Daily COVID-19 Research Articles

Effects of climatic factors on COVID-19 transmission in Ethiopia

Climatic conditions play a key role in the transmission and pathophysiology of respiratory tract infections, either directly or indirectly. However, their impact on the COVID-19 pandemic propagation is yet to be studied. This study aimed to evaluate the effects of climatic factors such as temperature, rainfall, relative humidity, sunshine duration, and wind speed on the number of daily COVID-19 cases in Addis Ababa, Ethiopia. Data on confirmed COVID-19 cases were obtained from the National Data Management Center at the Ethiopian Public Health Institute for the period 10th March 2020 to 31st October 2021. Data for climatic factors were obtained from the Ethiopia National Meteorology Agency. The correlation between daily confirmed COVID-19 cases and climatic factors was measured using the Spearman rank correlation test. The log-link negative binomial regression model was used to fit the effect of climatic factors on COVID-19 transmission, from lag 0 to lag 14 days. During the study period, a total of 245,101 COVID-19 cases were recorded in Addis Ababa, with a median of 337 new cases per day and a maximum of 1903 instances per day. A significant correlation between COVID-19 cases and humidity was observed with a 1% increase in relative humidity associated with a 1.1% [IRRs (95%CI) 0.989, 95% (0.97–0.99)] and 1.2% [IRRs (95%CI) 0.988, (0.97–0.99)] decrease in COVID-19 cases for 4 and 5 lag days prior to detection, respectively. The highest increase in the effect of wind speed and rainfall on COVID-19 was observed at 14 lag days prior to detection with IRRs of 1.85 (95%CI 1.26–2.74) and 1.078 (95%CI 1.04–1.12), respectively. The lowest IRR was 1.109 (95%CI 0.93–1.31) and 1.007 (95%CI 0.99–1.02) both in lag 0, respectively. The findings revealed that none of the climatic variables influenced the number of COVID-19 cases on the day of case detection (lag 0), and that daily average temperature and sunshine duration were not significantly linked with COVID-19 risk across the full lag period (p > 0.05). Climatic factors such as humidity, rainfall, and wind speed influence the transmission of COVID-19 in Addis Ababa, Ethiopia. COVID-19 cases have shown seasonal variations with the highest number of cases reported during the rainy season and the lowest number of cases reported during the dry season. These findings suggest the need to design strategies for the prevention and control of COVID-19 before the rainy seasons.

P001 Role of Stress Management on Perceived COVID-19 Health Risk Associated with Sleep (Quantity and Quality) of Undergraduate Business Students

Abstract Introduction Since the COVID-19 pandemic began in 2020, it has significantly caused disruptions in various aspects of daily life. In particular, students have been negatively affected by these changes due to unexpected alterations in educational modalities. Stress management can help students to adapt new normality easier and monitor their daily activities, which can reduce their COVID-19 risk while improving sleep quality for better health and academic performance. Methods For this pilot study, a Google form–based survey was distributed among 87 volunteer undergraduate business students from Tecnologico de Monterrey, Mexico. The survey was distributed during May 2021. The designed survey contains questionnaires related to perceived COVID-19 health risk, stress management, and sleep (quality and quantity). To test the mediating effects of stress management, we applied a mediation test using the “medmod” plugin of the Jamovi software. Results Analyzed results of the path estimate show that perceived health risk has a significant association with stress management (Estimate=.418, Z=5.58, p<.001), and stress management has a significant association with sleep (Estimate=.618, Z=5.18, p<.001). The value of mediation estimates calculated as =.618, Z=5.63, p<.001, 95% confidence intervals (LLCI=0.403 – ULCI=0.833). The analysis confirms that stress management mediates perceived COVID-19 health risk and sleep (quantity and quality) of undergraduate students. Conclusion Based on the analyzed results of this pilot study, we conclude that stress management helps undergraduate students to accept and adopt new norms, cope with daily COVID-19 challenges, take greater preventive measures to decrease COVID-19 health risks, and improve the quantity and quality of sleep.

Comparative Evaluation of the Multilayer Perceptron Approach with Conventional ARIMA in Modeling and Prediction of COVID-19 Daily Death Cases.

COVID-19 continues to pose a dangerous global health threat, as cases grow rapidly and deaths increase day by day. This increasing phenomenon does not only affect economic policy but also international policy around the world. In this paper, Pakistan daily death cases of COVID-19, from February 25, 2020, to March 23, 2022, have been modeled using the long-established autoregressive-integrated moving average (ARIMA) model and the machine learning multilayer perceptron (MLP) model. The most befitting model is selected based on the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). Values of the key performance indicator (KPI) showed that the MLP model outperformed the ARIMA model. The MLP model with 20 hidden layers, which emerged as the overall most apt model, was used to predict future daily COVID-19 deaths in Pakistan to enable policymakers and health professionals to put in place systematic measures to reduce death cases. We encourage the Government of Pakistan to intensify its vaccination campaign and encourage everyone to get vaccinated.

Bayesian Inference for the Negative Binomial-Quasi Lindley Model for Time Series Count Data on the COVID-19 Pandemic

In statistical models, the generalized linear model (GLMs) plays a role in studying to describe a response variable as a function of 1 or more predictor variables. Computational methods and mixed distributions are frequently used to build predictive models to perform time-to-event data analysis. To develop a statistical model so that the model can make predictions appropriately and accurately, it starts with developing a suitable distribution for the nature of the actual data. This paper proposes a new mixed negative binomial distribution for count data with over-dispersion, the so-called negative binomial-quasi Lindley (NB-QL) distribution. A new GLMs framework for the NB-QL model to build the time series count data model is introduced, and its application is carried out based on the actual data sets of the COVID-19 epidemic in Thailand. The models are related to GLMs as they are linear relationships between outcome variables and covariates. Where the response variable was in the form of time series count data under the exponential family distribution function, with the random components and link functions. In this study, we study the factors that affect the number of COVID-19 death cases in Thailand and provide the predictive modeling of the number of the COVID-19 death cases from 1 January 2020 to 31 December 2020, for which this data set has the observed sample of 366 days. In contrast, a model with an NB-QL distribution and NB has approached the uniform. Based on the deviance, DIC, and the probability integral transform histogram, we can see that the proposed model is also suitable for forecasting the number of the COVID-19 death cases daily in Thailand, indicating that the NB-QL time series model was another efficient alternative to modeling count data that has an over-dispersion problem. According to the NB-QL time series model about the number of the COVID-19 death cases daily in Thailand, it is indicated that the average number of daily COVID-19 deaths is influenced by the number of the COVID-19 death cases in the previous 3 days. The average number of COVID-19 death cases in Thailand is also influenced by the previous 2 days. At the same time, the number of infected cases daily in Thailand is influenced by the number of the COVID-19 death cases daily. In addition, there are also the components interventions of internal covariate effects due to the data, as there was a surge in the number of the COVID-19 death cases daily in Thailand at the time. HIGHLIGHTS A new mixture NB distribution to be a flexible alternative to analyze count data with over-dispersion. The new distribution is a mix of the NB and QL distributions; a name is Negative Binomial-Quasi Lindley A new mixed negative binomial distribution for time series count data with over-dispersion, and the Bayesian approach is the method used to estimate the parameters of the proposed model. We will apply the GLMs framework to build the time series count data The new mixed NB distribution in this study is an extremely effective alternative for modeling count data in the context of over-dispersion GRAPHICAL ABSTRACT

Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic

IntroductionVaccines are the most important instrument for bringing the pandemic to a close and saving lives and helping to reduce the risks of infection. It is important that everyone has equal access to immunizations that are both safe and effective. There is no one who is safe until everyone gets vaccinated. COVID-19 vaccinations are a game-changer in the fight against diseases. In addition to examining attitudes toward these vaccines in Africa, Asia, Oceania, Europe, North America, and South America, the purpose of this paper is to predict the acceptability of COVID-19 vaccines and study their predictors.Materials and methodsKaggle datasets are used to estimate the prediction outcomes of the daily COVID-19 vaccination to prevent a pandemic. The Kaggle data sets are classified into training and testing datasets. The training dataset is comprised of COVID-19 daily data from the 13th of December 2020 to the 13th of June 2021, while the testing dataset is comprised of COVID-19 daily data from the 14th of June 2021 to the 14th of October 2021. For the prediction of daily COVID-19 vaccination, four well-known machine learning algorithms were described and used in this study: CUBIST, Gaussian Process (GAUSS), Elastic Net (ENET), Spikes, and Slab (SPIKES).ResultsAmong the models considered in this paper, CUBIST has the best prediction accuracy in terms of Mean Absolute Scaled Error (MASE) of 9.7368 for Asia, 2.8901 for America, 13.2169 for Oceania, and 3.9510 for South America respectively.ConclusionThis research shows that machine learning can be of great benefit for optimizing daily immunization of citizens across the globe. And if used properly, it can help decision makers and health administrators to comprehend immunization rates and create strategies to enhance them.

Uncertain imputation for time-series forecasting: Application to COVID-19 daily mortality prediction.

The object of this study is to put forward uncertainty modeling associated with missing time series data imputation in a predictive context. We propose three imputation methods associated with uncertainty modeling. These methods are evaluated on a COVID-19 dataset out of which some values have been randomly removed. The dataset contains the numbers of daily COVID-19 confirmed diagnoses ("new cases") and daily deaths ("new deaths") recorded since the start of the pandemic up to July 2021. The considered task is to predict the number of new deaths 7 days in advance. The more values are missing, the higher the imputation impact is on the predictive performances. The Evidential K-Nearest Neighbors (EKNN) algorithm is used for its ability to take into account labels uncertainty. Experiments are provided to measure the benefits of the label uncertainty models. Results show the positive impact of uncertainty models on imputation performances, especially in a noisy context where the number of missing values is high.

Covid-19 pandemic in north-west Italy: the potential role of meteorology, air pollution and pollens

Abstract Background Italy was the first western country severely affected by the Covid-19 pandemic attesting more than 16 million cases since the outbreak began. Po Valley regions have been most afflicted, with Piedmont ranking sixth at 25,899 cases/100,000 inhabitants. Within this area, air dispersion is hampered making Po Valley a recognised air pollution hotspot. We aimed to explore the potential association between the environment and Covid-19 incidence. Methods Daily key air pollutants (NO2, NO, CO, O3, PM10, and PM2.5), meteorological parameters (temperature, %humidity, wind speed and solar radiation), pollens and Covid-19 cases were collected from 01/01 to 31/12/2021 in Turin, Italy. This ecological study preliminarily tested correlations (Spearman) between air pollutants and Covid-19 cases. Results The Covid-19 pandemic followed a seasonal trend with the highest number of cases (/100,000 inhabitants) in winter and spring (3.1) followed by autumn (1.3) and summer (0.5) (KW test p < 0.0001). Likewise, all air pollutants showed peaks in winter and autumn and sensibly decreased during spring and summer apart from pollens and O3. O3 follows the photochemical processes reaching its peak in the sunniest periods, while pollens undergo their natural vegetative process. Daily Covid-19 cases were positively correlated with daily-averaged NO2 (0.50, p < 0.0001), NO (0.48, p < 0.0001), CO (0.81, p < 0.0001), PM10 (0.36, p < 0.0001), PM2.5 (0.39, p < 0.0001), pollens (0.15, p = 0.073) and inversely with O3 (-0.44, p < 0.0001). We plan future analyses to test the hypothesized association by enhanced models with lagged air pollution variables, with demographic characteristics and meteorological data as potential confounders. Conclusions Results from ecological studies may support researchers’ preliminary understanding of the interplay between environment and Public Health issues, including pandemics. A multidisciplinary approach is mandatory to deepen the complexity of this topic across European regions Key messages • The Covid-19 pandemic may be associated with environmental conditions and air pollution but further research is needed. • Atmospheric particulate matter, including aeroallergens, can favour many airborne-related diseases by acting as immune suppressor and/or carrier, but these hypotheses deserve future research.

Prediction of COVID-19 Infections for Municipalities in the Netherlands: Algorithm Development and Interpretation.

BackgroundCOVID-19 was first identified in December 2019 in the city of Wuhan, China. The virus quickly spread and was declared a pandemic on March 11, 2020. After infection, symptoms such as fever, a (dry) cough, nasal congestion, and fatigue can develop. In some cases, the virus causes severe complications such as pneumonia and dyspnea and could result in death. The virus also spread rapidly in the Netherlands, a small and densely populated country with an aging population. Health care in the Netherlands is of a high standard, but there were nevertheless problems with hospital capacity, such as the number of available beds and staff. There were also regions and municipalities that were hit harder than others. In the Netherlands, there are important data sources available for daily COVID-19 numbers and information about municipalities.ObjectiveWe aimed to predict the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands, using a data set with the properties of 355 municipalities in the Netherlands and advanced modeling techniques.MethodsWe collected relevant static data per municipality from data sources that were available in the Dutch public domain and merged these data with the dynamic daily number of infections from January 1, 2020, to May 9, 2021, resulting in a data set with 355 municipalities in the Netherlands and variables grouped into 20 topics. The modeling techniques random forest and multiple fractional polynomials were used to construct a prediction model for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands.ResultsThe final prediction model had an R2 of 0.63. Important properties for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality in the Netherlands were exposure to particulate matter with diameters <10 μm (PM10) in the air, the percentage of Labour party voters, and the number of children in a household.ConclusionsData about municipality properties in relation to the cumulative number of confirmed infections in a municipality in the Netherlands can give insight into the most important properties of a municipality for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality. This insight can provide policy makers with tools to cope with COVID-19 and may also be of value in the event of a future pandemic, so that municipalities are better prepared.

COVID-19, climate change, and the finite pool of worry in 2019 to 2021 Twitter discussions

Climate change mitigation has been one of the world's most salient issues for the past three decades. However, global policy attention has been partially diverted to address the COVID-19 pandemic for the past 2 y. Here, we explore the impact of the pandemic on the frequency and content of climate change discussions on Twitter for the period of 2019 to 2021. Consistent with the "finite pool of worry" hypothesis both at the annual level and on a daily basis, a larger number of COVID-19 cases and deaths is associated with a smaller number of "climate change" tweets. Climate change discussion on Twitter decreased, despite 1) a larger Twitter daily active usage in 2020 and 2021, 2) greater coverage of climate change in the traditional media in 2021, 3) a larger number of North Atlantic Ocean hurricanes, and 4) a larger wildland fires area in the United States in 2020 and 2021. Further evidence supporting the finite pool of worry is the significant relationship between daily COVID-19 cases/deaths on the one hand and the public sentiment and emotional content of climate change tweets on the other. In particular, increasing COVID-19 numbers decrease negative sentiment in climate change tweets and the emotions related to worry and anxiety, such as fear and anger.

Detection of multiple waves for COVID-19 and its optimal control through media awareness and vaccination: study based on some Indian states.

COVID-19 is a highly infectious disease, and in very recent times, it has shown a massive impact throughout the globe. Several countries faced the COVID-19 infection waves multiple times. These later waves are more aggressive than the first wave and drastically impact social and economic factors. We developed a mechanistic model with imperfect lockdown effect, reinfection, transmission variability between symptomatic & asymptomatic, and media awareness to focus on the early detection of multiple waves and their control measures. Using daily COVID-19 cases data from six states of India, we estimated several important model parameters. Moreover, we estimated the home quarantine, community, and basic reproduction numbers. We developed an algorithm to carry out global sensitivity analysis (Sobol) of the parameters that influence the number of COVID-19 waves (W_mathrm{C}) and the average number of COVID-19 cases in a wave (A_mathrm{W}). We have identified some critical controlling parameters that mainly influenced W_mathrm{C} and A_mathrm{W}. Our study also revealed the best COVID-19 control strategy/strategies among vaccination, media awareness, and their combination using an optimal cost-effective study. The detailed analysis suggests that the severity of asymptomatic transmission is around 10% to 29% of that of symptomatic transmission in all six locations. About 1% to 4% of the total population under lockdown may contribute to new COVID-19 infection in all six locations. Optimal cost-effective analysis based on interventions, namely only vaccination (VA), only media awareness (ME), and a combination of vaccination & media (VA+ME), are projected for the period March 14, 2020, to August 31, 2021, for all the six locations. We have found that a large percentage of the population (26% to 45%) must be vaccinated from February 13 to August 31, 2021, to avert an optimal number of COVID-19 cases in these six locations.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11071-022-07887-5.

The Chaotic Behavior of the Spread of Infection during the COVID-19 Pandemic in Japan.

In December 2019, China reported a new virus identified as SARS-CoV-2, causing COVID-19, which soon spread to other countries and led to a global pandemic. Although many countries imposed strict actions to control the spread of the virus, the COVID-19 pandemic resulted in unprecedented economic and social consequences in 2020 and early 2021. To understand the dynamics of the spread of the virus, we evaluated its chaotic behavior in Japan. A 0–1 test was applied to the time-series data of daily COVID-19 cases from January 26, 2020 to August 5, 2021 (3 days before the end of the Tokyo Olympic Games). Additionally, the influence of hosting the Olympic Games in Tokyo was assessed in data including the post-Olympic period until October 8, 2021. Even with these extended time period data, although the time-series data for the daily infections across Japan were not found to be chaotic, more than 76.6% and 55.3% of the prefectures in Japan showed chaotic behavior in the pre- and post-Olympic Games periods, respectively. Notably, Tokyo and Kanagawa, the two most populous cities in Japan, did not show chaotic behavior in their time-series data of daily COVID-19 confirmed cases. Overall, the prefectures with the largest population centers showed non-chaotic behavior, whereas the prefectures with smaller populations showed chaotic behavior. This phenomenon was observed in both of the analyzed time periods (pre- and post-Olympic Games); therefore, more attention should be paid to prefectures with smaller populations, in which controlling and preventing the current pandemic is more difficult.

A SEIAR mathematical model to analyse the effect of COVID-19 pandemic over the crowded and dense populated regions

In this study, an attempt has been made to formulate a SEIR-type mathematical model for the dynamics of SARS-CoV-2 (COVID-19) pandemic by incorporating an additional asymptomatic compartment. The formulated model contains a system of ordinary differential equations. The basic reproduction number R 0 is obtained by applying the next-generation matrix method. For India, daily death cases and cumulative COVID-19 death cases were used to fit the model from March 20, 2020 to May 10, 2020. A tool MATLAB is used for numerical simulation and validation of analytical results. It is found that the coronavirus infection rate depends on the compactness of the population i.e., the infection rate in a particular region either increases or decreases depending on the population density of the region. The infection dynamics of coronavirus are also reduced when masks are worn and social distance is maintained.

Geographical and Temporal Analysis of Tweets Related to COVID-19 and Cardiovascular Disease in the US

ABSTRACT The COVID-19 pandemic has resulted in more than 600 million confirmed cases worldwide since December 2021. Cardiovascular disease (CVD) is both a risk factor for COVID-19 mortality and a complication that many COVID-19 patients develop. This study uses Twitter data to identify the spatiotemporal patterns and correlation of related tweets with daily COVID-19 cases and deaths at the national, regional, and state levels. We collected tweets mentioning both COVID-19 and CVD-related words from February to July 2020 (Eastern Time) and geocoded the tweets to the state level using GIScience techniques. We further proposed and validated that the Twitter user registration state can be a feasible proxy of geotags. We applied geographical and temporal analysis to investigate where and when people talked about COVID-19 and CVD. Our results indicated that the trend of COVID-19 and CVD-related tweets is correlated to the trend of COVID-19, especially the daily deaths. These social media messages revealed widespread recognition of CVD’s important role in the COVID-19 pandemic, even before the medical community started to develop consensus and theory supports about CVD aspects of COVID-19. The second wave of the pandemic caused another rise in the related tweets but not as much as the first one, as tweet frequency increased from February to April, decreased till June, and bounced back in July. At the regional level, four regions (Northeast, Midwest, North, and West) had the same trend of related tweets compared to the country as a whole. However, only the Northeast region had a high correlation (0.8–0.9) between the tweet count, new cases, and new deaths. For the second wave of confirmed new cases, the major contributing regions, South and West, did not ripple as many related tweets as the first wave. Our understanding is that the early news attracted more attention and discussion all over the U.S. in the first wave, even though some regions were not impacted as much as the Northeast at that time. The study can be expanded to more geographic and temporal scales, and with more physical and socioeconomic variables, with better data acquisition in the future.

Sectoral volatility spillovers and their determinants in Vietnam

Using the vector autoregression (VAR) connectedness approach, this paper investigates dynamic volatility spillovers across 14 sectors in Vietnam’s stock market over the period 2012–2021. The study also explores the differences in sectoral spillovers before and after the outbreak of Covid-19 pandemic. Additionally, the paper also investigates the effects of the current pandemic and macroeconomic fundamentals on intersectoral connectedness in Vietnam. Our findings show that volatility transmission across sectors fluctuates significantly over the research period and spikes during the Covid-19 pandemic. The total spillover index is approximately 64.23 per cent, indicating that volatility spillovers across the Vietnamese sectors are substantial. The risks from the stock market appear to spread quickly and easily across sectors in Vietnam. Among these 14 sectors, food, fisheries, and oil and gas act as net senders of risks while real estate and pharmacy are the greatest receivers of risk. The findings also confirm that the commerce, transportation, manufacturing, and service sectors are more sensitive to the Covid-19 pandemic crisis than other sectors in Vietnam. Furthermore, the empirical results show that an increase in daily Covid-19 infections increases volatility spillover across sectors. Policy implications have emerged based on these findings from this paper for the Vietnamese government and other emerging countries.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10644-022-09446-9.

Forecasting impact of Covid-19 on airline sector: a case study of Turkey

Purpose- An infectious virus called COVID-19 originated in China spread over the globe in a short time and became a pandemic. The aim of this study is to determine the relationship between the COVID-19 and the airline companies in Turkey. Thus, we went over daily stock prices of the companies as well as daily COVID-19 cases reported across the nation. As a result, two companies were chosen among all of them. From 11 March 2020 to 30 May 2022, we retrieved data of daily stock prices, COVID-19 cases and COVID-19 deaths recorded. Methodology- Regression analysis was used to determine the association between COVID-19 and prices. The results were as expected; both companies demonstrated significance, and we concluded that they responded to the COVID-19. The second aim of the study is to utilize regression analysis to predict, between June 2022 and June 2023, both the number of verified COVID-19 cases and the prices of Airline companies. Findings- The results were as expected; both companies were found significant, and we concluded that they responded to the COVID-19. By June 30, 2023, it is likely to see more verified cases if the corona virus spread rate in Turkey does not change. Data for COVID-19 instances will differ depending on the transmission rate. Briefly stated, COVID-19 cases and BIST 100 Travel Sector cases have a direct relationship. Because of a rise in COVID-19 vaccinations, both businesses exhibit an increasing tendency. Conclusion- The Travel Sector in BIST 100 also exhibits a favorable correlation with COVID-19 cases. But both businesses exhibit a bullish or rising tendency as a result of vaccination which started in 2021. To stop the corona virus from spreading, precautions should be taken, such as keeping a safe social distance, washing hands regularly, and avoiding unwanted facial contact. Keywords: COVID-19, BIST-100 Index, travel &amp; leisure sector, forecasting, relationship between price and COVID, regression analysis. JEL Codes: G15, G17, H84

Prayer frequency and COVID-19 vaccine hesitancy among older adults in Europe

AimDifferences in levels of vaccine uptake have emerged across Europe, and this may partly be explained by religious beliefs. Our aim is to study the association between religiosity, measured by prayer frequency, and vaccine hesitancy, and to examine how this association varies across European countries and regions. MethodsThis study was based on 42,583 adults aged 50 years and above from 27 European countries in the Survey of Health, Ageing and Retirement in Europe (SHARE), waves 1–8, and the 2nd SHARE COVID-19 Survey. Logistic regression models were used to investigate the associations. ResultsParticipants were more likely to be vaccine-hesitant when praying ‘weekly or less’ (odds ratio (OR) 1.32 95 % confidence interval (CI) 1.23–1.42) or daily (OR 1.78 95 % CI 1.65–1.92). Praying ‘weekly or less’ was associated with increased vaccine hesitancy in Southern Europe (OR 1.48 95 % CI 1.17–1.87) and Central and Eastern (OR 1.35 95 % CI 1.24–1.47) Europe, while daily praying was associated with vaccine hesitancy in Western (OR 1.77 95 % CI 1.51–2.08), Southern (OR 1.30 95 % CI 1.03–1.64), Central and Eastern (OR 1.89 95 % CI 1.73–2.06) and Northern (OR 2.75 95 % CI 1.54–4.89) Europe. ConclusionsThese findings provide support for an association between daily prayer frequency and COVID-19 vaccine hesitancy, with a consistent pattern across European regions.

The Association Between State-Issued Mask Mandates and County COVID-19 Hospitalization Rates.

Context:Mask mandates are one form of nonpharmaceutical intervention that has been utilized to combat the spread of SARS-CoV2, the virus that causes COVID-19.Objective:This study examines the association between state-issued mask mandates and changes in county-level and hospital referral region (HRR)–level COVID-19 hospitalizations across the United States.Design:Difference-in-difference and event study models were estimated to examine the association between state-issued mask mandates and COVID-19 hospitalization outcomes.Participants:All analyses were conducted with US county-level data.Interventions:State-issued mask mandates. County-level data on the mandates were collected from executive orders identified on state government Web sites from April 1, 2020, to December 31, 2020.Main Outcome Measures:Daily county-level (and HRR-level) estimates of inpatient beds occupied by patients with confirmed or suspected COVID-19 were collected by the US Department of Health and Human Services.Results:The state issuing of mask mandates was associated with an average of 3.6 fewer daily COVID-19 hospitalizations per 100 000 people (P < .05) and a 1.2-percentage-point decrease in the percentage of county beds occupied with COVID-19 patients (P < .05) within 70 days of taking effect. Event study results suggest that this association increased the longer mask mandates were in effect. In addition, the results were robust to analyses conducted at the HRR level.Conclusions:This study demonstrated that state-issued mask mandates were associated with reduction in COVID-19 hospitalizations across the United States during the earlier portion of the pandemic. As new variants of the virus cause spikes in COVID-19 cases, reimposing mask mandates in indoor and congested public areas, as part of a layered approach to community mitigation, may reduce the spread of COVID-19 and lessen the burden on our health care system.

Impact of school closures and reopening on COVID-19 caseload in 6 cities of Pakistan: An Interrupted Time Series Analysis.

Schools were closed all over Pakistan on November 26, 2020 to reduce community transmission of COVID-19 and reopened between January 18 and February 1, 2021. However, these closures were associated with significant economic and social costs, prompting a review of effectiveness of school closures to reduce the spread of COVID-19 infections in a developing country like Pakistan. A single-group interrupted time series analysis (ITSA) was used to measure the impact of school closures, as well as reopening schools, on daily new COVID-19 cases in 6 major cities across Pakistan: Lahore, Karachi, Islamabad, Quetta, Peshawar, and Muzaffarabad. However, any benefits were contingent on continued closure of schools, as cases bounced back once schools reopened. School closures are associated with a clear and statistically significant reduction in COVID-19 cases by 0.07 to 0.63 cases per 100,000 population, while reopening schools is associated with a statistically significant increase. Lahore is an exception to the effect of school closures, but it too saw an increase in COVID-19 cases after schools reopened in early 2021. We show that closing schools was a viable policy option, especially before vaccines became available. However, its social and economic costs must also be considered.

The Impact of Covid-19 on the US Stock Market: Evidence from Time Series Model

In this study, we conduct a time series analysis of the US stock market’s response to the COVID-19 pandemic. Using both US and global daily COVID-19 newly confirmed cases and stock market returns data represented by Nasdaq, S&amp;P 500, and Dow Jones over the period 31 December 2019 to 30 December 2021, we examine a time-series impact of COVID-19 on the US stock market. We employ our input into a vector autoregression model (VAR) and ARMA-GARCH model to characterize the dynamic relationship between both domestic and global COVID-19 infections and the performance of the US stock market. The findings show that COVID-19 has an initial negative shock on the stock market with large volatility clustering within 60 days after the initial pandemic outbreak. After around 200 to 300 days, the number of new COVID-19 cases per day does not have a statistically significant impact on the US stock market.

Lag Effect of Temperature and Humidity on COVID-19 Cases in 11 Chinese Cities

The global transmission of COVID-19 has caused considerable health burdens, and epidemiological studies have proven that temperature and humidity play an important role in the transmission of infectious respiratory diseases. This effect may not be immediate and can be delayed by days to weeks. In this study, the comprehensive effect of temperature and humidity on COVID-19 was evaluated using the discomfort index (DI). We analyzed the lag effect of the DI on COVID-19 from 21 January to 29 February 2020 in 11 Chinese cities by designing a generalized additive model (GAM). We classified the 11 Chinese cities into southern cities and northern cities to compare the potential effects in these two types of cities. The results reveal that the DI had the same negative correlation and different lag effects on daily COVID-19 cases. There was a significant negative correlation between the DI and daily COVID-19 cases (p &lt; 0.05), except in Wuhan. The lag effect was stronger in the cities located further north. In northern cities, each unit decrease in the DI increased the COVID-19 risk from 7 to 13 lag days. In southern China, each unit decrease in the DI increased the COVID-19 risk from 0 to 7 lag days, especially in Shanghai, Guangzhou, and Shenzhen.