Random forest regression on joint role of meteorological variables, demographic factors, and policy response measures in COVID-19 daily cases: global analysis in different climate zones.

Different sources of factors in environment can affect the spread of COVID-19 by influencing the diffusion of the virus transmission, but the collective influence of which has hardly been considered. This study aimed to utilize a machine learning algorithm to assess the joint effects of meteorological variables, demographic factors, and government response measures on COVID-19 daily cases globally at city level. Random forest regression models showed that population density was the most crucial determinant for COVID-19 transmission, followed by meteorological variables and response measures.

Association between meteorological factors and COVID-19 transmission in low- and middle-income countries: A time-stratified case-crossover study.

Background: Evidence is limited regarding the association between meteorological factors and COVID-19 transmission in low- and middle-income countries (LMICs).

Objective: To investigate the independent and interactive effects of temperature, relative humidity (RH), and ultraviolet (UV) radiation on the spread of COVID-19 in LMICs.

Methods: We collected daily data on COVID-19 confirmed cases, meteorological factors and non-pharmaceutical interventions (NPIs) in 2143 city- and district-level sites from 6 LMICs during 2020.