Cumulative Number Of COVID-19 Cases Research Articles

Lived experiences of American adults who survive COVID-19: Implications for physical activity and interpersonal stress.

The cumulative number of COVID-19 cases has surpassed 579 million globally. Symptoms during and after COVID-19 infection vary from mild cold symptoms to severe multisystem illness. Given the wide range of symptom presentations and complications post-COVID-19, the purpose of this study was to describe the lived experience of American adults surviving COVID-19. This study employed an exploratory qualitative description design. Semi-structured interviews were conducted with a sample of 35 individuals (White [94%], female [71%], Mage = 43.7 years), with proximity to a university in an urban Midwest American city. Interviews occurred between May and August 2021, 3 or more months after participants tested positive for COVID-19. Forty percent of the 35 participants experienced prolonged COVID-19 symptoms impacting their lifestyle. Four themes characterized the impacts of the post-COVID-19 condition on the lives of the participants within the context of a global pandemic: (a) disruptions in health and well-being, (b) persistent uncertainty, (c) disruptions in interpersonal relationships, (d) beneficent outcomes and adaptation. This study of COVID-19 has identified important implications for physical activity and interpersonal stress. Prolonged COVID-19 symptoms led to disruptions in the health, well-being, and interpersonal relationships of participants. Health care professionals need to attend to symptoms post-COVID-19, assess interpersonal functioning, and provide guidance on physical activity. Future studies are recommended to track consequences of COVID-19's impact on long-term health and well-being. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

COVID-19 Contact Tracing Experience at a Tertiary Healthcare Center in Saudi Arabia.

Introduction As of May 2023, the end of the pandemic, the cumulative number of COVID-19 cases reached more than 841,000 cases. Healthcare workers (HCWs) especially have been at the frontline during this pandemic and are at a higher chance of contracting COVID-19. Approximately half of all high-risk exposures were to healthcare personnel with COVID-19. While several tools for contact tracing were developed for the general public, contact tracing for infectious diseases in the healthcare setting is limited, and global testing of HCWs, or in-hospital digital tracing, is not performed in most facilities. The King Faisal Specialist Hospital and Research Center (KFSH&RC) collaborated with the Infection Control and Health Information Technology Affairs (HITA)to create an automated COVID-19 contact tracing tool specifically for HCWs who worked at the institute. This study aims to describe the contact tracing experience at KFSH&RC. Methods A retrospective study was conducted to describe the use of an automated tool that was developed to assist in the contact tracing process and that was to be used by KFSH&RC employees who had been in contact with a COVID-19-positiveindividual. This tool is utilized for the early identification of possible COVID-19 cases and risk stratification of the exposed individuals. The tool can be accessed through the KFSH&RC website; it also collects information about the COVID-19 exposure rate among the different departments such as administration, capital projects/facilities, and healthcare at a tertiary care hospital in Riyadh, Saudi Arabia Results The tool has been utilized 7,353 times by contact cases. Approximately 7% of those tested later developed a COVID-19 infection. When assessing the positivity rates per department, The Environmental Services Department had the highest positivity rate of 28.21%, followed by Health Information Technology and Analytics (HITA), and then the Central Transportation Department. Conclusion This study acts as the first of its kind to describe the successful use of the healthcare contact tracing system in one of Saudi Arabia's largest hospitals (KFSH&RC) and describe the infection trends in different departments of the hospital. Through the tracing system, the departments with the highest COVID-19 infection occurrences at the hospital were identified in a timely manner, and safety protocols were implemented.

Determinants of Poverty in East Java During The COVID-19 Pandemic

The global COVID-19 pandemic has infected million people in Indonesia. East Java has experienced Indonesia’s epicentre of positive COVID-19 cases. The economic disruption in East Java due to COVID-19 pandemic has led to increase the number of poor people. This study aims to examine the determinants of poverty during the pandemic outbreak. In this study, we employed multiple linear regression. The results reveals that simultaneously the cumulative number of COVID-19, unemployment rate, Gini Ratio, population density, human development index (HDI), and GRDP per capita affect the risk of poverty in East Java. Partially, the cumulative number of COVID-19, unemployment rate, population density, and HDI shows a significant effect to poverty. While the Gini ratio and GRDP per capita has an insignificant effect. The increase on cumulative number of COVID-19 cases is likely to increase the risk of poverty. Similarly, unemployment has a positive significant affect on poverty. The increase on unemployment rate tends to increase the number of poor people. Contrary, the HDI and population density have a negatively significant effect to poverty. The increase on HDI and population density tends to increase the number of poor people.

Estimation of the size of the COVID-19 pandemic using the epidemiological wavelength model: results from OECD countries

ObjectivesThis study aimed to simplify the previously developed epidemiological wavelength model and to expand the scope of the model with additional variables to estimate the magnitude of the COVID-19 pandemic. The applicability of the extended wavelength model was tested in Organisation for Economic Cooperation and Development (OECD) member countries. Study designThe epidemiological wavelengths of OECD member countries for the years 2020, 2021 and 2022 were estimated comparatively, considering the cumulative number of COVID-19 cases. MethodsThe size of the COVID-19 pandemic was estimated using the wavelength model. The scope of the wavelength model was expanded to include additional variables. The extended estimation model was improved by adding population density and human development index variables, in addition to the number of COVID-19 cases and number of days since the first case reported from the previous estimation model. ResultsAccording to the findings obtained from the wavelength model, the country with the highest epidemiological wavelength for the years 2020, 2021 and 2022 was the United States (We = 29.96, We = 28.63 and We = 28.86, respectively), and the country with the lowest wavelength was Australia (We = 10.50, We = 13.14 and We = 18.44, respectively). The average wavelength score of OECD member countries was highest in 2022 (We = 24.32) and lowest in 2020 (We = 22.84). The differences in the periodic wavelengths of OECD countries were analysed with the dependent t-test for paired samples in two periods, 2020–2021 and 2021–2022. There was a statistically significant difference between wavelengths in the 2020–2021 and 2021–2022 groups (t(36) = −3.670; P < 0.001). ConclusionsDecision-makers can use the extended wavelength model to easily follow the progress of the epidemic and to make quicker and more reliable decisions.

COVID-19 multiwaves as multiphase percolation: a general N-sigmoidal equation to model the spread.

The aim of the current study is to investigate the spread of the COVID-19 pandemic as a multiphase percolation process. Mathematical equations have been developed to describe the time dependence of the number of cumulative infected individuals, , and the velocity of the pandemic, , as well as to calculate epidemiological characteristics. The study focuses on the use of sigmoidal growth models to investigate multiwave COVID-19. Hill, logistic dose response and sigmoid Boltzmann models fitted successfully a pandemic wave. The sigmoid Boltzmann model and the dose response model were found to be effective in fitting the cumulative number of COVID-19 cases over time 2 waves spread (N = 2). However, for multiwave spread (N > 2), the dose response model was found to be more suitable due to its ability to overcome convergence issues. The spread of N successive waves has also been described as multiphase percolation with a period of pandemic relaxation between two successive waves.

Covid-19 Surveillance Testing and Resident Outcomes in Nursing Homes

BackgroundDespite widespread adoption of surveillance testing for coronavirus disease 2019 (Covid-19) among staff members in skilled nursing facilities, evidence is limited regarding its relationship with outcomes among facility residents.MethodsUsing data obtained from 2020 to 2022, we performed a retrospective cohort study of testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among staff members in 13,424 skilled nursing facilities during three pandemic periods: before vaccine approval, before the B.1.1.529 (omicron) variant wave, and during the omicron wave. We assessed staff testing volumes during weeks without Covid-19 cases relative to other skilled nursing facilities in the same county, along with Covid-19 cases and deaths among residents during potential outbreaks (defined as the occurrence of a case after 2 weeks with no cases). We reported adjusted differences in outcomes between high-testing facilities (90th percentile of test volume) and low-testing facilities (10th percentile). The two primary outcomes were the weekly cumulative number of Covid-19 cases and related deaths among residents during potential outbreaks.ResultsDuring the overall study period, 519.7 cases of Covid-19 per 100 potential outbreaks were reported among residents of high-testing facilities as compared with 591.2 cases among residents of low-testing facilities (adjusted difference, −71.5; 95% confidence interval [CI], −91.3 to −51.6). During the same period, 42.7 deaths per 100 potential outbreaks occurred in high-testing facilities as compared with 49.8 deaths in low-testing facilities (adjusted difference, −7.1; 95% CI, −11.0 to −3.2). Before vaccine availability, high- and low-testing facilities had 759.9 cases and 1060.2 cases, respectively, per 100 potential outbreaks (adjusted difference, −300.3; 95% CI, −377.1 to −223.5), along with 125.2 and 166.8 deaths (adjusted difference, −41.6; 95% CI, −57.8 to −25.5). Before the omicron wave, the numbers of cases and deaths were similar in high- and low-testing facilities; during the omicron wave, high-testing facilities had fewer cases among residents, but deaths were similar in the two groups.ConclusionsGreater surveillance testing of staff members at skilled nursing facilities was associated with clinically meaningful reductions in Covid-19 cases and deaths among residents, particularly before vaccine availability.

Vietnam and Other ASEAN Countries During the COVID-19 Pandemic

The author analyzes the impact of the COVID-19 pandemic on the economy of Vietnam and other ASEAN member states. Although the mentioned countries experienced an economic downturn for the first time in 22 years in 2020, growth rates of macroeconomic indicators quickly recovered. Vietnam was able to withstand the pandemic shock better than other ASEAN members, despite the fact that it had the largest number of cumulative COVID-19 cases within the Association. During the first year of the pandemic, ASEAN coped with it quite successfully. However, the situation in Vietnam and ASEAN as a whole has changed dramatically with the appearance of the Delta strain in 2021. Vaccination in most ASEAN countries is successful (Brunei and Singapore are the best, Myanmar is the worst; Vietnam and Laos share the 2nd place), allowing to gradually reduce restrictions. Despite the pandemic, in November 2020, 15 states of the region signed the Regional Comprehensive Economic Partnership (RCEP) agreement. According to experts, Vietnam will benefit the most among RCEP member states.

The Catalan Surveillance Network of SARS-CoV-2 in Sewage: design, implementation, and performance

Wastewater-based epidemiology has shown to be an efficient tool to track the circulation of SARS-CoV-2 in communities assisted by wastewater treatment plants (WWTPs). The challenge comes when this approach is employed to help Health authorities in their decision-making. Here, we describe the roadmap for the design and deployment of SARSAIGUA, the Catalan Surveillance Network of SARS-CoV-2 in Sewage. The network monitors, weekly or biweekly, 56 WWTPs evenly distributed across the territory and serving 6 M inhabitants (80% of the Catalan population). Each week, samples from 45 WWTPs are collected, analyzed, results reported to Health authorities, and finally published within less than 72 h in an online dashboard (https://sarsaigua.icra.cat). After 20 months of monitoring (July 20–March 22), the standardized viral load (gene copies/day) in all the WWTPs monitored fairly matched the cumulative number of COVID-19 cases along the successive pandemic waves, showing a good fit with the diagnosed cases in the served municipalities (Spearman Rho = 0.69). Here we describe the roadmap of the design and deployment of SARSAIGUA while providing several open-access tools for the management and visualization of the surveillance data.

High-Speed railways and the spread of Covid-19

High-speed railways (HSRs) greatly decrease transportation costs and facilitate the movement of goods, services, and passengers across cities. In the context of the Covid-19 pandemic, however, HSRs may contribute to the cross-regional spread of the new coronavirus. This paper evaluates the role of HSRs in spreading Covid-19 from Wuhan to other Chinese cities. We use train frequencies in 1971 and 1990 as instrumental variables. Empirical results from gravity models demonstrate that one more HSR train originating from Wuhan each day before the Wuhan lockdown increases the cumulative number of Covid-19 cases in a city by about 10 percent. The empirical analysis suggests that other transportation modes, including normal-speed trains and airline flights, also contribute to the spread of Covid-19, but their effects are smaller than the effect of HSRs. This paper’s findings indicate that transportation infrastructures, especially HSR trains originating from a city where a pandemic broke out, can be important factors promoting the spread of an infectious disease.

The impact of environmental and climate parameters on the incidence and mortality of COVID-19 in the six Gulf Cooperation Council countries: A cross-country comparison study.

BackgroundEnvironmental factors can influence the epidemiological dynamics of COVID-19. To estimate the true impact of these factors on COVID-19, climate and disease data should be monitored and analyzed over an extended period of time. The Gulf Cooperation Council (GCC) countries are particularly lacking in such studies. This ecological study investigates the association between climate parameters and COVID-19 cases and deaths in the GCC.MethodsData on temperature, wind-speed and humidity and COVID-19 cases and deaths from the six countries of the GCC were collected between 29/1/2020 and 30/3/2021. Using Spearman’s correlation coefficient, we examined associations between climate parameters and COVID-19 cases and deaths by month, over four different time periods. A two-step cluster analysis was conducted to identify distinct clusters of data using climate parameters and linear regression analysis to determine which climate parameters predicted COVID-19 new cases and deaths.ResultsThe United Arab Emirates (UAE) had the highest cumulative number of COVID-19 cases while Bahrain had the highest prevalence rate per 100,000. The Kingdom of Saudi Arabia (KSA) reported the highest cumulative number of deaths while Oman recorded the highest death rate per 100,000. All GCC countries, except the UAE, reported a positive correlation between temperature and cases and deaths. Wind speed was positively correlated with cases in Qatar, but negatively correlated with cases in the UAE and deaths in KSA. Humidity was positively correlated with cases and deaths in Oman, negatively correlated in Bahrain, Kuwait, Qatar and KSA but there was no correlation in the UAE. The most significant predictors in cluster analysis were temperature and humidity, while in the regression analysis, temperature, humidity and wind speed predicted new COVID-19 cases and deaths.ConclusionThis study provides comprehensive epidemiological information on COVID-19 and climate parameters and preliminary evidence that climate may play a key role in the transmission of the COVID-19 virus. This study will assist decision makers in translating findings into specific guidelines and policies for the prevention and elimination of COVID-19 transmission and infection.

Correlation between Population Density and COVID-19 Cases during the Third Wave in Malaysia: Effect of the Delta Variant

In this study, we describe the incidence and distribution of COVID-19 cases in Malaysia at district level and determine their correlation with absolute population and population density, before and during the period that the Delta variant was dominant in Malaysia. Methods: Data on the number of locally transmitted COVID-19 cases in each of the 145 districts in Malaysia, between 20 September 2020 and 19 September 2021, were manually extracted from official reports. The cumulative number of COVID-19 cases, population and population density of each district were described using choropleth maps. The correlation between population and population density with the cumulative number of COVID-19 cases in each district in the pre-Delta dominant period (20 September 2020–29 June 2021) and during the Delta dominant period (30 June 2021–19 September 2021) were determined using Pearson’s correlation. Results: COVID-19 cases were strongly correlated with both absolute population and population density (Pearson’s correlation coefficient (r) = 0.87 and r = 0.78, respectively). A majority of the districts had higher numbers of COVID-19 cases during the Delta dominant period compared to the pre-Delta period. The correlation coefficient in the pre-Delta dominant period was r = 0.79 vs. r = 0.86 during the Delta dominant period, whereas the pre-Delta dominant population density was r = 0.72, and in the Delta dominant period, r = 0.76. Conclusion: More populous and densely populated districts have a higher risk of transmission of COVID-19, especially with the Delta variant as the dominant circulating strain. Therefore, extra and more stringent control measures should be instituted in highly populated areas to control the spread of COVID-19.

Turnout in the time of corona: evidence from the legislative elections in Korea

ABSTRACT This paper reports empirical evidence on the impact of the prevalence of COVID-19 on voter turnout, using the data from the legislative elections in Korea. To control for the time-invariant constituency-specific factors, I regress the first-difference in turnout, i.e. the change from the previous election, on the cumulative number of COVID-19 cases per resident. The results show a substantially negative impact. The share of old-age voters, who are known to face worse health risks of COVID-19 infection, amplifies this impact. These findings raise the question whether it is more democratic to hold elections as scheduled, as opposed to postponing them, during a pandemic.

SARS-CoV-2 antibody seroprevalence in Lebanon: findings from the first nationwide serosurvey

BackgroundLebanon, a small country in the Middle East, remains severely affected by the COVID-19 pandemic. Seroprevalence surveys of anti-SARS-CoV-2 antibodies provide accurate estimates of SARS-CoV-2 infection and hence evaluate the extent of the pandemic. The present study aimed to evaluate the prevalence of SARS-CoV-2 antibodies in Lebanon and to compare the estimated cumulative number of COVID-19 cases with the officially registered number of laboratory-confirmed cases up to January 15, 2021.MethodsA nationwide population-based serosurvey study was conducted in Lebanon between December 7, 2020, and January 15, 2021, before the initiation of the national vaccination program. The nCOVID-19 IgG & IgM point-of-care (POCT) rapid test was used to detect the presence of anti-SARS-COV-2 immunoglobulin G (IgG) in the blood. Seroprevalence was estimated after weighting for sex, age, and area of residence and adjusting for the test performance.ResultsOf the 2058 participants, 329 were positive for IgG SARS-COV-2, resulting in a crude seroprevalence of 16.0% (95% CI 14.4–17.6). The weighed seroprevalence was 15.9% (95% CI of 14.4 and 17.4). After adjusting for test performance, the population weight-adjusted seroprevalence was 18.5% (95% CI 16.8–20.2). This estimate implies that 895,770 individuals of the general population were previously infected by COVID-19 up to January 15, 2021 in Lebanon. The overall estimated number of subjects with previous SARS-CoV-2 infection was three times higher than the officially reported cumulative number of confirmed cases. Seroprevalence was similar across age groups and sexes (p-value > 0.05). However, significant differences were revealed across governorates.ConclusionsOur results suggest that the Lebanese population is still susceptible to SARS-CoV-2 infection and far from achieving herd immunity. These findings represent an important contribution to the surveillance of the COVID-19 pandemic in Lebanon and to the understanding of how this virus spreads. Continued surveillance for COVID-19 cases and maintaining effective preventive measures are recommended to control the epidemic spread in conjunction with a national vaccination campaign to achieve the desired level of herd immunity against COVID-19.

Number of public health nurses and COVID-19 incidence rate by variant type: an ecological study of 47 prefectures in Japan.

BackgroundCommunity health activities by public health nurses (PHNs) are known to improve lifestyle habits of local residents, and may encourage the practice of infectious disease prevention behaviors during the COVID-19 pandemic. We investigated the association between prefecture-level COVID-19 incidence rate and the number of PHNs per population in Japan, by the COVID-19 variant type.MethodsOur data were based on government surveys where prefectural-level data are accessible to the public. The outcome variable was the COVID-19 incidence rate (i.e., the cumulative number of COVID-19 cases per 100,000 population for each variant type in 47 prefectures). The explanatory variable was the number of PHNs per 100,000 population by prefecture. Covariates included socioeconomic factors, regional characteristics, healthcare resources, and health behaviors. The generalized estimating equations of the multivariable Poisson regression models were used to estimate adjusted incidence rate ratio (IRR) and 95% confidence interval (CI) for the COVID-19 cases. We performed stratified analyses by variant type (i.e., wild type, alpha variant, and delta variant).ResultsA total of 1,705,224 confirmed COVID-19 cases (1351.6 per 100,000 population) in Japan were reported as of September 30, 2021. The number of PHNs per 100,000 population in Japan was 41.9. Multivariable Poisson regression models showed that a lower number of PHNs per population was associated with higher IRR of COVID-19. Among all COVID-19 cases, compared to the highest quintile group of the number of PHNs per population, the adjusted IRR of the lowest quintile group was consistently significant in the models adjusting for socioeconomic factors (IRR: 3.76, 95% CI: 2.55–5.54), regional characteristics (1.73, 1.28–2.34), healthcare resources (3.88, 2.45–6.16), and health behaviors (2.17, 1.39–3.37). These significant associations were unaffected by the variant type of COVID-19.ConclusionWe found that the COVID-19 incidence rate was higher in prefectures with fewer PHNs per population, regardless of the COVID-19 variant type. By increasing the number of PHNs, it may be possible to contain the spread of COVID-19 in Japan and provide an effective human resource to combat emerging infectious diseases in the future.Supplementary informationThe online version contains supplementary material available at https://doi.org/10.1265/ehpm.22-00013.

Medical Students’ Mental Health Status and Coping Strategies in Their Quarantine Period During COVID-19 Pandemic in Universiti Putra Malaysia

Movement control order (MCO) was enforced by the Malaysian government on 18th March 2020 due to sudden increase in the cumulative number of COVID-19 cases. University students, including medical students, were among the group of the population which were significantly affected. This study was conducted among medical students in Universiti Putra Malaysia (UPM) to compare the mental health status and coping strategies, between gender and phase of the clinical study related to their quarantine status between 18th March 2020 to 9th June 2020. This was a cross-sectional study involving the undergraduate medical students in UPM in the academic year of 2019/2020 during the MCO. A stratified random sampling method was used; a total of 173 respondents which includes students of Year 1 to Year 5 participated. The study was conducted after five months since the first day of MCO, for one week period of data collection. The mental health status was assessed using Depression, Anxiety and Stress Scale 21-item (DASS-21) questionnaire. Four-point Likert scale was scored to determine the degree of severity. The coping strategies was assessed using Brief-COPE (Coping Orientation to Problems Experienced) in which four-point Likert scale was used as well, and the total marks were grouped into four coping strategies. This study found higher scores of DASS-21 in the non-quarantined group and approach method was considered as a favourable coping strategy in both groups. In the quarantined group, female and clinical students showed higher scores of DASS-21. In the non-quarantined group, clinical students showed a significantly higher score of stress while male students significantly used their humour as coping strategies.

Excess mortality in Russia and its regions compared to high income countries: An analysis of monthly series of 2020

BackgroundRussia has been portrayed in media as having one of the highest death tolls due to the COVID-19 pandemic in the world. However, the precise scale of excess mortality is still unclear. We provide the first estimates of excess mortality in Russia as a whole and its regions in 2020, placing this in an international context. MethodsWe used monthly death rates for Russia and 83 regions plus the equivalent for 36 comparator countries. Expected mortality was derived in two ways using averages in the same months in preceding years and the same averages adjusted for secular trends. Excess death rates were estimated for the whole year and the last 3 quarters. We also estimated the relationships between excess mortality and reported COVID-19 cases and deaths across countries and Russian regions. ResultsEstimating excess deaths rates based on the trend-adjusted average, Russia had the highest excess mortality of any of the 37 countries considered. Using the simple average, Russia had the third highest. Most of the excess deaths were recorded in the 4th quarter of 2020 and the level and trajectory of excess mortality in Russia and most of Eastern European countries differed from that in Western countries. While both the cumulative number of COVID-19 cases and deaths showed positive correlations with excess mortality across countries (r=0.65 and r=0.75, p<0.001), the association across the Russian regions was, surprisingly, negative for cases (r=-0.34, p<0.01) and deaths (r=-0.09, p=0.42). When we replaced reported deaths with final data from death certificates the correlation was positive (r=0.38, p<0.001). ConclusionRussia has one of the largest absolute burden of excess mortality in 2020 but there is a counter-intuitive negative association between excess mortality and cumulative incidence at the regional level. Under-recording of COVID-19 cases seems to be a problem in some regions.

Assessing the reaction of the Baltic stock market to the spread of the COVID-19 pandemic

This research aims to assess the impact of the spread of the COVID-19 pandemic on the Baltic stock market. To reach this aim, the methods of bivariate (OLS) regression and VAR-based impulse response functions are employed. We use daily new cases of COVID-19 as well as the cumulative number of COVID-19 cases as independent and OMX Baltic Benchmark GI index as dependent variables for our research. The research period, covering data from 2020 March 1st to 2020 November 21st, is divided into three separate periods, reflecting the different phases of the spread of the COVID-19 pandemic. The results of the research revealed that the market reaction differs depending on the period; moreover, the Baltic stock market index was affected by new cases and total cases in a slightly different manner.

Association between air pollution and COVID-19 mortality and morbidity

Coronavirus disease (COVID-19) pandemic is affecting the world unevenly. One of the highest numbers of cases were recorded in the most polluted regions worldwide. The risk factors for severe COVID-19 include diabetes, cardiovascular, and respiratory diseases. It has been known that the same disease might be worsened by chronic exposure to air pollution. The study aimed to determine whether long-term average exposure to air pollution is associated with an increased risk of COVID-19 cases and deaths in Poland. The cumulative number of COVID-19 cases and deaths for each voivodeship (the main administrative level of jurisdictions) in Poland were collected from March 4, 2020, to May 15, 2020. Based on the official data published by Chief Inspectorate of Environmental Protection voivodeship-level long-term exposure to main air pollution: PM2.5, PM10, NO2, SO2, O3 (averaged from 2013 to 2018) was established. There were statistically significant correlation between COVID-19 cases (per 100,000 population) and annual average concentration of PM2.5 (R2 = 0.367, p = 0.016), PM10 (R2 = 0.415, p = 0.009), SO2 (R2 = 0.489, p = 0.003), and O3 (R2 = 0.537, p = 0.0018). Moreover, COVID-19 deaths (per 100,000 population) were associated with annual average concentration of PM2.5 (R2 = 0.290, p = 0.038), NO2 (R2 = 0.319, p = 0.028), O3 (R2 = 0.452, p = 0.006). The long-term exposure to air pollution, especially PM2.5, PM10, SO2, NO2, O3 seems to play an essential role in COVID-19 prevalence and mortality. Long-term exposure to air pollution might increase the susceptibility to the infection, exacerbates the severity of SARS-CoV-2 infections, and worsens the patients’ prognosis. The study provides generalized and possible universal trends. Detailed analyzes of the phenomenon dedicated to a given region require taking into account data on comorbidities and socioeconomic variables as well as information about the long-term exposure to air pollution and COVID-19 cases and deaths at smaller administrative level of jurisdictions (community or at least district level).

Estimation of the Number of Cumulative COVID-19 Cases by Day in Thailand Based on a Flattened Curve Policy

Since December 2019, the world has been facing an emerging infectious disease named coronavirus disease 2019. Thailand has also been affected by the spread of the coronavirus. The Thai government have announced policies to protect people, based on the emergency decree and curfew law for flattening the curve of the number of the coronavirus disease 2019 cases without vaccination in Thailand. This research estimated of the number of total infectious cases of coronavirus disease 2019 in Thailand. Two growth curves, including an exponential growth curve under a non-flattened curve policy (herd immunity policy without vaccination), and a logistic growth curve under a flattened curve policy without vaccination, were selected to estimate the parameters of the curves by the least square method to represent the number of the total infectious cases in Thailand. Moreover, the maximum infectious cases of coronavirus disease 2019 and the speed of spreading for coronavirus disease 2019 in Thailand were also explored. Based on the number of the total infectious cases of coronavirus disease 2019 in Thailand, the findings demonstrated that the coefficient of determination of the logistic growth curve was greater than the exponential growth curve and the root means squared percentage error of the logistic growth curve was less than the exponential growth curve. These results suggest that the logistic growth curve is suitable for describing the number of total infectious cases of coronavirus disease 2019 in Thailand under the fattened curve policy. GRAPHICAL ABSTRACT

When can physical distancing be relaxed? A health production function approach for COVID-19 control policy

BackgroundTo assess if physical distancing measures to control the COVID-19 pandemic can be relaxed, one of the key indicators used is the reproduction number R. Many developing countries, however, have limited capacities to estimate R accurately. This study aims to demonstrate how health production function can be used to assess the state of COVID-19 transmission and to determine a risk-based relaxation policy.MethodsThe author employs a simple “bridge” between epidemiological models and production economics to establish the cumulative number of COVID-19 cases as a short-run total product function and to derive the corresponding marginal product, average product, and production elasticity. Three crucial dates defining the states of transmission, labelled red, yellow, and green zones, are determined. Relaxation policy is illogical in the “red zone” and is not recommended in the “yellow zone”. In the “green zone”, relaxation can be considered. The Bayesian probability of near term’s daily cases meeting a policy target is computed. The method is applied to France, Germany, Italy, the UK, and the US, and to Indonesia as an example of application in developing countries.ResultsThis study uses data from the WHO COVID-19 Dashboard, beginning from the first recording date for each country until February 28, 2021. As of June 30, 2020, France, Germany, Italy, and the UK had arrived at the “green zone” but with a high risk of transmission re-escalations. In the following weeks, their production elasticities were rising, giving a signal of accelerated transmissions. The signal was corroborated by these countries’ rising cases, making them leaving the “green zone” in the later months. By February 28, 2021, the UK had returned to the “green zone”, France, Germany, and Italy were still in the “yellow zone”, while the US reached the “green zone” at a very high number of cases. Despite being in the “red zone”, Indonesia relaxed its distancing measures, causing a sharp rise of cases.ConclusionsHealth production function can show the state of COVID-19 transmission. A rising production elasticity gives an early warning of transmission escalations. The elasticity is a useful parameter for risk-based relaxation policy.