Cumulative Coronavirus Disease 2019 Research Articles

Change in the Proportion of Death at Home during the COVID-19 Pandemic and Its Associated Factors in the Municipality Level: A Nationwide Study in Japan.

The coronavirus disease 2019 (COVID-19) pandemic may have led to an increase in home deaths due to hospital bed shortage and hospital visitation restrictions. This study aimed to examine changes in the proportion of home deaths before and after the COVID-19 pandemic and identify associated factors. We used publicly available nationwide data to describe the proportion of home deaths among total deaths from 2015 to 2021. Furthermore, we used municipal-level data to examine the factors associated with the increase in the proportion of home deaths from 2019 to 2021. The dependent variable was the absolute change in the proportion of home deaths from 2019 to 2021. The independent variables included each municipality's 2019 home death percentage, medical and long-term care (LTC) resources divided by the population of older people, population density, and cumulative number of COVID-19 cases. A multivariable linear regression analysis was conducted after the standardization of each variable. The proportions of home deaths in 2015, 2019, and 2021 were 12.7%, 13.6%, and 17.2%, respectively, indicating a sharp increase in home death rate after the COVID-19 pandemic. In the multivariable linear regression analysis that included 1,696 municipalities, conventional home care support clinics and hospitals (HCSCs) (coefficient [95% confidence intervals (CIs)], 0.19 [0.01-0.37]), enhanced HCSCs (0.53 [0.34-0.71]), home-visiting nurses (0.26 [0.06-0.46]), population density (0.44 [0.21-0.67]), and cumulative COVID-19 cases (0.49 [0.27-0.70]) were positively associated with the increase in home deaths, whereas beds of LTC welfare facilities (-0.55 [-0.74--0.37]) and the proportion of home deaths in 2019 (-1.24 [-1.44--1.05]) were negatively associated with the increase. During the COVID-19 pandemic, home deaths significantly increased, particularly in densely populated areas with high cumulative COVID-19 cases. HCSCs, especially enhanced HCSCs, are crucial for meeting the demand for home-based end-of-life care.

Exploring the reported adverse effects of COVID-19 vaccines among vaccinated Arab populations: a multi-national survey study

The coronavirus disease 2019 (COVID-19) pandemic has been a major challenge worldwide for the past years with high morbidity and mortality rates. While vaccination was the cornerstone to control the pandemic and disease spread, concerns regarding safety and adverse events (AEs) have been raised lately. A cross-sectional study was conducted between January 1st and January 22nd, 2022, in six Arabic countries namely Saudi Arabia, Egypt, Syria, Libya, Iraq, and Algeria. We utilized a self-administered questionnaire validated in Arabic which encompassed two main parts. The first was regarding sociodemographic data while the second was about COVID-19 vaccination history, types, doses, and experienced AEs. A multistage sampling was employed in each country, involving the random selection of three governorates from each country, followed by the selection of one urban area and one rural area from each governorate. We included the responses of 1564 participants. The most common AEs after the first and second doses were local AEs (67.9% and 46.6%, respectively) followed by bone pain and myalgia (37.6% and 31.8%, respectively). After the third dose, the most common AEs were local AEs (45.7%) and fever (32.4%). Johnson and Johnson, Sputnik Light, and Moderna vaccines showed the highest frequency of AEs. Factors associated with AEs after the first dose included an increase in age (aOR of 61–75 years compared to the 12–18 years group: 2.60, 95% CI: 1.59–4.25, p = 0.001) and male gender (OR: 0.72, 95% CI: 0.63–0.82, p < 0.001). The cumulative post-vaccination COVID-19 disease was reported with Sinovac (16.1%), Sinopharm (15.8%), and Johnson and Johnson (14.9) vaccines. History of pre-vaccination SARS-CoV-2 infection significantly increases the risk of post-vaccination COVID-19 after the first, second, and booster doses (OR: 3.09, CI: 1.9–5.07, p < 0.0001; OR: 2.56, CI: 1.89–3.47, p < 0.0001; and OR: 2.94, CI: 1.6–5.39, p = 0.0005 respectively). In conclusion, AEs were common among our participants, especially local AEs. Further extensive studies are needed to generate more generalizable data regarding the safety of different vaccines.

Geographic Information Database of Herbs against COVID-19 in Thailand: The Medicinal Plants those Folk Healers Commonly Used for Treatment and Boosting People's Immunity

The rapid outbreak of coronavirus disease 2019 (COVID-19) has demonstrated the need for the development of new vaccine candidates and therapeutic drugs to fight against the underlying virus, severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Currently, no antiviral treatment is available to treat COVID-19, as treatment is mostly directed at relieving the symptoms, and retrospectively, herbal medicinal plants have been used for thousands of years as a medicinal alternative, including for the treatment of various viral illnesses. The aim of this study is to conduct a survey in terms of identifying the area where the population commonly uses the medicinal plant in comparison to the cumulative number of COVID-19 reports in each area, including the classification of medicinal plants by type and a stepwise approach shown in the form of geographic information maps in those areas. An observational study on the cultivation of medicinal plants those folk healers commonly used for healing. beneficial for treatment and strengthening the immunity of the people in 9 provinces of Thailand. According to the situation of the spread of COVID-19, there are people infected in Thailand. In each area where medicinal plants were used, there was a significant positive result when compared to the cumulative COVID-19 incidence; the majority was with the lowest cumulative COVID-19 incidence and the most commonly used medicinal plants, such as Artemisia annua, Harrisonia perforate (Blanco) Merr, Capparis micracantha, Tacca leontopetaloides, Andrographis paniculata, Phyllanthus emblica, Ficus carica, Tiliacora triandra, Terminalia bilaria, and Cannabis indica. This study exercise may lend enough credence to the potential value of Thai medicinal plants (herbs) as possible leads in anti-COVID-19 drug discovery through research and development.

Effects of cumulative COVID-19 cases on mental health: Evidence from multi-country survey.

Depression and anxiety were both ranked among the top 25 leading causes of global burden of diseases in 2019 prior to the coronavirus disease 2019 (COVID-19) pandemic. The pandemic affected, and in many cases threatened, the health and lives of millions of people across the globe and within the first year, global prevalence of anxiety and depression increased by 25% with the greatest influx in places highly affected by COVID-19. To explore the psychological impact of the pandemic and resultant restrictions in different countries using an opportunistic sample and online questionnaire in different phases of the pandemic. A repeated, cross-sectional online international survey of adults, 16 years and above, was carried out in 10 countries (United Kingdom, India, Canada, Bangladesh, Ukraine, Hong Kong, Pakistan, Egypt, Bahrain, Saudi Arabia). The online questionnaire was based on published approaches to understand the psychological impact of COVID-19 and the resultant restrictions. Five standardised measures were included to explore levels of depression [patient health questionnaire (PHQ-9)], anxiety [generalized anxiety disorder (GAD) assessment], impact of trauma [the impact of events scale-revised (IES-R)], loneliness (a brief loneliness scale), and social support (The Multi-dimensional Scale of Perceived Social support). There were two rounds of the online survey in 10 countries with 42866 participants in Round 1 and 92260 in Round 2. The largest number of participants recruited from the United Kingdom (112985 overall). The majority of participants reported receiving no support from mental health services throughout the pandemic. This study found that the daily cumulative COVID-19 cases had a statistically significant effect on PHQ-9, GAD-7, and IES-R scores. These scores significantly increased in the second round of surveys with the ordinary least squares regression results with regression discontinuity design specification (to control lockdown effects) confirming these results. The study findings imply that participants' mental health worsened with high cumulative COVID-19 cases. Whist we are still living through the impact of COVID-19, this paper focuses on its impact on mental health, discusses the possible consequences and future implications. This study revealed that daily cumulative COVID-19 cases have a significant impact on depression, anxiety, and trauma. Increasing cumulative cases influenced and impacted education, employment, socialization and finances, to name but a few. Building a database of global evidence will allow for future planning of pandemics, particularly the impact on mental health of populations considering the cultural differences.

A Simple Epidemiologic Model for Predicting Impaired Neutralization of New SARS-CoV-2 Variants.

This study is aimed at developing a simple epidemiologic model that could help predict the impaired neutralization of new SARS-CoV-2 variants. We explored the potential association between neutralization of recent and more prevalent SARS-CoV-2 sublineages belonging to the Omicron family (i.e., BA.4/5, BA.4.6, BA.2.75.2, BQ.1.1 and XBB.1) expressed as FFRNT50 (>50% suppression of fluorescent foci fluorescent focus reduction neutralization test) in recipients of four doses of monovalent mRNA-based coronavirus disease 2019 (COVID-19) vaccines, with epidemiologic variables like emergence date and number of spike protein mutations of these sublineages, cumulative worldwide COVID-19 cases and cumulative number of COVID-19 vaccine doses administered worldwide at the time of SARS-CoV-2 Omicron sublineage emergence. In the univariate analysis, the FFRNT50 value for the different SARS-CoV-2 Omicron sublineages was significantly associated with all such variables except with the number of spike protein mutations. Such associations were confirmed in the multivariate analysis, which enabled the construction of the equation: “−0.3917 × [Emergence (date)] + 1.403 × [COVID-19 cases (million)] − 121.8 × [COVID-19 Vaccine doses (billion)] + 18,250”, predicting the FFRNT50 value of the five SARS-CoV-2 Omicron sublineages with 0.996 accuracy (p = 0.013). We have shown in this work that a simple mathematical approach, encompassing a limited number of widely available epidemiologic variables, such as emergence date of new variants and number of COVID-19 cases and vaccinations, could help identifying the emergence and surge of future lineages with major propensity to impair humoral immunity.

Forecasting the Cumulative COVID-19 Cases in Indonesia Using Flower Pollination Algorithm

Coronavirus disease 2019 (COVID-19) was declared as a global pandemic by the World Health Organization (WHO) on 12 March 2020. Indonesia is reported to have the highest number of cases in Southeast Asia. Accurate prediction of the number of COVID-19 cases in the upcoming few days is required as one of the considerations in making decisions to provide appropriate recommendations in the process of mitigating global pandemic infectious diseases. In this research, a metaheuristics optimization algorithm, the flower pollination algorithm, is used to forecast the cumulative confirmed COVID-19 cases in Indonesia. The flower pollination algorithm is a robust and adaptive method to perform optimization for curve fitting of COVID-19 cases. The performance of the flower pollination algorithm was evaluated and compared with a machine learning method which is popular for forecasting, the recurrent neural network. A comprehensive experiment was carried out to determine the optimal hyperparameters for the flower pollination algorithm and recurrent neural network. There were 24 and 72 combinations of hyperparameters for the flower pollination algorithm and recurrent neural network, respectively. The best hyperparameters were used to develop the COVID-19 forecasting model. Experimental results showed that the flower pollination algorithm performed better than the recurrent neural network in long-term (two weeks) and short-term (one week) forecasting of COVID-19 cases. The mean absolute percentage error (MAPE) for the flower pollination algorithm model (0.38%) was much lower than that of the recurrent neural network model (5.31%) in the last iteration for long-term forecasting. Meanwhile, the MAPE for the flower pollination algorithm model (0.74%) is also lower than the recurrent neural network model (4.8%) in the last iteration for short-term forecasting of the cumulative COVID-19 cases in Indonesia. This research provides state-of-the-art results to help the process of mitigating the global pandemic of COVID-19 in Indonesia.

Impact of COVID-19 pandemic on HCV care cascade in Rwanda: Ecological study from July 2019 to June 2021.

Content available: Author Interview and Audio Recording

Predicting Managers' Mental Health Across Countries: Using Country-Level COVID-19 Statistics.

BackgroundThere is limited research focusing on publicly available statistics on the Coronavirus disease 2019 (COVID-19) pandemic as predictors of mental health across countries. Managers are at risk of suffering from mental disorders during the pandemic because they face particular hardship.ObjectiveWe aim to predict mental disorder (anxiety and depression) symptoms of managers across countries using country-level COVID-19 statistics.MethodsA two-wave online survey of 406 managers from 26 countries was performed in May and July 2020. We used logistic panel regression models for our main analyses and performed robustness checks using ordinary least squares regressions. In the sample, 26.5% of managers reached the cut-off levels for anxiety (General Anxiety Disorder-7; GAD-7) and 43.5% did so for depression (Patient Health Questionnaire-9; PHQ-9) symptoms.FindingsWe found that cumulative COVID-19 statistics (e.g., cumulative cases, cumulative cases per million, cumulative deaths, and cumulative deaths per million) predicted managers' anxiety and depression symptoms positively, whereas daily COVID-19 statistics (daily new cases, smoothed daily new cases, daily new deaths, smoothed daily new deaths, daily new cases per million, and smoothed daily new cases per million) predicted anxiety and depression symptoms negatively. In addition, the reproduction rate was a positive predictor, while stringency of governmental lockdown measures was a negative predictor. Individually, we found that the cumulative count of deaths is the most suitable single predictor of both anxiety and depression symptoms.ConclusionsCumulative COVID-19 statistics predicted managers' anxiety and depression symptoms positively, while non-cumulative daily COVID-19 statistics predicted anxiety and depression symptoms negatively. Cumulative count of deaths is the most suitable single predictor of both anxiety and depression symptoms. Reproduction rate was a positive predictor, while stringency of governmental lockdown measures was a negative predictor.

Exposure factors associated with SARS-CoV-2 seroprevalence during the first eight months of the COVID-19 pandemic in the Netherlands: A cross-sectional study

BackgroundThe availability of valid Severe Acute Respiratory Syndrome Coronvirus-2 (SARS-CoV-2) serological tests overcome the problem of underestimated cumulative Coronavirus Disease 2019 (COVID-19) cases during the first months of the pandemic in The Netherlands. The possibility to reliably determine the number of truly infected persons, enabled us to study initial drivers for exposure risk in the absence of routine testing. Numerous activities or circumstances can accelerate virus spread, here defined as exposure factors. Hence, we aimed to evaluate a wide variety of demographic, behavioural and social exposure factors associated with seropositivity during the first eight months of the pandemic in Limburg, The Netherlands.MethodsSARS-CoV-2 point-seroprevalence was determined cross-sectionally to indicate previous infection in a convenience sample of minimal 10,000 inhabitants of the study province. All adult (18+ years) inhabitants of the study province were eligible to register themselves for participation. Once the initial 10,000 registrations were reached, a reserve list was kept to ensure sufficient participants. Possible exposure factors were mapped by means of an extensive questionnaire. Associated exposure factors were determined using univariable and multivariable logistic regression models.ResultsSeropositivity was established in 19.5% (n = 1,948) of the 10,001 participants (on average 49 years old (SD = 15; range 18–90 years), majority women (n = 5,829; 58.3%). Exposure factors associated with seropositivity included current education, working in healthcare and not working from home, and being a member of three or four associations or clubs. Specifically for February-March 2020, visiting an après-ski bar during winter sports in Austria, travelling to Spain, celebrating carnival, and participating in a singing activity or ball sport were associated with seropositivity.ConclusionsOur results confirm that relevant COVID-19 exposure factors generally reflected circumstances where social distancing was impossible, and the number and duration of contacts was high, in particular for indoor activities.

Exposure factors associated with SARS-CoV-2 seroprevalence during the first eight months of the COVID-19 pandemic in the Netherlands: A cross-sectional study.

The availability of valid Severe Acute Respiratory Syndrome Coronvirus-2 (SARS-CoV-2) serological tests overcome the problem of underestimated cumulative Coronavirus Disease 2019 (COVID-19) cases during the first months of the pandemic in The Netherlands. The possibility to reliably determine the number of truly infected persons, enabled us to study initial drivers for exposure risk in the absence of routine testing. Numerous activities or circumstances can accelerate virus spread, here defined as exposure factors. Hence, we aimed to evaluate a wide variety of demographic, behavioural and social exposure factors associated with seropositivity during the first eight months of the pandemic in Limburg, The Netherlands. SARS-CoV-2 point-seroprevalence was determined cross-sectionally to indicate previous infection in a convenience sample of minimal 10,000 inhabitants of the study province. All adult (18+ years) inhabitants of the study province were eligible to register themselves for participation. Once the initial 10,000 registrations were reached, a reserve list was kept to ensure sufficient participants. Possible exposure factors were mapped by means of an extensive questionnaire. Associated exposure factors were determined using univariable and multivariable logistic regression models. Seropositivity was established in 19.5% (n = 1,948) of the 10,001 participants (on average 49 years old (SD = 15; range 18-90 years), majority women (n = 5,829; 58.3%). Exposure factors associated with seropositivity included current education, working in healthcare and not working from home, and being a member of three or four associations or clubs. Specifically for February-March 2020, visiting an après-ski bar during winter sports in Austria, travelling to Spain, celebrating carnival, and participating in a singing activity or ball sport were associated with seropositivity. Our results confirm that relevant COVID-19 exposure factors generally reflected circumstances where social distancing was impossible, and the number and duration of contacts was high, in particular for indoor activities.

Interval forecasts of weekly incident and cumulative COVID-19 mortality in the United States: A comparison of combining methods.

A combined forecast from multiple models is typically more accurate than an individual forecast, but there are few examples of studies of combining in infectious disease forecasting. We investigated the accuracy of different ways of combining interval forecasts of weekly incident and cumulative coronavirus disease-2019 (COVID-19) mortality. We considered weekly interval forecasts, for 1- to 4-week prediction horizons, with out-of-sample periods of approximately 18 months ending on 8 January 2022, for multiple locations in the United States, using data from the COVID-19 Forecast Hub. Our comparison involved simple and more complex combining methods, including methods that involve trimming outliers or performance-based weights. Prediction accuracy was evaluated using interval scores, weighted interval scores, skill scores, ranks, and reliability diagrams. The weighted inverse score and median combining methods performed best for forecasts of incident deaths. Overall, the leading inverse score method was 12% better than the mean benchmark method in forecasting the 95% interval and, considering all interval forecasts, the median was 7% better than the mean. Overall, the median was the most accurate method for forecasts of cumulative deaths. Compared to the mean, the median's accuracy was 65% better in forecasting the 95% interval, and 43% better considering all interval forecasts. For all combining methods except the median, combining forecasts from only compartmental models produced better forecasts than combining forecasts from all models. Combining forecasts can improve the contribution of probabilistic forecasting to health policy decision making during epidemics. The relative performance of combining methods depends on the extent of outliers and the type of models in the combination. The median combination has the advantage of being robust to outlying forecasts. Our results support the Hub's use of the median and we recommend further investigation into the use of weighted methods.

A predictive model for daily cumulative COVID-19 cases in Ghana.

Background: Coronavirus disease 2019 (COVID-19) is a pandemic that has affected the daily life, governments and economies of many countries all over the globe. Ghana is currently experiencing a surge in the number of cases with a corresponding increase in the cumulative confirmed cases and deaths. The surge in cases and deaths clearly shows that the preventive and management measures are ineffective and that policy makers lack a complete understanding of the dynamics of the disease. Most of the deaths in Ghana are due to lack of adequate health equipment and facilities for managing the disease. Knowledge of the number of cases in advance would aid policy makers in allocating sufficient resources for the effective management of the cases. Methods: A predictive tool is necessary for the effective management and prevention of cases. This study presents a predictive tool that has the ability to accurately forecast the number of cumulative cases. The study applied polynomial and spline models on the COVID-19 data for Ghana, to develop a generalized additive model (GAM) that accurately captures the growth pattern of the cumulative cases. Results: The spline model and the GAM provide accurate forecast values. Conclusion: Cumulative cases of COVID-19 in Ghana are expected to continue to increase if appropriate preventive measures are not enforced. Vaccination against the virus is ongoing in Ghana, thus, future research would consider evaluating the impact of the vaccine.

Infection fatality rate of COVID-19 in community-dwelling elderly populations

This mixed design synthesis aimed to estimate the infection fatality rate (IFR) of Coronavirus Disease 2019 (COVID-19) in community-dwelling elderly populations and other age groups from seroprevalence studies. Protocol: https://osf.io/47cgb. Eligible were seroprevalence studies done in 2020 and identified by any of four existing systematic reviews; with ≥ 500 participants aged ≥ 70 years; presenting seroprevalence in elderly people; aimed to generate samples reflecting the general population; and whose location had available data on cumulative COVID-19 deaths in elderly (primary cutoff ≥ 70 years; ≥ 65 or ≥ 60 also eligible). We extracted the most fully adjusted (if unavailable, unadjusted) seroprevalence estimates; age- and residence-stratified cumulative COVID-19 deaths (until 1 week after the seroprevalence sampling midpoint) from official reports; and population statistics, to calculate IFRs adjusted for test performance. Sample size-weighted IFRs were estimated for countries with multiple estimates. Thirteen seroprevalence surveys representing 11 high-income countries were included in the main analysis. Median IFR in community-dwelling elderly and elderly overall was 2.9% (range 1.8–9.7%) and 4.5% (range 2.5–16.7%) without accounting for seroreversion (2.2% and 4.0%, respectively, accounting for 5% monthly seroreversion). Multiple sensitivity analyses yielded similar results. IFR was higher with larger proportions of people > 85 years. The IFR of COVID-19 in community-dwelling elderly is lower than previously reported.

A predictive model for daily cumulative COVID-19 cases in Ghana

Background: Coronavirus disease 2019 (COVID-19) is a pandemic that has affected the daily life, governments and economies of many countries all over the globe. Ghana is currently experiencing a surge in the number of cases with a corresponding increase in the cumulative confirmed cases and deaths. The surge in cases and deaths clearly shows that the preventive and management measures are ineffective and that policy makers lack a complete understanding of the dynamics of the disease. Most of the deaths in Ghana are due to lack of adequate health equipment and facilities for managing the disease. Knowledge of the number of cases in advance would aid policy makers in allocating sufficient resources for the effective management of the cases. Methods: A predictive tool is necessary for the effective management and prevention of cases. This study presents a predictive tool that has the ability to accurately forecast the number of cumulative cases. The study applied polynomial and spline models on the COVID-19 data for Ghana, to develop a generalized additive model (GAM) that accurately captures the growth pattern of the cumulative cases. Results: The spline model and the GAM provide accurate forecast values. Conclusion: Cumulative cases of COVID-19 in Ghana are expected to continue to increase if appropriate preventive measures are not enforced. Vaccination against the virus is ongoing in Ghana, thus, future research would consider evaluating the impact of the vaccine.

Severe Acute Respiratory Syndrome Coronavirus 2 Seroprevalence and Reported Coronavirus Disease 2019 Cases in US Children, August 2020-May 2021.

BackgroundCase-based surveillance of pediatric coronavirus disease 2019 (COVID-19) cases underestimates the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections among children and adolescents. Our objectives were to estimate monthly SARS-CoV-2 antibody seroprevalence and calculate ratios of SARS-CoV-2 infections to reported COVID-19 cases among children and adolescents in 8 US states.MethodsUsing data from the Nationwide Commercial Laboratory Seroprevalence Survey, we estimated monthly SARS-CoV-2 antibody seroprevalence among children aged 0–17 years from August 2020 through May 2021. We calculated and compared cumulative incidence of SARS-CoV-2 infection extrapolated from population-standardized seroprevalence of antibodies to SARS-CoV-2, cumulative COVID-19 case reports since March 2020, and infection-to-case ratios among persons of all ages and children aged 0–17 years for each state.ResultsOf 41 583 residual serum specimens tested, children aged 0–4, 5–11, and 12–17 years accounted for 1619 (3.9%), 10 507 (25.3%), and 29 457 (70.8%), respectively. Median SARS-CoV-2 antibody seroprevalence among children increased from 8% (range, 6%–20%) in August 2020 to 37% (range, 26%–44%) in May 2021. Estimated ratios of SARS-CoV-2 infections to reported COVID-19 cases in May 2021 ranged by state from 4.7–8.9 among children and adolescents to 2.2–3.9 for all ages combined.ConclusionsThrough May 2021 in selected states, the majority of children with serum specimens included in serosurveys did not have evidence of prior SARS-CoV-2 infection. Case-based surveillance underestimated the number of children infected with SARS-CoV-2 more than among all ages. Continued monitoring of pediatric SARS-CoV-2 antibody seroprevalence should inform prevention and vaccination strategies.

Prioritizing COVID-19 vaccination. Part 2: Real-time comparison between single-dose and double-dose in Japan

&lt;abstract&gt; &lt;p&gt;Japan successfully implemented a mass vaccination program for coronavirus disease 2019 (COVID-19), immunizing more than 1 million persons a day by July 2021. Given the COVID-19 vaccination capacity limitations, an urgent question was raised regarding whether it would be better to (ⅰ) complete double-dose COVID-19 vaccination among healthcare personnel and older adults before beginning double-dose vaccination of younger adults (double-dose strategy) or (ⅱ) allocate a single dose of COVID-19 vaccine to all adults regardless of age before administering the second dose (single-dose-first strategy). We used an age-structured susceptible-infectious-recovered (SIR) compartment model to compare the effectiveness of possible COVID-19 vaccination strategies and the length of public health and social measures (PHSM) to minimize the cumulative COVID-19 disease risk and death toll. Our results indicate that if the single-dose-first strategy was taken, an estimated total of 1,387,078 persons, i.e., 263,315 children, 928,518 young adults, and 195,245 older adults, would develop COVID-19, resulting in 15,442 deaths. In contrast, if the double-dose strategy was taken instead, an estimated total of 1,900,172 persons, i.e., 377,107 children, 1,315,927 young adults, and 207,138 older adults, would develop COVID-19, yielding 17,423 deaths. Real-time investigation favored the disease transmission blocking option, i.e., single-dose vaccination strategy. Applying the single-dose-first strategy should yield a smaller epidemic size than applying the double-dose strategy; however, for both strategies, PHSM will be essential by the time second-dose COVID-19 vaccination is complete among all adults.&lt;/p&gt; &lt;/abstract&gt;

Social Capital and COVID-19 Deaths: An Ecological Analysis in Japan.

Social contextual factors could determine mortality by the coronavirus disease 2019 (COVID-19), with social capital as a potential determinant. This study aimed to examine the association between prefecture-level social capital and COVID-19 deaths in Japan. Data on the cumulative number of COVID-19 deaths per 100,000 individuals between 1 October 2020 and 30 June 2021 in 47 prefectures were obtained from the government open-access database. Prefecture-level social capital was collected from a large-scale web-based nationwide survey conducted between August and September 2020. We included trust in neighbors, norm of reciprocity in the neighborhood, and trust in the national government as cognitive social capital, and neighborhood ties and social participation as structural social capital. The cumulative COVID-19 deaths per 100,000 individuals (1 October 2020 to 30 June 2021) ranged from 0.15 to 27.98 in 47 prefectures. A multiple regression analysis after adjusting for covariates showed that a greater norm of reciprocity and government trust were associated with fewer COVID-19 deaths during the first and second 3-month periods of observation. In the third 3-month period, the association between COVID-19 deaths and government trust became nonsignificant. Trust in neighbors, neighborhood ties, and social participation were not related to COVID-19 deaths during any time period. The disparity of COVID-19 deaths by prefecture in Japan can be explained by cognitive social capital. This study suggests that the association between social capital and COVID-19 deaths may vary according to the dimension of social capital and time period.

Quantifying the impacts of human mobility restriction on the spread of coronavirus disease 2019: an empirical analysis from 344 cities of China.

Background:Since the outbreak of coronavirus disease 2019 (COVID-19), human mobility restriction measures have raised controversies, partly because of the inconsistent findings. An empirical study is promptly needed to reliably assess the causal effects of the mobility restriction. The purpose of this study was to quantify the causal effects of human mobility restriction on the spread of COVID-19.Methods:Our study applied the difference-in-difference (DID) model to assess the declines of population mobility at the city level, and used the log–log regression model to examine the effects of population mobility declines on the disease spread measured by cumulative or new cases of COVID-19 over time after adjusting for confounders.Results:The DID model showed that a continual expansion of the relative declines over time in 2020. After 4 weeks, population mobility declined by −54.81% (interquartile range, −65.50% to −43.56%). The accrued population mobility declines were associated with the significant reduction of cumulative COVID-19 cases throughout 6 weeks (ie, 1% decline of population mobility was associated with 0.72% [95% CI: 0.50%–0.93%] reduction of cumulative cases for 1 week, 1.42% 2 weeks, 1.69% 3 weeks, 1.72% 4 weeks, 1.64% 5 weeks, and 1.52% 6 weeks). The impact on the weekly new cases seemed greater in the first 4 weeks but faded thereafter. The effects on cumulative cases differed by cities of different population sizes, with greater effects seen in larger cities.Conclusions:Persistent population mobility restrictions are well deserved. Implementation of mobility restrictions in major cities with large population sizes may be even more important.

COVID-19 effects on the S&P 500 index

ABSTRACT This paper investigates the effects of the coronavirus disease 2019 (COVID-19) cases in the US on the S&P 500 Index using daily data covering the period between 21st January, 2020 and 10th August, 2021. The investigation is achieved by using a structural vector autoregression model, where a measure of the global economic activity and the spread between 10-year treasury constant maturity and the federal funds rate are also included. The empirical results suggest that having of an increase in cumulative daily COVID-19 cases in the US results in about of a cumulative reduction in the S&P 500 Index after 1 day and about of a reduction after 1 week. Historical decomposition of the S&P 500 Index further suggests that the negative effects of COVID-19 cases in the US on the S&P 500 Index have been mostly observed during March 2020.

Letter from Trinidad and Tobago: No final battles in the weary war on COVID‐19?

Letter from Trinidad and Tobago: No final battles in the weary war on <scp>COVID</scp>‐19?