Does climate change affect the transmission of COVID-19? A Bayesian regression analysis.
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...
| Publicado en: | Journal of Public Health: From Theory to Practice (2198-1833) Vol. 32; no. 8; pp. 1307 - 1318 |
|---|---|
| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Aug2024
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=179536545&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 179536545 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 21981833 NENI jtl: Journal of Public Health: From Theory to Practice (2198-1833) issn: 21981833 maglogo: N pubinfo: dt: Aug2024 vid: 32 iid: 8 pid: 237 pub: Springer Nature artinfo: ui: 179536545 10.1007/s10389-023-01860-1 ppf: 1307 ppct: 11 formats: tig: atl: Does climate change affect the transmission of COVID-19? A Bayesian regression analysis. aug: au: Karim, Rezaul Akter, Nazmin affil: https://ror.org/04ywb0864 Department of Statistics, Jahangirnagar University, Savar, Bangladesh su: Bangladesh Climate change Causes of death Infectious disease transmission Disease risk factors Prevention of infectious disease transmission Risk assessment Cold (Temperature) Poisson distribution Prediction models Probability theory Cell proliferation Descriptive statistics Heat Cell survival COVID-19 SARS-CoV-2 Regression analysis sug: subj: Climate change Causes of death Infectious disease transmission Disease risk factors Bangladesh Prevention of infectious disease transmission Risk assessment Cold (Temperature) Poisson distribution Prediction models Probability theory Cell proliferation Descriptive statistics Heat Cell survival COVID-19 SARS-CoV-2 Regression analysis keyword: Bayesian semiparametric regression Coronavirus Gibbs sampling Markov chain Monte Carlo Bayesian semiparametric regression Coronavirus Gibbs sampling Markov chain Monte Carlo ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|