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Seasonal variation in SARS-CoV-2 transmission in temperate climates
Tomas Gavenciak; Joshua Teperowski Monrad; Gavin Leech; Mrinank Sharma; Soren Mindermann; Jan Markus Brauner; Samir Bhatt; Jan Kulveit.
Affiliation
  • Tomas Gavenciak; Epidemic Forecasting
  • Joshua Teperowski Monrad; Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, UK
  • Gavin Leech; Department of Computer Science, University of Bristol, UK
  • Mrinank Sharma; Department of Statistics, University of Oxford, UK
  • Soren Mindermann; Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, UK
  • Jan Markus Brauner; Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, UK
  • Samir Bhatt; Section of Epidemiology, Department of Public Health, University of Copenhagen, Denmark
  • Jan Kulveit; Future of Humanity Institute, University of Oxford, UK
Preprint in En | PREPRINT-MEDRXIV | ID: ppmedrxiv-21258647
ABSTRACT
While seasonal variation has a known influence on the transmission of several respiratory viral infections, its role in SARS-CoV-2 transmission remains unclear. As previous analyses have not accounted for the implementation of non-pharmaceutical interventions (NPIs) in the first year of the pandemic, they may yield biased estimates of seasonal effects. Building on two state-of-the-art observational models and datasets, we adapt a fully Bayesian method for estimating the association between seasonality and transmission in 143 temperate European regions. We find strong seasonal patterns, consistent with a reduction in the time-variable Rt 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.
License
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Full text: 1 Collection: 09-preprints Database: PREPRINT-MEDRXIV Type of study: Observational_studies / Prognostic_studies Language: En Year: 2021 Document type: Preprint
Full text: 1 Collection: 09-preprints Database: PREPRINT-MEDRXIV Type of study: Observational_studies / Prognostic_studies Language: En Year: 2021 Document type: Preprint