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. 2024 Nov 11;8(6):e338.
doi: 10.1097/EE9.0000000000000338. eCollection 2024 Dec.

Meteorological factors, population immunity, and COVID-19 incidence: A global multi-city analysis

Denise Feurer  1   2 , Tim Riffe  3   4   5 , Maxi Stella Kniffka  5   6 , Enrique Acosta  5   7 , Ben Armstrong  8 , Malcolm Mistry  9   10 , Rachel Lowe  11   12   13 , Dominic Royé  14   15 , Masahiro Hashizume  16   17 , Lina Madaniyazi  16 , Chris Fook Sheng Ng  17 , Aurelio Tobias  18 , Carmen Íñiguez  19 , Ana Maria Vicedo-Cabrera  20   21 , Martina S Ragettli  22   23 , Eric Lavigne  24   25 , Patricia Matus Correa  26 , Nicolás Valdés Ortega  26 , Jan Kyselý  27   28 , Aleš Urban  27   28 , Hans Orru  29 , Ene Indermitte  29 , Marek Maasikmets  30 , Marco Dallavalle  31 , Alexandra Schneider  31 , Yasushi Honda  32   33 , Barrak Alahmad  34 , Antonella Zanobetti  34 , Joel Schwartz  34 , Gabriel Carrasco  35 , Iulian Horia Holobâca  36 , Ho Kim  37 , Whanhee Lee  38 , Michelle L Bell  39   40 , Noah Scovronick  41 , Fiorella Acquaotta  42 , Micheline de Sousa Zanotti Stagliorio Coélho  43 , Magali Hurtado Diaz  44 , Eunice Elizabeth Félix Arellano  44 , Paola Michelozzi  45 , Massimo Stafoggia  45 , Francesca de'Donato  45 , Shilpa Rao  46 , Francesco Di Ruscio  46 , Xerxes Seposo  17   47 , Yuming Guo  48   49 , Shilu Tong  50   51 , Pierre Masselot  9 , Antonio Gasparrini  9 , Francesco Sera  8   52
Affiliations

Meteorological factors, population immunity, and COVID-19 incidence: A global multi-city analysis

Denise Feurer et al. Environ Epidemiol. .

Abstract

Objectives: While COVID-19 continues to challenge the world, meteorological variables are thought to impact COVID-19 transmission. Previous studies showed evidence of negative associations between high temperature and absolute humidity on COVID-19 transmission. Our research aims to fill the knowledge gap on the modifying effect of vaccination rates and strains on the weather-COVID-19 association.

Methods: Our study included COVID-19 data from 439 cities in 22 countries spanning 3 February 2020 - 31 August 2022 and meteorological variables (temperature, relative humidity, absolute humidity, solar radiation, and precipitation). We used a two-stage time-series design to assess the association between meteorological factors and COVID-19 incidence. For the exposure modeling, we used distributed lag nonlinear models with a lag of up to 14 days. Finally, we pooled the estimates using a random effect meta-analytic model and tested vaccination rates and dominant strains as possible effect modifiers.

Results: Our results showed an association between temperature and absolute humidity on COVID-19 transmission. At 5 °C, the relative risk of COVID-19 incidence is 1.22-fold higher compared to a reference level at 17 °C. Correlated with temperature, we observed an inverse association for absolute humidity. We observed a tendency of increased risk on days without precipitation, but no association for relative humidity and solar radiation. No interaction between vaccination rates or strains on the weather-COVID-19 association was observed.

Conclusions: This study strengthens previous evidence of a relationship of temperature and absolute humidity with COVID-19 incidence. Furthermore, no evidence was found that vaccinations and strains significantly modify the relationship between environmental factors and COVID-19 transmission.

Keywords: COVID-19; Distributed lag nonlinear models; Humidity; Multi-Country Multi-City Collaborative Research Network; Precipitation; Solar radiation; Temperature; Time-series design.

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Figures

Figure 1.
Figure 1.
Map of cities included in the analysis, with the majority of data coming from North America, Europe, and Latin America. Additional cities included from South Africa, Australia, and Asia.
Figure 2.
Figure 2.
Time series of COVID-19 cases per 100,000 inhabitants aggregated by country, with number of included cities ranging from 1 (e.g., Kuwait) to 204 (USA).
Figure 3.
Figure 3.
Pooled association curves between COVID-19 cases and meteorological variables, with a lag of up to 14 days.
Figure 4.
Figure 4.
Pooled association curves between COVID-19 cases and meteorological variables by periods with low (<60%) and high (>60%) vaccination coverage.
Figure 5.
Figure 5.
Pooled association curves between COVID-19 cases and meteorological variables by periods with different dominant strains [Initial (First wave), Delta, Omicron].
See this image and copyright information in PMC

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