Effects of demographic and weather parameters on COVID-19 basic reproduction number

Abstract

Timely prediction of the COVID-19 progression is not possible without a comprehensive understanding of environmental factors that may affect the infection transmissibility. Studies addressing parameters that may influence COVID-19 progression relied on either the total numbers of detected cases and similar proxies and/or a small number of analyzed factors, including analysis of regions that display a narrow range of these parameters. We here apply a novel approach, exploiting widespread growth regimes in COVID-19 detected case counts. By applying nonlinear dynamics methods to the exponential regime, we extract basic reproductive number R0 (i.e., the measure of COVID-19 inherent biological transmissibility), applying to the completely naive population in the absence of social distancing, for 118 different countries. We then use bioinformatics methods to systematically collect data on a large number of demographics and weather parameters from these countries, and seek their correlations with the rate of COVID-19 spread. In addition to some of the already reported tendencies, we show a number of both novel results and those that help settle existing disputes: the absence of dependence on wind speed and air pressure, negative correlation with precipitation; significant positive correlation with society development level (human development index) irrespective of testing policies, and percent of the urban population, but an absence of correlation with population density per se. We find a strong positive correlation of transmissibility on alcohol consumption, and the absence of correlation on refugee numbers, contrary to some widespread beliefs. Significant tendencies with health-related factors are reported, including a detailed analysis of the blood type group showing consistent tendencies on Rh factor, and a strong positive correlation of transmissibility with cholesterol levels.

arXiv comment28 pages, 6 figures, 7 tables

References in corpus (4)

• Effective containment explains sub-exponential growth in confirmed cases of recent COVID-19 outbreak in Mainland China
• Modeling and forecasting of the COVID-19 pandemic in India
• Forecasting the daily and cumulative number of cases for the COVID-19 pandemic in India
• Influence of Absolute Humidity, Temperature and Population Density on COVID-19 Spread and Decay Durations: Multi-prefecture Study in Japan

Cited by in corpus (4)

• PM${}_{2.5}$ as a major predictor of COVID-19 basic reproduction number in the USA
• Reaction-diffusion equations in mathematical models arising in epidemiology
• A systems biology approach to COVID-19 progression in a population
• Inferring the main drivers of SARS-CoV-2 global transmissibility by feature selection methods