Different forms of superspreading lead to different outcomes: Heterogeneity in infectiousness and contact behavior relevant for the case of SARS-CoV-2.


Journal

PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922

Informations de publication

Date de publication:
08 2022
Historique:
received: 01 03 2022
accepted: 29 06 2022
revised: 01 09 2022
pubmed: 23 8 2022
medline: 9 9 2022
entrez: 22 8 2022
Statut: epublish

Résumé

Superspreading events play an important role in the spread of several pathogens, such as SARS-CoV-2. While the basic reproduction number of the original Wuhan SARS-CoV-2 is estimated to be about 3 for Belgium, there is substantial inter-individual variation in the number of secondary cases each infected individual causes-with most infectious individuals generating no or only a few secondary cases, while about 20% of infectious individuals is responsible for 80% of new infections. Multiple factors contribute to the occurrence of superspreading events: heterogeneity in infectiousness, individual variations in susceptibility, differences in contact behavior, and the environment in which transmission takes place. While superspreading has been included in several infectious disease transmission models, research into the effects of different forms of superspreading on the spread of pathogens remains limited. To disentangle the effects of infectiousness-related heterogeneity on the one hand and contact-related heterogeneity on the other, we implemented both forms of superspreading in an individual-based model describing the transmission and spread of SARS-CoV-2 in a synthetic Belgian population. We considered its impact on viral spread as well as on epidemic resurgence after a period of social distancing. We found that the effects of superspreading driven by heterogeneity in infectiousness are different from the effects of superspreading driven by heterogeneity in contact behavior. On the one hand, a higher level of infectiousness-related heterogeneity results in a lower risk of an outbreak persisting following the introduction of one infected individual into the population. Outbreaks that did persist led to fewer total cases and were slower, with a lower peak which occurred at a later point in time, and a lower herd immunity threshold. Finally, the risk of resurgence of an outbreak following a period of lockdown decreased. On the other hand, when contact-related heterogeneity was high, this also led to fewer cases in total during persistent outbreaks, but caused outbreaks to be more explosive in regard to other aspects (such as higher peaks which occurred earlier, and a higher herd immunity threshold). Finally, the risk of resurgence of an outbreak following a period of lockdown increased. We found that these effects were conserved when testing combinations of infectiousness-related and contact-related heterogeneity.

Identifiants

pubmed: 35994497
doi: 10.1371/journal.pcbi.1009980
pii: PCOMPBIOL-D-22-00314
pmc: PMC9436127
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e1009980

Déclaration de conflit d'intérêts

I have read the journal’s policy and the authors of this manuscript have the following competing interests: FV contributed to this work as a full time employee of the University of Antwerp. As of 21 March 2022, after his contributions to this work ended, FV is employed by the GSK group of companies.

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Auteurs

Elise J Kuylen (EJ)

Centre for Health Economic Research and Modeling Infectious Diseases, University of Antwerp, Antwerp, Belgium.
Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium.

Andrea Torneri (A)

Centre for Health Economic Research and Modeling Infectious Diseases, University of Antwerp, Antwerp, Belgium.

Lander Willem (L)

Centre for Health Economic Research and Modeling Infectious Diseases, University of Antwerp, Antwerp, Belgium.

Pieter J K Libin (PJK)

Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium.
Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels, Belgium.
Rega Institute for Medical Research, Clinical and Epidemiological Virology, University of Leuven, Leuven, Belgium.

Steven Abrams (S)

Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium.
Global Health Institute, University of Antwerp, Antwerp, Belgium.

Pietro Coletti (P)

Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium.

Nicolas Franco (N)

Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium.
Namur Institute for Complex Systems, Department of Mathematics, University of Namur, Namur, Belgium.

Frederik Verelst (F)

Centre for Health Economic Research and Modeling Infectious Diseases, University of Antwerp, Antwerp, Belgium.

Philippe Beutels (P)

Centre for Health Economic Research and Modeling Infectious Diseases, University of Antwerp, Antwerp, Belgium.
School of Public Health and Community Medicine, The University of New South Wales, Sydney, NSW, Australia.

Jori Liesenborgs (J)

Expertise Centre for Digital Media, Hasselt University - transnational University Limburg, Hasselt, Belgium.

Niel Hens (N)

Centre for Health Economic Research and Modeling Infectious Diseases, University of Antwerp, Antwerp, Belgium.
Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium.

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