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. 2021 Oct 13;12(1):5968.
doi: 10.1038/s41467-021-25914-8.

A cross-sectional analysis of meteorological factors and SARS-CoV-2 transmission in 409 cities across 26 countries

Francesco Sera  1   2 , Ben Armstrong  3 , Sam Abbott  4   5 , Sophie Meakin  4   5 , Kathleen O'Reilly  4   5 , Rosa von Borries  6 , Rochelle Schneider  3   7   8   9 , Dominic Royé  10 , Masahiro Hashizume  11   12   13 , Mathilde Pascal  14 , Aurelio Tobias  12   15 , Ana Maria Vicedo-Cabrera  16   17 ; MCC Collaborative Research Network; CMMID COVID-19 Working Group; Antonio Gasparrini  3   7   18 , Rachel Lowe  19   20   21   22
Collaborators, Affiliations

A cross-sectional analysis of meteorological factors and SARS-CoV-2 transmission in 409 cities across 26 countries

Francesco Sera et al. Nat Commun. .

Abstract

There is conflicting evidence on the influence of weather on COVID-19 transmission. Our aim is to estimate weather-dependent signatures in the early phase of the pandemic, while controlling for socio-economic factors and non-pharmaceutical interventions. We identify a modest non-linear association between mean temperature and the effective reproduction number (Re) in 409 cities in 26 countries, with a decrease of 0.087 (95% CI: 0.025; 0.148) for a 10 °C increase. Early interventions have a greater effect on Re with a decrease of 0.285 (95% CI 0.223; 0.347) for a 5th - 95th percentile increase in the government response index. The variation in the effective reproduction number explained by government interventions is 6 times greater than for mean temperature. We find little evidence of meteorological conditions having influenced the early stages of local epidemics and conclude that population behaviour and government interventions are more important drivers of transmission.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Effective reproduction number and mean temperature in the observation window for 409 cities.
Bivariate plot of effective reproduction number (Re) and mean temperature (Ta) (°C) in the observation window for each of the 409 study cities. Dark purple circles represent cities with both high Re and high Ta, while pale purple circles show areas with both low Re and low Ta. Red circles represent cities with low Re and high Ta and blue circles depict areas with high Re and low Ta. The bar chart (bottom right) represents the number of cities in each category defined in the bivariate legend (bottom left).
Fig. 2
Fig. 2. Effective reproduction number vs key weather variables by climate zone.
a Mean temperature (°C), b relative humidity (%), c absolute humidity (g/m3) and d solar surface radiation (J/m2) vs effective reproduction number (Re) by climate zone (409 cities). The area of the circles is proportional to the precision (inverse of the variance) of Re estimates.
Fig. 3
Fig. 3. Associations between weather variables, non-pharmaceutical interventions and the effective reproduction number.
Non-linear associations between (a) mean temperature (°C), (b) relative humidity (%), (c) absolute humidity (g/m3) and (d) OxCGRT Government Response Index and predicted Re difference. Curves and their 95% confidence intervals show the predicted difference in Re with respect to a reference value set to the value at the trough of the curve for meteorological variables (a–c), or for the OxCGRT Government Response Index = 50 (d). Two-sided Wald test p values and adjusted curves with 95% confidence intervals were obtained from multivariable meta-regression multilevel models adjusted by population (log scale), population density (log scale), GDP (log scale), % population >65 years of age, PM2.5 (μg/m3, log scale) and OxCGRT Government Response Index, with cities nested within countries. The marginal distribution along the x-axis represents the observed data for that covariate.
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