Are meteorological factors enhancing COVID-19 transmission in Bangladesh? Novel findings from a compound Poisson general
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RESEARCH ARTICLE
Are meteorological factors enhancing COVID-19 transmission in Bangladesh? Novel findings from a compound Poisson generalized linear modeling approach Abu Reza Md. Towfiqul Islam 1 & Md. Hasanuzzaman 1 & Mashura Shammi 2 & Roquia Salam 1 & Md. Bodrud-Doza 3 & Md. Mostafizur Rahman 2 & Md. Abdul Mannan 4 & Saleemul Huq 5 Received: 28 May 2020 / Accepted: 15 October 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Novel coronavirus (SARS-CoV-2) causing COVID-19 disease has arisen to be a pandemic. Since there is a close association between other viral infection cases by epidemics and environmental factors, this study intends to unveil meteorological effects on the outbreak of COVID-19 across eight divisions of Bangladesh from March to April 2020. A compound Poisson generalized linear modeling (CPGLM), along with a Monte-Carlo method and random forest (RF) model, was employed to explore how meteorological factors affecting the COVID-19 transmission in Bangladesh. Results showed that subtropical climate (mean temperature about 26.6 °C, mean relative humidity (MRH) 64%, and rainfall approximately 3 mm) enhanced COVD-19 onset. The CPGLM model revealed that every 1 mm increase in rainfall elevated by 30.99% (95% CI 77.18%, − 15.20%) COVID-19 cases, while an increase of 1 °C of diurnal temperature (TDN) declined the confirmed cases by − 14.2% (95% CI 9.73%, − 38.13%) on the lag 1 and lag 2, respectively. In addition, NRH and MRH had the highest increase (17.98% (95% CI 22.5%, 13.42%) and 19.92% (95% CI: 25.71%, 14.13%)) of COVID-19 cased in lag 4. The results of the RF model indicated that TDN and AH (absolute humidity) influence the COVID-19 cases most. In the Dhaka division, MRH is the most vital meteorological factor that affects COVID-19 deaths. This study indicates the humidity and rainfall are crucial factors affecting the COVID-19 case, which is contrary to many previous studies in other countries. These outcomes can have policy formulation for the suppression of the COVID-19 outbreak in Bangladesh. Keywords Bangladesh . COVID-19 . Compound Poisson generalized linear modeling (CPGLM) . Meteorological factors . Time-series analysis . Random forest (RF)
Introduction Responsible Editor: Gerhard Lammel * Abu Reza Md. Towfiqul Islam [email protected] * Md. Mostafizur Rahman [email protected] 1
Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh
2
Department of Environmental Sciences, Jahangirnagar University, Dhaka 1342, Bangladesh
3
Climate Change Programme, BRAC, Dhaka, Bangladesh
4
Bangladesh Meteorological Department, Meteorological Complex Agargaon, Dhaka 1207, Bangladesh
5
ICCCAD, Independent University Bangladesh, Dhaka, Bangladesh
Novel coronavirus (SARS-CoV-2) causing COVID-19 disease has arisen to be a pandemic and a dangerous component in strategic planning, critical policymaking, and time-worthy decisions in public health sectors worldwide. One of the significant challenges fighting against the COVID-19 virus was to un
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