Version 1
: Received: 22 June 2020 / Approved: 24 June 2020 / Online: 24 June 2020 (09:44:59 CEST)
How to cite:
Ahmed, J.; Jaman, M.H.; Saha, G.; Ghosh, P. Effect of Temperatures, Humidity and Population Density on the Spreading of Covid-19 at 70 Cities/Provinces. Preprints2020, 2020060292 (doi: 10.20944/preprints202006.0292.v1).
Ahmed, J.; Jaman, M.H.; Saha, G.; Ghosh, P. Effect of Temperatures, Humidity and Population Density on the Spreading of Covid-19 at 70 Cities/Provinces. Preprints 2020, 2020060292 (doi: 10.20944/preprints202006.0292.v1).
Cite as:
Ahmed, J.; Jaman, M.H.; Saha, G.; Ghosh, P. Effect of Temperatures, Humidity and Population Density on the Spreading of Covid-19 at 70 Cities/Provinces. Preprints2020, 2020060292 (doi: 10.20944/preprints202006.0292.v1).
Ahmed, J.; Jaman, M.H.; Saha, G.; Ghosh, P. Effect of Temperatures, Humidity and Population Density on the Spreading of Covid-19 at 70 Cities/Provinces. Preprints 2020, 2020060292 (doi: 10.20944/preprints202006.0292.v1).
Abstract
The main goal of this article is to demonstrate the impact of environmental data on the spreading of Covid-19. In this research, data has been collected from 70 cities/provinces that are affected by Covid-19. Here, environmental data refers to temperatures, humidity and population density in each of these cities/provinces. This data has been analyzed using statistical models such as Poisson, Quasi-Poisson and negative Binomial. It is found that a negative Binomial regression model is the best fit for our data. Our results reveal that average high temperature is the vital factor to slow down the spread of Covid-19. In addition, higher population density found to be an important factor for the quick spreading of Covid-19 where it is quite impossible to maintain the social distance and the virus can spread easily.
Keywords
temperatures; humidity; population density; environment; Covid-19; generalized linear model
Subject
MATHEMATICS & COMPUTER SCIENCE, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
