Evidence map›Paper›PMID 36026483›Full record

ArticlePLoS computational biology2022

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

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
7.4field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

36 citing papers in PubMed, 58 citations in OpenAlex.

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  12. On the SARS-CoV-2 Variants.Infectious disease reports · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 3 countries.

Tomáš GavenčiakCentre for Theoretical Studies, Charles University, Prague, Czech Republic.ORCID 0000-0003-1119-2426
Joshua Teperowski MonradFuture of Humanity Institute, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-7377-2074
Gavin LeechDepartment of Computer Science, University of Bristol, Bristol, United Kingdom.ORCID 0000-0002-9298-1488
Mrinank SharmaFuture of Humanity Institute, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-4304-7963
Sören MindermannOxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-0315-9821
Samir BhattFaculty of Medicine, School of Public Health, Imperial College London, London, United Kingdom.ORCID 0000-0002-0891-4611
Jan BraunerFuture of Humanity Institute, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-1588-5724
Jan KulveitCentre for Theoretical Studies, Charles University, Prague, Czech Republic.ORCID 0000-0002-3791-8355
University of Oxford · GBCharles University · CZUniversity of Bristol · GBUniversity of Copenhagen · DK

Funding

Department of HealthMedical Research Council MC_PC_19012Medical Research Council MR/R015600/1Medical Research Council MR/V038109/1
6 · The paper itself

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 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.

Indexed as

COVID-19SARS-CoV-2Bayes TheoremClimateHumansSeasons

Identifiers

PMID36026483
PMCPMC9455844
OpenAlexW4293780013

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.