk of an outbreak in February 2021. The permanent population of Guangzhou will not increase significantly in February 2021; however, February 14, 2021, is the Chinese Lunar New Year. Thus, the whole month falls within the Spring Festival travel season. During the 2019 Chinese Lunar New Year, the population mobility across all of China exceeded 3,000,000,000 individual trips ( Zhang et al., 2020c ). Therefore, during the Spring Festival travel season of 2021, the population mobility in Guangzhou is bound to reach a new peak, and a large number of population movements are likely to exacerbate the risk of COVID-19 transmission.

Analyzing the risk distribution of COVID-19 between February 2021 and 2020 intuitively shows that the risk of COVID-19 is most directly related to the population concentration and mobility. Therefore, the risk of COVID-19 transmission can be greatly reduced if the population concentration and mobility can be inhibited to a certain extent.

Preliminary Accuracy Test Model

Verification of the risk level of COVID-19 is an important condition for the generalization of research results. Therefore, in order to test the accuracy of the risk assessment of COVID-19 based on spatio-temporal geoepidemiological data, confusion matrix and ROC curve verification are used in this study to verify the accuracy of the results ( Shu et al., 2020 ). Firstly, the dataset of epidemiological data is classified into training data and validation data through the Sklearn module, in which the training data accounts for 70% and validation data accounts for 30% ( Abedini et al., 2017 ). Then, cross-validation is conducted for training data and verification data of different classifications, and the obtained verification indexes are accuracy, precision and recall. Finally, the verification indexes obtained from the training data and test data of different classifications are returned in the form of array to get the final accuracy verification results.

Verification of the Confusion Matrix

The preliminary accuracy test is a crucial step in verifying the reliability and predictability of the model ( Kranji et al., 2019 ). In this study, a confusion matrix (the average value of verification indexes obtained from different training data and verification data) is used to conduct a preliminary accuracy test of the prediction of COVID-19 in February 2021. Confusion matrix test results are shown in Figure 9 . The preliminary accuracies of the risk areas and risk-free areas are 0.9932 and 0.8949, respectively. Both of these values are greater than 0.85, demonstrating that the model has high accuracy in its prediction of epidemic risk, but the accuracy of the risk-free areas is relatively low, which may be due to the smaller number of risk-free areas and samples. The precision and recall are 0.9439 and 0.8995 for the risk areas and 0.9392 and 0.8849 for the risk-free areas, respectively. From the perspective of precision, recall and accuracy, the LR model for COVID-19 prediction has relatively high accuracy.

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FIGURE 9 . Heat map of the verification of the confusion matrix of the logistic regression model (the verification result of the logistic regression confusion matrix is shown in the heat map. The color in the figure ranges from black to white, indicating that the verification result value ranges from 0 to 5. The higher the thermal value is, the more accurate the verification result of the confusion matrix).

Verification of the Receiver Operating Characteristic Curve

The area under the curve (AUC) value was used to comprehensively test and evaluate the predictive accuracy of the LR model for receiver operating characteristic (ROC) curve validation ( Chirisa et al., 2020 ). When the AUC value is greater than 0.5, the closer it is to 1, the higher the predictive accuracy of the model. Figure 10 shows that the after logistic regression, the average AUC values of the cross-checking of training samples, verification samples and all data of different categories are 0.9934, 0.9932 and 0.9933, respectively. All of these values are higher than 0.99 and close to 1, showing that the model has fairly high predictive accuracy and further illustrating the important role of LR models in predicting the COVID-19 risk distribution.

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FIGURE 10 . ROC curve verification results (the verification results of the ROC curve include the test ROC curve, train ROC curve and total ROC curve, and the three values together determine the accuracy of the results. The closer the area under different curves is to 1, the higher the accuracy will be).

Analysis of Influencing Factors

1) Risk factor detector

The factor test results in Table 2 show that in COVID-19 risk assessment, population mobility is the most important factor determining COVID-19 infections in cities, followed by the density of the resident population. This finding is not only consistent with previous COVID-19 risk assessments and predictions but also demonstrates that the most effective way to prevent COVID-19 is to avoid the mobility and excessive agglomeration of people. On the other hand, the densities of public transit stations, shopping malls, and restaurants and the distance to supermarkets have similar influences. That is, the influences of these factors are all slightly lower than those of population mobility, indicating that to prevent the population from being exposed to the public environment for a long period of time, reducing population mobility and interaction in population agglomeration areas is a reasonable means of epidemic prevention. The factors that have the lowest impact on the risk level of COVID-19 are the distance to fever clinics and hotel density because, on the one hand, even if someone tests positive for COVID-19, he or she can be promptly transferred to a fever clinic for treatment; on the other hand, hotels mainly play a role in isolation. During an epidemic, more people choose home isolation, and there is less time to go to a hotel, which makes the population density of the hotel very low; as a result, hotels have little influence as a spatial factor.

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TABLE 2 . Risk factor detector.

The results of the interaction test of different factors are shown in Table 3 . The results showed that the risk level of the epidemic in Guangzhou could be best explained by the interaction between the permanent population density and the floating population. When the Q value is 0.67, the effect of the epidemic risk is interpreted to be greater than that of a single impact factor after the interaction of the two indicators, illustrating that epidemic prevention and control can achieve the maximum effect if the floating population and permanent resident population can be effectively controlled.

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TABLE 3 . Interaction detector.

The ecological test results ( Table 4 ) are obtained based on the assumption that the test value of F is 0.05, where Y represents a significant difference and N represents no significant difference. In the risk distribution of a COVID-19 epidemic, the results for the densities of the permanent population and floating population are significantly different from the results for other influencing factors, indicating that the densities of the permanent population and floating population are the most important factors affecting the risk of COVID-19. Moreover, the floating population has a greater impact on the risk of COVID-19 than the permanent population. Compared with the population factor, there is no significant difference in other factors, including the distributions of supermarkets, hotels and shopping malls. Therefore, under the premise of reasonably and safely controlling population factors, the distribution of other urban facilities can reasonably provide basic life services for urban residents.

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TABLE 4 . Ecological detector.

In order to avoid the interaction between influencing factors caused by repeated calculation of the algorithm, this study carries out a second cross-validation of the results obtained by the Interaction Detector and Ecological Detector, which shows that there are no significant differences between final verification result and the first one. In other words, the index calculation results between Interaction Detector and Ecological Detector are reasonableness and interdependency.

This study proposes a risk assessment and prediction model of COVID-19 based on spatiotemporal geographic epidemiological data, an LR model and geographic detectors. The risk levels of COVID-19 in January, February and August 2020 are obtained, and the areas at high risk of COVID-19 in February 2021 are predicted. The spatial variability and attribute associations among different influencing factors are also analyzed to identify the main factors influencing the spread of COVID-19.

After the outbreak of COVID-19, the assessment of COVID-19 risk transmission based on the geographical perspective were initially mainly focused on the macro scale, including regional, national and global epidemic assessments ( Chakraborty and Maity, 2020 ). With the popularization of epidemiological data applications for population mobility, the risk assessment of COVID-19 has taken the meso and micro perspectives. That is, studies have started to explore the reasons for the spread of the epidemic from the perspective of the population mobility between communities ( Ouyang et al., 2020 ; Yan et al., 2021 ). However, such studies continue to place greater emphasis on discussing the impact of population mobility on epidemic risk, and they do not objectively assess and predict the current epidemic risk from the spatiotemporal perspective ( Chen et al., 2021 ). In this study, using multisource spatiotemporal geographic epidemiological data, machine learning-based simulations were conducted, taking into account the resident population, the floating population and all urban spatial factors that may affect the spread of the epidemic in geographical space. Finally, the primary and secondary factors affecting the risk of an epidemic are discussed, and the verification results show that the simulation method is quite accurate.

The areas at high risk of COVID-19 are mainly concentrated in areas with resident populations and floating populations, and this result is basically similar to that of previous studies on COVID-19 ( Cokun et al., 2021 ). Since humans are the main carriers of COVID-19 and other infectious diseases, the mobility and interaction of the population are the most important factors contributing to the high risk of COVID-19 ( Nguyen et al., 2020 ; Xu et al., 2020b ). Compared with current studies related to epidemic risk assessment and prediction, this study focuses on the analysis of the impact of urban spatial factors on epidemic risk from the perspective of spatial-temporal geography, allowing the spread of the epidemic to be expressed in terms of geographical location, which is conducive to preventing and controlling the epidemic in the community at the micro scale.

Finally, this study leaves some areas that require further exploration. Guangzhou, China, was selected as the case for analysis in this study ( Peirlinck et al., 2020 ). To better prevent and control the global pandemic, it is necessary to conduct further assessments and simulations of specific epidemics in cities with severe outbreaks around the world.

Risk assessment and prediction of the COVID-19 epidemic and analysis of the main influencing factors hold great practical value for the construction of urban public health safety spaces. In this study, spatiotemporal geographic epidemiological data such as Tencent-migration data and POI data as well as LR and geographical detector models are used to assess the risk of COVID-19 in Guangzhou in January, February and August 2020 and to predict the risk distribution of COVID-19 in February 2021. In addition, the main factors affecting the areas at high risk of COVID-19 are analyzed, and the following conclusions are drawn:

1) The risk of COVID-19 in 2020 mainly exhibited a downward trend and then an upward trend. Although the “home quarantine” policy implemented by the Chinese government has effectively contained the spread of COVID-19 and further reduced the risk of the epidemic for a short time, with the increase in population mobility and interaction degree as well as the recovery of production and the activities of daily life, regional epidemic risk is beginning to show an upward trend.

2) The prediction results of the epidemic situation in February 2021 show that the COVID-19 risk of major external transport hubs in Guangzhou increased significantly due to the arrival of the Spring Festival travel rush, except for areas with dense population movement and interaction. The accuracy of the risk prediction of COVID-19 is greater than 99%, which indicates that the prediction of COVID-19 is highly reliable.

3) The main factors affecting the epidemic risk level are the distribution of the floating population and resident population, and the interaction between the floating population and the resident population also explains the risk distribution of the epidemic to the greatest extent. Therefore, if population agglomeration is limited, then the rational distribution of other urban spatial factors will not have an important impact on the risk of the epidemic.

On the basis of using spatiotemporal geographic epidemiological data, the risk assessment and prediction models for COVID-19 are highly practical and accurate. This study objectively and accurately assesses and predicts areas at high risk of COVID-19, which is conducive to not only preventing and controlling a second outbreak but also providing solutions to urban public security problems for epidemic prevention agencies.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary files, further inquiries can be directed to the corresponding author.

Author Contributions

XH and CZ: Conceptualization; YW: Methodology; XY: Software; XH and CZ: Validation; XH: Formal analysis; XH: Investigation; CZ: Resources; YW: Data curation; XH: Writing—original draft preparation; XH and CZ: Writing—review and editing.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: Guangzhou, logistic regression, influencing factors, interaction, spatiotemporal geography

Citation: He X, Zhou C, Wang Y and Yuan X (2021) Risk Assessment and Prediction of COVID-19 Based on Epidemiological Data From Spatiotemporal Geography. Front. Environ. Sci. 9:634156. doi: 10.3389/fenvs.2021.634156

Received: 07 December 2020; Accepted: 20 July 2021; Published: 30 July 2021.

Reviewed by:

Copyright © 2021 He, Zhou, Wang and Yuan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Chunshan Zhou, [email protected]

  • Information
  • Covid-19 Risk Assessments

Guidelines for risk assessing your group’s activities or your community venue to prevent the spread of Covid-19

Although legal restrictions to prevent the spread of Covid-19 in the UK mostly ended in 2022, the  virus remains prevalent. Your group might choose to continue thinking through how you can keep the risk of Covid-19 infection down for people who participate in your group’s events and activities.   

This information sheet includes:

What is a Covid-19 risk assessment?

Direct contact between people, shared equipment and facilities, sample covid-19 risk assessment for an activity, sample covid-19 risk assessment for a community venue.

A risk assessment lists the hazards that people might face when engaging in a particular activity or event, and the measures you are taking to mitigate the risk from those hazards. See our Risk assessment  information sheet for a general guide.

A Covid-19 risk assessment specifically addresses the hazard of spreading or catching the Covid-19 virus.

Doing a risk assessment is a useful tool to help your group consider how the virus might be spread during any activity that you organise, and what you will do to reduce the likelihood of it being spread, in order to keep everyone in your group as safe as possible.

How could the virus be spread in our group activity or community space?

The Covid-19 virus is transmitted from one person to another through: 1) airborne particles (droplets and aerosols); and 2) particles on surfaces (fomites). This means that, in basic terms, the virus is spread when people are close together and breathing the same air and/or touching the same things. Some people who are infected with the virus do not have any symptoms and do not know they are infected. They can still transmit the virus to others, however, through talking, breathing, coughing, sneezing, and sharing of equipment and facilities.

Regulations and guidelines that the government developed during the height of the pandemic – like social distancing, mask wearing, increased cleaning of hands and equipment – were all designed to reduce the risk of virus transmission. Your group might still choose to use some or all of these measures, even though there are no longer any legal requirements to do so.

Key ways in which the virus could be spread in any group activity are listed below. Your risk assessment should include consideration of all these risks, and outline what your group will do to reduce the likelihood of them happening. You should also include any other risks that might be specific to your group members or activity e.g. if your group includes people who are clinically vulnerable, are there extra measures you need to put in place to minimise risk to them?

Staying away if you have symptoms

If you are running a community space or organising an activity, you should ask anyone who has  symptoms not to come to the venue or event. The three main symptoms of Covid-19 originally identified were: cough, high temperature, and loss of smell (anosmia). Other key symptoms have since been identified, including fatigue, sore throat, headache and diarrhoea. A full list of identified Covid-19 symptoms can be found on the Zoe Covid Study website.

Symptoms can vary depending on a number of factors, such as the individual, whether they have been vaccinated, and which variant of the virus they have. Your group might decide to ask anyone who is feeling unwell, with any set of symptoms, not to attend your venue or activity. Or you might ask them to stay away unless they have taken a Covid-19 test and had a negative result since they started having the symptoms.

Testing in advance of an event

Some groups and events might choose to ask all attendees to take a lateral flow test (LFT) on the day of the event. LFTs are no longer available for free, but can still be purchased from most pharmacies. They cost about £2 each.

Social distancing

‘Social distancing’ originally referred to government guidance that people who do not live together should keep 2 metres apart from one another. Although there are no longer government guidelines requiring this, you can still think through how crowded your activity or event will be and make choices about numbers and spacing that can reduce the risk of spreading Covid-19.

If you are running a community space, you should think about:

  • The size of each room and how many people can fit into it safely. You can find online space calculators to help you with this, although they can be quite complicated.
  • ‘Pinch points’ (where people would be forced to move closer together e.g. through doorways). Is it possible to set up a one-way system so that people come in one door and leave through another? Do you need signs to make this clear? If you are in Brighton & Hove, you can get signs printed at the Resource Centre.

If you are organising an activity, you should think about:

  • The maximum number of people you can safely allow at your event. Plan how you will limit numbers – will people have to book in advance? You could use an online ticketing service such as Eventbrite for advance bookings (there’s no cost if you are not charging for your tickets).
  • Whether your event or meeting would work as a hybrid event , where some people join via Zoom. If you are in Brighton & Hove, the Resource Centre has equipment  that can help you to organise events like this.
  • How you will set up the space and mark distances. It is a good idea to have someone from the committee set up the space before other people arrive e.g. by setting out chairs or marker cones that are appropriately distanced for your activity. You can hire free-standing signs from the Resource Centre and print your own information to display in them.
  • How to minimise the need for people to be too close together as they are arriving and leaving. You might decide to have staggered arrival times so people do not end up queuing to get in, or a one-way system so that people are not going in and out of the same door.

Face coverings

The most recent government guidelines suggest you should wear a face mask when:

  • you are coming into close contact with someone at higher risk of becoming seriously unwell from Covid-19 or other respiratory infections
  • Covid-19 rates are high and you will be in close contact with other people, such as in crowded and enclosed spaces
  • there are a lot of respiratory viruses circulating, such as in winter, and you will be in close contact with other people in crowded and enclosed spaces

If you are running a community venue, you should think about:

  • Whether your staff would still like to wear face coverings, or have screens between them and venue-users.
  • If your group would like to ask people to wear masks during the activity
  • How you will let people know in advance about your decisions on wearing masks
  • You may want to supply masks at the door, for people who don’t remember to bring their own mask with them

Hand washing

Making sure people wash or sanitise their hands frequently can help reduce the risk of passing on Covid-19 through direct contact or shared surfaces.

  • Providing information about good handwashing technique in the toilets or other places where people can wash their hands
  • Providing hand sanitiser at the entrance and asking people to sanitise their hands as soon as they arrive in the building
  • Providing hand sanitiser for people arriving at your event or activity.

There is  evidence  that the virus can spread between people who are not close together, if they are in an enclosed space and breathing the same air. The main way to reduce this risk is to improve ventilation.  This app can help you think through the measures you can take to decrease risk of indoor transmission of the virus.

  • Advising users of your venue to open windows and doors during their activities
  • Monitoring the level of carbon dioxide (CO2) inside your venue, to give you an indication of how well the space is ventilated. According to this article , “Outdoors, CO2 levels are just above 400 parts per million (ppm). A well ventilated room will have around 800 ppm of CO2. Any higher than that and it is a sign the room might need more ventilation.” If your group is based in Brighton & Hove, you can hire a CO2 monitor from the Resource Centre. Otherwise, or if you need one long-term, you can buy them for between £30 and £60.
  • Installing a mechanical ventilation system (eg extractor fans) in rooms where it is not possible to open the windows.
  • Purchasing an air purifier, which filters the air and can remove particles the size of those that typically contain the virus. Look for one which has a HEPA filter. You can buy an air purifier powerful enough to cover a typical 70m 2 room for around £300. Be aware that an air purifier will not reduce the level of CO2 in the air.
  • Is it possible to organise your activity outdoors?
  • If you need to be indoors, ask your venue about ventilation
  • Make sure you have windows and doors open during the activity
  • If your group is based in Brighton & Hove, you can hire an air purifier from the Resource Centre

Covid-19 transmission can occur through touching contaminated surfaces, according to the World Health Organisation .

The main mitigation for the risk of passing on the virus after touching shared surfaces is frequent handwashing. This is covered above, in the Direct contact between people section. Nevertheless, you may want to introduce additional cleaning measures, and minimise shared equipment.

If you are running a community venue, you could think about:

  • Using paper towels or a hand dryer instead of shared hand towels
  • Setting up a routine to clean equipment between hires of your venue – you could ask hirers to do this, or use your own staff or volunteers.

If you are organising an activity, you could think about:

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Risk assessment of airborne COVID-19 exposure in social settings

Affiliations.

  • 1 Institute of High Performance Computing, Agency for Science, Technology and Research, 1 Fusionopolis Way, Singapore 138632.
  • 2 Institute of Materials Research and Engineering, Agency for Science, Technology and Research, 2 Fusionopolis Way, Singapore 138634.
  • 3 National Centre for Infectious Diseases, Tan Tock Seng Hospital, 16 Jalan Tan Tock Seng, Singapore 308443.
  • 4 Ministry of Health Singapore, College of Medicine Building, 16 College Road, Singapore 169854.
  • 5 Land Transport Authority, 1 Hampshire Road, Singapore 219428.
  • PMID: 34552314
  • PMCID: PMC8450907
  • DOI: 10.1063/5.0055547

The COVID-19 pandemic has led to many countries oscillating between various states of lock-down as they seek to balance keeping the economy and essential services running and minimizing the risk of further transmission. Decisions are made about which activities to keep open across a range of social settings and venues guided only by ad hoc heuristics regarding social distancing and personal hygiene. Hence, we propose the dual use of computational fluid dynamic simulations and surrogate aerosol measurements for location-specific assessment of risk of infection across different real-world settings. We propose a 3-tiered risk assessment scheme to facilitate classification of scenarios into risk levels based on simulations and experiments. Threshold values of <54 and >840 viral copies and <5% and >40% of original aerosol concentration are chosen to stratify low, medium, and high risk. This can help prioritize allowable activities and guide implementation of phased lockdowns or re-opening. Using a public bus in Singapore as a case study, we evaluate the relative risk of infection across scenarios such as different activities and passenger positions and demonstrate the effectiveness of our risk assessment methodology as a simple and easily interpretable framework. For example, this study revealed that the bus's air-conditioning greatly influences dispersion and increases the risk of certain seats and that talking can result in similar relative risk to coughing for passengers around an infected person. Both numerical and experimental approaches show similar relative risk levels with a Spearman's correlation coefficient of 0.74 despite differing observables, demonstrating applicability of this risk assessment methodology to other scenarios.

© 2021 Author(s).

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  • Open access
  • Published: 02 May 2023

Risk assessment of retinal vascular occlusion after COVID-19 vaccination

  • Jing-Xing Li   ORCID: orcid.org/0000-0003-3776-5042 1 , 2 , 3 ,
  • Yu-Hsun Wang 4 ,
  • Henry Bair 5 , 6 ,
  • Shu-Bai Hsu   ORCID: orcid.org/0009-0009-9698-5695 7 , 8 ,
  • Connie Chen 9 , 10 ,
  • James Cheng-Chung Wei 4 , 11 , 12 , 13 &
  • Chun-Ju Lin   ORCID: orcid.org/0000-0002-7880-9198 2 , 5 , 14  

npj Vaccines volume  8 , Article number:  64 ( 2023 ) Cite this article

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  • Epidemiology
  • Retinal diseases

Coronavirus disease 2019 (COVID-19) vaccines are associated with several ocular manifestations. Emerging evidence has been reported; however, the causality between the two is debatable. We aimed to investigate the risk of retinal vascular occlusion after COVID-19 vaccination. This retrospective cohort study used the TriNetX global network and included individuals vaccinated with COVID-19 vaccines between January 2020 and December 2022. We excluded individuals with a history of retinal vascular occlusion or those who used any systemic medication that could potentially affect blood coagulation prior to vaccination. To compare the risk of retinal vascular occlusion, we employed multivariable-adjusted Cox proportional hazards models after performing a 1:1 propensity score matching between the vaccinated and unvaccinated cohorts. Individuals with COVID-19 vaccination had a higher risk of all forms of retinal vascular occlusion in 2 years after vaccination, with an overall hazard ratio of 2.19 (95% confidence interval 2.00–2.39). The cumulative incidence of retinal vascular occlusion was significantly higher in the vaccinated cohort compared to the unvaccinated cohort, 2 years and 12 weeks after vaccination. The risk of retinal vascular occlusion significantly increased during the first 2 weeks after vaccination and persisted for 12 weeks. Additionally, individuals with first and second dose of BNT162b2 and mRNA-1273 had significantly increased risk of retinal vascular occlusion 2 years following vaccination, while no disparity was detected between brand and dose of vaccines. This large multicenter study strengthens the findings of previous cases. Retinal vascular occlusion may not be a coincidental finding after COVID-19 vaccination.

Introduction

The extremely contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the coronavirus disease 2019 (COVID-19). Since the end of 2020, many vaccines have been developed, including messenger RNA (mRNA) vaccines (BNT162b2 [Pfizer-BioNTech] and mRNA-1273 [Moderna]), adjuvanted recombinant protein vaccines (Novavax), and adenoviral vector vaccines (ChAdOx1-S vaccine [Oxford/AstraZeneca] and Ad26.COV2.S [Janssen-Johnson&Johnson]). Consequently, several possible complications have been documented due to the increased vaccination rates.

Retinal vein occlusion (RVO) is the second most prevalent cause of visual loss related to retinal vascular diseases, after diabetic retinopathy. RVO is related to thromboembolism caused by vessel compression, vasospasm, or degeneration of vascular walls 1 . Retinal artery occlusion (RAO) is caused by vasospasm, vasculitis, reduced arterial perfusion, and thromboembolism of the retinal arteries originating from the ipsilateral carotid artery, aortic arch, or heart chambers. Based on the location of the occlusion, RAO and RVO can be further classified into central and branch forms. SARS-CoV-2 infection can precipitate retinal vascular events 2 , 3 . RVO following COVID-19 vaccination is uncommon. However, there is growing literature including case reports on retinal vascular occlusion following vaccination 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 . Intriguingly, some studies on retinal vascular occlusion have been related to mRNA COVID-19 vaccination 14 , 15 ; the vaccines implicated include mRNA vaccines, mRNA-1273 10 and BNT162b2 7 , 11 , 16 , as well as the viral vector-based vaccine ChAdOx1 6 , 12 , 17 . However, the quality of these data was insufficient to establish a causal relationship between retinal vascular occlusion and COVID-19 vaccination.

This study aimed to determine whether COVID-19 vaccines are related to an increased risk of retinal vascular occlusion and to raise awareness about the probability of retinal vascular events due to an increased thrombotic inflammatory state associated with COVID-19 vaccinations.

Patient characteristics and stratified analysis

The TriNetX network collected information on a total of 95,156,967 individuals, of whom 7,318,437 met the inclusion criteria. Figure 1 presents the flowchart of patient selection. After excluding cases with confirmation of COVID-19 diagnosis, 6,755,737 individuals were separated into two cohorts: 883,177 vaccinated and 5,871,737 unvaccinated individuals. In both cohorts, any diagnosis of retinal vascular occlusion six months prior to the index date was excluded. We also considered the effect of systemic medications and excluded cases with the use of any antiplatelets, anticoagulants, diuretics, contraceptives, or antihemorrhages 4 weeks prior to the index date. Ultimately, 745,041 vaccinated and 3,874,458 unvaccinated individuals remained. We matched 739,066 vaccinated cohorts to the unvaccinated cohort at a ratio of 1:1. Table 1 presents the baseline characteristics of the study population. After matching, the average age of the vaccinated group was 52.5 ± 18.5 years, whereas the unvaccinated group was 52.2 ± 18.2 years. There were no differences in any of the variables between the two cohorts. Table 2 reveals the stratified analysis based on age, sex, and race. Individuals aged from 18 to 64 years has increased risk of retinal vascular occlusion except for CRAO.

figure 1

Access date to the TriNetX database was Feburary 15, 2023.

Brand of vaccines

In the subgroup analysis, the first dose was defined as individuals receiving a single dose of the COVID-19 vaccine, and the second dose was defined as individuals receiving a second dose that was identical to the first dose. Table 3 presents the results of vaccination with various brands of COVID-19 vaccines. The risk of retinal vascular occlusion increased significantly after the first and second doses of BNT162b2 or mRNA-1273 in a 2-year period. The risks were not different between BNT162b2 and mRNA-1273 recipients. Though the risk of retinal vascular occlusion was elevated following the first dose of Ad26.COV2.S, the risk was not significant. Twelve weeks after vaccination of all brands of vaccines, the risk of retinal vascular occlusion increased non-significantly.

Risk of retinal vascular occlusion at 2 years and 12 weeks

Figure 2 presents the risk of retinal vascular occlusion at 2 years and 12 weeks after the uptake of COVID-19 vaccines. Supplementary Table 1 shows the original data with number of events and incidence rate. The overall risk of retinal vascular occlusion in the vaccinated cohort was 2.19 times higher than that in the unvaccinated cohort at 2 years (95% Cl 2.00–2.39). Two years after vaccination, the chances of all subtypes (BRAO, BRVO, CRAO and BRVO) of retinal vascular occlusion increased significantly in the vaccinated cohort. The hazards of retinal vascular occlusion and its subtypes were higher within 12 weeks than those at 2 years. Considering the potential acute consequences of COVID-19 vaccinations and its temporary effect, we investigated the bi-weekly incidence of the four forms of retinal vascular occlusion within 12 weeks of COVID-19 vaccination.

figure 2

A 2-year, and ( B ) 12-week. HR hazard ratio, LCL lower confidence limit, UCL upper confidence limit, US United States.

Bi-weekly risk of retinal vascular occlusion in 12 weeks

Figure 3 presents the results of the subgroup analysis of bi-weekly risks of retinal vascular occlusion. Supplementary Table 2 provides the original data with number of events and incidence rate. Cox multivariate analysis showed that the risk of retinal vascular occlusion in the vaccinated group was higher than that in the unvaccinated group at within 2 weeks of vaccination, which persisted for 12 weeks. This effect affected all subtypes except CRAO at 10–12 weeks after vaccination.

figure 3

A retinal vascular occlusion, ( B ) branch retinal artery occlusion, ( C ) branch retinal vein occlusion, ( D ) central retinal artery occlusion, and ( E ) central retinal vein occlusion. HR hazard ratio, LCL lower confidence limit, UCL upper confidence limit.

Figure 4 demonstrated the results of Kaplan–Meier analysis, which revealed that the cumulative incidence of retinal vascular occlusion and its subtypes were significantly increase in vaccinated than in the unvaccinated cohorts two years after vaccination (log-rank p  < 0.001). This trend was also observed within 12 weeks after COVID-19 vaccination (log-rank p  < 0.001) (Supplementary Fig. 1 ).

figure 4

A retinal vascular occlusion, ( B ) branch retinal artery occlusion, ( C ) branch retinal vein occlusion, ( D ) central retinal artery occlusion, and ( E ) central retinal vein occlusion.

Time course of risk of retinal vascular occlusion

Figure 5 depicts the time evolution of risks associated with retinal vascular occlusion and its subtypes. The risk of retinal vascular occlusion increased 27 days following vaccination against COVID-19. The risk of branch retinal vascular occlusion was greater at 6 and 3 days for BRAO and BRVO, respectively. In contrast, the probability of central retinal vascular occlusion was greater at 15 and 45 days for CRAO and CRVO. Supplementary Table 3 enumerates hazards of retinal vascular occlusion and its subtypes in 3-day interval after vaccination.

figure 5

Time course of hazard ratios of retinal vascular occlusion and its subtypes following COVID-19 vaccination was demonstrated. Of note, the risk of branch retinal vascular occlusion was extremely high after vaccination.

We demonstrated a higher risk and incidence rate of retinal vascular occlusion following COVID-19 vaccination, after adjusting for potential confounding factors 18 . The risk of retinal vascular occlusion, except for CRAO, has been promptly observed in individuals receiving vaccines against SARS-CoV-2. The risk factors for retinal vascular occlusion include diabetes, hypertension, obesity, coronary artery disease, and stroke 19 , 20 , 21 . To ensure the reliability of the results, we appropriately balanced the baseline characteristics in both cohorts before analysis.

The widespread occurrence of microvascular thrombosis in COVID-19 patients have been demonstrated 22 . Vaccination with ChAdOx1 nCoV-19 can result in the rare development of immune thrombotic thrombocytopenia mediated by platelet-activating antibodies against platelet factor 4 (PF4), which clinically mimics autoimmune heparin-induced thrombocytopenia 23 . A large cohort study 24 showed that the risk of VTE slightly increased 1.10-fold 8–14 days after ChAdOx1 nCoV-19 vaccination but found no difference for individuals who were administered BNT162b2 vaccination; the risk of ATE following ChAdOx1 nCoV-19 and BNT162b2 vaccination increased 1.21-fold and 1.06-fold, respectively.

Thrombosis that manifests before thrombocytopenia is referred to as vaccine-induced immune thrombotic thrombocytopenia (VITT). Two adenoviral vector-based immunizations, ChAdOx1 nCoV-19 and Ad26.COV2.S, have been associated with the development of VITT. VITT cerebral venous sinus thrombosis is predominantly from adenovirus viral vector vaccines. The pathological mechanism of thrombosis has been hypothesized to entail either an innate or adaptive response, involving the activation of B and T cells and CD4 T cells are essential for regulating the production of PF4/heparin-specific antibodies 25 .

VITT is a very rare, life-threatening adverse complication with a 23% overall mortality rate 26 . Certain inflammatory vaccine adjuvants and delivery techniques may induce immune cell recruitment during the VITT. Antibodies that detect platelet-bound PF4 are the cause of VITT. These antibodies are immunoglobulin G (IgG) molecules that activate platelets by binding to platelet FcγIIa with a modest affinity 27 . VITT typically appears as uncommon thromboses (cerebral venous sinus thrombosis and splanchnic vein thrombosis), although it can also manifest as typical thromboses (stroke, pulmonary embolism, and deep vein thrombosis) with severe thrombocytopenia.

Thrombosis with thrombocytopenia syndrome (TTS) is a more general descriptive name for the syndrome of thrombosis and thrombocytopenia of any cause following COVID-19 vaccination. Some individuals with TTS may not have been evaluated for anti-PF4 antibodies; or have causes of thrombosis and thrombocytopenia other than VITT, such as antiphospholipid syndrome, cancer-associated thrombosis and thrombocytopenia, thrombotic thrombocytopenic purpura, or disseminated intravascular coagulation.

A series of 65 individuals with serologically confirmed VITT who repeated functional assays over time found that the functional assays became negative in 74% of individuals, at a median of 15.5 weeks (95% Cl, 5–28 weeks) 28 . VITT plays a fundamental role in retinal vascular disease and may well explain the significantly increased risk of all forms of retinal vascular occlusion in 12 weeks observed in the subgroup analysis. In an examination of the temporal change of the risk of retinal vascular occlusion, which increased significantly shortly after vaccination, especially BRAO and BRVO. The highest hazards of subtypes of retinal vascular occlusion varied. The riskiest period after COVID vaccination for BRAO, BRVO, CRAO, and CRVO was 6, 3, 15, and 45 days, respectively. For BRAO and BRVO, direct embolism may be the preferred mechanism, whereas for CRAO and CRVO, VITT secondary to immunization may be the cause. VITT has a predilection for venous thrombosis in the CNS, splanchnic or adrenal veins, with patients presenting neurologic signs in addition to fever and mild bruising as early as 4–28 and up to 30 days post-COVID-19 vaccination. The relevant literatures on it are extremely limited 29 .

The Netherlands’ Lareb 30 showed that the incidence rate of VITT and TTS in individuals receiving the ChAdOx1-S vaccine was 7.7 per million vaccinations. Among them, 13.4 per million people who received the first dose and 1.7 per million people who received the second dose. The reported rates of retinal vascular occlusion for Ad26.COV2.S, BNT162b2, and mRNA-1273 per million vaccines were 5.7, 0.05, and 0.2, respectively. The Netherlands Pharmacovigilance Centre Lareb has received three reports of VITT/TTS with BNT162b2 and mRNA-1273; however, the associations are not sufficiently strong. A large international network cohort study 31 demonstrated a 30% greater risk of thrombocytopenia after a single dose of the ChAdOx1-S vaccine, as well as a trend toward an increased risk of venous TTS after vaccination of Ad26.COV2.S compared with BNT162b2. In this study, though higher risk of retinal vascular occlusion on Ad26.COV2.S recipients was observed in 2-year and 12-week periods, there is no significant increase. Intriguingly, a trend was noted that the risk is more pronounced following immunization with Ad26.COV2.S than BNT162b2 or mRNA-1273.

The SARS-CoV-2 genome encodes ten genes, two-thirds of which are nonstructural. The other one-third of the genome comprises four major structural genes, including spike, envelope, matrix, and nucleocapsid proteins, as well as five auxiliary proteins 32 . Messenger RNA vaccines contain fully functional mRNAs that can be directly translated into the S protein 33 , 34 . BNT162b2 and mRNA-1273, two mRNA vaccines currently in broad use, are technologically extremely similar. They comprise codon-optimized sequences for effective production of the whole S protein and utilize the actual signal sequence for its biosynthesis. Molecular mimicry of the S protein, which shares sequence homology with human proteins, may play a central role in retinal vascular occlusion 35 .

The global prevalence of RVO, BRVO, and CRVO in individuals aged 30–89 years was 0.77%, 0.64%, and 0.13% 36 . In the United States, the prevalence of RVO, BRVO, and CRVO is 0.7%–0.8%, 0.6%, and 0.1%–0.2%, respectively 37 , 38 . However, studies on the prevalence of RAO are limited. The current study revealed a strong correlation between vaccination with a mRNA vaccine and retinal vascular occlusion. However, we recommend that individuals without a history of severe allergic reaction to any component of the vaccine be vaccinated to protect against COVID-19, owing to the lack of definite causation between retinal vascular occlusion and vaccinations. Based on the official COVID-19 death reports, it is estimated that vaccinations have prevented 14.4 million excess COVID-19 deaths worldwide between December 2020 and December 2021 39 . Thus, vaccination is the most effective method for preventing the spread of SARS-CoV-2.

The number of reported ophthalmic complications has remained low, and vaccine-related retinal vascular occlusion is very rare, although the number of COVID-19 vaccinations is enormous. As of August 2 2022, 223.04 million people had completed a primary series of COVID-19 vaccines in the US 39 . However, we still suggest that patients on medications that may alter blood osmolarity should be aware of this possibility of adverse effects. Additional research is required to draw a solid conclusion regarding the association between retinal vascular occlusion and COVID-19 vaccines.

Strengths and weakness

Emerging cases of retinal vascular occlusion in outpatient settings have prompted us to address this concern. However, since this is the first study on this topic, these discoveries may have a significant impact on public health. To ensure the validity of the analysis, we conducted a comprehensive evaluation of confounding factors. However, this study had several limitations. First, since the existence of retinal vascular occlusion was defined by diagnostic codes, the diagnostic accuracy cannot be further confirmed. Second, the HR can be calculated using the TriNetX database; however, the p -value is not provided. Third, despite the fact that multiple confounding variables were accounted for, residual confounding variables may still exist and bias the results. Additional clinical investigations are required to validate the efficacy of mRNA vaccination against retinal vascular occlusion. Fourth, underprivileged people are more difficult to seek medical help under COVID-19 pandemic thought they do not have to pay for COVID-19 vaccines. Moreover, retinal vascular occlusion with no or mild symptoms may not be noted. Thus, under-reporting of retinal vascular occlusion and vaccination may bias the study to some extent. Lastly, TriNetX collects patient information only when the patient receives care from one of the participating healthcare organizations. The inclusion of care obtained from other institutions was not possible in this analysis. Loss to follow-up has the potential to distort the distributions of covariates and occurrence of outcomes. In brief, the data should be evaluated critically and cautiously owing to the retrospective nature of this investigation.

This large-scale cohort spanning two years investigate the association between retinal vascular occlusion and COVID-19 vaccination. A 2.19-fold increased risk of retinal vascular occlusion after COVID-19 vaccination was observed. Limited evidence and low frequency of the disease has complicated the establishment of a definitive association between both. The current findings support the conclusions of this case series. This emphasizes the necessity for a thorough study and ophthalmologists to consider the likelihood of retinal vascular occlusion in vulnerable patients following the administration of COVID-19 vaccines. Vaccination is suggested to protect against COVID-19, since the incidence of retinal vascular occlusion remains extremely low.

Study design and participants

This retrospective cohort study was based on data provided by the TriNetX global network, a large and federated research network; numerous renowned studies have employed this database 40 , 41 , 42 , 43 , 44 . Data for this analysis were restricted to patient data from the United States collected between January 1, 2020 and December 31, 2022, derived from 52 health care organizations. The TriNetX federated network received a waiver from the Western institutional review board since it only aggregated counts and statistical summaries of de-identified information; however, protected health information was not collected, and no study-specific activities were performed in retrospective analyses. The study protocol was approved by institutional review board of Chung Shang Medical University Hospital.

Outcomes and covariates

The International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes H34.1 and H34.2, respectively, define central retinal artery occlusion (CRAO) and branch retinal artery occlusion (BRAO). The ICD-10-CM codes H34.81 and H34.83 define central retinal vein occlusion (CRVO) and branch retinal vein occlusion (BRVO), respectively. ICD-10-CM code H34 indicated retinal vascular occlusion. Participants with COVID-19 infection identified by a positive polymerase chain reaction or immunoassay result for immunoglobulin A, G, or M in the plasma or serum were excluded. Participants who received mNRA vaccines BNT162b2 or mRNA-1273 which were documented in electronic medical records during the study period were included. The control group consisted of individuals who had not received any vaccinations. Only individuals with a first-time diagnosis of retinal vascular occlusion during the study period were included in both the case and control groups.

Patients were excluded if a diagnosis of retinal vascular occlusion was made six months before the index date (the earliest date of COVID-19 vaccination) or if they had received antithrombotics, diuretics, oral contraceptives, or antihemorrhagics four weeks prior to the index date. Antithrombotic agents include antiplatelet agents (aspirin, receptor P2Y12 antagonists, and glycoprotein IIb/IIIa [GPIIb/IIIa] inhibitors), anticoagulant (heparin, warfarin, direct oral anticoagulants, and direct thrombin inhibitors), and fibrinolytics (plasminogen activator inhibitors). COVID-19 was identified using TriNetX-provided criteria and ICD-10-CM codes in accordance with the Centers for Disease Control and Prevention coding guidelines. Supplementary Table 4 listed codes of laboratory, diagnosis, and medications adopted on TriNetX platform, as well as ICD-10-CM codes of comorbidities and Anatomical Therapeutic Chemical codes of common-used medications.

Statistical analysis

We used 1:1 propensity score matching of age, sex, race, comorbidities, medications and previous hospitalization to reduce selection bias and to optimize the variates of the case and control cohorts. The closest propensity scores for the cases and controls were estimated. We used the nearest-neighbor algorithm to derive matched pairs, with values of standardized mean difference <0.1, to indicate a significant difference between the cases and controls. Crude and multivariable-adjusted Cox proportional hazards models were used to compare the risk of outcomes between the cases and controls. The results of the comparisons are presented as HRs and 95% confidence intervals (CIs).

Chi-square (χ 2 ) tests were performed to analyze the homogeneity of category variables, including age, sex, race, and comorbidities, between the vaccinated and unvaccinated groups. Comorbidities included hypertensive diseases (ICD-10-CM codes I10–I16), overweight and obesity (ICD-10-CM code E66), type 2 diabetes mellitus (ICD-10-CM code E11), dyslipidemia (ICD-10-CM code E78), cerebrovascular diseases (ICD-10-CM codes I60–I69), ischemic heart diseases (ICD-10-CM code I82), glaucoma (ICD-10-CM code H40), arterial thromboembolism (ATE) (ICD-10-CM code I74), and venous thromboembolism (VTE) (ICD-10-CM code I82). The incidence rates of the retinal vascular occlusion subtypes were calculated for both groups. The 95% confidence interval (CIs) for the risk of retinal vascular occlusion was calculated. The Kaplan–Meier survival curve was plotted to describe the cumulative incidence of retinal vascular occlusion between the two groups, and the differences between the two groups were evaluated using the log-rank test. Statistical significance was set at p  < 0.05. TriNetX obscures counts in studies with counts less than 10 to protect patient health information by rounding it to the nearest 10. Whenever such rounding occurred in the analysis conducted in this study, it was identified and reported.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

Data is available from the TriNetX global network. Requests for data can be sent as logging on TriNetX platform ( https://live.trinetx.com/ ).

Code availability

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Jing-Xing Li

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Li, JX., Wang, YH., Bair, H. et al. Risk assessment of retinal vascular occlusion after COVID-19 vaccination. npj Vaccines 8 , 64 (2023). https://doi.org/10.1038/s41541-023-00661-7

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risk assessment of covid 19

Urine oxygenation predicts COVID-19 risk

  • Review article
  • Open access
  • Published: 24 February 2024

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  • Eisei Noiri   ORCID: orcid.org/0000-0002-9515-7582 1 ,
  • Daisuke Katagiri 2 ,
  • Yusuke Asai 3 ,
  • Takeshi Sugaya 4 &
  • Katsushi Tokunaga 1  

Since February, 2023, the omicron variant has accounted for essentially all new coronavirus infections in Japan. If future infections involve mutant strains with the same level of infectivity and virulence as omicron, the government’s basic policy will be to prevent the spread of infection, without compromising socioeconomic activities. Objectives include protecting pregnant women and elderly persons, and focusing on citizens requiring hospitalization and those at risk of serious illness, without imposing new social restrictions. Although the government tries to raise public awareness through education, most people affected by COVID-19 stay at home, and by the time patients become aware of the seriousness of their disease, it has often reached moderate or higher severity. In this review, we discuss why this situation persists even though the disease seems to have become milder with the shift from the delta variant to omicron. We also propose a pathophysiological method to determine the risk of severe illness. This assessment can be made at home in the early stages of COVID-19 infection, using urine analysis. Applicability of this method to drug discovery and development is also discussed.

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Introduction

Three years have passed since the COVID-19 outbreak in Wuhan, China, which spread throughout the world, and became entirely too familiar to people everywhere. Fortunately, compared to the early days of the pandemic, when treatment of this infection was nonexistent, it is now possible in developed countries to manage and treat moderate and severe cases with therapeutants and medical equipment. The virus has evolved through several variants, initially becoming progressively more virulent and infectious, until omicron, arguably the most contagious of all, but fortunately less serious [ 1 , 2 ]. This has decreased the frequency of severe cases, although the population remains susceptible to infection. The number of infections leading to severe cases, hospitalization and death, has decreased since the days of the delta variant [ 3 ]. Even so, deaths occasionally occur while patients are recovering at home. Sometimes these deaths are solitary, but often problems arise because emergency medical teams are unable to find hospitals that can accept such patients, even though compared to the delta variant era, COVID-19 critical patients currently occupy fewer hospital beds in urban areas [ 4 , 5 , 6 ].

Why do such problems occur? Regardless of hospital size, infection control is obviously of paramount importance for both medical care and hospital management. In the case of highly contagious COVID-19 infections, horizontal transmission is facile and infection control is difficult. Therefore, infection cycles often occur between hospitalized patients and medical workers, leading to institutional clusters of cases. Once a cluster is identified, patient admissions to that hospital are restricted by various means. Therefore, because COVID-19 outbreaks restrict access to hospitals, lives that could have been saved before the pandemic may not be saved now, though this is not to suggest that infection control should be neglected.

Why urinalysis is appropriate?

In the beta and delta stages of the pandemic, COVID-19-positive patients assigned to home rest, lived in fear that they might become severely ill. Now, if COVID-19-positive subjects who are at risk of serious illness can be identified during early stages of infection, morbidity, mortality, and social disruption can be prevented. Early detection of those at risk for severe illness is like searching for pebbles in an ocean, because the overwhelming majority of people remain mildly ill or asymptomatic.

Since the time of Hippocrates, urine has proven a very useful and non-invasive indicator of the condition of the whole body. The practice became so divinatory that its use eventually fell into disrepute after the Middle Ages. However, with the invention of the microscope, diagnosis of urinary sediment became possible, and urine stick tests enabled qualitative identification of proteinuria, urinary sugar, urinary occult blood, and nitrite. These assays are now used in daily medical practice. In Japan, urinary screening is a mandatory part of annual school and workplace health examinations, so the public is well familiar with the ease of urine screening. Such tests are non-invasive and can be used to screen a large number of subjects in a short time, requiring no special skill for stick tests or the equivalent. Moreover, since urine collection is self-administered, there is less risk of transferring infections to others than with blood collection. Nonetheless, coronavirus, which has a molecular mass of 150–200 kDa, does not appear in urine in mild cases [ 7 ]. When testing urine for L-FABP, we initially performed urine nucleic acid amplification tests (NAAT) on 200 COVID-19-positive patients to evaluate the possibility of environmental contamination by COVID-19 urine, but all 200 mild to severe cases were negative, soon after diagnosis. Just as urinalysis is useful to identify diabetes mellitus by detecting urinary sugar or useful to identify kidney disease by detecting proteinuria in medical checkups of mostly normal, healthy subjects, likewise, urinalysis makes it easy to distinguish persons at risk of moderate or severe COVID-19 from those who are likely to remain mild or asymptomatic.

Suitability of urinary L-FABP for risk assessment of COVID-19 infection

Given the global spread of novel coronavirus infections, we are investigating and proposing a novel urinary assay to predict the severity of COVID-19. This research is still ongoing, but among multiple urinary biomarkers, in this review we will focus on urinary L-type fatty acid-binding protein (L-FABP, FABP1). In Europe and Japan, L-FABP has already been approved as an in vitro product to diagnose renal injury [ 8 ].

A unique hypoxic condition called “happy hypoxia” appears in moderate or severe cases of coronavirus infection. For this reason, many countries have dealt with this problem by measuring SpO 2 using pulse oximeters to identify COVID-19-positive patients who require serious interventions. In Japan, among other countries, if patient SpO 2 values worsen, patients report this and receive intensive treatment through hospitalization. However, SpO 2 values are prone to fluctuations, especially as the pulse wave becomes weaker with lower oxygenation, leading to unstable values. Sixty torr of PaO 2 is considered equivalent to 96% SpO 2 in room air, which is a safety threshold for starting nasal oxygen administration. However, it is difficult for lay persons to determine what action to take if SpO 2 is oscillating from 93 to 96 to 94, and sometimes the decision to seek hospitalization comes too late. What would happen if we apply the urine biomarker L-FABP to determine the course of action? Below, we will explain how we came to think that L-FABP might work as a urine oxygenation biomarker for COVID-19 patients and the molecular biological mechanism by which L-FABP reflects systemic oxygenation.

Method for the proof of concept

COVID-19 was introduced to Japan as the Wuhan-1 variant. The disease broke out during a cruise on the Diamond Princess, a luxury liner from Hong Kong, which was forced to make port in Yokohama. The incident attracted worldwide attention because of a series of horizontal infections on the ship, some of which became severe. The National Center for Global Health and Medicine (NCGM) was the first Japanese hospital to admit infected patients in Japan. At that time, all COVID-19-positive patients in Japan had to be managed at a limited number of hospitals designated by the government. The data presented here come from the first 45 cases sent to the NCGM. On board the passenger ship, patients who tested positive for COVID-19 in RT-PCR tests on consecutive days for suspected horizontal infection were transferred to NCGM. Vital signs necessary for follow-up and disease monitoring included temperature, blood pressure, pulse, SpO2, blood counts and blood biochemistry tests, and urinalysis.

Deterioration of oxygenation can be seen in patient respiratory management as an increase in the fraction of inspiratory oxygen (FiO 2 ), i.e., ΔFiO 2 . We examined whether SpO 2 and other indicators at the time of admission could capture this increase during that one-week period. The maximum value of ΔFiO 2 was taken as the maximum value during a week. A chest X-ray was performed on the day of admission. In cases with elevated ΔFiO 2 , the patient is at increased risk of developing pneumonia or requiring severe respiratory management. We classified respiratory symptom severity into four groups: (1) no change; (2) COVID-19-related pneumonia, but no need of oxygen therapy; (3) COVID-19-related pneumonia with need of oxygen therapy; and (4) COVID-19-related pneumonia with need of mechanical ventilation therapy. While keeping these as surrogate markers of severe outcomes, urinary L-FABP was evaluated for proof of concept in two groups, with a cut-off of 10 ng/mL, which is also the upper limit for healthy normal individuals.

Initial clinical data analysis for proof of concept

In the aforementioned procedures, the indicator that could have predicted an increase in ΔFiO 2 and the severity of respiratory management during the first week of onset was L-FABP in the urine (Figs.  1 , 2 ). Urinary L-FABP was classified into two categories with a cut-off of 10 ng/mL (Fig.  1 ), and data were plotted individually based on SpO 2 values (horizontal axis). The line shows median values of SpO 2 in the two groups, with a median value of 97% in the group below 10 and 96% in the group more than 10. The right panel of Fig.  1 shows whether additional oxygenation was required after admission based on the maximum ΔFiO 2 increment within 1 week after hospitalization. Magenta demonstrates the ratio of patients required more oxygen after admission for respiratory management. Some patients required intubation for ventilator management. Since a 1% variation in SpO 2 is within the margin of error for the SpO 2 measurement method, it is difficult to determine a definitive risk of respiratory worsening within a week. On the other hand, the urine L-FABP test is able to detect this risk.

figure 1

SpO 2 level at COVID-19 diagnosis and maximum ΔFiO 2 one week thereafter. Left: Patients were classified into two groups based on a net L-FABP value (10 ng/mL), and SpO 2 values in the horizontal axis were plotted individually upon admission. The median value was 97% for patients above 10, and 96% for patients below 10. There was statistical difference between two groups, p  < 0.05 using t -test. Right: The proportion of patients who required additional oxygenation after admission was demonstrated through the evaluation of the maximum ΔFiO 2 increment (FiO 2 increase) within 1 week after admission. Magenta color indicates the percentage of cases requiring more oxygen after admission for respiratory management. Chi-square detected significant difference between two groups ( p  < 1 × 10 –9 )

figure 2

Respiratory severity with or without pneumonia at the time of COVID-19 diagnosis and one week thereafter. Left: Patients were classified into two groups based on a net L-FABP value of 10 ng/mL, and the percentage of patients with or without pneumonia by chest X-ray. Chi-square found significant differences in the presence of pneumonia on admission between two groups ( p  < 1 × 10 –21 ). Right: Respiratory severity class percentages 1 week after admission. The severity classification is as follows: 1. No symptoms in radiogram (green), 2. Pneumonia diagnosed by chest X-ray (light green), 3. Oxygen supply needed (orange), 4. Respiratory mechanical ventilation required (red). Colors indicate disease severity. The most severe condition in a week was assigned in each case. Chi-square found statistical differences between groups ( p  < 1 × 10 –20 ) and further found the oxygen requirement in higher L-FABP group (ratio of orange and red; p  < 1 × 10 –10 )

Urinary L-FABP was used to allocate patients into two groups, as described above (Fig.  2 ). The right panel of Fig.  2 represents the worst of the four conditions above within the first week after admission. There are many cases of severe disease among those with L-FABP > 10 ng/mL. This small, preliminarily study shows that, surprisingly, L-FABP 10 ng/mL, which is almost the upper limit of the normal range used in Europe and Japan to detect renal damage, indicates a patient’s risk of severe COVID 1 week after a positive diagnosis.

Sensitivity and specificity of risk assessment by urine L-FABP

Based on the above preliminary data, in a previous report [ 9 ], we reported the usefulness of this method in 58 COVID-19-positive patients. However, the number of patients needs to be increased and the results reconfirmed before such a novel approach can be used in clinical practice. A prospective study was conducted at a couple of centers in Japan, and 522 patients were enrolled [ 10 ]. Of 224 cases that met inclusion criteria (patients over 18 years old, without end-stage renal disease, and with urinalysis data during hospitalization) there were 173 mild cases and 51 moderately severe cases. Analysis of patient specimens occurred within 10 days of COVID-19 onset, and within 4 days of hospitalization, including weekend admissions. The AUC of adjusted L-FABP predicting severe outcomes was 84.3% (95% CI 73.5–92.0%) with a cut-off of 24.1 (Fig.  3 ). Sensitivity was 90.0% and specificity was 78.0%. The AUC of unadjusted L-FABP predicting severe outcomes was 83.9% (95% CI 73.4–92.7%) with a cut-off of 32.1. Sensitivity was 80.0% and specificity was 78.0%. The AUC of adjusted L-FABP predicting mild outcomes was 85.4% (95% CI 79.8–90.3%) with a cut-off of 6.07. Sensitivity was 86.4% and specificity was 70.3%. The AUC of non-adjusted L-FABP predicting mild outcomes was 82.3% (95% CI 76.3–87.7%) with a cut-off 7.51. Sensitivity was 87.9% and specificity was 63.3%. In this study, only 7 patients showed acute kidney injury (AKI) on admission. In other patients, serum creatinine generally remained below 1 mg/dL. Kidney dysfunction was minimal among the current cases.

figure 3

ROC curves to detect severe or mild COVID-19 cases (All cases: n  = 224). a – d , ROC curve analysis was performed for all cases ( n  = 224) to detect severe or mild groups, using either adjusted or unadjusted urinary L-FABP levels. Resulting cut-off values were; adjusted L-FABP to discriminate severe cases a 24.1 μg/gCre (specificity 78.0% and sensitivity 90.0%, area under the receiver operating characteristic curve [AUC] 84.3%) and mild cases b 6.1 μg/gCre (specificity 70.3% and sensitivity 86.4%, AUC 85.4%), and unadjusted L-FABP to discriminate severe cases c 32.1 ng/mL (specificity 78.0% and sensitivity 80.0%, AUC 83.9%), and mild cases d 7.51 ng/mL (specificity 63.3% and sensitivity 87.9%, AUC 82.3%)

The public wants severe cases to be detected at infection onset, when symptoms are mild, since persons with mild cases remain at home without being admitted to the hospital. For this purpose, information from our recent study, in which we prospectively examined mild cases and looked at changes in severity, is very useful. Before examining the data, a few characteristics of urinary data require explanation. The water volume of urine changes constantly to maintain an appropriate intravascular water balance. Therefore, it is better to adjust urine solute values by the urinary creatinine value to enhance measurement accuracy.

Of 173 initially mild cases, 3 became severe and 154 remained mild (Fig.  4 ). The AUC of L-FABP adjusted for urine creatinine to predict severe outcomes was 96.3% (95% CI 92.6–98.8%) with a cut-off value 35.9. Sensitivity was 100% and specificity was 93.5% (Fig.  4 a). This is extremely good predictive capacity. Data without adjustment by urinary creatinine are shown below.

figure 4

ROC curves to detect severe or mild cases of COVID-19 of initially mild cases. a – d ROC curve analysis was performed for mild cases at diagnosis of COVID-19 ( n  = 173) to detect severe or mild groups using adjusted urinary L-FABP. Resulting cut-off values were; severe cases a 35.9 μg/gCre (specificity 93.6% and sensitivity 100%, AUC 96.3%), mild cases, b : 6.1 μg/gCre (specificity 71.4% and sensitivity 89.5%, AUC 85.0%). ROC curve analysis to detect severe or mild groups using adjusted urinary L-FABP. Resulting cut-off values were; severe cases c 31.0 ng/mL (specificity 85.3% and sensitivity 100%, AUC 93.6%), mild cases d 10.9 ng/mL (specificity 70.1% and sensitivity 89.5%, AUC 84.9%)

The AUC of L-FABP (unadjusted for urine creatinine) for predicting severe outcome was 93.5% (95% CI 85.3–98.8%) with a cut-off value of 31.0. Sensitivity was 100% and specificity was 85.3% (Fig.  4 b). The fact that the results are quite good even without adjustment suggests that the point-of-care (POC) approach may also show good predictive power. Next, we extend the framework for predicting severity of illness to find moderate or severe cases as well.

Among the 173 mild cases, 19 developed into either moderate or severe cases (Fig.  4 c). The AUC of L-FABP adjusted for urine creatinine for predicting moderate or severe outcomes was 85.0% (95% CI 75.9–92.3%) with a cut-off 6.07. Sensitivity was 89.5% and specificity was 71.4% (Fig.  2 b). The AUC of L-FABP (unadjusted for urine creatinine) for predicting moderate or severe outcomes was 84.9% (95% CI 75.3–92.5%) with a cut-off of 10.9. Sensitivity was 89.5% and specificity was 70.1% (Fig.  4 d). Although inclusion of moderate outcomes reduces the predictive capacity, it might be safer to include broader coverage (moderate or severe outcome) in risk management. In the POC operation, the cut-off value of unadjusted L-FABP was 31.0 for severe outcomes only, while it was 6.7 for moderate or severe outcome, suggesting that semi-quantitative evaluation could be used.

How can urinary L-FABP predict worsening respiratory status?

Urine L-FABP has been approved by the Pharmaceuticals and Medical Devices Agency (PMDA) and CE as an in vitro diagnostic agent that can assist in early diagnosis of renal injury. Its pathophysiology has been extensively studied in acute kidney injury in mice. In rodent kidney, the so-called silencing sequence upstream, nucleotides − 4000 to − 597, of the L-FABP gene are expressed and repress gene expression ( 11 ), whereas in humans that sequence is absent and the gene is expressed. Previous L-FABP-related mouse studies employed humanized mice, in which the entire length of the mouse L-FABP gene, including the silencing sequence, was replaced with the human L-FABP gene [ 12 ]. The assay system also used a monoclonal antibody that recognizes human L-FABP only and was an ELISA method approved for in vitro diagnostic use.

We have reported many studies of AKI, including ischemic injury. Urinary L-FABP proved to be a highly sensitive, specific biomarker for ischemic damage to the kidneys [ 13 , 14 , 15 ]. The kidneys are well vascularized organs that receive 20% of cardiac output and that control systemic water and solute balance. Erythropoietin is produced by peritubular capillary endothelial cells in the kidney and serves as a hypoxia sensor. Insufficient oxygenation causes an increase in its expression, leading to an increase in red blood cells and improved systemic oxygenation. Hypoxia sensing depends on a molecular structure called the hypoxia responding element (HRE), upstream of the gene. The L-FABP gene also has an HRE upstream and L-FABP gene expression is also increased in response to hypoxia. This means that even hypoxic conditions that do not result in acute kidney injury are likely to increase urinary L-FABP levels. For proof of concept, we investigated a hypoxic model in which lipopolysaccharides (LPS) were administered to lungs of mice via bronchial tubes to induce hypoxia resulting from a consequent cytokine storm in the lungs [ 16 ]. Urinary L-FABP increased in a dose-dependent manner (Fig.  5 ). A small dose of LPS elevated it transiently, and it soon returned thereafter to the level of control saline-treated mice. In severe COVID-19 patients, the condition known as “happy hypoxia” results from lung-only injury and is very similar to the current mouse LPS model. For this reason, we hypothesized that urinary L-FABP could predict the risk of severe COVID-19 in the absence of acute kidney injury.

figure 5

Urinary L-FABP in a mouse intratracheal LPS injection model 16 . Bronchoalveolar lavage fluid protein levels differ between the 50 µg and 200 µg group ( n  = 5–7/group, p  < 0.05 vs. saline injection), denoting the difference of severity

Comparison of urine L-FABP and serum IL-6

In severe COVID-19 cases, immune overreaction is associated with worsening of the disease, and an increase in IL-6 is particularly well correlated with disease severity. This led to development of tocilizumab, a humanized anti-human IL-6 receptor monoclonal antibody, as a potential therapeutic agent. Although initial clinical studies did not demonstrate efficacy, the RECOVERY trial in Japan showed a significantly lower 28 day mortality rate in the tocilizumab group (RR 0.85, 95% CI 0.76–0.94, p  = 0.0028), particularly in combination with steroid administration. A subsequent meta-analysis of 27 RCT studies was reported in July 2021 with similar results [ 17 ]. Serum IL-6 has been approved by the FDA and PMDA as an in vitro diagnostic agent because it is an indicator of disease activity.

We argue that urine L-FABP can reveal severe illness trajectory in patients who test positive for COVID-19. How does this compare to serum IL-6? Serum IL-6 was not routinely measured in all cases. In a preliminary study, we examined the correlation between serum IL-6 and urinary L-FABP in cases where they could be measured on the same day. We found that urinary L-FABP correlated very well with serum IL-6 ( r  = 0.78, p  < 0.001, n  = 15). Although the number of cases used here was small, ROC analysis showed the following. Among these 15 cases, the AUC of unadjusted L-FABP for predicting severe outcomes was 88.6% with a cut-off of 51.4 ng/dL. Sensitivity was 100% and specificity was 81.8% (Fig.  6 a). Six cases progressed to either moderate or severe disease, whereas nine remained mild. The AUC of unadjusted L-FABP for predicting moderate or severe outcomes was 100% with a cut-off 51.4. Sensitivity was 100% and specificity was 100% (Fig.  6 b). This suggests that the L-FABP POC test will show good predictive performance in this small cohort. In contrast, the AUC of IL-6 for predicting severe outcomes was 81.8% with a cut-off of 5.2 pg/mL. Sensitivity was 100% and specificity was 72.7% (Fig.  6 c). The AUC of IL-6 in predicting moderate or severe outcomes was 92.6% with a cut-off 5.2. Sensitivity was 100% and specificity was 88.9% (Fig.  6 d). Since the ROC analysis of L-FABP was comparable to that of IL-6, we decided to plot the measurements versus time for each case with multiple measurement points during the course of hospitalization.

figure 6

Correlation analysis and ROC analysis to detect severe or mild COVID-19 between urine unadjusted L-FABP and serum IL-6. a – d ROC curve analysis was performed to detect severe or mild cases using unadjusted urinary L-FABP or serum IL-6. Resulting cut-off values were unadjusted L-FABP; severe cases a 51.4 ng/mL (specificity 81.8% and sensitivity 100%, area under the receiver operating characteristic curve [AUC] 88.6%), mild cases b 51.4 ng/mL (specificity 100% and sensitivity 100%, AUC 100%), and IL-6; severe cases c 5.2 pg/mL (specificity 72.7% and sensitivity 100%, AUC 81.8%), mild cases d 5.2 pg/mL (specificity 88.9% and sensitivity 100%, AUC 92.6%)

Dynamics of L-FABP ( n  = 228) and IL-6 ( n  = 60) were evaluated in all subjects in whom both indicators were measured from onset to 15 days later (Fig.  7 ). As time passes from onset, the slope becomes − 0.03 for L-FABP and − 0.029 for IL-6. This shows that the values of both L-FABP and IL-6 decrease overtime. In severe cases, the value of L-FABP tends to be high soon after the onset of symptoms, and to remain high until at least 15th day. On the other hand, the value for moderate and mild remain low. The difference in severe cases was remarkable in L-FABP, and the mixed-effect model found a significant difference ( p  < 0.0001) to moderate or mild cases. Regarding IL-6, severe cases tended to show higher values in severe cases, but the difference was not significant to moderate or mild. Fewer cases and limited time points for serum collection in IL-6 might affect to statistical insignificance. IL-6 was reportedly high in severe cases around 10 days from onset. However, there are some severe cases showing the decrease of IL-6 level. It seems impossible to conclude in this analysis that IL-6 will take high value around 10 days on average value basis. Even though IL-6 was approved by the FDA as an indicator to discriminate severe cases, it frequently fails to distinguish mild from moderate cases. L-FABP does so with much greater certainty during the first 15 days. These dynamics are more evident in the earlier phase of infection.

figure 7

Dynamics of L-FABP and IL-6 during the first 15 days after COVID-19 onset. Urine L-FABP levels ( a upper panel) and serum IL-6 levels ( b lower panel) are shown using all cases from NCGM; L-FABP, 228 cases and IL-6, 60 cases. The thick maroon line represents the mean of severe cases. The thick blue line denotes the mean of moderate and mild cases. A wider space between the two lines (maroon and blue), represents a greater likelihood of discriminating severe cases. Mixed-effect model was used to compare L-FABP and IL-6. The slope of L-FABP becomes − 0.03 and that of IL-6 − 0.029 as time passes from onset, indicating that the value of both L-FABP and IL-6 decreases overtime. In severe cases, the value of L-FABP tends to be high soon after the onset of symptoms, and to remain high until at least 15th day. Regarding groups, there is a significant difference in L-FABP to discriminate severe cases ( p  < 0.0001), but IL-6 was p  = 0.344 and no significance. This suggest that L-FABP is more suitable to discriminate severe cases

Operations to increase the probability of success in clinical trials

Most drugs in clinical use for COVID-19 have been developed for other infections or diseases, and have been applied as viral RNA-dependent RNA polymerase inhibitors, rheumatoid arthritis drugs, lupus drugs, etc. Clinical trials for treatment of COVID-19 infection have not progressed well, despite the growing need of the public. Especially in early stages, clinical trials faced difficulties caused by enrolling a large number of cases that would recover without medical intervention. It is important to note here that drugs with a solid companion diagnosis, such as tocilizumab, were developed quickly. If therapeutic intervention is not directed toward high-risk patients, drug development will be extremely difficult in infectious diseases in which spontaneous resolution is possible. As mentioned above, urinary L-FABP is an in vitro diagnostic agent that can serve as a companion diagnostic tool.

Early risk assessment, where COVID-19 infection clusters occur, can facilitate subsequent management for both the government and patients. We developed a point-of-care test (POCT) using immuno-lateral flow of L-FABP and recently reported that the risk of severe disease in mild COVID-19 positive patients can be detected with 88.9% accuracy (sensitivity 100%, specificity 87.7%) by ROC analysis [ 10 ]. This has attracted much interest, especially in Europe and the United States, where self-care using OTC home tests is advancing. Although we have discussed COVID-19 here, this urinary L-FABP approach will be efficacious not only for COVID-19 cases, but also for SIRS and MARS, viral infections that display similar pathologies. Therefore, we expect this to be a useful risk management tool against future, unknown viral respiratory infections.

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A part of this review data is supported by Japan Agency for Medical Research and Development (AMED: Project #: 20he0822003j0001), National Center Global Health and Medicine (NCGM) Intramural Research Fund (Project #: 20A2013, 21A2002).

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Eisei Noiri & Katsushi Tokunaga

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Daisuke Katagiri

Antimicrobial Resistance Clinical Reference Center, Disease Control and Prevention Center, National Center for Global Health and Medicine (NCGM), Tokyo, Japan

Yusuke Asai

Nephrology, St Marianna University, Kawasaki, Japan

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DK: collected patient data and samples, managed data matrix, and facilitated the study, including public relations. YA: independently analyzed data and supplied comprehensive insight. TS: organized the multiple-center study both locally and internationally. TS: developed the POCT system with advice from EN. KT: managed research field suitable to complete current study in the midst of COVID-19 and gave advice. EN: designed this project and wrote this review.

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Noiri, E., Katagiri, D., Asai, Y. et al. Urine oxygenation predicts COVID-19 risk. Clin Exp Nephrol (2024). https://doi.org/10.1007/s10157-023-02456-5

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Risk Assessment and Management of COVID-19 Among Travelers Arriving at Designated U.S. Airports, January 17–September 13, 2020

Weekly / November 13, 2020 / 69(45);1681–1685

Philip Dollard, MPH 1 ,2 ,3 ; Isabel Griffin, PhD 1 ,2 ,4 ; Andre Berro, MPH 1 ,2 ; Nicole J. Cohen, MD 1 ,2 ; Kimberly Singler, DrPH 1 ,2 ; Yoni Haber, MBA 1 ,2 ; Chris de la Motte Hurst, MPH 1 ,2 ; Amber Stolp, MPAff 1 ,2 ; Sukhshant Atti, MD 1 ; Leslie Hausman, MPH 1 ,2 ; Caitlin E. Shockey, JD 1 ,2 ; Shahrokh Roohi, MPH 1 ,2 ; Clive M. Brown, MBBS 1 ,2 ; Lisa D. Rotz, MD 1 ,2 ; Martin S. Cetron, MD 1 ,2 ; CDC COVID-19 Port of Entry Team; Francisco Alvarado-Ramy, MD 1 ,2 ( View author affiliations )

What is already known about this topic?

As an early effort to prevent importation of SARS-CoV-2, CDC established entry screening at designated airports for passengers from certain countries.

What is added by this report?

Passenger entry screening was resource-intensive with low yield of laboratory-diagnosed COVID-19 cases (one case per 85,000 travelers screened). Contact information was missing for a substantial proportion of screened travelers in the absence of manual data collection.

What are the implications for public health practice?

Symptom-based screening programs are ineffective because of the nonspecific clinical presentation of COVID-19 and asymptomatic cases. Reducing COVID-19 importation has transitioned to enhancing communication with travelers to promote recommended preventive measures, strengthening response capacity at ports of entry, and encouraging predeparture and postarrival testing. Collection of contact information from international air passengers before arrival would facilitate timely postarrival management when indicated.

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In January 2020, with support from the U.S. Department of Homeland Security (DHS), CDC instituted an enhanced entry risk assessment and management (screening) program for air passengers arriving from certain countries with widespread, sustained transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). The objectives of the screening program were to reduce the importation of COVID-19 cases into the United States and slow subsequent spread within states. Screening aimed to identify travelers with COVID-19–like illness or who had a known exposure to a person with COVID-19 and separate them from others. Screening also aimed to inform all screened travelers about self-monitoring and other recommendations to prevent disease spread and obtain their contact information to share with public health authorities in destination states. CDC delegated postarrival management of crew members to airline occupational health programs by issuing joint guidance with the Federal Aviation Administration.* During January 17–September 13, 2020, a total of 766,044 travelers were screened, 298 (0.04%) of whom met criteria for public health assessment; 35 (0.005%) were tested for SARS-CoV-2, and nine (0.001%) had a positive test result. CDC shared contact information with states for approximately 68% of screened travelers because of data collection challenges and some states’ opting out of receiving data. The low case detection rate of this resource-intensive program highlighted the need for fundamental change in the U.S. border health strategy. Because SARS-CoV-2 infection and transmission can occur in the absence of symptoms and because the symptoms of COVID-19 are nonspecific, symptom-based screening programs are ineffective for case detection. Since the screening program ended on September 14, 2020, efforts to reduce COVID-19 importation have focused on enhancing communications with travelers to promote recommended preventive measures, reinforcing mechanisms to refer overtly ill travelers to CDC, and enhancing public health response capacity at ports of entry. More efficient collection of contact information for international air passengers before arrival and real-time transfer of data to U.S. health departments would facilitate timely postarrival public health management, including contact tracing, when indicated. Incorporating health attestations, predeparture and postarrival testing, and a period of limited movement after higher-risk travel, might reduce risk for transmission during travel and translocation of SARS-CoV-2 between geographic areas and help guide more individualized postarrival recommendations.

On January 17, 2020, entry screening of air passengers arriving from Wuhan, Hubei Province, China, the epicenter of the COVID-19 outbreak at the time, began at three U.S. airports (Los Angeles International Airport, California; San Francisco International Airport, California; and John F. Kennedy International Airport, New York City, New York) receiving the highest volume of passengers arriving from Wuhan Tianhe International Airport ( Table 1 ) ( Figure ). Beginning February 3, entry screening expanded to all passengers arriving from mainland China after the issuance of a presidential proclamation † restricting entry to U.S. citizens, lawful permanent residents, and other excepted persons. These travelers were routed to one of 11 designated airports. On March 2, travelers from Iran were added. § As Europe became a new epicenter of COVID-19, travelers from 26 countries in the European Schengen Area ¶ (effective March 14), the United Kingdom, and Ireland** (effective for both March 17) were added, and the number of airports to which passengers were routed expanded to 13. When travelers from Brazil †† were added on May 28, screening expanded to 15 designated airports.

Screening consisted of three steps. First, U.S. Customs and Border Protection officers identified and referred travelers for screening if they had been in one of the specified countries during the previous 14 days. Next, initial screening was conducted, which included observation for signs of illness, a temperature check using a noncontact infrared thermometer (fever defined as temperature ≥100.4°F [38°C]), administration of a questionnaire about signs and symptoms (fever, cough, and difficulty breathing) in the preceding 24 hours or exposure to a person with COVID-19 in the preceding 14 days, and collection of travelers’ U.S. contact information. The third step included referral of ill travelers and those disclosing an exposure for additional public health assessment by an on-site medical officer; if indicated, travelers were sent to a local health care facility for medical evaluation. The threshold for sending symptomatic travelers for public health assessment and deciding which among those would be sent for medical evaluation varied during the evaluation period, reflecting evolution of CDC’s definition for “person under investigation” §§ and operational considerations (e.g., testing capacity). Until March 20, travelers from Hubei Province were quarantined for 14 days upon arrival under federal or state authority.

All screened travelers received a Travel Health Alert Notice, an information card that advised them to stay home (or in a comparable setting, such as a hotel room) for 14 days after arrival and provided messaging on self-monitoring for COVID-19 symptoms and actions to take if symptoms develop. Traveler contact information was transmitted securely to state health departments via CDC’s Epidemic Information Exchange (Epi-X). In addition to covering all costs for CDC personnel and contractors conducting screening, CDC transferred about $57 million to DHS to support the screening operation and incurred additional costs for equipment, travel, and housing of quarantined travelers. At the program’s peak volume on March 20, designated airports were staffed with approximately 750 screeners, plus other supporting personnel.

During January 17–September 13, 2020, 766,044 travelers were screened (Table 1), 298 (0.04%) of whom met CDC criteria for referral. Travelers were referred because they had either been in Hubei Province (16, 5.4%), reported contact with a person with COVID-19 (four, 1.3%), or had signs or symptoms that triggered a public health assessment (278, 93.3%). Among the 278 persons who had COVID-19–like symptoms, the most common signs or symptoms triggering assessment were cough (73%), self-reported fever (41%), measured fever (17%), and difficulty breathing (13%) ( Table 2 ). Forty (14%) of these travelers were medically evaluated at a local health care facility, and 35 (13%) were tested for SARS-CoV-2 using reverse transcription–polymerase chain reaction (RT-PCR); nine of the 35 tests returned positive results, representing 0.001% (one per 85,000) of all travelers screened. Fourteen additional travelers with laboratory-confirmed COVID-19 were identified through other mechanisms rather than as a direct result of entry screening: six via established processes with airlines and airport partners to detect ill travelers and notify CDC and eight through notifications about travelers who had received a positive test result in the United States or another country before travel.

CDC relied initially on existing federal traveler databases to obtain passenger contact information to share with states, but missing or inaccurate data prompted adding manual data collection to the screening process. Manual data collection resulted in 98.1% complete records (i.e., records contained both phone number and physical address). CDC sent state health departments contact information for approximately 68% of screened travelers. CDC did not send records processed 12 days after travelers’ arrival, with insufficient contact data, or belonging to six states that opted out of receiving travelers’ data because of competing response priorities. Analysis of traveler data submitted electronically by airlines during September 14–24, after discontinuation of manual data collection, and supplemented by previously untapped federal databases, showed that 22% of traveler contact information records had phone number and physical address.

These findings demonstrate that temperature and symptom screening at airports detected few COVID-19 cases and required considerable resources. The observed yield was approximately one identified case per 85,000 travelers screened. Reasons for the low yield were likely multifactorial and might have included an overall low COVID-19 prevalence in travelers; the relatively long incubation period; an illness presentation with a wide range of severity, afebrile cases, and nonspecific symptoms common to other infections; asymptomatic infections; and travelers who might deny symptoms or take steps to avoid detection of illness (e.g., through use of antipyretic or cough suppressant medications) ( 1 ).

SARS-CoV-2 presents a formidable control challenge because asymptomatic (i.e., never symptomatic) and presymptomatic (i.e., contagious infections before symptom onset) infections can result in substantial transmission, which was unknown early in the pandemic ( 2 , 3 ). The proxy for infectiousness, viral shedding in the upper respiratory tract, is greatest early in the course of infection, before prominent symptoms are apparent, suggesting peak infectiousness at or before symptom onset ( 3 ).

These findings are consistent with mathematical models examining the effectiveness of airport screening for COVID-19, which suggest that most infected travelers would be undetected by symptom-based screening at airports ( 4 , 5 ). Nonetheless, reductions in travel (e.g., associated with issuance of travel health notices to avoid nonessential travel and some entry restrictions) and airport-based activities might have lessened the incidence of COVID-19 in the United States early in the pandemic by discouraging symptomatic persons from traveling, limiting entry of potentially infected travelers, and promoting actions to prevent transmission from infected travelers, including a recommendation to stay home for 14 days after arrival ( 6 – 8 ).

Challenges associated with providing complete and accurate traveler contact information to health departments, the high volume of travelers to some locations, and competing health department priorities when jurisdictions were confronting outbreaks, precluded efforts to contact most travelers after arrival to oversee self-monitoring as recommended at the time ( 9 ). Manual data collection of traveler contact information on arrival is resource-intensive and poses a risk to travelers who might have to wait in crowded, enclosed spaces while the information is collected. CDC is working with government and industry partners to develop a framework to collect reliable contact information electronically for airline passengers before arrival in the United States and enable secure, real-time data transfer for any public health follow-up, including air travel-related contact tracing, when indicated.

The findings in this report are subject to at least three limitations. First, not all symptomatic travelers were referred for public health assessment because many COVID-19 symptoms are nonspecific and available data (for travelers who were not referred) are insufficient to determine the proportion who might have had some symptoms. Second, most travelers referred for public health assessment were not sent to a local health care facility or tested for SARS-CoV-2. Both could have been sources of selection bias toward underestimation of the number of cases in screened travelers. Third, screening was limited to travelers from certain countries, and current surveillance systems lack information to match COVID-19 cases reported by states to known international travelers. Therefore, this report is unable to provide definitive assessment of the outcomes of screened travelers who were not referred for medical evaluation or to compare outcomes for screened travelers with those arriving from countries not targeted for screening.

The hallmark of effective public health programs is reassessment of methods used for public health practice based on available evidence. Therefore, CDC recommended a shift from resource-intensive, low-yield, symptom-based screening of air travelers to an approach that better fits the current stage of the pandemic, and on September 14, 2020, the screening program was discontinued. Protecting travelers and destination communities during the pandemic will require continued emphasis on implementation of health precautions before, during, and after travel, and communicating these recommendations to travelers and the airline industry. ¶¶ , *** , ††† , §§§ After the removal of requirements for enhanced entry screening operations, CDC continued to invest in strengthening illness detection and response under CDC’s regulatory authorities, ¶¶¶ by training of partners at ports of entry, as well as overall public health response capacity at 20 CDC quarantine station locations. CDC, along with U.S. government partners, also issued recommendations for airlines, airports, and travelers**** , †††† to prevent COVID-19 transmission associated with air travel. All travelers should follow CDC recommendations for mask use, §§§§ hand hygiene, self-monitoring for symptoms, and social distancing during travel and after arrival to the United States. Travellers with higher exposure risk should take additional precautions, including postarrival testing, avoiding contact with persons at higher risk for severe disease, and staying home as recommended or required by jurisdictional public health authorities. Predeparture testing of travelers, ideally with specimen collection within 72 hours before departure, might reduce the risk for SARS-CoV-2 transmission during travel. Postarrival testing could allow for shortening of posttravel self-quarantine periods that protect against travel-associated imported (translocated) infections. ¶¶¶¶ Finally, progress in understanding immunity biomarkers and duration of protection, in developing one or more vaccines, and in testing hold promise for refining risk stratification and optimizing management of travelers to reduce COVID-19 transmission and translocation related to commercial air travel.

Acknowledgments

All airport screeners (CDC quarantine station staff members, deployers from across the U.S. government, and contractors); Countering Weapons of Mass Destructions Office and Customs and Border Protection, U.S. Department of Homeland of Security; Dan Reed, Heesoo Joo, Clelia Pezzi, Terrianna Woodard, Candice Gilliland.

Corresponding author: Francisco Alvarado-Ramy, [email protected] .

1 CDC COVID-19 Response Team; 2 Division of Global Migration and Quarantine, National Center for Emerging and Zoonotic Infectious Diseases, CDC; 3 Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee; 4 Kapili Services, LLC, Orlando, Florida.

All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

* https://www.faa.gov/other_visit/aviation_industry/airline_operators/airline_safety/safo/all_safos/media/2020/SAFO20009.pdf pdf icon external icon .

† https://www.whitehouse.gov/presidential-actions/proclamation-suspension-entry-immigrants-nonimmigrants-persons-pose-risk-transmitting-2019-novel-coronavirus/ external icon .

§ https://www.whitehouse.gov/presidential-actions/proclamation-suspension-entry-immigrants-nonimmigrants-certain-additional-persons-pose-risk-transmitting-coronavirus/ external icon .

¶ https://www.whitehouse.gov/presidential-actions/proclamation-suspension-entry-immigrants-nonimmigrants-certain-additional-persons-pose-risk-transmitting-2019-novel-coronavirus/ external icon .

** https://www.whitehouse.gov/presidential-actions/proclamation-suspension-entry-immigrants-nonimmigrants-certain-additional-persons-pose-risk-transmitting-coronavirus-2/ external icon .

†† https://www.whitehouse.gov/presidential-actions/proclamation-suspension-entry-immigrants-nonimmigrants-certain-additional-persons-pose-risk-transmitting-novel-coronavirus/ external icon .

§§ https://www.emergency.cdc.gov/han/2020.asp .

¶¶ https://www.cdc.gov/coronavirus/2019-ncov/travelers/when-to-delay-travel.html .

*** https://www.cdc.gov/coronavirus/2019-ncov/travelers/travel-during-covid19.html .

††† https://www.cdc.gov/coronavirus/2019-ncov/travelers/after-travel-precautions.html .

§§§ https://www.cdc.gov/quarantine/travel-restrictions.html .

¶¶¶ https://www.cdc.gov/quarantine/specificlawsregulations.html .

**** https://www.cdc.gov/coronavirus/2019-ncov/travelers/airline-toolkit.html .

†††† https://www.transportation.gov/sites/dot.gov/files/2020-07/Runway_to_Recovery_07022020.pdf pdf icon external icon .

§§§§ https://www.cdc.gov/quarantine/masks/mask-travel-guidance.html .

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Abbreviations: COVID-19 = coronavirus disease 2019; IATA = International Air Transport Association.

FIGURE . Number of travelers screened for COVID-19 and changes in screening program — 15 designated U.S. airports, January 17–September 13, 2020

Abbreviations: COVID-19 = coronavirus disease 2019; UK = United Kingdom.

Abbreviation: COVID-19 = coronavirus disease 2019.

Suggested citation for this article: Dollard P, Griffin I, Berro A, et al. Risk Assessment and Management of COVID-19 Among Travelers Arriving at Designated U.S. Airports, January 17–September 13, 2020. MMWR Morb Mortal Wkly Rep 2020;69:1681–1685. DOI: http://dx.doi.org/10.15585/mmwr.mm6945a4 external icon .

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  • Open access
  • Published: 17 February 2024

Implementing a pilot study of COVID-19 self-testing in high-risk populations and remote locations: results and lessons learnt

  • Elena Marbán-Castro 1 ,
  • Vladimer Getia 2 ,
  • Maia Alkhazashvili 2 ,
  • Maia Japaridze 1 ,
  • Ia Jikia 1 ,
  • Berra Erkosar 1 ,
  • Paula Del Rey-Puech 1 ,
  • Guillermo Z. Martínez-Pérez 1 ,
  • Paata Imnadze 2 ,
  • Amiran Gamkrelidze 2 ,
  • Olga Denisiuk 1 ,
  • Elena Ivanova Reipold 1 &
  • Sonjelle Shilton 1  

BMC Public Health volume  24 , Article number:  511 ( 2024 ) Cite this article

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Rapid antigen-detection tests for SARS-CoV-2 self-testing represent a useful tool for pandemic control and expanding access to community-level case screening. COVID-19 self-tests have been extensively used in high-income countries since 2021; however, their introduction and programmatic implementation in low- and middle-income countries was delayed. We aimed to identify and continuously improve a weekly COVID-19 self-testing model among staff at healthcare facilities and schools.

This mixed-methods, observational prospective study was conducted in 5 healthcare centres and 24 schools in Georgia, between June and December 2022. The study comprised the integration of COVID-19 self-testing into the national mandatory testing programme for high-risk groups, with primary distribution of self-tests among staff performed weekly, plus secondary distribution to their household members. These use cases were selected because NCDC was seeking to strengthen their already strong weekly testing programme, by investigating self-testing to ease the burden of testing in the healthcare system. Online surveys and semi-structured interviews were used for data collection.

In total, 2156 participants were enrolled (1963 female, 72%). At baseline and mid- and end-points, 88%, 97% and 99%, respectively, of participants agreed/strongly agreed they would self-test. Similarly, the majority were willing to report their self-testing results (88%, 98% and 96% at baseline and mid- and end-points, respectively). Weekly reporting of test results to the national COVID-19 database was high during all the implementation. There were 622 COVID-19 positive results reported, and linked to care, from 601 individuals (282 participants and 319 household members). Findings from qualitative interviews showed great satisfaction with self-testing for its convenience, ease of use, trust in the results, no need to travel for diagnostics, and increased perception of safety.

Conclusions

Our findings contribute to the evidence-base regarding self-testing strategies conducted via workplaces and secondary distribution to households. Willingness to perform a COVID-19 self-test increased after implementation. This pilot enhanced pandemic preparedness through expansion of the national self-testing reporting system, development of communications materials, changes in the national legal framework and coordination mechanisms, and improved perceptions around self-care in the community. The lessons learnt can inform operational aspects of the introduction and scale-up of self-care strategies.

Peer Review reports

The novel coronavirus disease 2019 (COVID-19) pandemic was one of the greatest challenges to public health in recent history. COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Prevention of COVID-19 is a key control strategy that requires early and accurate diagnosis and prompt isolation of cases [ 1 ]. Screening and testing are cost-effective measures for COVID-19 control, as they enable contact-tracing that helps to ensure individuals isolate during their infectious period and accelerate these individuals’ access to psychosocial and clinical care [ 2 , 3 , 4 ]. In high-transmission scenarios, weekly testing for COVID-19 is a cost-effective strategy [ 5 ]. The World Health Organization (WHO) Strategic Preparedness and Response Plan for COVID-19 emphasised the need to accelerate equitable access to new tools to tackle COVID-19, including diagnostics, to reduce exposure, empower communities and protect the vulnerable [ 6 ].

In March 2022, WHO released interim guidance with a strong recommendation to use SARS-CoV-2 rapid antigen diagnostic tests (RADTs) as self-tests either as a diagnostic or a screening tool, depending on the epidemiological situation, appearance of symptoms, or recent exposure, and to facilitate linkage-to-care [ 7 ]. RADTs for self-testing allow individuals to fully perform tests themselves, from self-sampling to the interpretation of results; this may take place in an unsupervised or a supervised environment [ 8 , 9 ]. Self-testing has particular benefits in limited-resource settings, as it does not require laboratory capacities, and it reduces the burden on the healthcare system as individuals can check their infection status without the need to attend a healthcare facility [ 10 ]. The WHO guidance highlights that in certain settings, such as schools and workplaces, serial COVID-19 self-testing may be recommended for the early detection of outbreaks [ 7 ]. Self-testing has been used worldwide to expand access to human immunodeficiency virus (HIV) diagnosis, especially in the most vulnerable populations [ 11 , 12 , 13 ]. In 2016, WHO recommended HIV self-testing as a safe, accurate and effective way to reach those in need of diagnosis [ 14 ]; in 2021, similar recommendations were made for hepatitis C virus (HCV) diagnosis [ 14 , 15 ].

Since early 2021, COVID-19 self-tests have been extensively used in high-income countries, with many such countries deploying a wide range of self-testing strategies to complement testing efforts across multiple user-segments, including the workplace, schools, mass gatherings and the wider community [ 16 , 17 ]. However, the introduction and programmatic implementation of COVID-19 self-tests in low- and middle-income countries (LMICs) was delayed by several months. This lag further contributed to the gap in access to COVID-19 testing between high-income countries and LMICs, exemplified by the inequities in testing rates across income groups. During the first quarter of 2021, daily testing rates per 1000 individuals in high-income countries were approximately 90 times higher than in low-income countries and approximately 11 times higher than in lower middle-income countries [ 18 ].

These inequities in access to testing during the COVID-19 pandemic highlight the importance of developing tools and models for the timely adoption and roll-out of self-testing that can be tailored to specific user-segments in LMICs. WHO stressed the importance of identifying optimal approaches to deliver SARS-CoV-2 self-tests, based on epidemiology, identified gaps in testing, and the broader response resources needed for prioritised population groups [ 7 ]. Any service delivery approach must be sufficiently agile to reflect the evolving epidemiology and be adaptable to suit the needs of the local public health system and community preferences [ 7 ]. Creating these strategies now can contribute towards pandemic preparedness efforts for other respiratory viruses, particularly in LMICs, and play a critical role in reducing the response time in the future.

This study forms part of a portfolio of projects led by FIND and multiple partners, which has included the implementation of COVID-19 screening models assisted by the distribution of SARS-CoV-2 self-test devices, in Brazil, Georgia, India, Malaysia and Viet Nam. This implementation research was designed to optimise and tailor SARS-CoV-2 self-testing approaches to specific contexts and defined user-segments, to improve service delivery models. Furthermore, this study was designed to be performed in Georgia, based on (i) geographical representation from different regions with different degrees of SARS-Cov-2 self-testing maturity; (ii) the importance of obtaining data from a country in the WHO European region, where there is a dearth of information relating to the feasibility of SARS-CoV-2 self-testing to increase case-detection rates; (iii) the availability of local partners with an interest in integrating SARS-CoV-2 self-testing as part of a national testing strategy; and (iv) previous experience of research into self-testing for other infectious diseases by the organisations implementing the study (FIND and the Georgia National Center for Disease Control and Public Health (NCDC)).

Georgia is an upper middle-income country, located at the intersection of Europe and Asia, and has a population of 3.7 million people [ 19 ]. Between the beginning of the pandemic in January 2020, to October 2022, the country reported more than 1.8 million COVID-19 cases [ 20 , 21 ]. Since the start of the pandemic, the Georgian government has developed specific public health measures that were focused on identifying cases and preventing the spread of the virus [ 22 ]. Several interventions were implemented to reduce viral transmission, including a pass for vaccinated people or those who had proof of a negative COVID-19 test, and there was regular and frequent testing for high-risk groups, initially by reverse transcription polymerase chain reaction (RT-PCR) (since November 2020) and subsequently using RADTs, since February 2022 [ 23 ]. In Georgia, the first self-test for the detection of SARS-CoV-2 was authorised for use in April 2022; however, self-tests were still not being delivered to the public by the Georgian health system [ 24 ]. Regular, mandatory testing was provided for high-risk groups until May 2023; since then, mandatory testing has been replaced by a “recommendation” to test.

Populations at high risk of exposure to SARS-CoV-2 and/or who have difficulty accessing COVID-19 testing facilities, such as those living in remote, mountainous areas, would benefit most from self-testing strategies, as they do not require the travelling time and financial effort needed to access conventional, professionally delivered COVID-19 testing.

To introduce and scale-up self-testing in a particular setting or target population, it is essential to tailor and optimise the distribution model used. With this study, we aimed to identify and continuously improve SARS-CoV-2 self-testing strategies and supportive packages of a weekly self-testing model among staff at healthcare facilities and schools. The results would then be used to inform the potential integration of self-testing as part of the Georgian national SARS-CoV-2 screening programme for high-risk groups. Our study assessed a screening model among workplace staff and their household members, considering two important use cases: staff at high risk of exposure (hospital, clinic, nursing home and school staff) and staff in remote areas with limited access to healthcare (e.g. school staff in the Svaneti region). These use cases were selected because NCDC was seeking to strengthen their already strong weekly testing programme, by investigating self-testing to ease burden of testing in the healthcare system. The specific objectives were to: (1) assess the feasibility of a weekly SARS-CoV-2 self-testing model by examining the process, logistics, and site capacity to report self-testing results; (2) assess self-testing uptake and the reporting of results among staff and household members; (3) assess linkage-to-care following the use of a self-test; (4) assess knowledge acquisition with regards to self-testing; and to (5) explore participants’ satisfaction with the model. The primary outcomes of the analysis were perceptions around individuals’ willingness to self-test and report their COVID-19 self-test results to the national database.

Study design

This was a mixed-methods, prospective study conducted in a diverse range of working environments in Georgia between June and December 2022. These working environments comprised two hospitals, one clinic and one nursing home in Tbilisi; one nursing home in Kutaisi; and 24 public schools in the Svaneti region. In each location, a novel COVID-19 screening model was implemented, supported by the distribution of SARS-CoV-2 self-tests. The model included primary distribution of self-tests among staff at participating sites; these self-tests were to be performed weekly, if a staff member had symptoms, or if they were a contact of a person diagnosed with COVID-19. The model also included secondary distribution of self-tests, via staff members, to their household members and the wider community, to be used for individuals who were symptomatic or were a contact of a case. Self-tests were distributed free of charge to staff and their household members. This study was flexible in its design, to allow continuous improvements based on the results and feedback from staff members and stakeholders involved in the pilot study.

Key components of the self-testing package were communications materials and the system for reporting results. Communications materials were designed in collaboration with NCDC colleagues, to target knowledge gaps identified during the formative phase. These materials included a brochure and frequently asked questions (FAQs) for pilot participants, in addition to an existing video provided by the manufacturer of the self-tests.

Formative phase and study periods

Prior to launching the model in the targeted workplaces, a needs assessment and gap analysis phase was conducted, between October 2021 and January 2022. The purpose of this phase was to identify examples of best practice, conduct landscape research, perform stakeholder mapping and engagement, and define user segments. From January 2022 to May 2022, a formative research phase was conducted to inform the design of the screening model and the creation of the protocol and data collection tools. Implementation took place from June 2022 to December 2022, including trainings, screening, obtaining participants’ written informed consent, distribution of self-tests, and data collection. The overarching framework for this study (Fig.  1 ) was co-developed by FIND and NCDC.

figure 1

Timeline of the study’s design and implementation

Study settings

NCDC selected all sites for participation during the needs assessment and gap analysis phase. The first criterion for selecting user segments and the sites was related to exposure, i.e. high-risk population groups who fell within the government’s mandatory COVID-19 screening programmes (weekly testing). The individuals who did not consent to enrol as participants in this study were advised to comply with government recommendations for healthcare provider-administered COVID-19 testing.

Tbilisi is the country’s capital and the most populous city in Georgia. Located in central-east Georgia, it has healthcare centres with some of the largest catchments in the country. The two largest government-run nursing homes in Georgia, located in Tbilisi and Kutaisi, were selected because of their high-risk populations (the elderly). Kutaisi, located in western Georgia, is one of the oldest cities in the country and the third-most populous.

The second criterion for selecting user segments and the sites was a lack of access to conventional SARS-CoV-2 testing due to geographical barriers. The Svaneti region, in Mestia district, is a mountainous area in the north-west of Georgia, where for most people there is no medical service within a radius of 30 km. Access to health resources in this area is very difficult, especially during the winter, due to heavy snow and transportation difficulties.

Study participants and recruitment criteria

Study staff were recruited from personnel already working at the sites and who were then trained to help with the study procedures. Study staff were responsible for engaging potential participants in the study, training them in the use of self-tests, distributing self-tests, receiving results and reporting results to the Georgia NCDC national COVID-19 laboratory (LabCov) database. Participants were responsible for reporting their own results and those of any household members to study staff.

All staff from the selected sites were invited to participate in the study. After signing an informed consent form, they became participants. Participants were able to invite members of their household to participate in the study if they needed to self-test. The inclusion criteria included: willingness to provide informed consent, being aged more than 17 years, working or volunteering at the study sites, and willingness to self-test on a weekly basis. Age restrictions did not apply for household members. Minors were required to provide their assent in addition to the consent of their parents or legal guardians. No data were directly collected from household members (Table  1 ). The selection of participants for qualitative interviews was based on purposive convenience criteria to shortlist potential interviewees among the sample of participants. During the purposive sampling process, efforts were made to include participants from various genders, sites, job professions, and other relevant factors. Shortlisted participants were approached by staff at sites, in person or by phone to be invited to participate in a SSI.

Implementation strategy

All recruited staff performed self-testing weekly, or more frequently in the case of symptoms or being a contact of a case, until 31 December 2022. Their household members used self-testing as needed if they were symptomatic or a contact of a case. As per the national guidelines, if a self-test result was negative, but the user had symptoms, they were advised to repeat a self-test after three days. If the result was positive, participants were asked to self-isolate. Participants knew their COVID-19 status within 15 min after self-testing and learnt to act accordingly. Counselling was provided, and all positive cases were linked to care according to the national guidelines. The internal channel used to communicate self-test results varied according to participants’ and study staff preferences, as well as the result (a positive result required instant communication). Reporting channels included phone calls, SMS, instant social messaging (such as WhatsApp®), a shared registry in Google Docs®, a paper-based sheet at work, and others. Participants could choose which channel to use to report their results.

The self-testing devices distributed were approved for use in Georgia and were considered to expose individuals to minimal risks. The COVID-19 self-test device used was the OnSite® COVID-19 Ag Self-Test (CTK Biotech, California, USA). The instructions for use were translated into Georgian by the manufacturer, reviewed by national stakeholders and optimised by potential participants based on cognitive interviews conducted during the formative phase.

Data collection and processing

The data collected included participants’ data regarding self-test use, participants’ responses to the online survey questionnaire, and qualitative data generated during the semi-structured interviews (SSIs) (Fig.  2 ).

figure 2

Flow diagram of study activities

Reporting data

COVID-19 self-test reporting data were collected in a study report log created in Microsoft Excel® (date, location, reason for performing the self-test, symptoms, and self-test result). Data from the reporting log were entered into the national LabCov database and validated according to NCDC database rules. National guidelines for linkage to care and surveillance procedures were followed for all results and all cases. Participants were responsible for reporting their household members’ self-test results to study staff. For the reporting of results, the NCDC and the Information Technology Agency (ITA) expanded the existing national LabCov database to incorporate COVID-19 self-testing results. In parallel, the national e-Health mobile application was expanded to incorporate reporting of self-testing results and was launched in the last weeks of the pilot study as an additional channel via which results could be reported. All results were linked to the existing COVID-19 national surveillance and care cascades. Cases that were positive by self-testing were tracked and managed according to the national protocol (reported to the Georgia Ministry of Health (MoH), to provide information about COVID-19 transmission trends and potentially lead to updates in national guidelines).

Surveys at enrolment, mid- and end-points

Alongside the implementation, an anonymous longitudinal online survey questionnaire was administered, to assess acceptability, knowledge and satisfaction. Participants were asked to complete the questionnaire at enrolment, mid-way through and at the end of the study, (Supporting information, Annex I ; this version has been edited for consistency and to facilitate readers’ understanding). The questionnaire was written in English, based on insights gathered during the formative phase, and then translated into Georgian. The online surveys were continuously monitored for validity, including review by local stakeholders and responses from participants during the formative phase and during study implementation itself. The questionnaire was set up using the online data collection software, Alchemer®. All questions were mandatory, to minimise the risk of missing data. Data cleaning was performed using the Python programming language (version 3.9.7) integrated in Jupyter Notebooks and managed using the Visual Studio Code development environment (version 1.79.2). The database handling library used was “pandas” version 1.3.4. Data manipulation techniques were employed to enhance data quality and facilitate the integration of the three survey databases (at the baseline and mid- and end-points. Text substitution was performed using the “replace” function from “pandas”, to avoid misspelling or errors due to the manual input of data. Initial data cleaning involved rectifying incorrect identification numbers (“ID numbers”) to ensure consistency. Responses that lacked identifiable unique ID numbers were excluded from the analysis to maintain data integrity and reliability. Free-text responses collected in Georgian were translated into English. Subsequently, the three databases were merged using the unique ID numbers.

Qualitative data

Three female interviewers conducted the SSIs (EM-C, IA and NB). Two were medical doctors (MDs) and one held a PhD. All were part of the study team, two of them working exclusively for the study and one MD working at a public health hospital. One MD had no previous experience of interviewing but was trained to conduct interviews and had existing relationships with the interviewees. The other two interviewers had previous experience of conducting interviews, but no existing relationships with the interviewees. All interviewees knew about the study before the interviews took place. For the SSIs, study staff invited participants (staff at sites and also those who had a role as study staff) to be interviewed, until the saturation point was reached [ 25 ]. Interviewers’ were trained to minimized biasing participants responses while asking the questions, and to be aware of their internal biases.

The qualitative data collected were used to explore participants’ perceptions of and experiences with the self-testing model, their success stories, and any potential social harms. Interviews were performed online, with participants at their homes or at any place decided by them, thus, potentially other people were present beside participants and researchers. The interviews lasted approximately 30 min, were audio-recorded and notes were taken. The interviewers used a guide that was specifically developed for this study and can be found in the Supporting information (Supporting information, Annex II ).

Data analysis

The various datasets collected were analysed separately, as outlined below.

The self-test results collected in the reporting logs were monitored using an Excel tool. The data were cleaned and analysed using R 4.2.2 statistical software. Descriptive statistics were calculated using the “vtable” package, “sumtable” function in R. The uptake and reporting of self-tests to the national database were analysed by sex at birth, site, type of user and self-test result.

Surveys at enrolment and the mid- and end-points

The descriptive statistics were calculated as described above for the reporting data. Linear regression analysis was conducted to investigate relationships between willingness to self-test and to report results and perceptions around being worried about COVID-19 and understanding the benefits of self-testing. Responses were recorded using a five-point Likert scale and were coded from –2 (“strongly disagree”) to 2 (“strongly agree”), with 0 being “neutral”; time was coded as 1 (baseline), 2 (mid-point) and 3 (end-point). The regressions were built using general linear models using the “lm” function in the “stats” package. Contrasts were defined using the “emmeans” function within the “emmeans” package. Visualisations were created using “ggplot2”. P -values were adjusted for multiple comparisons using "p.adjust” function in R and using Benjamini-Hochberg (BH) method. Due to the large sample size, the significance threshold for the p -value was defined as 0.001.

The SSIs included a set of questions that corresponded to (1) sociodemographic context and previous experiences with COVID-19, (2) perceptions and satisfaction with self-testing, (3) use of self-tests and value of the reporting mechanism, and 4) exploratory questions about willingness to pay for a COVID-19 self-test. The SSIs were audio-recorded, and notes were taken. Thematic analysis was conducted [ 26 ]. Meetings were held with the study team to identify and discuss common themes and codes. The coding process involved a combination of inductive and deductive approaches, with pre-existing concepts from the interview guide used to categorise the information (themes were deductive from the guide) and codes derived from the data (inductive codes). COREQ guidelines were followed (Supporting information, Annex III ) [ 27 ].

Ethics considerations and approval

The main risk that could derived from participation in the study would be social harm resulting from a breach in confidentiality. To minimise this risk and prevent its occurrence, study staff at all study sites were trained in ethics and confidentiality issues. All participants provided written informed consent. Household members provided oral consent when written informed consent could not be obtained. This study protocol was approved by the NCDC Institutional Review Board (Ref.: # 2022-049, May 24, 2022). The study was conducted in accordance with the Belmont Report [ 28 ], the Declaration of Helsinki [ 29 ] and applicable ICH Good Clinical Practice E6 (R2) standards [ 30 ].

Screening, enrolment, refusal to participate and withdrawal

A total of 2156 staff from the various sites were enrolled in the pilot study, which corresponded to 99% of the total number of staff (Fig.  3 and Table  2 ). Smaller sites (< 100 employees) enrolled 100% of their staff. Overall, just 19 participants refused to enrol, and 103 withdrew during the implementation. In addition, 582 household members were enrolled during the implementation of the pilot study, yielding a total of 2738 self-test users.

figure 3

Participant numbers and survey response proportions

Table 3 shows the participants’ basic sociodemographic data and household members who were screened and enrolled. Most participants (71.7%) were female, with the majority aged less than 31 years (25.5%) or aged 51 to 60 years (21.4%). Of all self-testing users, 20% were household members (Table  3 ).

Sociodemographic characteristics of participants

More detailed sociodemographic data were collected from participants who completed the online enrolment survey (57% of those enrolled) (Table  4 ). Among the participants, 80% self-identified as female, and the mean age was 47.2 years. Regarding educational background, 65% of participants had completed university studies. Participants from healthcare centres and schools had a higher level of education compared with participants from nursing homes (67% and 69% university level vs. 39%, respectively). More than 20% of participants at all sites lived with four or more household members, but for the majority just one of them had been employed in the past three months. Smartphone ownership was lower among participants from schools in the Svaneti region (65%) compared with participants from healthcare centres in Tbilisi (86%) and nursing homes in Tbilisi and Kutaisi (87%). Most participants from the schools in Svaneti had not received any dose of COVID-19 vaccine (67%). More than half of participants at all sites had been previously diagnosed with COVID-19, especially those from nursing homes (84%). While most participants from healthcare centres (77.2%) and schools (83.3%) declared they had no medical condition/risk factors for COVID-19, just 2.6% of participants from nursing homes declared they had no medical condition/risk factors for COVID-19.

COVID-19 self-testing reporting

A total of 52,985 self-tests were reported to the national COVID-19 database (Table  5 ). Of these self-tests, 41,443 (78%) were performed by females. Just 3.3% of COVID-19 self-tests were performed in the presence of symptoms. There were 622 COVID-19 positive results reported from 601 individuals (282 participants and 319 household members). A total of 1080 self-tests were used and reported by household members, of which 31% were positive. The majority of COVID-19 self-tests (95%) were performed in households.

More than 78% of all self-tests reported by participants were performed by females, compared to 35% to 47% of female household members (Supporting information, Annex IV ). A significant percentage of household members tested positive, 29.4% in healthcare centres compared with 64.3% in nursing homes, and 51.4% in schools (Supporting information, Annex V ). While most COVID-19 self-tests performed as part of the participants’ weekly monitoring were negative (97%), among all positive cases, 81% of them self-tested because they had symptoms (Table  6 ). Asymptomatic infections were detected in 19% of positive self-tests performed by participants and in 6.9% of those performed by household members (Table  6 ).

Since all study sites started enrolment, self-testing reporting rates, among participants and their household members, remained high among the 26 weeks of the implementation (Supporting information, Annex VI ).

COVID-19 perceptions, willingness to self-test and to report results

Baseline and mid- and end-point online surveys were completed by 1326 (57%), 1481 (69%) and 1507 (70%) participants, respectively. Most participants at all sites and times agreed/strongly agreed with the idea of self-testing for SARS-CoV-2 (88% at baseline, 97% at mid- and 95% at end-point). Similarly, the majority of participants were willing to report their results after self-testing (88% at baseline, 98% at mid-point and 96% at end-point). Willingness to perform and report self-testing results increased during implementation, especially in nursing homes (Fig.  4 ).

figure 4

Reported perceptions about COVID-19 and willingness to self-test and report results, by site

Participants’ understanding of the benefits of COVID-19 self-testing increased during the implementation period. However, participants had differing levels of worry regarding COVID-19, at different times and across the various sites, with participants in nursing homes reporting being more worried about COVID-19 at the end-point (December 2022) (Supporting information, Annex VII ). To have a deeper understanding of the trends in the perceptions over time in different type of sites, we have analysed the data using linear regressions (Supporting information, Annex VII ). In all cases, main effects (Time and Site type) were significant overall (adjusted p -value < 001) highlighting a general increase over time, and site to site differences. However, the most obvious trend was the change of opinion in time that was different by site type: Worries about COVID-19, understanding the benefits of self-testing, willingness to report and willingness to perform a self-test highly increased over time in nursing homes but not necessarily in schools or health care centres. This was supported by a statistically significant p -value for the interaction term in all cases (adjusted p -value < 0.001 for all questions (Supporting information, Annex VII ). Age and sex were not significant in any of the regressions.

Knowledge about COVID-19 self-testing

Overall, there was an increase seen in the knowledge levels from the baseline data to the end-of-study data. At baseline, 80% of participants correctly answered where from their body they should take a sample for self-testing, compared with 91% at end of the intervention (Table  7 ). Regarding what a positive result from a self-test means, knowledge levels were very similar at the baseline (88%), mid- (92%) and end-points (89%) of the study. A correct understanding of what the faint line in a self-test cassette means was understood at baseline by 53% of participants, followed by 59% and 68% at the mid- and end-points, respectively. However, some knowledge gaps remained; for example, 20% of participants thought that after a positive result with a faint line, they would need to repeat a self-test.

Key qualitative themes: participants’ perceptions and experiences

During the implementation pilot study, 54 SSIs were performed; most participants were female, aged more than 41 years and from healthcare centres. At the mid-point, 32 SSIs were performed, with most participants being female, aged 41 to 50 years and from healthcare centres (Supporting information, Annex VIII ). At the end-point, 22 SSIs were performed, 16 with participants and 6 with participants who were also study staff. Most of the participants were female, aged more than 41 years and from healthcare centres. Themes identified in advance, following the interview guide included: previous experiences with COVID-19, COVID-19 self-testing experiences, advantages, disadvantages, feelings and willingness to pay for COVID-19 self-testing (Supporting information, Annex IX ).

Among all participants interviewed, the most commonly reported advantages of COVID-19 self-testing were that it was comfortable/painless, time-saving, simple/easy and convenient. Only two people expressed concerns about self-testing, in interviews performed at the mid-point; these concerns were related to fears/doubts about other individuals not reporting their results. Most participants reported that it would be very valuable to have COVID-19 self-tests available for the general population, as exemplified by the following quote:

“When people outside work heard about the project and self-test availability they were jealous, and wanted also to buy them” (Female, 34-year-old, caregiver)

Participants shared how weekly self-testing made them feel. Most responses were related to feeling calm knowing that their work colleagues were being screened and were testing negative, and that they could easily access a simple self-test if they or their household members had symptoms or had been in contact someone who had COVID-19.

“The project caused us peace” (Female, 52-year-old, accountant)

Although it depends on the price, most participants reported they would be willing to buy COVID-19 self-tests if they were available in pharmacies or shops. In total, 45 participants responded to the question regarding the price they would pay for a self-test. Of them, 40% stated that the price should be approximately 5 lari (1.9 USD), 20% stated it should be from 5 to 10 lari (1.9 to 3.8 USD), with other participants stating prices that ranged from 0.5 to 30 lari. Participants from the Svaneti region and Kutaisi were less likely to identify a price compared with participants from Tbilisi city. The lowest price, 0.5 lari (0.2 USD), that would be paid for a self-test was stated by a participant from Svaneti, and the maximum price, 20 to 30 lari (7.7 to 11.6 USD), was stated by a participant from Tbilisi. Participants noted the need for the government to adapt the price of self-tests to ensure they were available to the most vulnerable populations, such as the elderly, those who cannot afford to pay for tests, school students etc.

“Previously, school staff shared transportation to the testing location, and if one tested positive, all of us had to self-isolate” (Female, 39-year-old, teacher)

By the end-point of the study, most participants’ perceptions about COVID-19 self-testing had changed in a positive way. Most of their concerns at the beginning were related to uncomfortable experiences during PCR or professional antigen testing. During the pilot study, the participants realised that the nasal swabs used for self-tests were very comfortable and painless. Furthermore, none of the participants reported any concerns regarding a lack of trust in their self-test results or any privacy issues regarding the handling of their data.

Questions about participants’ behaviour upon a receiving a positive COVID-19 result were only asked during the end-of-study interviews, to the final six participants. Three of them disclosed that they self-tested positive for COVID-19 during the pilot study, which enabled them to promptly self-isolate. Participants also related that on some occasion they had an invalid result, and they knew what to do, to call their assigned study staff and perform another self-test, as illustrated in the following quote.

“ One invalid result was yesterday, the test did not show any result, I was informed that this kind of case might happened, so I knew what to do, took another test and notified my facilitator accordingly .” (Male, 19 years old, administrative)

Similarly, one participant shared that he had a faint line result in one self-test and knew what he needed to do, as he had no doubts about the result being positive:

“In 2022 July, I was positive, used ST, the line was very faint but for sure considered as a positive and self-isolated” (Male, 35 years old, medical doctor).

Six study staff were interviewed at the end of the study, not only to share their experiences as participants but also as study staff. No difficulties were reported in terms of building positive and trusting relationships with participants. Study staff recognised that their role in the pilot was essential for creating a welcoming environment, where participants felt comfortable sharing their self-testing results, concerns and experiences, and identifying any challenges so they could be appropriately addressed. Some study staff, especially those living in Svaneti, saw the value in self-testing and declared that they would continue helping staff at their site and other staff to enable self-testing to continue. This may have been due to their bad experiences with transportation for the previous testing method and their good experiences with self-testing. During implementation, self-testing increased participants’ perception of safety (at work and in their wider environment). At a mid-point workshop, preliminary results were shown to the study staff. As most positive cases were detected among those who self-tested when they had symptoms, discussions were held regarding whether self-testing should occur only in symptomatic individuals. However, due to the participants’ increased perception of safety, the study staff preferred to continue with weekly self-testing.

Implementation results

For this study, several legal documents had to be signed by Georgia’s MoH to register, import and distribute COVID-19 self-tests in the country. The expansion of the existing reporting system for recording COVID-19 testing results (the LabCov database), to incorporate self-testing results, enabled the pilot study data to be integrated into the existing national data management and linkage-to-care systems. The platform was ready for use when the pilot study started in June 2022. An expansion of the national e-Health mobile application was carried out, with the engagement of the ITA, in parallel with the pilot study implementation and was launched in December 2022. Participants were able to directly upload their self-testing results with minimal data entry, and their results were reflected in LabCov in real-time. The e-Health application was initially rolled out among study staff and then expanded to the other participants in January 2023. Additionally, various communications materials were co-designed by FIND and NCDC to address specific knowledge gaps identified during the formative research phase. Communications materials that were provided to study staff during the train the trainer sessions included FAQs and training materials about the study’s procedures. Communications materials that were given to study participants by the study staff during the distribution of self-tests included FAQs, a brochure and a link to the manufacturer’s video. Additional gaps in knowledge identified during the pilot implementation and preliminary analysis of results, were addressed with workshops and trainings targeted for the specific sites. Additionally, four videos with information and case studies about self-testing were created, in the Georgian language, for use by the general population.

The present study aimed to assess and improve the distribution models used for self-testing for SARS-CoV-2 in healthcare centres and schools in Georgia and to generate data to inform the potential inclusion of self-testing as part of the national testing programme. We employed a mixed-methods, observational, prospective approach. The findings of this study have provided valuable insights into the feasibility and acceptability of self-testing as a strategy for COVID-19 control, particularly in workplace settings and in households of individuals at high risk of exposure and who are in remote areas. Additionally, the study has provided information about the operational aspects of implementing and scaling up self-testing in resource-limited settings.

Georgia has extensive experience in designing and implementing self-testing strategies for HIV and HCV. However, at the time this pilot study was initiated, COVID-19 self-tests were not widely available in the country and were not part of national policies. Despite the multiple differences in disease epidemiology and risk factors between these infections, Georgia’s previous experience in self-testing played a key role in the success of the implementation of this study.

Our results suggested that routine monitoring for COVID-19 using self-tests was feasible and acceptable among staff at healthcare and education centres. Depending on the epidemiological situation, and following updated guidelines, regular self-testing can be well-received and integrated into routine testing practices, enhancing case screening at the community level. Other studies have found similarly high acceptability of regular COVID-19 self-testing in various populations, including students [ 31 ], children at day care centres [ 32 ] and primary school children [ 33 ]. Our pilot study highlights the feasibility of using existing human resources and systems in place at both the site and country level to operationalise self-testing strategies.

The implementation of the self-testing pilot study was successful, as evidenced by the high enrolment rates and the substantial number of self-tests reported to the national COVID-19 database and thus linked to care. A high proportion of participants actively participated in the self-testing programme, with high weekly reporting rates during the 26 weeks of implementation. This indicates a high level of engagement with and adherence to the self-testing protocol, further supporting the feasibility of self-testing as a widespread screening strategy and the importance of providing flexibility in the reporting channels. While most positive cases were detected among symptomatic individuals (81%), our engagement with pilot study staff and other national stakeholders during the implementation revealed a preference for weekly testing, based on a corresponding increased perception of safety.

Notably, through the self-testing pilot study, a considerable number of COVID-19 positive cases were identified, both among participants and their household members. The detection of positive cases among participants and their household members highlights the potential of self-testing to identify infected individuals and facilitate timely linkage-to-care. This finding underscores the importance of self-testing for detecting and containing viral spread, especially in remote settings, among individuals who are far from a healthcare centre and employees at high risk of exposure. The low rate of detection of asymptomatic infections in this pilot (18% in participants and 7% in household members) suggests that a scaled-up program may benefit from targeted testing based on factors such as symptoms, exposure risk, and community case pressure, while there is not an epidemic peak. Further studies are needed to establish the cost-effectiveness of different testing strategies depending on the epidemic stage to optimize the best use of resources. The high proportion (31%) of cases detected among household members provides evidence of the importance of the secondary distribution of tests to further increase detection.

This study provides evidence for the importance of developing and tailoring self-testing support packages, in particular to increase knowledge and awareness about both testing and self-testing. The knowledge surveys we conducted indicated an increase in participants’ knowledge from the baseline to the end-point. This was likely due to the continuous provision of communications materials, the role of the study staff (who were always available to answer questions and address any doubts), and targeted discussions and trainings held to address specific knowledge gaps identified during implementation. The role of the study staff to be the contact point during the implementation for their assigned participants was key for participants to build confidence, to empower them to self-test, to solve concerns, and to explain when and who to self-test (for example, in case of household members with symptoms), and to understand their self-test results.

The success of the pilot study contributed to the expansion of self-testing in other healthcare centres and among the general population in different areas of the country. When the pilot started, self-testing was offered by NCDC as an additional testing requirement to professional testing. However, as evidence from the pilot study emerged and the severe phase of the COVID-19 pandemic was declared to be over, NCDC and the MoH decided to continue with the self-testing approach among those sites participating in the pilot study. Self-testing was also expanded to patients on dialysis, medical staff at emergency centres, etc. The pilot study provided valuable insights into the operational aspects of self-testing, informing the scale-up process and enabling NCDC to expand their national database for reporting self-testing results. The integration of self-testing into the national e-Health mobile application demonstrates the potential for technology-based solutions to enhance self-testing implementation and data management. Community-based asymptomatic testing has been associated with substantial reductions in COVID-19-related hospital admissions [ 34 ].

This study contributes to the evidence base on the use of self-testing in workplaces, particularly for staff at high risk of exposure, and the secondary distribution of self-tests to household members and the community. The findings highlight the acceptance by individuals of incorporating self-testing into comprehensive testing strategies during health emergencies, especially in resource-limited settings, and how self-testing can play a role in changing people’s mindset and culture around self-care, in a context of routine care.

The information resulting from the assessment of this enhanced screening model enabled the creation of self-testing implementation resources for Georgia and other public health resource-constrained countries. These resources can subsequently be used to rapidly deploy and scale-up self-testing strategies as part of pandemic preparedness. As there is a dearth of evidence on the costs of self-testing, as well as how to deploy and scale-up self-testing for outbreaks and health emergencies, the lessons learnt from this study can also inform self-testing modalities for future pandemics or for health emergencies that are endemic in many LMICs.

As suggested by qualitative studies conducted in Indonesia and Brazil [ 10 , 35 ], the use of self-testing could reduce the demand on health facilities while addressing many of the usual barriers to the uptake of services, leading to more timely testing of greater numbers of individuals. It is also hoped that the findings will influence policies on self-sampling, both nationally and internationally. Lessons learnt from this study may be used to tailor and optimise self-testing delivery packages and models; drive demand-generation for diagnosis and self-testing in Georgia, the wider region and other countries around the world; and support the gaining of market approval for self-testing devices in jurisdictions where self-tests remain unregulated.

Operational lessons learnt from our COVID-19 self-testing pilot, however, must be considered in context for other diseases that are more stigmatised, such as HIV, where self-tests have the capacity to decrease the gap in testing [ 36 ], but, for example, reporting mechanisms for individuals to disclose their infection status might need to be adapted. Another mixed-methods study, also conducted in Georgia, to investigate self-testing for HCV among populations at increased risk of this infection, such as people who inject drugs, men who have sex with men, and transgender people, again found that people considered self-testing very convenient and easy to use [ 37 ].

Our study had some limitations. These include the potential bias towards acceptability of the implementation or increased satisfaction with self-tests, as study staff were also considered participants. However, there were only 65 study staff who were also participants, corresponding to just 3% of the total number of participants. Further limitations included the risk of memory bias, social desirability and observer bias in the interviews. Another potential limitation of our study was the presence of inconsistencies in the ID numbers within the dataset. While data manipulation procedures were instrumental in ensuring dataset integrity, it was not feasible to correct all ID numbers due to the number of initial inconsistencies. Despite this limitation, the remaining dataset contained all responses per timepoint and provided valuable insights for our analysis. Finally, there could have been some limitations due to the self-test specificities and sensitivities and potential false-negative results in asymptomatic individuals, or issues with individuals’ errors in the use of self-tests and the interpretation of results [ 38 , 39 ]. However, RADTs have been shown to have high sensitivity and excellent specificity [ 8 , 40 ]. Nevertheless, to assign a diagnosis of COVID-19, the interpretation of self-test results must be considered in combination with clinical information and according to updated national guidelines. Despite some potential limitations, RADTs for SARS-CoV-2are recommended by WHO to be offered as self-tests, due to the evidence in support of users being able to reliably and accurately self-test and because they reduce inequalities in access to testing [ 41 ]. Inherent to the study design, a limitation of the study was that we were not able to obtain socio-demographic information from those who did not answer the to compare their data with the ones who responded.

Strengths of our study include that the design was sufficiently flexible to be adapted to the study’s needs, the results were linked to the existing national COVID-19 surveillance database, trust was built among stakeholders, staff at sites and participants, and that a mobile application was launched nationwide to facilitate the reporting of self-test results.

While we understand that there is a gender skew among participants in this study, following global trends where women constitute 70% of the healthcare and education sectors, it is important to learn from this implementation and tailor approaches (trainings, workshops, sensitization materials) to reach other workplaces and population groups, including children and adolescents. The findings of our study have implications for the broader adoption of self-testing in diverse settings and beyond COVID-19 and can guide the operational aspects of introducing and scaling up self-testing for various diseases within the community. Future research should focus on evaluating the long-term sustainability and cost-effectiveness of self-testing programmes, while also exploring strategies to further enhance uptake of and adherence to self-testing initiatives for various diseases and to bridge the diagnostic gap.

This study has produced valuable evidence regarding the feasibility and acceptability of self-testing in workplace settings and as part of a national testing programme for groups at high risk of infections, which subsequently informed the successful scale-up of COVID-19 self-testing in various healthcare centres across different regions of Georgia. Enrolment and participation rates in the COVID-19 self-testing pilot study were high, with consistent weekly reporting over a six-month period. This pilot study successfully detected more than 600 COVID-19 cases, half of which were identified among household members. Self-testing increased participants’ perception of safety. After implementation, there was a slight increase in individuals’ willingness to perform COVID-19 self-tests and report the results, and people’s knowledge of self-testing increased. Participants expressed a high degree of satisfaction with the use of self-testing, especially those residing in remote areas who no longer needed to travel long distances for diagnosis. Notably, self-testing greatly improved access to testing for teaching staff and their household members in rural villages.

In Georgia, this pilot study has improved pandemic preparedness and strengthened capabilities to incorporate self-testing for other diseases through the expansion of the national self-testing reporting system, the development of self-testing communications materials, changes in the national legal framework, the establishment of self-testing coordination mechanisms within sites and within NCDC, and by changing perceptions around self-testing and self-care, both among study participants and national stakeholders. This research contributes to the evidence on the use of self-testing strategies in workplaces for staff at high risk of exposure and in remote locations and highlights the importance of secondary distribution. Lessons learnt from this study have the potential to inform operational aspects of the introduction and scale-up of self-testing for other diseases during health emergencies or routine care, in other countries and settings, particularly resource-limited settings.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We would like to thank our partners and the study sites for their contributions to this study: Georgia Ministry of Health (MOH); National Center for Disease Control and Public Health (NCDC); Information Technology Agency; Academician N. Kipshidze University Clinic; Infectious Diseases, AIDS and Clinical Immunology Research Center; Curatio clinic; Agency for state care and assistance for the (statutory) victims of human trafficking. We would also like to thank Tbilisi nursing home, Kutaisi nursing home, Public Health Center Svaneti (PHC), Becho, Daba Mestia N1, Daba Mestia N2, Daba Mestia N3, Etseri, Idliani, Ieli, Iphari, Kala, Latali, Lakhamula, 17 Lenjeri N1, Lenjeri N2, Mazeri, Mulakhi, Muzhali, Nakra, Ushguli, Phari, Kar-Sgurishi, Tskhumari, Tsvirmi, Chuberi, and Khaishi public schools. Furthermore, we would like to thank the US CDC office in Tbilisi and the Health Research Union (HRU). Editorial support was provided by Adam Bodley, according to Good Publication Practice and funded by FIND.

This research was funded by the German Federal Ministry of Education and Research (Bundesministe-rium für Bildung und Forschung, BMBF) under grant number KFW-TBBU02. The funders played no role in the study design or the collection, management, analysis or interpretation of data; writing of the report; or the decision to submit the report for publication.

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Contributions

Conceptualisation, E.I. and S.S.; methodology, E.M-C.; K.G.; M.A.; M.J.; I.A.; P.DR-P; and N.L.; formal analysis, E.M-C.; M.J.; I.J., and N.L.; writing-original draft preparation, E.M-C.; writing; review and editing, K.G.; M.A.; P.DR-P; M.J.; I.J.; N.L., S.S.; O.D.; G.Z.M-P.; and E.I.; supervision, K.G.; M.A.; E.I.; and S.S.; funding acquisition, E.I. and S.S. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Sonjelle Shilton .

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Ethics approval and consent to participate.

All participants provided written informed consent. Household members provided oral consent when written informed consent could not be obtained. The study protocol was approved by the NCDC Institutional Review Board (Ref.: # 2022-049, May 24, 2022). The study was conducted in accordance with the protocol and with the ethical principles derived from the Belmont Report, the Declaration of Helsinki and applicable ICH Good Clinical Practice E6 (R2) standards.

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Supplementary Information

Additional file 1: annex i..

Structured questionnaire administered at baseline, mid-point and end-point.  Annex II. Knowledge and satisfaction survey.  Annex III. COREQ (COnsolidated criteria for REporting Qualitative research) Checklist.  Annex IV. Uptake of COVID-19 self-tests, reported to the national database, by type of user, gender and site.  Annex V. Uptake of COVID-19 self-tests, reported to the national database, by type of user, self-test result, and site.  Annex VI. Trends and potential associations among COVID-19 perceptions and willingness to self-test and report results, by time point and sites.  Annex VII. Analysis of variance (ANOVA) among COVID-19 perceptions and willingness to self-test and report results, by time points and sites.  Annex VIII. Socio-demographic characteristics of participants interviewed in the semi-structured interviews.  Annex IX. Coding tree for the COVID-19 self-testing pilot in Georgia based on semi-structured interviews: main theme, sub-themes and codes.

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Marbán-Castro, E., Getia, V., Alkhazashvili, M. et al. Implementing a pilot study of COVID-19 self-testing in high-risk populations and remote locations: results and lessons learnt. BMC Public Health 24 , 511 (2024). https://doi.org/10.1186/s12889-024-17930-2

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DOI : https://doi.org/10.1186/s12889-024-17930-2

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risk assessment of covid 19

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Shielded Patient List

NHS Digital published the Shielded Patient List (SPL), to enable partner organisations across government to support and protect those who were shielding.

Shielded Patient List closure 7 November 2022 update

Shielded Patient List (SPL) web pages have been archived. This followed the government announcement about the end of shielding in September 2021 and the subsequent closure of the supporting service within NHS Digital on 30 June 2022. This is to align with the end of the  COVID-19 Control of Patient Information (COPI) notices .

The latest information for people who are considered at high risk from coronavirus (COVID-19) .

What was the Shielded Patient List?

Between March 2020 and September 2021, the UK Chief Medical Officers (CMOs) identified and regularly reviewed the underlying clinical conditions for which people should be considered at high risk of complications from COVID-19 infection.  

NHS Digital developed a clinical methodology, a ruleset to identify patients who met these clinical conditions based on coded information in their health records. This methodology was updated as appropriate to reflect the guidance provided by the CMOs. 

NHS Digital created and maintained a Shielded Patient List (SPL) for England. The SPL included patients registered for healthcare in England and identified nationally using the clinical methodology, as well as patients routinely identified as at high risk by their General Practice or hospital specialist. Patients considered at high risk were also referred to as clinically extremely vulnerable. The list was updated weekly from March 2020 to September 2021. 

As part of the government’s shielding programme, the SPL was used by specific partner organisations across the NHS and government to provide advice, guidance and support to help people at high risk to protect themselves from COVID-19 infection during and between periods of national restrictions.

Guidance for health and care professionals

The SNOMED codes were introduced for the purposes of identifying those who were at high risk will remain in GP patient records. However, these codes are not used nationally as an indicator of clinical vulnerability or as an indicator of eligibility for any type of support.

Transparency notice

Version 8: 30 June 2022. We processed users' personal data in order to provide the shielded patient list for people at high clinical risk from coronavirus. This notice outlines what data was collected, how it was processed, and what we do with it now that the shielded patient list is closed.

Data retention

The SPL was established in response to the COVID-19 pandemic, with data dissemination being underpinned by NHS Digital’s COPI notice or the Health and Social Care Act 2012. Organisations with whom NHS Digital shared personal data had obligations to keep it for no longer than was necessary for the agreed purposes and in compliance with any terms of release issued by NHS Digital.

At the expiry of the COPI notice (on 30 June 2022) organisations who received the NHS SPL needed to securely delete the information held except where it formed part of the patient’s medical record. NHS Digital issued further guidance on the process to those who had received data during June 2022.

Control of patient information (COPI) notice

Publication

The coronavirus (COVID-19) shielded patient list enabled partner organisations across government to support and protect those who needed shielding. The open data about the Shielded Patient List (SPL), and how it had been used, will remain available until 31 March 2023. 

Additional information about the SPL - how did the SPL work?

A set of clinical conditions and a supporting methodology were used to determine whether a patient was at high, moderate or low risk of complications from COVID-19 infection.  

The list was dynamic and changed each week to reflect the changing status of patients’ risk from COVID-19 infection. Each week, individuals were: 

  • added to the high-risk category by the national methodology 
  • added to or removed from the high-risk category by their general practice or hospital consultant depending on their changing health circumstances 
  • removed from the SPL where a patient had died or no longer registered for care within the NHS in England 

GPs, hospital trusts and mental health providers were informed of the risk status of their patients through routine provision of SPL data. This was to enable them to be aware of COVID-19 risk to those patients in their care. 

Others involved in the pandemic response for whom 'letters of release' were put in place to support dissemination of SPL data for specific purposes, were also routinely provided with relevant SPL data.

Other organisations who received the SPL

The cabinet office .

The Cabinet Office received the list for the purpose of delivering the government's extremely vulnerable persons service, which provided additional support to clinically extremely vulnerable people (including food parcels) as well as to the local authorities affected by national and local restrictions. This sharing ceased on 17 September 2021 based upon the government’s announcement of the end of shielding in England. 

Supermarkets did not receive the SPL. When patients registered with the government’s extremely vulnerable persons service, they were able to request support for food deliveries. It was this information that flowed to supermarkets and/or food distributors.

Local authorities 

Local authorities received the list to identify clinically extremely vulnerable people and provide targeted support as part of the local COVID-19 response. This included offering help, social care and support in coordination with other relevant organisations.   

Local authorities were permitted to link SPL data with local authority NHS Test and Trace data to identify people who were clinically extremely vulnerable that came into contact with people recorded by NHS Test and Trace. They could then identify people who were clinically extremely vulnerable that were directed to isolate by the NHS Test and Trace Service. In both cases, this was to offer them appropriate advice and direct care. 

NHS clinical commissioning groups

Clinical commissioning groups (CCGs) received the list for the purpose of providing GP practices with support, and patients in their CCG area with support and care. 

Capita 

Capita received the list for the purpose of distributing letters to shielded patients, on behalf of NHS England and Improvement, as their data processor. 

APS Group Limited 

APS Group received the list for the purpose of distributing letters to shielded patients, on behalf of the Department of Health and Social Care (DHSC) as their data processor. 

NHS Business Services Authority

The NHS Business Services Authority received the list for the purpose of sending text messages to shielded patients, on behalf of DHSC as their data processor. 

NHS Business Service Authority also operated the Prescription Payment Verification assurance function and following direction by NHS England and Improvement, included those functions for shielded patient pharmacy, post payment verification. This was to ensure that the prescription delivery services provided to previously clinically extremely vulnerable patients, who were shielding and required prescription delivery support were operating effectively, and to ensure that public funds assigned for COVID-19 services to patients were being managed appropriately. 

Gov.Notify 

On behalf of DHSC and NHS England and Improvement, NHS Digital’s data processor Gov.Notify, received the list for the purpose of sending letters, emails and text messages to patients previously identified as clinically extremely vulnerable. 

Emails were used in addition to letters to ensure patients received timely information on their risk status, shielding policy, advice and guidance. 

NHS England and Improvement and TPP Group Ltd

NHS England/NHS Improvement and TPP received the list for the purpose of identifying patients on the SPL who were in the detained estate and who needed advice, support, and care. 

NHS England and Improvement 

Data was disseminated to NHS England and Improvement for the purposes of strategic commissioning. Data was pseudonymised and held on the NHS England data warehouse - the National Central Data Repository or NCDR. The NHS COVID‑19 Reference Library described the datasets used in the NHS COVID‑19 Data Store, and the sources of those datasets. 

Data was also disseminated to NHS England and Improvement for the purposes of providing an SPL feed into the COVID-19 vaccination programme (using National Immunisation Management System (NIMS)). This supported the Joint Commission for Vaccinations and Immunisations (JCVI) who identified that patients who are clinically extremely vulnerable should be offered a priority vaccination. 

National Services Scotland

National Services Scotland (NSS) were responsible for the Scottish Shielded Patient List. The purpose of sharing this information was so that NSS could contact Scottish residents who were identified as clinically extremely vulnerable as a result of direct care received in the NHS in England, to provide them with details of support they could obtain locally from Scottish local authorities where they were resident, including food parcels. 

NHS Wales Informatics Service

NHS Wales Informatics Service (NWIS) were responsible for the Welsh Shielded Patient List. The purpose of sharing this information was so that NWIS could contact the Welsh residents who were identified as clinically extremely vulnerable as a result of direct care received in the NHS in England, to provide them with details of support they could obtain locally from Welsh local authorities where they were resident, including food parcels. 

Redbridge CCG 

Redbridge CCG were responsible for the 111 call handling systems for London. The purpose of sharing this information was to support an NHS England and Improvement pilot programme, so that patients on the Shielded Patient List who called 111 and required referral to an emergency department would be flagged as being on the Shielded Patient List - enabling clinicians to make appropriate care arrangements. 

Mental health providers 

Mental health providers were responsible for mental health services commissioned by CCGs. The purpose of sharing this information was to support an NHS England and Improvement policy, so that patients on the Shielded Patient List in receipt of mental health services from the provider could be proactively contacted and have their care plans reviewed. This was because shielding was an extreme course of action likely to have had significant impact on the shielding individual’s mental health before, during and after periods of national and local restrictions. 

Public Health England  

Data was disseminated to Public Health England (PHE) for the purposes of COVID-19 vaccination programme surveillance. PHE had a role to monitor the delivery, safety and effectiveness of immunisation programmes in England and required the SPL Data to improve the data set in order to analyse the impact of COVID-19 vaccines for people previously identified as clinically extremely vulnerable.  

Department of Health and Social Care (DHSC)

Data was disseminated to DHSC’s service provider, Paragon, for the purposes of supplying a vitamin D supplement to those who were clinically extremely vulnerable and who had registered for a free vitamin D supplement been December 2020 and February 2021 via the 'Get Vitamin D Supplements' service.  

University of Oxford 

SPL data was shared with the University of Oxford, for the development and validation of a risk prediction QCovid® risk calculator to estimate short term adverse outcomes from COVID-19. This was a risk stratification tool to support national shielding policy.

Last edited: 20 February 2024 4:17 pm

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