Modeling and Preliminary Analysis of the Impact of Meteorological Conditions on the COVID-19 Epidemic.

Since the COVID-19 epidemic outbreak at the end of 2019, many studies regarding the impact of meteorological factors on the attack have been carried out, and inconsistent conclusions have been reached, indicating the issue's complexity. To more accurately identify the effects and patterns of meteorological factors on the epidemic, we used a combination of logistic regression (LgR) and partial least squares regression (PLSR) modeling to investigate the possible effects of common meteorological factors, including air temperature, relative humidity, wind speed, and surface pressure, on the transmission of the COVID-19 epidemic. Our analysis shows that: (1) Different countries and regions show spatial heterogeneity in the number of diagnosed patients of the epidemic, but this can be roughly classified into three types: "continuous growth", "staged shock", and "finished"; (2) Air temperature is the most significant meteorological factor influencing the transmission of the COVID-19 epidemic. Except for a few areas, regional air temperature changes and the transmission of the epidemic show a significant positive correlation, i.e., an increase in air temperature is conducive to the spread of the epidemic; (3) In different countries and regions studied, wind speed, relative humidity, and surface pressure show inconsistent correlation (and significance) with the number of diagnosed cases but show some regularity.

Nonlinear modulation of COVID-19 transmission by climate conditions

Abstract COVID-19 is spreading rapidly worldwide, posing great threats to public health and economy. This study aims to examine how the transmission of COVID-19 is modulated by climate conditions, which is of great importance for better understanding of the seasonal feature of COVID-19. Constrained by the accurate observations we can make, the basic reproduction numbers (R0) for each country were inferred and linked to temperature and relative humidity (RH) with statistical analysis. Using R0 as the measure of COVID-19 transmission potential, we find stronger transmission of COVID-19 under mildly warm (0°C?< T <?20°C) and humid (RH?>?60%) climate conditions, while extremely low (T <??2°C) and high (T >?20°C) temperature or a dry climate (RH?<?60%) weakens transmission. The established nonlinear relationships between COVID-19 transmission and climate conditions suggest that seasonal climate variability may affect the spread and severity of COVID-19 infection, and temperate coastal regions with mildly warm and humid climate would be susceptible to large-scale outbreaks.