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

Abstract

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.

Do Weather Conditions Still Have an Impact on the COVID-19 Pandemic? An Observation of the Mid-2022 COVID-19 Peak in Taiwan

“…Our study showed a negative correlation between WS and newly confirmed COVID-19 cases in Regions a, e, and f, aligning with earlier research [8,17,38,39,42,43] and supporting the hypothesis that higher wind speeds might dilute airborne virus particles, reducing transmission [44].…”

High-resolution short-term prediction of the COVID-19 epidemic based on spatial-temporal model modified by historical meteorological data

“…Epidemics exhibit distinct transmission characteristics in varied spatial and temporal contexts. The spread of COVID-19, characterized by spatial heterogeneity, is significantly influenced by meteorological factors [34] . Changes in meteorological factors (temperature, humidity, wind, air pressure, and diffusion conditions, etc.)…”

Dynamic variations in and prediction of COVID-19 with omicron in the four first-tier cities of mainland China, Hong Kong, and Singapore

“…In late 2019, coronavirus disease 2019 (COVID- 19), the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first reported; subsequently, a pandemic ensued and was characterized by person-to-person transmission and asymptomatic patients (1,2). For at least half a year, COVID-19 rapidly spread worldwide over more than 200 countries and regions.…”

“…Due to the huge impacts that COVID-19 exerted on social economics and human lives worldwide, a large number of prevention and control measures have been employed in different countries (7,(13)(14)(15). Because the accurate simulation and prediction of infectious diseases can provide the scientific basis for adopting reasonable measurements, many COVID-19 models have been established to investigate disease variations and related impact factors and to predict future trends (16)(17)(18)(19). Among these models, ARIMA and SARIMA models have been widely applied to analyze the linear, nonlinear and seasonal characteristics of COVID-19 (20)(21)(22).…”