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Effects of meteorological parameters on COVID-19 transmission trends in Bangladesh

ABSTRACT:

Abstract

Understanding the influence of meteorological parameters in relation to COVID-19 transmission may be a convenient way to predict the ongoing pandemic towards its adaptive control measures. This study aims to explore the association between COVID-19 cases and meteorological parameters and to predict COVID-19 transmission for an extended period covering different climatic patterns. The number of COVID-19 cases, daily records of rainfall, temperature, relative humidity and wind speed data were collected for April and May 2020 from the eight major divisions in Bangladesh. The basic statistical analyses and auto regressive integrated moving average (ARIMA) using SPSS tool were applied to evaluate and explore association between meteorological parameters and COVID-19 cases and its transmission trend. A greater number of significant positive associations (r = 0.24–0.58) is found to exist between the relative humidity and COVID-19 cases across the cities, while with temperature both positive and negative associations (r = − 0.23 to 0.72) were revealed. Furthermore, both the rainfall and wind speed exhibit positive correlations. ARIMA model portrayed predictive trend of COVID-19 transmission, from its inception on 8 March 2020 to September 2020, in Bangladesh. The month of July showed the highest daily COVID-19 cases prior to lowering at steady rate till September illustrating the influnce of meteorological parameters.

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Supplementary Information

The online version contains supplementary material available at 10.1007/s42398-021-00195-5.

SUBMITTER: Pal S

PROVIDER: S-EPMC8180358 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Coronavirus disease 2019 (COVID-19) was first detected in December 2019 in Wuhan, China, with 11,669,259 positive cases and 539,906 deaths globally as of July 8, 2020. The objective of the present study was to determine whether meteorological parameters and air quality affect the transmission of COVID-19, analogous to SARS. We captured data from 29 provinces, including numbers of COVID-19 cases, meteorological parameters, air quality and population flow data, between Jan 21, 2020 and Apr 3, 2020. To evaluate the transmissibility of COVID-19, the basic reproductive ratio (R0) was calculated with the maximum likelihood "removal" method, which is based on chain-binomial model, and the association between COVID-19 and air pollutants or meteorological parameters was estimated by correlation analyses. The mean estimated value of R0 was 1.79 ± 0.31 in 29 provinces, ranging from 1.08 to 2.45. The correlation between R0 and the mean relative humidity was positive, with coefficient of 0.370. In provinces with high flow, indicators such as carbon monoxide (CO) and 24-h average concentration of carbon monoxide (CO_24 h) were positively correlated with R0, while nitrogen dioxide (NO2), 24-h average concentration of nitrogen dioxide (NO2_24 h) and daily maximum temperature were inversely correlated to R0, with coefficients of 0.644, 0.661, -0.636, -0.657, -0.645, respectively. In provinces with medium flow, only the weather factors were correlated with R0, including mean/maximum/minimum air pressure and mean wind speed, with coefficients of -0.697, -0.697, -0.697 and -0.841, respectively. There was no correlation with R0 and meteorological parameters or air pollutants in provinces with low flow. Our findings suggest that higher ambient CO concentration is a risk factor for increased transmissibility of the novel coronavirus, while higher temperature and air pressure, and efficient ventilation reduce its transmissibility. The effect of meteorological parameters and air pollutants varies in different regions, and requires that these issues be considered in future modeling disease transmissibility.