Spatial modeling of COVID-19 transmission in Bangladesh

Abstract

The purpose of the research was to investigate and identify the demographic risk factors behind the transmission of COVID-19 in Bangladesh based on spatial and statistical modeling. Number of COVID-19 confirmed cases per thousand population as the dependent variable and nine demographic explanatory variables were considered. Different spatial (i.e., Spatial Lag and Spatial Error Model) and non-spatial (Classic Model) regression techniques were employed in the research to detect the geographical relevance of potential risk factors affecting the transmission of COVID-19. Results indicate that population density was crucial for explaining the pattern of COVID-19 transmission in Bangladesh. Spatial Auto-correlation suggests that the spatial pattern of population density were significantly clustered at a confidence interval of 95%. Again, the regression analysis also shows that population density is an influential determinant for the propagation of COVID-19 in Bangladesh, with densely populated districts like Dhaka and Narayanganj also being among the worst affected areas. The findings of this research will help the government agencies and communities for effective and well-informed decision making in order to develop and implement strategies to contain the further spread of COVID-19 in Bangladesh.