The influence of weather conditions on the COVID-19 epidemic

Abstract

Studying the influence of weather conditions on the COVID-19 epidemic is an emerging field. However, existing studies in this area tend to utilize time-series data, which have certain limitations and fail to consider individual, social, and economic factors. Therefore, this study aimed to fill this gap. In this paper, we explored the influence of weather conditions on the COVID-19 epidemic using COVID-19-related prefecture-daily panel data collected in mainland China between January 1, 2020, and February 19, 2020. A two-way fixed effect model was applied taking into account factors including public health measures, effective distance to Wuhan, population density, economic development level, health, and medical conditions. We also used a piecewise linear regression to determine the relationship in detail. We found that there is a conditional negative relationship between weather conditions and the epidemic. Each 1 °C rise in mean temperature led to a 0.49% increase in the confirmed cases growth rate when mean temperature was above −7 °C. Similarly, when the relative humidity was greater than 46%, it was negatively correlated with the epidemic, where a 1% increase in relative humidity decreased the rate of confirmed cases by 0.19%. Furthermore, prefecture-level administrative regions, such as Chifeng (included as “warning cities”) have more days of “dangerous weather”, which is favorable for outbreaks. In addition, we found that the impact of mean temperature is greatest in the east, the influence of relative humidity is most pronounced in the central region, and the significance of weather conditions is more important in the coastal region. Finally, we found that rising diurnal temperatures decreased the negative impact of weather conditions on the spread of COVID-19. We also observed that strict public health measures and high social concern can mitigate the adverse effects of cold and dry weather on the spread of the epidemic. To the best of our knowledge, this is the first study which applies the two-way fixed effect model to investigate the influence of weather conditions on the COVID-19 epidemic, takes into account socio-economic factors and draws new conclusions.

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

“…Numerous studies have been conducted on the impact of weather variables on the COVID-19 pandemic since the pandemic began in 2019, and heterogeneous results have been reported. While a great number of regional studies from different countries have shown an association between weather conditions and the COVID-19 pandemic [7][8][9][10][11], a large study review claimed the opposite [6]. Given the dynamic relationship among the environment, viruses, and populations, our study divided Taiwan into six regions to closely examine these dynamics.…”

“…A large study review declared that there was no association between weather conditions and the COVID-19 pandemic [6], but several regional studies have shown a correlation [7][8][9][10]. Research focused on China highlights a positive correlation between temperature and the COVID-19 epidemic [7,11], while other studies suggest that rising temperatures may mitigate the expansion of the pandemic to some extent [12,13]. Ng et al (2024) provide similar insights for Malaysia, suggesting that regional climatic conditions significantly influence transmission dynamics across the country [14].…”

Assessing eco-geographic influences on COVID-19 transmission: a global analysis

“…The existing literature mainly utilizes a generalized additive model 21 , generalized linear model, Spearman's correlation analysis 50 , 51 and Pearson's correlation analysis 52 . Regarding sample selection, the use of national-level samples alone fails to account for the regional variations in weather conditions among countries with large areas and uneven population distribution, such as the United States, China, and Brazil 53 . Moreover, solely selecting geographic areas with confirmed cases as samples for statistical analysis is prone to sample selection bias 53 .…”

“…Regarding sample selection, the use of national-level samples alone fails to account for the regional variations in weather conditions among countries with large areas and uneven population distribution, such as the United States, China, and Brazil 53 . Moreover, solely selecting geographic areas with confirmed cases as samples for statistical analysis is prone to sample selection bias 53 . However, these methods have certain limitations and may lead to estimation bias since the data often fail to meet the underlying assumptions of the methods.…”

Impact of geographical, meteorological, demographic, and economic indicators on the trend of COVID-19: A global evidence from 202 affected countries

“…The COVID-19 spread might influence by political, social, geographical, and climatic factors [ 7 ]. Generally, it is believed that weather variables play a significant role in infectious diseases like Spanish influenza, SARS-COV, and MERS-COV [ 8 ]. In a study, Dalziel et al [ 9 ] mentioned that virus spread is linked to factors like temperature, population density, and humidity.…”