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

Bibliographic Data

ID22331985
AuthorsLing Tan (0009-0004-9958-0566, Nanjing University of Information Science and Technology), David M Schultz (0000-0002-7393-7086, University of Manchester)
Year2022
Volume14
Issue1
Pages237-255
Publication date2022-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueWeather, Climate, and Society (JOURNAL)
Journal identifiersISSN: 1948-8327 • E-ISSN: 1948-8335
PublisherAmerican Meteorological Society (PUBLISHER • US)
DOI10.1175/wcas-d-21-0132.1
OpenAlexW3211297110
LanguageEN
Citations received7
References cited68

Because many viral respiratory diseases show seasonal cycles, weather conditions could affect the spread of coronavirus disease 2019 (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 metaregression analysis was performed on their 4793 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 to 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

Climatology · Disease · Distributed lag · Econometrics · Geography · Lag · Meta-analysis · Outbreak · Pandemic · Regression analysis · Sociology · Statistics · Computer Science · COVID-19 epidemiological studies · COVID-19 impact on air quality · COVID-19 Pandemic Impacts · Demography · Environmental Science · Mathematics · Medicine · Virology

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