How Is COVID-19 Affected by Weather? Metaregression of 158 Studies and Recommendations for Best Practices in Future Research

Abstract

Because many viral respiratory diseases show seasonal cycles, weather conditions could affect the spread of COVID-19. Although many studies pursued this possible link early in the pandemic, their results were inconsistent. Here, we assembled 158 quantitative empirical studies examining the link between weather and COVID-19. A meta-regression analysis was performed on their 4,793 correlation coefficients to explain these inconsistent results. We found four principal findings. First, 80 of the 158 studies did not state the time lag between infection and reporting, rendering these studies ineffective in determining the weather–COVID-19 relationship. Second, the research outcomes depended on the statistical analysis methods employed in each study. Specifically, studies using correlation tests produced outcomes that were functions of the geographical locations of the data from the original studies, whereas studies using linear regression produced outcomes that were functions of the analyzed weather variables. Third, Asian countries had more positive associations for air temperature than other regions, possibly because the air temperature was undergoing its seasonal increase from winter to spring during the rapid outbreak of COVID-19 in these countries. Fourth, higher solar energy was associated with reduced COVID-19 spread, regardless of statistical analysis method and geographical location. These results help interpret the inconsistent results and motivate recommendations for best practices in future research. These recommendations include calculating the effects of a time lag between the weather and COVID-19, using regression analysis models, considering nonlinear effects, increasing the time period considered in the analysis to encompass more variety of weather conditions and to increase sample size, and eliminating multicollinearity between weather variables.

A Model for Highly Fluctuating Spatio-Temporal Infection Data, with Applications to the COVID Epidemic

“…Intervention or environmental variables may also be included in this regression. For example, there is increasing evidence of links between COVID infection and weather conditions [ 44 ].…”

Biological Properties of SARS-CoV-2 Variants: Epidemiological Impact and Clinical Consequences

“…A recent metaregression of 158 studies conducted by Tan et al (2022) revealed that Asian countries had more positive associations between COVID-19 spread and rising temperatures compared to other regions. In addition, regardless of the statistical analytic method or geographic location, increased sun energy was linked to reduced COVID-19 spread [377].…”

“…R t = 1.8-3.1 in December 2021 (Italy) [346]. R t = 1.34-3.57 depending on the location and vaccination rate, according to data obtained in many areas as of January 2022 (India) [377].…”

The influence of meteorological factors on COVID-19 spread in Italy during the first and second wave

“…Though several studies have indicated that meteorological factors could influence the spread of COVID-19, inconsistencies remain regarding effect size and direction for temperature, humidity and ultraviolet (UV) radiation ( Byun et al, 2021 ; Chen et al, 2022 ; Jerrett et al, 2022 ; Kaplin et al, 2021 ; Li et al, 2022 ; Majumder and Ray, 2021 ; Romero Starke et al, 2021 ; Zheng et al, 2021 ). Explanations for these inconsistencies might include residual confounding due to pharmacological or non-pharmacological interventions ( Chen et al, 2022 ; Nottmeyer et al, 2022 ), differences in virus lineage and variant in different regions and populations ( Neagu et al, 2021 ; Zhao et al, 2022 ), changes in season or geospatial scale considered, and other factors ( Babin, 2020 ; Kerr et al, 2021 ; Li et al, 2022 ; Tan and Schultz, 2022 ). Additional uncertainty stems from the unknown lag time between meteorological determinants and COVID-19 endpoints, if they do have an effect ( Xiao et al, 2021 ), or the possibility that weather variables have indirect effects mediated through changes in mobility, social contacts and other behavioral variables ( Barceló and Saez, 2021 ; Paraskevis et al, 2021 ).…”