Luise Nottmeyer1, Ben Armstrong2, Rachel Lowe3, Sam Abbott4, Sophie Meakin4, Kathleen O'Reilly4, Rosa von Borries5, Rochelle Schneider6, Dominic Royé7, Masahiro Hashizume8, Mathilde Pascal9, Aurelio Tobias10, Ana Maria Vicedo-Cabrera11, Eric Lavigne12, Patricia Matus Correa13, Nicolás Valdés Ortega13, Jan Kynčl14, Aleš Urban15, Hans Orru16, Niilo Ryti17, Jouni Jaakkola17, Marco Dallavalle18, Alexandra Schneider19, Yasushi Honda20, Chris Fook Sheng Ng21, Barrak Alahmad22, Gabriel Carrasco23, Iulian Horia Holobâc24, Ho Kim25, Whanhee Lee25, Carmen Íñiguez26, Michelle L Bell27, Antonella Zanobetti22, Joel Schwartz22, Noah Scovronick28, Micheline de Sousa Zanotti Stagliorio Coélho29, Paulo Hilario Nascimento Saldiva29, Magali Hurtado Diaz30, Antonio Gasparrini31, Francesco Sera32. 1. Faculty of Engineering Sciences, Heidelberg University, Heidelberg, Germany. Electronic address: luise.nottmeyer1@alumni.lshtm.ac.uk. 2. Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, UK. 3. Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK; ICREA Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS), Life & Medical Sciences, Barcelona, Spain; European Centre for Medium-Range Weather Forecast (ECMWF), Reading, UK. 4. Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK. 5. Charité Universitätsmedizin, Berlin, Germany. 6. Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK; Φ-Lab, European Space Agency, Frascati, Italy; European Centre for Medium-Range Weather Forecast (ECMWF), Reading, UK. 7. Department of Geography, University of Santiago de Compostela, CIBER of Epidemiology and Public Health (CIBERESP), Spain. 8. Department of Paediatric Infectious Disease, Institute of Tropical Medicine, Nagasaki University, Japan; School of Tropical Medicine and Global Health, Nagasaki University, Japan; Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. 9. Santé Publique France, Department of Environmental Health, French National Public Health Agency, Saint Maurice, France. 10. School of Tropical Medicine and Global Health, Nagasaki University, Japan; Institute of Environmental Assessment and Water Research (IDAEA), Spanish Council for Scientific Research (CSIC), Barcelona, Spain. 11. Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.; Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland. 12. School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada; Air Health Science Division, Health Canada, Ottawa, Canada. 13. Department of Public Health, Universidad de los Andes, Santiago, Chile. 14. Department of Infectious Diseases Epidemiology, National Institute of Public Health, Prague, Czech Republic; Department of Epidemiology and Biostatistics, Third Faculty of Medicine, Charles University, Prague, Czech Republic. 15. Institute of Atmospheric Physics of the Czech Academy of Sciences, Prague, Czech Republic; Faculty of Environmental Sciences, Czech University of Life Sciences, Prague, Czech Republic. 16. Department of Family Medicine and Public Health, University of Tartu, Tartu, Estonia. 17. Center for Environmental and Respiratory Health Research (CERH), University of Oulu, Oulu, Finland; Medical Research Center Oulu (MRC Oulu), Oulu University Hospital and University of Oulu, Oulu, Finland. 18. Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Neuherberg, Germany; Department of Epidemiology, Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilians-Universität München, Munich, Germany. 19. Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Neuherberg, Germany. 20. Center for Climate Change Adaptation, National Institute for Environmental Studies, Tsukuba, Japan; Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Japan. 21. Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; School of Tropical Medicine and Global Health, Nagasaki University, Japan. 22. Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, USA. 23. Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru. 24. Faculty of Geography, Babes-Bolyai University, Cluj-Napoca, Romania. 25. Department of Public Health Science, Graduate School of Public Health, & Institute of Health and Environment, Seoul National University, Seoul, Republic of Korea. 26. Department of Statistics and Computational Research, Universitat de València, València, Spain. 27. School of the Environment, Yale University, New Haven, CT, USA. 28. Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, USA. 29. Institute of Advanced Studies, University of São Paulo, São Paulo, Brazil. 30. Department of Environmental Health, National Institute of Public Health, Cuernavaca, Morelos, Mexico. 31. Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, UK; Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK; Centre for Statistical Modelling, London School of Hygiene & Tropical Medicine, London, UK. 32. Department of Statistics, Computer Science and Applications "G. Parenti", University of Florence, Florence, Italy. Electronic address: francesco.sera@unifi.it.
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.
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.