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Author Notes:

Luise Nottmeyer, luise.nottmeyer1@alumni.lshtm.ac.uk

Francesco Sera, francesco.sera@unifi.it

Luise Nottmeyer: Data curation, Formal analysis, Writing – original draft. Ben Armstrong: Methodology, Writing – review & editing. Rachel Lowe: Writing – review & editing. Sam Abbott: Writing – review & editing. Sophie Meakin: Writing – review & editing. Kathleen M. O'Reilly: Writing – review & editing. Rosa von Borries: Writing – review & editing. Rochelle Schneider: Data curation, Writing – review & editing. Dominic Royé: Visualization, Writing – review & editing. Masahiro Hashizume: Data curation, Writing – review & editing. Mathilde Pascal: Data curation, Writing – review & editing. Aurelio Tobias: Data curation, Writing – review & editing. Ana Maria Vicedo-Cabrera: Writing – review & editing. Eric Lavigne: Data curation, Writing – review & editing. Patricia Matus Correa: Data curation, Writing – review & editing. Nicolás Valdés Ortega: Data curation, Writing – review & editing. Jan Kynčl: Data curation, Writing – review & editing. Aleš Urban: Data curation, Writing – review & editing. Hans Orru: Data curation, Writing – review & editing. Niilo Ryti: Writing – review & editing. Jouni Jaakkola: Data curation, Writing – review & editing. Marco Dallavalle: Data curation, Writing – review & editing. Alexandra Schneider: Writing – review & editing. Yasushi Honda: Writing – review & editing. Chris Fook Sheng Ng: Data curation, Writing – review & editing. Barrak Alahmad: Data curation, Writing – review & editing. Gabriel Carrasco-Escobar: Data curation, Writing – review & editing. Iulian Horia Holobâc: Data curation, Writing – review & editing. Ho Kim: Writing – review & editing. Whanhee Lee: Data curation, Writing – review & editing. Carmen Íñiguez: Data curation, Writing – review & editing. Michelle L. Bell: Writing – review & editing. Antonella Zanobetti: Writing – review & editing. Joel Schwartz: Writing – review & editing. Noah Scovronick: Data curation, Writing – review & editing. Micheline de Sousa Zanotti Stagliorio Coélho: Data curation, Writing – review & editing. Paulo Hilario Nascimento Saldiva: Data curation, Writing – review & editing. Magali Hurtado Diaz: Data curation, Writing – review & editing. Antonio Gasparrini: Methodology, Writing – review & editing. Francesco Sera: Supervision, Conceptualization, Methodology, Data curation, Writing – original draft, Writing – review & editing.

This paper used Copernicus Climate Change Service (C3S) and Copernicus Atmosphere Monitoring Service (CAMS) information. Hence, the authors would like to thank the European Centre for Medium-Range Weather Forecasts (ECMWF) for its data generation on behalf of the European Union.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Subjects:

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Environmental Sciences
  • Environmental Sciences & Ecology
  • Temperature
  • Humidity
  • UV radiation
  • COVID-19
  • Distributed lag non-linear modelling
  • Global analysis
  • TIME-SERIES REGRESSION
  • RELATIVE-HUMIDITY
  • INFLUENZA
  • TRANSMISSION
  • WEATHER

The association of COVID-19 incidence with temperature, humidity, and UV radiation - A global multi-city analysis

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Journal Title:

SCIENCE OF THE TOTAL ENVIRONMENT

Volume:

Volume 854

Publisher:

, Pages 158636-158636

Type of Work:

Article | Final Publisher PDF

Abstract:

Background and aim: The associations between COVID-19 transmission and meteorological factors are scientifically debated. Several studies have been conducted worldwide, with inconsistent findings. However, often these studies had methodological issues, e.g., did not exclude important confounding factors, or had limited geographic or temporal resolution. Our aim was to quantify associations between temporal variations in COVID-19 incidence and meteorological variables globally. Methods: We analysed data from 455 cities across 20 countries from 3 February to 31 October 2020. We used a time-series analysis that assumes a quasi-Poisson distribution of the cases and incorporates distributed lag non-linear modelling for the exposure associations at the city-level while considering effects of autocorrelation, long-term trends, and day of the week. The confounding by governmental measures was accounted for by incorporating the Oxford Governmental Stringency Index. The effects of daily mean air temperature, relative and absolute humidity, and UV radiation were estimated by applying a meta-regression of local estimates with multi-level random effects for location, country, and climatic zone. Results: We found that air temperature and absolute humidity influenced the spread of COVID-19 over a lag period of 15 days. Pooling the estimates globally showed that overall low temperatures (7.5 °C compared to 17.0 °C) and low absolute humidity (6.0 g/m3 compared to 11.0 g/m3) were associated with higher COVID-19 incidence (RR temp =1.33 with 95%CI: 1.08; 1.64 and RR AH =1.33 with 95%CI: 1.12; 1.57). RH revealed no significant trend and for UV some evidence of a positive association was found. These results were robust to sensitivity analysis. However, the study results also emphasise the heterogeneity of these associations in different countries. Conclusion: Globally, our results suggest that comparatively low temperatures and low absolute humidity were associated with increased risks of COVID-19 incidence. However, this study underlines regional heterogeneity of weather-related effects on COVID-19 transmission.

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