ABSTRACT
Aim: Coronavirus is an airborne and infectious disease and it is crucial to check the impact of climatic risk factors on the transmission of COVID-19. The main objective of this study is to determine the effect of climate risk factors using Bayesian regression analysis. Methods: Coronavirus disease 2019, due to the effect of the SARS-CoV-2 virus, has become a serious global public health issue. This disease was identified in Bangladesh on March 8, 2020, though it was initially identified in Wuhan, China. This disease is rapidly transmitted in Bangladesh due to the high population density and complex health policy setting. To meet our goal, The MCMC with Gibbs sampling is used to draw Bayesian inference, which is implemented in WinBUGS software. Results: The study revealed that high temperatures reduce confirmed cases and deaths from COVID-19, but low temperatures increase confirmed cases and deaths. High temperatures have decreased the proliferation of COVID-19, reducing the virus's survival and transmission. Conclusions: Considering only the existing scientific evidence, warm and wet climates seem to reduce the spread of COVID-19. However, more climate variables could account for explaining most of the variability in infectious disease transmission.
ABSTRACT
Alcohol-based hand sanitizers (ABHSs) containing ethanol (EtOH) or isopropyl alcohol (IPA) to inactivate microorganisms help prevent the spread of respiratory diseases. These products have become very popular during the COVID-19 pandemic. Apart from vaccines or other preventative antiseptic measures, the majority of consumers have relied on different types of ABHSs to disinfect their hands. As a result, there has been a global rush in the demand for these ABHSs and other antiseptic hygiene products. This has resulted in the formation of many new commercial sanitizer producers. There are around fifty companies of varying sizes that have been marketing their ABHSs in Bangladesh, most of which have only been manufacturing their products for the first time since the COVID-19 pandemic. To monitor the quality and components of these products, the Bangladesh Council of Scientific and Industrial Research (BCSIR) analyzed approximately 200 different hand sanitizer samples using GC-FID method. All samples were alcohol-based except for 3 which were alcohol-free aqueous hand sanitizers. Of the supplied formulated ABHSs, 80 samples were found to contain only IPA and 54 contained only EtOH. However, 28 samples were found to be contaminated with methanol (MeOH), 7 samples contained only MeOH and 18 samples contained both EtOH and IPA. This is the first study to explore the analysis of alcohol content in formulated ABHSs and their marketing status in Bangladesh, but the findings could be of use in other jurisdictions as similar issues have been raised in many parts of the world.
ABSTRACT
Infectious diseases such as severe acute respiratory syndrome (SARS) and influenza are influenced by weather conditions. Climate variables, for example, temperature and humidity, are two important factors in the severity of COVID-19's impact on the human respiratory system. This study aims to examine the effects of these climate variables on COVID-19 mortality. The data are collected from March 08, 2020, to April 30, 2022. The parametric regression under GAM and semiparametric regression under GAMLSS frameworks are used to analyze the daily number of death due to COVID-19. Our findings revealed that temperature and relative humidity are commencing to daily deaths due to COVID-19. A positive association with COVID-19 daily death counts was observed for temperature range and a positive association for humidity. In addition, one-unit increase in daily temperature range was only associated with a 1.08% (95% CI: 1.06%, 1.10%), and humidity range was only associated with a 1.03% (95% CI: 1.02%, 1.03%) decrease in COVID-19 deaths. A flexible regression model within the framework of Generalized Additive Models for Location Scale and Shape is used to analyze the data by adjusting the time effect. We used two adaptable predictor models, such as (i) the Fractional polynomial model and (ii) the B-spline smoothing model, to estimate the systematic component of the GAMLSS model. According to both models, high humidity and temperature significantly (and drastically) lessened the severity of COVID-19 death. The findings on the epidemiological trends of the COVID-19 pandemic and weather changes may interest policymakers and health officials.