Impact of meteorological parameters on COVID-19 transmission in Bangladesh: a spatiotemporal approach

Abstract

It has been more than 10 months since the first COVID-19 case was reported in Wuhan, China, still menacing the world with a possible second wave. This study aimed to analyze how meteorological variables can affect the spread of local COVID-19 transmission in Bangladesh. Nine spatial units were considered from a meteorological standpoint to characterize COVID-19 transmission in Bangladesh. The daily COVID-19 incidence and meteorological variable (e.g., mean temperature, relative humidity, precipitation, and wind speed) data from April 5 to September 20, 2020, were collected. The Spearman rank correlation, heat maps, and multivariate quasi-Poisson regression were employed to understand their association. The effect of meteorological variables on COVID-19 transmission was modeled using a lag period of 10 days. Results showed that mean temperature, relative humidity, and wind speed are substantially associated with an increased risk of COVID-19. On the other hand, daily precipitation is significantly associated with a decreased risk of COVID-19 incidence. The relative risks (RR) of mean temperature for daily COVID-19 incidences were 1.222 (95% confidence interval [CI], 1.214–1.232). For wind speed, the RR was 1.087 (95% CI, 1.083–1.090). For relative humidity, the RR was 1.027 (95% CI, 1.025–1.029). Overall, this study found the profound effect of meteorological parameters on COVID-19 incidence across selected nine areas in Bangladesh. This study is probably the first study to explore the impact of region-specific meteorological conditions on COVID-19 incidence in Bangladesh. Moreover, adjustments on the areal-aggregated and regional levels were made for three confounding factors, including lockdown, population density, and potential seasonal effects. The study’s findings suggest that SARS-CoV-2 can be transmitted in high temperatures and humidity conditions, which contradicts many other countries’ prior studies. The research outcomes will provide implications for future control and prevention measures in Bangladesh and other countries with similar climate conditions and population density.

Spatio-temporal estimation of basic and effective reproduction number of COVID-19 and post-lockdown transmissibility in Bangladesh

“…[ 33 ] and Hridoy et al . [ 4 ] found a profound effect of meteorological parameters on COVID-19 transmission in Bangladesh. Population density is also an influential factor for COVID-19 transmission in Bangladesh [ 34 , 35 ].…”

“…Bangladesh is one of the world's most populous countries, with a high poverty rate and dense population, implying greater COVID-19 exposure [ 4 ]. Jahangiri et al [ 5 ] mentioned that transmission of COVID-19 has a high sensitivity to population size.…”

Statistical study on the impact of different meteorological changes on the spread of COVID-19 pandemic in Egypt and its latitude

“…The quasi-Poisson regression is considered as s typical model for modeling over distributed count variables (Imai et al 2015 ). Recently a log-linear quasi-Poisson regression model is used to correlate the climate variables and daily counts ( Hridoy et al 2021 ). The quasi-Poisson model as a function of the meteorological parameters can be given by the following equation: where log(U) refers to the logarithmic of the daily new cases, Y is the fitting model, β0 is the overall coefficient, and α o , α 1 ( T C ), α 2 (hum), α 3 (DP), and α 4 ( v wind ) represent coefficients for mean temperature (°C), relative humidity (%), dew point (°C), and wind speed (km/h) respectively.…”

“…The authors employed descriptive statistics, basic correlation coefficients, and simple regression analysis to predict and model the spread of COVID-19 (Hridoy et al 2021 ). Because COVID-19 records are distinct count variables, using linear regression to model the relationship between meteorological variables and daily confirmed cases could result in inaccurate and biased results, which can lower the findings' dependability (Briz-Redón and Serrano-Aroca 2020 ; Hoffmann 2004 ; Long 1997 ).…”

“…Recently, a distributed log nonlinear model was used to correlate the temperature, relative humidity, and wind speed with the spread of SARS-CoV2 (DLNM) (Islam et al 2020a , b ). Also, a log-linear quasi-Poisson regression model was used to correlate the meteorological variables with the daily COVID-19 counts (Hridoy et al 2021 ).…”