Estimating the COVID-19 infection rate: Anatomy of an inference problem

Non-healthcare system interventions and COVID-19 daily cases: a multilevel time series analysis

BACKGROUND

The global COVID-19 pandemic has significantly impacted public health and socio-economic development worldwide. This study aims to investigate the effects of non-healthcare system interventions on the daily new cases of COVID-19 from January 2020 to October 2022.

METHODS

With the aid of multilevel approach, we identified income group, region and country as stratification factors that affect the number of COVID-19 daily new cases. Data on COVID-19 cases collected by Johns Hopkins University were used, and policy implementation details were recorded through the Oxford COVID-19 Government Response Tracker dataset. To analyze the effects of national, regional, and income group factors on the number of daily new COVID-19 cases, we implemented three multilevel sequential mixed-effects models and applied restricted maximum likelihood to estimate the variance of random effects.

RESULTS

Our results indicate a correlation between income group and the rise in intercepts of random effects in the multilevel sequential mixed-effects models. High-income countries recorded the highest intercept at 713.26, while low-income countries showed the lowest at -313.79. Under the influence of policies, the implementation of "Canceling public events" and "International travel restrictions" has been shown to significantly reduce the daily number of new COVID-19 cases. In contrast, "Restrictions on gatherings" appear to have the opposite effect, potentially leading to an increase in daily new COVID-19 cases.

CONCLUSIONS

In designing epidemic control policies, due consideration should be given to factors such as income group, as well as medical, demographic, and social differences among nations influenced by economic factors. In policy-making, policymakers should pay greater attention to policy implementation and people's responses, in order to maximize the effectiveness and adherence of such policies.

A comparative analysis of COVID-19 seroprevalence rates, observed infection rates, and infection-related mortality

Objectives

The COVID-19 pandemic highlighted the need for data-driven decision making in managing public health crises. This study aims to extend previous research by incorporating infection-related mortality (IRM) to evaluate the discrepancies between seroprevalence data and infection rates reported to the Centers for Disease Control and Prevention (CDC), and to assess the implications for public health policy.

Study design

We conducted a comparative analysis of seroprevalence data collected as part of an NIH study and CDC-reported infection rates across ten U.S. regions, focusing on their correlation with IRM calculations.

Methods

The analysis includes a revision of prior estimates of IRM using updated seroprevalence rates. Correlations were calculated and their statistical relevance assessed.

Results

Findings indicate that COVID-19 is approximately 2.7 times more prevalent than what CDC infection data suggest. Utilizing the lower CDC-reported rates to calculate IRM leads to a significant overestimation by a factor of 2.7. When both seroprevalence and CDC infection data are combined, the overestimation of IRM increases to a factor of 3.79.

Conclusion

The study highlights the importance of integrating multiple data dimensions to accurately understand and manage public health emergencies. The results suggest that public health agencies should enhance their capacity for collecting and analyzing seroprevalence data regularly, given its stronger correlation with IRM than other estimates. This approach will better inform policy decisions and direct effective interventions.

Inferring effects of mutations on SARS-CoV-2 transmission from genomic surveillance data

New and more transmissible variants of SARS-CoV-2 have arisen multiple times over the course of the pandemic. Rapidly identifying mutations that affect transmission could improve our understanding of viral biology and highlight new variants that warrant further study. Here we develop a generic, analytical epidemiological model to infer the transmission effects of mutations from genomic surveillance data. Applying our model to SARS-CoV-2 data across many regions, we find multiple mutations that substantially affect the transmission rate, both within and outside the Spike protein. The mutations that we infer to have the largest effects on transmission are strongly supported by experimental evidence from prior studies. Importantly, our model detects lineages with increased transmission even at low frequencies. As an example, we infer significant transmission advantages for the Alpha, Delta, and Omicron variants shortly after their appearances in regional data, when they comprised only around 1-2% of sample sequences. Our model thus facilitates the rapid identification of variants and mutations that affect transmission from genomic surveillance data.

Contact Tracing Different Age Groups During the COVID-19 Pandemic: Retrospective Study From South-West Germany

BACKGROUND

Contact tracing was implemented in many countries during the COVID-19 pandemic to prevent disease spread, reduce mortality, and avoid overburdening health care systems. In several countries, including Germany, new systems were needed to trace potentially infected individuals.

OBJECTIVE

Using data collected in the Rhine-Neckar and Heidelberg (RNK/HD) districts in southwest Germany (population: 706,974), this study examines the overall effectiveness and efficiency of contact tracing in different age groups and stages of the pandemic.

METHODS

From January 27, 2020, to April 30, 2022, the RNK/HD Health Authority collected data on COVID-19 infections, quarantines, and deaths. Data on infection, quarantine, and death was grouped by age (young: 0-19 years; adult: 20-65 years; and senior citizens: >65 years) and pandemic phase (infectious wave plus subsequent lull periods) and analyzed for proportion, risk, and relative risk (RR). The overall effectiveness and efficiency of contact tracing were determined by calculating quarantine sensitivity (proportion of the infected population captured in quarantine), positive predictive value (PPV; proportion of the quarantined population that was infected), and the weighted Fβ-score (combined predictive performance).

RESULTS

Of 706,974 persons living in RNK/HD during the study period, 192,175 (27.2%) tested positive for SARS-CoV-2, 74,810 (10.4%) were quarantined, and 932 (0.132%) died following infection. Compared with adults, the RR of infection was lower among senior citizens (0.401, 95% CI 0.395-0.407) and while initially lower for young people, was ultimately higher for young people across all 5 phases (first-phase RR 0.502, 95% CI 0.438-0.575; all phases RR 1.35, 95% CI 1.34-1.36). Of 932 COVID-19-associated deaths during the study period, 852 were senior citizens (91.4%), with no deaths reported among young people. Relative to adults, senior citizens had the lowest risk of quarantine (RR 0.436, 95% CI 0.424-0.448), while young people had the highest RR (2.94, 95% CI 2.90-2.98). The predictive performance of contact tracing was highest during the second and third phases of the pandemic (Fβ-score=0.272 and 0.338, respectively). In the second phase of the pandemic, 5810 of 16,814 COVID-19 infections were captured within a total quarantine population of 39,687 (sensitivity 34.6%; PPV 14.6%). In the third phase of the pandemic, 3492 of 8803 infections were captured within a total quarantine population of 16,462 (sensitivity 39.7%; PPV 21.2%).

CONCLUSIONS

The use of quarantine aligned with increasing risks of COVID-19 infection and death. High levels of quarantine sensitivity before the introduction of the vaccine show how contact tracing systems became increasingly effective at capturing and quarantining the infected population. High levels of PPV and Fβ-scores indicate, moreover, that contact tracing became more efficient at identifying infected individuals. Additional analysis of transmission pathways is needed to evaluate the application of quarantine in relation to infection and death risks within specific age groups.

Science and sanity: A social epistemology of misinformation, disinformation, and the limits of knowledge

Recent challenges to scientific authority in relation to the COVID pandemic, climate change, and the proliferation of conspiracy theories raise questions about the nature of knowledge and conviction. This article considers problems of social epistemology that are central to current predicaments about popular or public knowledge and the status of science. From the perspective of social epistemology, knowing and believing are not simply individual cognitive processes but based on participation in social systems, networks, and niches. As such, knowledge and conviction can be understood in terms of the dynamics of epistemic communities, which create specific forms of authority, norms, and practices that include styles of reasoning, habits of thought and modes of legitimation. Efforts to understand the dynamics of delusion and pathological conviction have something useful to teach us about our vulnerability as knowers and believers. However, this individual psychological account needs to be supplemented with a broader social view of the politics of knowledge that can inform efforts to create a healthy information ecology and strengthen the civil institutions that allow us to ground our action in well-informed picture of the world oriented toward mutual recognition, respect, diversity, and coexistence.

Estimating actual SARS-CoV-2 infections from secondary data

Eminent in pandemic management is accurate information on infection dynamics to plan for timely installation of control measures and vaccination campaigns. Despite huge efforts in diagnostic testing of individuals, the underestimation of the actual number of SARS-CoV-2 infections remains significant due to the large number of undocumented cases. In this paper we demonstrate and compare three methods to estimate the dynamics of true infections based on secondary data i.e., (a) test positivity, (b) infection fatality and (c) wastewater monitoring. The concept is tested with Austrian data on a national basis for the period of April 2020 to December 2022. Further, we use the results of prevalence studies from the same period to generate (upper and lower bounds of) credible intervals for true infections for four data points. Model parameters are subsequently estimated by applying Approximate Bayesian Computation-rejection sampling and Genetic Algorithms. The method is then validated for the case study Vienna. We find that all three methods yield fairly similar results for estimating the true number of infections, which supports the idea that all three datasets contain similar baseline information. None of them is considered superior, as their advantages and shortcomings depend on the specific case study at hand.

Vaccination coverage against COVID-19 among rural population in Haryana, India: A cross-sectional study

BACKGROUND

Conducting a study in rural pre-dominant areas will help to understand the penetration of the vaccination campaign during the COVID-19 health crisis. This study aimed to investigate vaccination coverage against COVID-19 among the rural adult population in India and to identify factors associated with vaccination coverage.

METHODS

A population-based cross-sectional study was conducted among the rural population in one district of north India from January to February 2023. A semi-structured questionnaire was designed on the SurveyMonkey digital platform for interviewing the participants, which consisted of questions related to socio-demographic profile, health problems, vaccination status, types of vaccine, re-infection after vaccination, and functional difficulties. The data regarding infection with COVID-19 was collected based on self-reported positive testing for SARS-CoV 2 on RT-PCR.

FINDINGS

A total of 3700 eligible individuals were enumerated for the survey, out of which 2954 (79.8%) were interviewed. The infection rate of past COVID-19 infection, based on self-report of testing positive, was 6.2% (95%CI: 5.3-7.1). Covishield vaccine was received by most participants (81.3%, 2380) followed by Covaxin (12.3%, 361) and Pfizer manufactured vaccine (0.03,1). The coverage for first, second, and booster doses of the vaccine was 98.2% (2902), 94.8% (2802), and 10.7% (315) respectively. The risk of reinfection at 12 months or more among participants with two doses of vaccine was 1.6% (46/2802, 95%CI: 1.2-2.1). The coverage among those with severe functional difficulties was lesser as compared to those with some or no difficulties.

INTERPRETATION

Vaccination coverage against COVID-19 in rural Haryana, India is not dependent on factors like gender or occupation but is dependent on age and education. Although the full and partial vaccination coverage is high, the booster dose coverage is poor. In addition, the presence of severe disability was significantly associated with reduced vaccination coverage.

Estimating hidden relationships in dynamical systems: Discovering drivers of infection rates of COVID-19

Discovering causal influences among internal variables is a fundamental goal of complex systems research. This paper presents a framework for uncovering hidden relationships from limited time-series data by combining methods from nonlinear estimation and information theory. The approach is based on two sequential steps: first, we reconstruct a more complete state of the underlying dynamical system, and second, we calculate mutual information between pairs of internal state variables to detail causal dependencies. Equipped with time-series data related to the spread of COVID-19 from the past three years, we apply this approach to identify the drivers of falling and rising infections during the three main waves of infection in the Chicago metropolitan region. The unscented Kalman filter nonlinear estimation algorithm is implemented on an established epidemiological model of COVID-19, which we refine to include isolation, masking, loss of immunity, and stochastic transition rates. Through the systematic study of mutual information between infection rate and various stochastic parameters, we find that increased mobility, decreased mask use, and loss of immunity post sickness played a key role in rising infections, while falling infections were controlled by masking and isolation.

Multi-feature SEIR model for epidemic analysis and vaccine prioritization

The SEIR (susceptible-exposed-infected-recovered) model has become a valuable tool for studying infectious disease dynamics and predicting the spread of diseases, particularly concerning the COVID pandemic. However, existing models often oversimplify population characteristics and fail to account for differences in disease sensitivity and social contact rates that can vary significantly among individuals. To address these limitations, we have developed a new multi-feature SEIR model that considers the heterogeneity of health conditions (disease sensitivity) and social activity levels (contact rates) among populations affected by infectious diseases. Our model has been validated using the data of the confirmed COVID cases in Allegheny County (Pennsylvania, USA) and Hamilton County (Ohio, USA). The results demonstrate that our model outperforms traditional SEIR models regarding predictive accuracy. In addition, we have used our multi-feature SEIR model to propose and evaluate different vaccine prioritization strategies tailored to the characteristics of heterogeneous populations. We have formulated optimization problems to determine effective vaccine distribution strategies. We have designed extensive numerical simulations to compare vaccine distribution strategies in different scenarios. Overall, our multi-feature SEIR model enhances the existing models and provides a more accurate picture of disease dynamics. It can help to inform public health interventions during pandemics/epidemics.

Face mask is an efficient tool to fight the Covid-19 pandemic and some factors increase the probability of its adoption

This study examines the dynamic impact of face mask use on both infected cases and fatalities at a global scale by using a rich set of panel data econometrics. An increase of 100% of the proportion of people declaring wearing a mask (multiply by two) over the studied period lead to a reduction of around 12 and 13.5% of the number of Covid-19 infected cases (per capita) after 7 and 14 days respectively. The delay of action varies from around 7 days to 28 days concerning infected cases but is more longer concerning fatalities. Our results hold when using the rigorous controlling approach. We also document the increasing adoption of mask use over time and the drivers of mask adoption. In addition, population density and pollution levels are significant determinants of heterogeneity regarding mask adoption across countries, while altruism, trust in government and demographics are not. However, individualism index is negatively correlated with mask adoption. Finally, strict government policies against Covid-19 have a strong significant effect on mask use.

Optimal timing of non-pharmaceutical interventions during an epidemic

In response to the recent outbreak of the SARS-CoV-2 virus governments have aimed to reduce the virus's spread through, inter alia, non-pharmaceutical intervention. We address the question when such measures should be implemented and, once implemented, when to remove them. These issues are viewed through a real-options lens and we develop an SIRD-like continuous-time Markov chain model to analyze a sequence of options: the option to intervene and introduce measures and, after intervention has started, the option to remove these. Measures can be imposed multiple times. We implement our model using estimates from empirical studies and, under fairly general assumptions, our main conclusions are that: (1) measures should be put in place not long after the first infections occur; (2) if the epidemic is discovered when there are many infected individuals already, then it is optimal never to introduce measures; (3) once the decision to introduce measures has been taken, these should stay in place until the number of susceptible or infected members of the population is close to zero; (4) it is never optimal to introduce a tier system to phase-in measures but it is optimal to use a tier system to phase-out measures; (5) a more infectious variant may reduce the duration of measures being in place; (6) the risk of infections being brought in by travelers should be curbed even when no other measures are in place. These results are robust to several variations of our base-case model.

Rapidly Establishing an Ultra-Cold Supply Chain of Vaccines in Israel: Evidence for the Efficacy of Inoculation to Mitigate the COVID-19 Pandemic

The agenda of this research was to investigate how to mitigate the spread of coronaviruses by rapidly establishing an ultra-cold supply chain of vaccines. Data analysis was conducted by linear regression utilizing a dataset publicly available from the Israel Ministry of Health regarding the daily rates of people vaccinated, tested, hospitalized, etc., since the start of the pandemic. The data provide statistical evidence for the efficacy of the Pfizer vaccines in diminishing a wide variety of disease factors, such as the number of patients who were lightly, moderately, or severely sick, and daily deaths, as well as the rate of spread (R-ratio) and number/percentage of people infected. Insightfully, the data corroborate how the first and second doses of the vaccines were able to decrease the wave of COVID-19, which hit Israel in January 2021, while the booster third dose was able to diminish a subsequent COVID-19 wave occurring in Israel in July 2021.

A hybrid compartmental model with a case study of COVID-19 in Great Britain and Israel

Given the severe impact of COVID-19 on several societal levels, it is of crucial importance to model the impact of restriction measures on the pandemic evolution, so that governments are able to make informed decisions. Even though there have been countless attempts to propose diverse models since the rise of the outbreak, the increase in data availability and start of vaccination campaigns calls for updated models and studies. Furthermore, most of the works are focused on a very particular place or application and we strive to attain a more general model, resorting to data from different countries. In particular, we compare Great Britain and Israel, two highly different scenarios in terms of vaccination plans and social structure. We build a network-based model, complex enough to model different scenarios of government-mandated restrictions, but generic enough to be applied to any population. To ease the computational load we propose a decomposition strategy for our model.

Epidemic responses under uncertainty

We examine how policymakers react to a pandemic with uncertainty around key epidemiological and economic policy parameters by embedding a macroeconomic SIR model in a robust control framework. Uncertainty about disease virulence and severity leads to stricter and more persistent quarantines, while uncertainty about the economic costs of mitigation leads to less stringent quarantines. On net, an uncertainty-averse planner adopts stronger mitigation measures. Intuitively, the cost of underestimating the pandemic is out-of-control growth and permanent loss of life, while the cost of underestimating the economic consequences of quarantine is more transitory.

Time varying Markov process with partially observed aggregate data: An application to coronavirus

A major difficulty in the analysis of Covid-19 transmission is that many infected individuals are asymptomatic. For this reason, the total counts of infected individuals and of recovered immunized individuals are unknown, especially during the early phase of the epidemic. In this paper, we consider a parametric time varying Markov process of Coronavirus transmission and show how to estimate the model parameters and approximate the unobserved counts from daily data on infected and detected individuals and the total daily death counts. This model-based approach is illustrated in an application to French data, performed on April 6, 2020.

COVID-19: Tail risk and predictive regressions

The paper focuses on econometrically justified robust analysis of the effects of the COVID-19 pandemic on financial markets in different countries across the World. It provides the results of robust estimation and inference on predictive regressions for returns on major stock indexes in 23 countries in North and South America, Europe, and Asia incorporating the time series of reported infections and deaths from COVID-19. We also present a detailed study of persistence, heavy-tailedness and tail risk properties of the time series of the COVID-19 infections and death rates that motivate the necessity in applications of robust inference methods in the analysis. Econometrically justified analysis is based on heteroskedasticity and autocorrelation consistent (HAC) inference methods, recently developed robust t-statistic inference approaches and robust tail index estimation.

What cannot be cured must be endured: The long-lasting effect of a COVID-19 infection on workplace productivity

The COVID-19 pandemic has triggered economic shock waves across the globe. Exploiting a natural experiment, this paper estimates how being infected with the virus shapes individual-level productivity after having recovered. Studying the performance of professional athletes in Germany and Italy and applying a staggered difference-in-differences design, we find that individual performance drops by around 6 percent after a previously infected athlete returns to the pitch. This striking deterioration remains persistent over time - amounting to 5% eight months after the infection. The effect increases with age and infection severity, and is spread disproportionally over the course of a match. We detect no productivity effects for other respiratory infections. We take these findings as first evidence that the pandemic might cause long-lasting effects on worker productivity and economic growth.

Responses to COVID-19 with probabilistic programming

The COVID-19 pandemic left its unique mark on the twenty-first century as one of the most significant disasters in history, triggering governments all over the world to respond with a wide range of interventions. However, these restrictions come with a substantial price tag. It is crucial for governments to form anti-virus strategies that balance the trade-off between protecting public health and minimizing the economic cost. This work proposes a probabilistic programming method to quantify the efficiency of major initial non-pharmaceutical interventions. We present a generative simulation model that accounts for the economic and human capital cost of adopting such strategies, and provide an end-to-end pipeline to simulate the virus spread and the incurred loss of various policy combinations. By investigating the national response in 10 countries covering four continents, we found that social distancing coupled with contact tracing is the most successful policy, reducing the virus transmission rate by 96% along with a 98% reduction in economic and human capital loss. Together with experimental results, we open-sourced a framework to test the efficacy of each policy combination.

Cities in a pandemic: Evidence from China

This paper studies the impact of urban density, city government efficiency, and medical resources on COVID-19 infection and death outcomes in China. We adopt a simultaneous spatial dynamic panel data model to account for (i) the simultaneity of infection and death outcomes, (ii) the spatial pattern of the transmission, (iii) the intertemporal dynamics of the disease, and (iv) the unobserved city-specific and time-specific effects. We find that, while population density increases the level of infections, government efficiency significantly mitigates the negative impact of urban density. We also find that the availability of medical resources improves public health outcomes conditional on lagged infections. Moreover, there exists significant heterogeneity at different phases of the epidemiological cycle.

How many asymptomatic cases were unconfirmed in the US COVID-19 pandemic? The evidence from a serological survey

A serological survey from CDC revealed more than 10% of individuals in America probably resolving or past infection with SARS-CoV-2 at the end of 2020, which illustrated there were massive unconfirmed asymptomatic infected people by contrast with the reported cases numbers. Asymptomatic patients as one of the crucial reasons for the COVID-19 pandemic being tough to contain, estimating the number of unconfirmed ones including the active infected and having cured in this population, is of great guiding significance for formulating epidemic prevention and control policies. This paper proposes a varying coefficient Susceptible-Infected-Removed-Susceptible (vSIRS) model to obtain the time series data of the unconfirmed asymptomatic infected numbers. Moreover, due to the time-varying coefficients, we can effectively track the situation changes of the COVID-19 intervened by related policy support and medical care level through this epidemiological model. A novel two-stage approach with a programming problem is correspondingly developed to accomplish the estimation of the unknown parameters in the vSIRS model. Subsequently, by leveraging seroprevalence data, daily reported cases data, and other clinical information, we apply the vSIRS model to analyze the evolution of COVID-19 in America. The modeling results show millions of active asymptomatic infected individuals were unconfirmed during the autumn and winter of 2020, which was a momentous factor for driving American COVID-19 pandemic.

Behavioral Economics in the Epidemiology of the COVID-19 Pandemic: Theory and Simulations

We provide a game-theoretical epidemiological model for the COVID-19 pandemic that takes into account that: (1) asymptomatic individuals can be contagious, (2) contagion is behavior-dependent, (3) behavior is determined by a game that depends on beliefs and social interactions, (4) there can be systematic biases in the perceptions and beliefs about the pandemic. We incorporate lockdown decisions by the government into the model. The citizens’ and government’s beliefs can exhibit several biases that we discuss from the point of view of behavioral economics. We provide simulations to understand the effect of lockdown decisions and the possibility of “nudging” citizens in the right direction by improving the accuracy of their beliefs.

Association between the Human Development Index and Confirmed COVID-19 Cases by Country

It is important to understand the ultimate control of COVID-19 in all countries around the world in relation to the characteristics of developed countries, LDCs, and the variety of transmission characteristics of COVID-19. Therefore, this study aimed to identify factors associated with confirmed cases of COVID-19 with a focus on the Human Development Index (HDI). The units of analysis used for the current study were countries, and dataset were aggregated from multiple sources. This study used COVID-19 data from Our World in Data, the Global Health Security Index, and the WORLD BANK. A total of 171 countries were included in the analysis. A multi-variable linear regression with a hierarchical framework was employed to investigate whether the HDI is associated with confirmed COVID-19 cases after controlling for the demographic and healthcare system characteristics of the study countries. For Model 2, which controlled for demographic and healthcare system characteristics, HDI (β = 0.46, p < 0.001, 95% CI = 2.64−10.87) and the number of physicians per 1000 people (β = 0.34, p < 0.01, 95% CI = 0.21−0.75) had significant associations with the total number of confirmed COVID-19 cases per million people. Countries with a high HDI level are able to conduct higher per capita testing, resulting in higher numbers of confirmed cases than in countries with lower HDI levels. This study has shown evidence that could be used by governments and international organizations to identify national characteristics and provide the international cooperation necessary to develop effective prevention and intervention methods to deal with the global pandemic.

Convergence of testing and positivity rates for the COVID-19 pandemic: evidence from Germany and Italy

Purpose

This paper aims to analyze the convergence of the testing and positivity rates for the COVID-19 pandemic focusing on Germany and Italy.

Design/methodology/approach

The authors employ the two-regime threshold autoregressive (TAR) panel unit root test by using the weekly data in the period of 2020:03-2021:04.

Findings

Following finding out that the testing and positivity rates are nonlinear, the authors determine that the transition country between the two regimes is Italy for the testing rates and Germany for the positivity rates. Their findings support the partial convergence for the testing rates for Germany and Italy. On the other hand, the authors could not find any convergence for the positivity rates of these two countries.

Originality/value

This paper contributes to academic literature in several ways. Firstly, to the best of their knowledge, this paper is the first study that analyzes the convergence of testing and positivity data. This paper further focuses on two Euro-Area countries which have suffered significantly from the COVID-19 pandemic. In addition, the authors employ the two-regime threshold autoregressive (TAR) panel unit root proposed by Beyaert and Camacho (2008) in their empirical analysis. This recent panel data methodology aims to test real convergence in a nonlinear framework by incorporating the threshold model, panel data unit root test and the calculation of critical values by bootstrap simulation.

Measuring Resilience and Fatality Rate During the First Wave of COVID-19 Pandemic in Northern Italy: A Note

Background: This Policy Brief aims to contribute to the debate on the resilience of the healthcare systems during the pandemic by discussing whether mortality indicators are appropriate for assessing resilience or whether other statistics should be employed.

Evidence: During the first wave of the COVID-19, much emphasis was placed on case-fatality rates to offer a preliminary assessment of the resilience of healthcare systems. However, these statistics are often biased and do not consider the real figure of the population that has been infected.

Policy Options and Recommendations: Comparing data obtained with different approaches based on statistical inference and large-scale serological survey, the brief highlights, that great care must be taken when using case-fatality data, which in the absence of careful analysis, can lead to erroneous conclusions.

Conclusion: Using case-fatality rate gives us no sounding information about the real capability of healthcare systems to save lives during the pandemic. However, even in the absence of detailed epidemiological data new advancements in statistical methods can be useful to provide a more sounding evaluation of the resilience of the healthcare systems.

Key links in network interactions: Assessing route-specific travel restrictions in China during the Covid-19 pandemic

We consider a model of network interactions where the outcome of a unit depends on the outcomes of the connected units. We determine the key network link, i.e., the network link whose removal results in the largest reduction in the aggregate outcomes, and examine a measure that quantifies the contribution of a network link to the aggregate outcomes. We provide an example examining the spread of Covid-19 in China. Travel restrictions were imposed to limit the spread of infectious diseases. As uniform restrictions can be inefficient and incur unnecessarily high costs, we examine the design of restrictions that target specific travel routes. Our approach may be generalized to multiple countries to guide policies during epidemics ranging from ex ante route-specific travel restrictions to ex post health measures based on travel histories, and from the initial travel restrictions to the phased reopening.

Hybrid drug-screening strategy identifies potential SARS-CoV-2 cell-entry inhibitors targeting human transmembrane serine protease

The spread of coronavirus infectious disease (COVID-19) is associated with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has risked public health more than any other infectious disease. Researchers around the globe use multiple approaches to identify an effective approved drug (drug repurposing) that treats viral infections. Most of the drug repurposing approaches target spike protein or main protease. Here we use transmembrane serine protease 2 (TMPRSS2) as a target that can prevent the virus entry into the cell by interacting with the surface receptors. By hypothesizing that the TMPRSS2 binders may help prevent the virus entry into the cell, we performed a systematic drug screening over the current approved drug database. Furthermore, we screened the Enamine REAL fragments dataset against the TMPRSS2 and presented nine potential drug-like compounds that give us clues about which kinds of groups the pocket prefers to bind, aiding future structure-based drug design for COVID-19. Also, we employ molecular dynamics simulations, binding free energy calculations, and well-tempered metadynamics to validate the obtained candidate drug and fragment list. Our results suggested three potential FDA-approved drugs against human TMPRSS2 as a target. These findings may pave the way for more drugs to be exposed to TMPRSS2, and testing the efficacy of these drugs with biochemical experiments will help improve COVID-19 treatment.

Supplementary information

The online version contains supplementary material available at 10.1007/s11224-022-01960-w.

From the lockdown to the new normal: individual mobility and local labor market characteristics following the COVID-19 pandemic in Italy

Italy was among the first countries to introduce drastic measures to reduce individual mobility in order to slow the diffusion of COVID-19. The first measures imposed by the central authorities on March 8, 2020, were unanticipated and highly localized, focusing on 26 provinces. Additional nationwide measures were imposed after one day, and were removed only after June 3. Looking at these watershed moments of the pandemic, this paper explores the impact of the adoption of localized restrictions on changes in individual mobility in Italy using a spatial discontinuity approach. Results show that these measures lowered individual mobility by 7 percentage points on top of the reduction in mobility recorded in the adjacent untreated areas. The study also fills a gap in the literature in that it looks at the changes in mobility after the nationwide restrictions were lifted and shows how the recovery in mobility patterns is related to various characteristics of local labour markets. Areas with a higher proportion of professions exposed to diseases, more suitable for flexible work arrangements, and with a higher share of fixed-term contracts before the pandemic are characterised by a smaller increase in mobility after re-opening.

Effectiveness of government policies in response to the first COVID-19 outbreak

This paper assesses the quantitative impact of government interventions on deaths related to the first COVID-19 outbreak. Using daily data for 32 countries and relying on the stringency of the conducted policies, we find that the greater the strength of government interventions at an early stage, the more effective these are in slowing down or reversing the growth rate of deaths. School closures have a significant impact on reducing the growth rate of deaths, which is less powerful compared to the case where a number of policy interventions are combined together. These results can be informative for governments in responding to future pandemics.

Resilience as a measure of preparedness for pandemic influenza outbreaks

The global crisis generated by COVID-19 has heightened awareness of pandemic outbreaks. From a public health preparedness standpoint, it is essential to assess the impact of a pandemic and also the resilience of the affected communities, which is the ability to withstand and recover quickly after a pandemic outbreak. The infection attack rate has been the common metric to assess community response to a pandemic outbreak, while it focuses on the number of infected it does not capture other dimensions such as the recovery time. The aim of this research is to develop community resilience measures and demonstrate their estimation using a simulated pandemic outbreak in a region in the USA. Three scenarios are analysed with different combinations of virus transmissibility rates and non-pharmaceutical interventions. I The inclusion of the resilience framework in the pandemics outbreak analysis will enable decision makers to capture the multi dimensional nature of community response.

COVID-19 severity: A new approach to quantifying global cases and deaths

As the COVID-19 pandemic has progressed, so too has the recognition that cases and deaths have been underreported, perhaps vastly so. Here, we present an econometric strategy to estimate the true number of COVID-19 cases and deaths for 61 and 56 countries, respectively, from 1 January 2020 to 3 November 2020. Specifically, we estimate a 'structural' model based on the SIR epidemiological model extended to incorporate underreporting. The results indicate significant underreporting by magnitudes that align with existing research and conjectures by public health experts. While our approach requires some strong assumptions, these assumptions are very different from the equally strong assumptions required by other approaches addressing underreporting in the assessment of the extent of the pandemic. Thus, we view our approach as a complement to existing methods.

Assessing alternative indicators for Covid-19 policy evaluation, with a counterfactual for Sweden

Using the synthetic control method, we construct counterfactuals for what would have happened if Sweden had imposed a lockdown during the first wave of the COVID-19 epidemic. We consider eight different indicators, including a novel one that we construct by adjusting recorded daily COVID-19 deaths to account for weakly excess mortality. Correcting for data problems and re-optimizing the synthetic control for each indicator, we find that a lockdown would have had sizable effects within one week. The much longer delay estimated by two previous studies focusing on the number of positives cases is mainly driven by the extremely low testing frequency that prevailed in Sweden in the first months of the epidemic. This result appears relevant for choosing the timing of future lockdowns and highlights the importance of looking at several indicators to derive robust conclusions. We also find that our novel indicator is effective in correcting errors in the COVID-19 deaths series and that the quantitative effects of the lockdown are stronger than previously estimated.

Threshold dynamics of a stochastic SIHR epidemic model of COVID-19 with general population-size dependent contact rate

In this paper, we propose a stochastic SIHR epidemic model of COVID-19. A basic reproduction number $ R_{0}^{s} $ is defined to determine the extinction or persistence of the disease. If $ R_{0}^{s} < 1 $, the disease will be extinct. If $ R_{0}^{s} > 1 $, the disease will be strongly stochastically permanent. Based on realistic parameters of COVID-19, we numerically analyze the effect of key parameters such as transmission rate, confirmation rate and noise intensity on the dynamics of disease transmission and obtain sensitivity indices of some parameters on $ R_{0}^{s} $ by sensitivity analysis. It is found that: 1) The threshold level of deterministic model is overestimated in case of neglecting the effect of environmental noise; 2) The decrease of transmission rate and the increase of confirmed rate are beneficial to control the spread of COVID-19. Moreover, our sensitivity analysis indicates that the parameters $ \beta $, $ \sigma $ and $ \delta $ have significantly effects on $ R_0^s $.

Pooled testing efficiency increases with test frequency

Pooled testing increases efficiency by grouping individual samples and testing the combined sample, such that many individuals can be cleared with one negative test. This short paper demonstrates that pooled testing is particularly advantageous in the setting of pandemics, given repeated testing, rapid spread, and uncertain risk. Repeated testing mechanically lowers the infection probability at the time of the next test by removing positives from the population. This effect alone means that increasing frequency by x times only increases expected tests by around [Formula: see text] However, this calculation omits a further benefit of frequent testing: Removing infections from the population lowers intragroup transmission, which lowers infection probability and generates further efficiency. For this reason, increasing testing frequency can paradoxically reduce total testing cost. Our calculations are based on the assumption that infection rates are known, but predicting these rates is challenging in a fast-moving pandemic. However, given that frequent testing naturally suppresses the mean and variance of infection rates, we show that our results are very robust to uncertainty and misprediction. Finally, we note that efficiency further increases given natural sampling pools (e.g., workplaces, classrooms) that induce correlated risk via local transmission. We conclude that frequent pooled testing using natural groupings is a cost-effective way to provide consistent testing of a population to suppress infection risk in a pandemic.

A statistical model for the dynamics of COVID-19 infections and their case detection ratio in 2020

The case detection ratio of coronavirus disease 2019 (COVID-19) infections varies over time due to changing testing capacities, different testing strategies, and the evolving underlying number of infections itself. This note shows a way of quantifying these dynamics by jointly modeling the reported number of detected COVID-19 infections with nonfatal and fatal outcomes. The proposed methodology also allows to explore the temporal development of the actual number of infections, both detected and undetected, thereby shedding light on the infection dynamics. We exemplify our approach by analyzing German data from 2020, making only use of data available since the beginning of the pandemic. Our modeling approach can be used to quantify the effect of different testing strategies, visualize the dynamics in the case detection ratio over time, and obtain information about the underlying true infection numbers, thus enabling us to get a clearer picture of the course of the COVID-19 pandemic in 2020.

An operationally implementable model for predicting the effects of an infectious disease on a comprehensive regional healthcare system

An operationally implementable predictive model has been developed to forecast the number of COVID-19 infections in the patient population, hospital floor and ICU censuses, ventilator and related supply chain demand. The model is intended for clinical, operational, financial and supply chain leaders and executives of a comprehensive healthcare system responsible for making decisions that depend on epidemiological contingencies. This paper describes the model that was implemented at NorthShore University HealthSystem and is applicable to any communicable disease whose risk of reinfection for the duration of the pandemic is negligible.

Modeling the spread of COVID-19 in New York City

This paper proposes an ensemble predictor for the weekly increase in the number of confirmed COVID-19 cases in the city of New York at zip code level. Within a Bayesian model averaging framework, the baseline is a Poisson regression for count data. The set of covariates includes autoregressive terms, spatial effects, and demographic and socioeconomic variables. Our results for the second wave of the coronavirus pandemic show that these regressors are more significant to predict the number of new confirmed cases as the pandemic unfolds. Both pointwise and interval forecasts exhibit strong predictive ability in-sample and out-of-sample.

Modeling and forecasting the COVID-19 pandemic with heterogeneous autoregression approaches: South Korea

This paper deals with time series analysis for COVID-19 in South Korea. We adopt heterogeneous autoregressive (HAR) time series models and discuss the statistical inference for various COVID-19 data. Seven data sets such as cumulative confirmed (CC) case, cumulative recovered (CR) case and cumulative death (CD) case as well as recovery rate, fatality rate and infection rates for 14 and 21 days are handled for the statistical analysis. In the HAR models, model selections of orders are conducted by evaluating root mean square error (RMSE) and mean absolute error (MAE) as well asR 2 , AIC, and BIC. As a result of estimation, we provide coefficients estimates, standard errors and 95% confidence intervals in the HAR models. Our results report that fitted values via the HAR models are not only well-matched with the real cumulative cases but also differenced values from the fitted HAR models are well-matched with real daily cases. Additionally, because the CC and the CD cases are strongly correlated, we use a bivariate HAR model for the two data sets. Out-of-sample forecastings are carried out with the COVID-19 data sets to obtain multi-step ahead predicted values and 95% prediction intervals. As for the forecasting performances, four accuracy measures such as RMSE, MAE, mean absolute percentage error (MAPE) and root relative square error (RRSE) are evaluated. Contributions of this work are three folds: First, it is shown that the HAR models fit well to cumulative numbers of the COVID-19 data along with good criterion results. Second, a variety of analysis are studied for the COVID-19 series: confirmed, recovered, death cases, as well as the related rates. Third, forecast accuracy measures are evaluated as small values of errors, and thus it is concluded that the HAR model provides a good prediction model for the COVID-19.

Embracing Uncertainty: The Value of Partial Identification in Public Health and Clinical Research

INTRODUCTION

This paper describes the methodology of partial identification and its applicability to empirical research in preventive medicine and public health.

METHODS

The authors summarize findings from the methodologic literature on partial identification. The analysis was conducted in 2020-2021.

RESULTS

The applicability of partial identification methods is demonstrated using 3 empirical examples drawn from published literature.

CONCLUSIONS

Partial identification methods are likely to be of considerable interest to clinicians and others engaged in preventive medicine and public health research.

Simulation and prediction of spread of COVID-19 in The Republic of Serbia by SEAIHRDS model of disease transmission

As a response to the pandemic caused by SARS-Cov-2 virus, on 15 March 2020, the Republic of Serbia introduced comprehensive anti-epidemic measures to curb COVID-19. After a slowdown in the epidemic, on 6 May 2020, the regulatory authorities decided to relax the implemented measures. However, the epidemiological situation soon worsened again. As of 7 February 2021, a total of 406,352 cases of SARSCov-2 infection have been reported in Serbia, 4,112 deaths caused by COVID-19. In order to better understand the epidemic dynamics and predict possible outcomes, we have developed an adaptive mathematical model SEAIHRDS (S-susceptible, E-exposed, A-asymptomatic, I-infected, H-hospitalized, R-recovered, d-dead due to COVID-19 infection, S-susceptible). The model can be used to simulate various scenarios of the implemented intervention measures and calculate possible epidemic outcomes, including the necessary hospital capacities. Considering promising results regarding the development of a vaccine against COVID-19, the model is extended to simulate vaccination among different population strata. The findings from various simulation scenarios have shown that, with implementation of strict measures of contact reduction, it is possible to control COVID-19 and reduce number of deaths. The findings also show that limiting effective contacts within the most susceptible population strata merits a special attention. However, the findings also show that the disease has a potential to remain in the population for a long time, likely with a seasonal pattern. If a vaccine, with efficacy equal or higher than 65%, becomes available it could help to significantly slow down or completely stop circulation of the virus in human population. The effects of vaccination depend primarily on: 1. Efficacy of available vaccine(s), 2. Prioritization of the population categories for vaccination, and 3. Overall vaccination coverage of the population, assuming that the vaccine(s) develop solid immunity in vaccinated individuals. With expected basic reproduction number of Ro=2.46 and vaccine efficacy of 68%, an 87% coverage would be sufficient to stop the virus circulation.

Controlling of pandemic COVID-19 using optimal control theory

In 2019, a new infectious disease called pandemic COVID-19 began to spread from Wuhan, China. In spite of the efforts to stop the disease, being out of the control of the governments it spread rapidly all over the world. From then on, much research has been done in the world with the aim of controlling this contagious disease. A mathematical model for modeling the spread of COVID-19 and also controlling the spread of the disease has been presented in this paper. We find the disease-free equilibrium points as trivial equilibrium (TE), virus absenteeism equilibrium (VAE) and virus incidence equilibrium (VIE) for the proposed model; and at the trivial equilibrium point for the presented dynamic system we obtain the Jacobian matrix so as to be used in finding the largest eigenvalue. Radius spectral method has been used for finding the reproductive number. In the following, by adding a controller to the model and also using the theory of optimal control, we can improve the performance of the model. We must have a correct understanding of the system i.e. how it works, the various variables affecting the system, and the interaction of the variables on each other. To search for the optimal values, we need to use an appropriate optimization method. Given the limitations and needs of the problem, the aim of the optimization is to find the best solutions, to find conditions that result in the maximum of susceptiblity, the minimum of infection, and optimal quarantination.

Meteorological factors against COVID-19 and the role of human mobility

In the vein of recent empirical literature, we reassessed the impact of weather factors on Covid-19 daily cases and fatalities in a panel of 37 OECD countries between 1st January and 27th July 2020. We considered five different meteorological factors. For the first time, we used a dynamic panel model and considered two different kinds of channels between climate and Covid-19 virus: direct/physical factors related to the survival and durability dynamics of the virus on surfaces and outdoors and indirect/social factors through human behaviour and individual mobility, such as walking or driving outdoors, to capture the impact of weather on social distancing and, thus, on Covid-19 cases and fatalities. Our work revealed that temperature, humidity and solar radiation, which has been clearly under considered in previous studies, significantly reduce the number of Covid-19 cases and fatalities. Indirect effects through human behaviour, i.e., correlations between temperature (or solar radiation) and human mobility, were significantly positive and should be considered to correctly assess the effects of climatic factors. Increasing temperature, humidity or solar radiation effects were positively correlated with increasing mobility effects on Covid-19 cases and fatalities. The net effect from weather on the Covid-19 outbreak will, thus, be the result of the physical/direct negative effect of temperature or solar radiation and the mobility/indirect positive effect due to the interaction between human mobility and those meterological variables. Reducing direct effects of temperature and solar radiation on Covid-19 cases and fatalities, when they were significant, were partly and slightly compensated for positive indirect effects through human mobility. Suitable control policies should be implemented to control mobility and social distancing even when the weather is favourable to reduce the spread of the Covid-19 virus.

The Molecular Basis of COVID-19 Pathogenesis, Conventional and Nanomedicine Therapy

In late 2019, a new member of the Coronaviridae family, officially designated as "severe acute respiratory syndrome coronavirus 2" (SARS-CoV-2), emerged and spread rapidly. The Coronavirus Disease-19 (COVID-19) outbreak was accompanied by a high rate of morbidity and mortality worldwide and was declared a pandemic by the World Health Organization in March 2020. Within the Coronaviridae family, SARS-CoV-2 is considered to be the third most highly pathogenic virus that infects humans, following the severe acute respiratory syndrome coronavirus (SARS-CoV) and the Middle East respiratory syndrome coronavirus (MERS-CoV). Four major mechanisms are thought to be involved in COVID-19 pathogenesis, including the activation of the renin-angiotensin system (RAS) signaling pathway, oxidative stress and cell death, cytokine storm, and endothelial dysfunction. Following virus entry and RAS activation, acute respiratory distress syndrome develops with an oxidative/nitrosative burst. The DNA damage induced by oxidative stress activates poly ADP-ribose polymerase-1 (PARP-1), viral macrodomain of non-structural protein 3, poly (ADP-ribose) glycohydrolase (PARG), and transient receptor potential melastatin type 2 (TRPM2) channel in a sequential manner which results in cell apoptosis or necrosis. In this review, blockers of angiotensin II receptor and/or PARP, PARG, and TRPM2, including vitamin D3, trehalose, tannins, flufenamic and mefenamic acid, and losartan, have been investigated for inhibiting RAS activation and quenching oxidative burst. Moreover, the application of organic and inorganic nanoparticles, including liposomes, dendrimers, quantum dots, and iron oxides, as therapeutic agents for SARS-CoV-2 were fully reviewed. In the present review, the clinical manifestations of COVID-19 are explained by focusing on molecular mechanisms. Potential therapeutic targets, including the RAS signaling pathway, PARP, PARG, and TRPM2, are also discussed in depth.

Discovering treatment effectiveness via median treatment effects-Applications to COVID-19 clinical trials

Comparing median outcomes to gauge treatment effectiveness is widespread practice in clinical and other investigations. While common, such difference-in-median characterizations of effectiveness are but one way to summarize how outcome distributions compare. This paper explores properties of median treatment effects (TEs) as indicators of treatment effectiveness. The paper's main focus is on decisionmaking based on median TEs and it proceeds by considering two paths a decisionmaker might follow. Along one, decisions are based on point-identified differences in medians alongside partially identified median differences; along the other decisions are based on point-identified differences in medians in conjunction with other point-identified parameters. On both paths familiar difference-in-median measures play some role yet in both the traditional standards are augmented with information that will often be relevant in assessing treatments' effectiveness. Implementing either approach is straightforward. In addition to its analytical results the paper considers several policy contexts in which such considerations arise. While the paper is framed by recently reported findings on treatments for COVID-19 and uses several such studies to explore empirically some properties of median-treatment-effect measures of effectiveness, its results should be broadly applicable.

Stochastic analysis of COVID-19 by a SEIR model with Lévy noise

We propose a Lévy noise-driven susceptible-exposed-infected-recovered model incorporating media coverage to analyze the outbreak of COVID-19. We conduct a theoretical analysis of the stochastic model by the suitable Lyapunov function, including the existence and uniqueness of the positive solution, the dynamic properties around the disease-free equilibrium and the endemic equilibrium; we deduce a stochastic basic reproduction number R₀ ^s for the extinction of disease, that is, if R₀ ^s≤1, the disease will go to extinction. Particularly, we fit the data from Brazil to predict the trend of the epidemic. Our main findings include the following: (i) stochastic perturbation may affect the dynamic behavior of the disease, and larger noise will be more beneficial to control its spread; (ii) strengthening social isolation, increasing the cure rate and media coverage can effectively control the spread of disease. Our results support the feasible ways of containing the outbreak of the epidemic.

Assessing the spread of the novel coronavirus in the absence of mass testing

BACKGROUND

Assessing why the spread of the COVID-19 virus slowed down in many countries in March through to May of 2020 is of great significance. The relative role of restrictions on behaviour ("lockdowns") and of a natural slowing for other reasons is difficult to assess when mass testing was not widely done. This paper assesses the evolution of the spread of the COVID-19 virus over this period when there was no data on test results for a large, random sample of the population.

METHOD

We estimate a version of the susceptible-infected-recovered model applied to data on the numbers who were tested positive in several countries over the period when the virus spread very fast and then its spread slowed sharply. Up to the end of April 2020, test data came from non-random samples of populations who were overwhelmingly those who displayed symptoms. Using data from a period when the criteria used for testing (which was that people had clear symptoms) was relatively consistent is important in drawing out the message from test results. We use this data to assess two things: how large might be the group of those infected who were not recorded and how effective were lockdown measures in slowing the spread of the infection.

RESULTS

We find that to match data on daily new cases of the virus, the estimated model favours high values for the number of people infected but not recorded.

CONCLUSIONS

Our findings suggest that the infection may have spread far enough in many countries by April 2020 to have been a significant factor behind the fall in measured new cases. Government restrictions on behaviour-lockdowns-were only one factor behind slowing in the spread of the virus.

Epidemics and macroeconomic outcomes: Social distancing intensity and duration

We analyze the determination of the optimal intensity and duration of social distancing policy aiming to control the spread of an infectious disease in a simple macroeconomic-epidemiological model. In our setting the social planner wishes to minimize the social costs associated with the levels of disease prevalence and output lost due to social distancing, both during and at the end of epidemic management program. Indeed, by limiting individuals' ability to freely move or interact with others (since requiring to wear face mask or to maintain physical distance from others, or even forcing some businesses to remain closed), social distancing has on the one hand the effect to reduce the disease incidence and on the other hand to reduce the economy's productive capacity. We analyze both the early and the advanced epidemic stage intervention strategies highlighting their implications for short and long run health and macroeconomic outcomes. We show that both the intensity and the duration of the optimal social distancing policy may largely vary according to the epidemiological characteristics of specific diseases, and that the balancing of the health benefits and economic costs associated with social distancing may require to accept the disease to reach an endemic state. Focusing in particular on COVID-19 we present a calibration based on Italian data showing how the optimal social distancing policy may vary if implemented at national or at regional level.

Evaluation of the number of undiagnosed infected in an outbreak using source of infection measurements

In times of outbreaks, an essential requirement for better monitoring is the evaluation of the number of undiagnosed infected individuals. An accurate estimate of this fraction is crucial for the assessment of the situation and the establishment of protective measures. In most current studies using epidemics models, the total number of infected is either approximated by the number of diagnosed individuals or is dependent on the model parameters and assumptions, which are often debated. We here study the relationship between the fraction of diagnosed infected out of all infected, and the fraction of infected with known contaminator out of all diagnosed infected. We show that those two are approximately the same in exponential models and across most models currently used in the study of epidemics, independently of the model parameters. As an application, we compute an estimate of the effective number of infected by the SARS-CoV-2 virus in various countries.

The US COVID-19 pandemic in the flood season

Flooding displaces large populations each season, which potentially increases the exposure of the vulnerable societies. Having failed to curve down the number of people infected with COVID-19 in the first wave of the pandemic, many states in the United States (U.S.) are now at high risk of the concurrence of the two disasters. Assessing this compound risk before the country enters the flood season is of vital importance. Therefore, we provide a prompt tool to assess the compound risk of COVID-19 at the county level over the U.S. We find that (1) the number of flood insurance house claims can proxy the displaced population accurately with more spatiotemporal detail, and (2) the high-risk areas of both flooding and COVID-19 are concentrated along the southern and eastern coasts and some parts of the Mississippi River. Our findings may trigger the interest of further exploring the topics related to the concurrence of COVID-19 and flooding.