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

Denise Feurer*, Tim Riffe, Maxi Stella Kniffka, Enrique Acosta, Ben Armstrong, Malcolm Mistry, Rachel Lowe, Dominic Royé, Masahiro Hashizume, Lina Madaniyazi, Chris Fook Sheng Ng, Aurelio Tobias, Carmen Íñiguez, Ana Maria Vicedo-Cabrera, Martina S. Ragettli, Eric Lavigne, Patricia Matus Correa, Nicolás Valdés Ortega, Jan Kyselý, Aleš UrbanHans Orru, Ene Indermitte, Marek Maasikmets, Marco Dallavalle, Alexandra Schneider, Yasushi Honda, Barrak Alahmad, Antonella Zanobetti, Joel Schwartz, Gabriel Carrasco, Iulian Horia Holobâca, Ho Kim, Whanhee Lee, Michelle L. Bell, Noah Scovronick, Fiorella Acquaotta, Micheline de Sousa Zanotti Stagliorio Coélho, Magali Hurtado Diaz, Eunice Elizabeth Félix Arellano, Paola Michelozzi, Massimo Stafoggia, Francesca de’Donato, Shilpa Rao, Francesco Di Ruscio, Xerxes Seposo, Yuming Guo, Shilu Tong, Pierre Masselot, Antonio Gasparrini, Francesco Sera

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Original languageEnglish
Pages (from-to)e338
JournalEnvironmental Epidemiology
Volume8
Issue number6
DOIs
StatePublished - 11 Nov 2024

Bibliographical note

Publisher Copyright:
Copyright © 2024 The Authors.

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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