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Relationship between COVID-19 infection rates and air pollution, geo-meteorological, and social parameters. | LitMetric

AI Article Synopsis

  • The study investigates the relationship between COVID-19 infection rates and various factors including air pollution, geo-meteorological data, and social parameters in Dhaka, Bangladesh.
  • It uses Geographically Weighted Regression (GWR) and Geographic Information System (GIS) to analyze data, revealing that air pollution and social factors like population density and poverty significantly correlate with higher infection rates.
  • The findings suggest that local agencies can use this information to develop targeted strategies for managing COVID-19 and similar infectious diseases in urban areas, with plans for future research to incorporate additional variables.

Article Abstract

Like all infectious diseases, the infection rate of COVID-19 is dependent on many variables. In order to effectively prepare a localized plan for infectious disease management, it is important to find the relationship between COVID-19 infection rate and other key variables. This study aims to understand the spatial relationships between COVID-19 infection rate and key variables of air pollution, geo-meteorological, and social parameters in Dhaka, Bangladesh. The relationship was analyzed using Geographically Weighted Regression (GWR) model and Geographic Information System (GIS) by means of COVID-19 infection rate as a dependent variable and 17 independent variables. This study revealed that air pollution parameters like PM (p < 0.02), AOT (p < 0.01), CO (p < 0.05), water vapor (p < 0.01), and O (p < 0.01) were highly correlated with COVID-19 infection rate while geo-meteorological parameters like DEM (p < 0.01), wind pressure (p < 0.01), LST (p < 0.04), rainfall (p < 0.01), and wind speed (p < 0.03) were also similarly associated. Social parameters like population density (p < 0.01), brickfield density (p < 0.02), and poverty level (p < 0.01) showed high coefficients as the key independent variables to COVID-19 infection rate. Significant robust relationships between these factors were found in the middle and southern parts of the city where the reported COVID-19 infection case was also higher. Relevant agencies can utilize these findings to formulate new and smart strategies for reducing infectious diseases like COVID-19 in Dhaka and in similar urban cities around the world. Future studies will have more variables including ecological, meteorological, and economical to model and understand the spread of COVID-19.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7781405PMC
http://dx.doi.org/10.1007/s10661-020-08810-4DOI Listing

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