Seasonal variation in SARS-CoV-2 transmission in temperate climates: A Bayesian modelling study in 143 European regions

Tomáš Gavenčiak; Joshua Teperowski Monrad; Gavin Leech; Mrinank Sharma; Sören Mindermann; Samir Bhatt; Jan Brauner; Jan Kulveit

Abstract: PLOS COMPUTATIONAL BIOLOGY RESEARCH ARTICLE Seasonal variation in SARS-CoV-2 transmission in temperate climates: A Bayesian modelling study in 143 European regions a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 Tomáš Gavenčiak ID1☯, Joshua Teperowski Monrad ID2,3,4☯*, Gavin Leech ID5, Mrinank Sharma ID2,6,7, Sören Mindermann ID8, Samir Bhatt ID9,10, Jan Brauner ID2,8, Jan Kulveit ID1,2☯ 1 Centre for Theoretical Studies, Charles University, Prague, Czech Republic, 2 Future of Humanity Institute, University of Oxford, Oxford, United Kingdom, 3 Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom, 4 Department of Health Policy, London School of Economics and Political Science, London, United Kingdom, 5 Department of Computer Science, University of Bristol, Bristol, United Kingdom, 6 Department of Statistics, University of Oxford, Oxford, United Kingdom, 7 Department of Engineering Science, University of Oxford, Oxford, United Kingdom, 8 Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, Oxford, United Kingdom, 9 Faculty of Medicine, School of Public Health, Imperial College London, London, United Kingdom, 10 Department of Public Health, University of Copenhagen, Copenhagen, Denmark OPEN ACCESS Citation: Gavenčiak T, Monrad JT, Leech G, Sharma M, Mindermann S, Bhatt S, et al. (2022) Seasonal variation in SARS-CoV-2 transmission in temperate climates: A Bayesian modelling study in 143 European regions. PLoS Comput Biol 18(8): e1010435. https://doi.org/10.1371/journal. pcbi.1010435 Editor: Claudio José Struchiner, Fundação Getúlio Vargas: Fundacao Getulio Vargas, BRAZIL Received: October 20, 2021 Accepted: July 25, 2022 Published: August 26, 2022 Copyright: © 2022 Gavenčiak et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Our Brauner et al. seasonal model implementation is based on the Brauner et al. codebase and can be obtained at https://github.com/gavento/covid_seasonal_ Brauner together with the datasets used, including the temperate Europe dataset where we restrict the dataset of Brauner et al. to the following 29 regions (out of 41 total): Albania, Andorra, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, ☯ These authors contributed equally to this work. * joshua.monrad@gmail.com Abstract Although seasonal variation has a known influence on the transmission of several respiratory viral infections, its role in SARS-CoV-2 transmission remains unclear. While there is a sizable and growing literature on environmental drivers of COVID-19 transmission, recent reviews have highlighted conflicting and inconclusive findings. This indeterminacy partly owes to the fact that seasonal variation relates to viral transmission by a complicated web of causal pathways, including many interacting biological and behavioural factors. Since analyses of specific factors cannot determine the aggregate strength of seasonal forcing, we sidestep the challenge of disentangling various possible causal paths in favor of a holistic approach. We model seasonality as a sinusoidal variation in transmission..

DOI record: { "DOI": "10.1371/journal.pcbi.1010435", "ISSN": [ "1553-7358" ], "URL": "http://dx.doi.org/10.1371/journal.pcbi.1010435", "abstract": "\n Although seasonal variation has a known influence on the transmission of several respiratory viral infections, its role in SARS-CoV-2 transmission remains unclear. While there is a sizable and growing literature on environmental drivers of COVID-19 transmission, recent reviews have highlighted conflicting and inconclusive findings. This indeterminacy partly owes to the fact that seasonal variation relates to viral transmission by a complicated web of causal pathways, including many interacting biological and behavioural factors. Since analyses of specific factors cannot determine the aggregate strength of seasonal forcing, we sidestep the challenge of disentangling various possible causal paths in favor of a holistic approach. We model seasonality as a sinusoidal variation in transmission and infer a single Bayesian estimate of the overall seasonal effect. By extending two state-of-the-art models of non-pharmaceutical intervention (NPI) effects and their datasets covering 143 regions in temperate Europe, we are able to adjust our estimates for the role of both NPIs and mobility patterns in reducing transmission. We find strong seasonal patterns, consistent with a reduction in the time-varying reproduction number\n R\n (\n t\n ) (the expected number of new infections generated by an infectious individual at time\n t\n ) of 42.1% (95% CI: 24.7%—53.4%) from the peak of winter to the peak of summer. These results imply that the seasonality of SARS-CoV-2 transmission is comparable in magnitude to the most effective individual NPIs but less than the combined effect of multiple interventions.\n ", "author": [ { "ORCID": "https://orcid.org/0000-0003-1119-2426", "affiliation": [], "authenticated-orcid": true, "family": "Gavenčiak", "given": "Tomáš", "sequence": "first" }, { "ORCID": "https://orcid.org/0000-0002-7377-2074", "affiliation": [], "authenticated-orcid": true, "family": "Monrad", "given": "Joshua Teperowski", "sequence": "additional" }, { "ORCID": "https://orcid.org/0000-0002-9298-1488", "affiliation": [], "authenticated-orcid": true, "family": "Leech", "given": "Gavin", "sequence": "additional" }, { "ORCID": "https://orcid.org/0000-0002-4304-7963", "affiliation": [], "authenticated-orcid": true, "family": "Sharma", "given": "Mrinank", "sequence": "additional" }, { "ORCID": "https://orcid.org/0000-0002-0315-9821", "affiliation": [], "authenticated-orcid": true, "family": "Mindermann", "given": "Sören", "sequence": "additional" }, { "ORCID": "https://orcid.org/0000-0002-0891-4611", "affiliation": [], "authenticated-orcid": true, "family": "Bhatt", "given": "Samir", "sequence": "additional" }, { "ORCID": "https://orcid.org/0000-0002-1588-5724", "affiliation": [], "authenticated-orcid": true, "family": "Brauner", "given": "Jan", "sequence": "additional" }, { "ORCID": "https://orcid.org/0000-0002-3791-8355", "affiliation": [], "authenticated-orcid": true, "family": "Kulveit", "given": "Jan", "sequence": "additional" } ], "container-title": "PLOS Computational Biology", "container-title-short": "PLoS Comput Biol", "content-domain": { "crossmark-restriction": false, "domain": [ "www.ploscompbiol.org" ] }, "created": { "date-parts": [ [ 2022, 8, 26 ] ], "date-time": "2022-08-26T14:02:51Z", "timestamp": 1661522571000 }, "deposited": { "date-parts": [ [ 2022, 9, 8 ] ], "date-time": "2022-09-08T13:37:30Z", "timestamp": 1662644250000 }, "editor": [ { 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