Detail
Článek
Článek online
FT
Medvik - BMČ
  • Je něco špatně v tomto záznamu ?

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

. 2022 ; 18 (8) : e1010435. [pub] 20220826

Jazyk angličtina Země Spojené státy americké

Typ dokumentu časopisecké články, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/bmc22024737

Grantová podpora
MR/R015600/1 Medical Research Council - United Kingdom
MR/V038109/1 Medical Research Council - United Kingdom
Department of Health - United Kingdom

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.

Citace poskytuje Crossref.org

000      
00000naa a2200000 a 4500
001      
bmc22024737
003      
CZ-PrNML
005      
20221031101321.0
007      
ta
008      
221017s2022 xxu f 000 0|eng||
009      
AR
024    7_
$a 10.1371/journal.pcbi.1010435 $2 doi
035    __
$a (PubMed)36026483
040    __
$a ABA008 $b cze $d ABA008 $e AACR2
041    0_
$a eng
044    __
$a xxu
100    1_
$a Gavenčiak, Tomáš $u Centre for Theoretical Studies, Charles University, Prague, Czech Republic $1 https://orcid.org/0000000311192426
245    10
$a Seasonal variation in SARS-CoV-2 transmission in temperate climates: A Bayesian modelling study in 143 European regions / $c T. Gavenčiak, JT. Monrad, G. Leech, M. Sharma, S. Mindermann, S. Bhatt, J. Brauner, J. Kulveit
520    9_
$a 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.
650    _2
$a Bayesova věta $7 D001499
650    12
$a COVID-19 $x epidemiologie $7 D000086382
650    _2
$a podnebí $7 D002980
650    _2
$a lidé $7 D006801
650    12
$a SARS-CoV-2 $7 D000086402
650    _2
$a roční období $7 D012621
655    _2
$a časopisecké články $7 D016428
655    _2
$a práce podpořená grantem $7 D013485
700    1_
$a Monrad, Joshua Teperowski $u Future of Humanity Institute, University of Oxford, Oxford, United Kingdom $u Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom $u Department of Health Policy, London School of Economics and Political Science, London, United Kingdom $1 https://orcid.org/0000000273772074
700    1_
$a Leech, Gavin $u Department of Computer Science, University of Bristol, Bristol, United Kingdom $1 https://orcid.org/0000000292981488
700    1_
$a Sharma, Mrinank $u Future of Humanity Institute, University of Oxford, Oxford, United Kingdom $u Department of Statistics, University of Oxford, Oxford, United Kingdom $u Department of Engineering Science, University of Oxford, Oxford, United Kingdom $1 https://orcid.org/0000000243047963
700    1_
$a Mindermann, Sören $u Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, Oxford, United Kingdom $1 https://orcid.org/0000000203159821
700    1_
$a Bhatt, Samir $u Faculty of Medicine, School of Public Health, Imperial College London, London, United Kingdom $u Department of Public Health, University of Copenhagen, Copenhagen, Denmark $1 https://orcid.org/0000000208914611
700    1_
$a Brauner, Jan $u Future of Humanity Institute, University of Oxford, Oxford, United Kingdom $u Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, Oxford, United Kingdom $1 https://orcid.org/0000000215885724
700    1_
$a Kulveit, Jan $u Centre for Theoretical Studies, Charles University, Prague, Czech Republic $u Future of Humanity Institute, University of Oxford, Oxford, United Kingdom $1 https://orcid.org/0000000237918355
773    0_
$w MED00008919 $t PLoS computational biology $x 1553-7358 $g Roč. 18, č. 8 (2022), s. e1010435
856    41
$u https://pubmed.ncbi.nlm.nih.gov/36026483 $y Pubmed
910    __
$a ABA008 $b sig $c sign $y p $z 0
990    __
$a 20221017 $b ABA008
991    __
$a 20221031101319 $b ABA008
999    __
$a ok $b bmc $g 1854464 $s 1176027
BAS    __
$a 3
BAS    __
$a PreBMC
BMC    __
$a 2022 $b 18 $c 8 $d e1010435 $e 20220826 $i 1553-7358 $m PLoS computational biology $n PLoS Comput Biol $x MED00008919
GRA    __
$a MR/R015600/1 $p Medical Research Council $2 United Kingdom
GRA    __
$a MR/V038109/1 $p Medical Research Council $2 United Kingdom
GRA    __
$p Department of Health $2 United Kingdom
LZP    __
$a Pubmed-20221017

Najít záznam

Citační ukazatele

Pouze přihlášení uživatelé

Možnosti archivace

Nahrávání dat ...