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

Elise J Kuylen; Andrea Torneri; Lander Willem; Pieter J K Libin; Steven Abrams; Pietro Coletti; Nicolas Franco; Frederik Verelst; Philippe Beutels; Jori Liesenborgs; Niel Hens

doi:10.1371/journal.pcbi.1009980

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

PLoS Comput Biol. 2022 Aug 22;18(8):e1009980. doi: 10.1371/journal.pcbi.1009980. eCollection 2022 Aug.

Authors

Elise J Kuylen^{1

2}, Andrea Torneri¹, Lander Willem¹, Pieter J K Libin^{2

3

4}, Steven Abrams^{2

5}, Pietro Coletti², Nicolas Franco^{2

6}, Frederik Verelst¹, Philippe Beutels^{1

7}, Jori Liesenborgs⁸, Niel Hens^{1

2}

Affiliations

¹ Centre for Health Economic Research and Modeling Infectious Diseases, University of Antwerp, Antwerp, Belgium.
² Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium.
³ Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels, Belgium.
⁴ Rega Institute for Medical Research, Clinical and Epidemiological Virology, University of Leuven, Leuven, Belgium.
⁵ Global Health Institute, University of Antwerp, Antwerp, Belgium.
⁶ Namur Institute for Complex Systems, Department of Mathematics, University of Namur, Namur, Belgium.
⁷ School of Public Health and Community Medicine, The University of New South Wales, Sydney, NSW, Australia.
⁸ Expertise Centre for Digital Media, Hasselt University - transnational University Limburg, Hasselt, Belgium.

Abstract

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.

Publication types

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

MeSH terms

Basic Reproduction Number
COVID-19* / epidemiology
Communicable Disease Control / methods
Disease Outbreaks
Humans
SARS-CoV-2*

Grants and funding

We acknowledge funding from the Flemish Government through IBOF (E.J.K., P.B. and N.H.: IBOF.21.027 Descartes project) and from the Research Foundation Flanders (FWO) (S.A. and N.H.: G0G2920N RESTORE project; E.J.K and N.H.: grant agreement R-7861). This work also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (A.T., P.J.K.L., N.F., F.V., P.B and N.H.: grant agreement 101003688 EpiPose; P.C. and N.H.: grant agreement 682540 TransMID). Furthermore, L.W. and P.J.K.L. gratefully acknowledge support from the Research Foundation Flanders (FWO) via postdoctoral fellowships 1234620N (L.W.) and 1242021N (P.J.K.L.). P.J.K.L. also acknowledges support from the Research council of the Vrije Universiteit Brussel (OZR-VUB) via grant number OZR3863BOF, and from the Flemish Government through the AI Research Program. Finally, P.B. and N.H. acknowledge funding from University of Antwerp via the Antwerp Study Center for Infectious Diseases (ASCID) and the Methusalem-Centre of Excellence consortium VAX–IDEA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.