A Statistical Investigation into the COVID-19 Outbreak Spread

Abstract

Objective: Coronavirus-19 (COVID-19) outbreaks have been reported in a range of climates worldwide, including Bangladesh. There is less evidence of a link between the COVID-19 pandemic and climatic variables. This research article's purpose is to examine the relationship between COVID-19 outbreaks and climatic factors in Dhaka, Bangladesh.

Methods: The daily time series COVID-19 data used in this study span from May 1, 2020, to April 14, 2021, for the study area, Dhaka, Bangladesh. The Climatic factors included in this study were average temperature, particulate matter ( $P{M}_{2.5}$ ), humidity, carbon emissions, and wind speed within the same timeframe and location. The strength and direction of the relationship between meteorological factors and COVID-19 positive cases are examined using the Spearman correlation. This study examines the asymmetric effect of climatic factors on the COVID-19 pandemic in Dhaka, Bangladesh, using the Nonlinear Autoregressive Distributed Lag (NARDL) model.

Results: COVID-19 widespread has a substantial positive association with wind speed (r = .781), temperature (r = .599), and carbon emissions (r = .309), whereas $P{M}_{2.5}$ (r = -.178) has a negative relationship at the 1% level of significance. Furthermore, with a 1% change in temperature, the incidence of COVID-19 increased by 1.23% in the short run and 1.53% in the long run, with the remaining variables remaining constant. Similarly, in the short-term, humidity was not significantly related to the COVID-19 pandemic. However, in the long term, it increased 1.13% because of a 1% change in humidity. The changes in PM_2.5 level and wind speed are significantly associated with COVID-19 new cases after adjusting population density and the human development index.

Keywords: Nonlinear autoregressive distributed lag (NARDL); Wald test; carbon emissions; coronavirus-19 (COVID-19); dynamic multiplier; particulate matter (PM2.5).

Figure 1.

Environmental factors and transmission of SARS-CoV-2.

Figure 2.

COVID-19 daily-confirmed cases versus climatic factors.

Figure 3.

Cumulative dynamic multipliers (CDM) effects on COVID-19 daily cases by climatic parameters.

Figure 4.

Cumulative sum (CSUM) and cumulative sum square (CSUM squares) test for model stability test.