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Seasonal variation in SARS-CoV-2 transmission in temperate climates: A Bayesian modelling study in 143 European regions
T. Gavenčiak, JT. Monrad, G. Leech, M. Sharma, S. Mindermann, S. Bhatt, J. Brauner, J. Kulveit
Jazyk angličtina Země Spojené státy americké
Typ dokumentu časopisecké články, práce podpořená grantem
Grantová podpora
MR/R015600/1
Medical Research Council - United Kingdom
MR/V038109/1
Medical Research Council - United Kingdom
Department of Health - United Kingdom
NLK
Directory of Open Access Journals
od 2005
Free Medical Journals
od 2005
Public Library of Science (PLoS)
od 2005
PubMed Central
od 2005
Europe PubMed Central
od 2005
ProQuest Central
od 2005-06-01
Open Access Digital Library
od 2005-01-01
Open Access Digital Library
od 2005-06-01
Open Access Digital Library
od 2005-01-01
Medline Complete (EBSCOhost)
od 2005-06-01
Health & Medicine (ProQuest)
od 2005-06-01
ROAD: Directory of Open Access Scholarly Resources
od 2005
- MeSH
- Bayesova věta MeSH
- COVID-19 * epidemiologie MeSH
- lidé MeSH
- podnebí MeSH
- roční období MeSH
- SARS-CoV-2 * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
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 R(t) (the expected number of new infections generated by an infectious individual at time t) 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.
Centre for Theoretical Studies Charles University Prague Czech Republic
Department of Computer Science University of Bristol Bristol United Kingdom
Department of Engineering Science University of Oxford Oxford United Kingdom
Department of Health Policy London School of Economics and Political Science London United Kingdom
Department of Public Health University of Copenhagen Copenhagen Denmark
Department of Statistics University of Oxford Oxford United Kingdom
Faculty of Medicine School of Public Health Imperial College London London United Kingdom
Future of Humanity Institute University of Oxford Oxford United Kingdom
Citace poskytuje Crossref.org
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