Replicating superspreader dynamics with compartmental models
Meehan, Michael T., Hughes, Angus, Ragonnet, Romain R., Adekunle, Adeshina I., Trauer, James M., Jayasundara, Pavithra, McBryde, Emma S., and Henderson, Alec S. (2023) Replicating superspreader dynamics with compartmental models. Scientific Reports, 13. 15319.
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Abstract
Infectious disease outbreaks often exhibit superspreader dynamics, where most infected people generate no, or few secondary cases, and only a small fraction of individuals are responsible for a large proportion of transmission. Although capturing this heterogeneity is critical for estimating outbreak risk and the effectiveness of group-specific interventions, it is typically neglected in compartmental models of infectious disease transmission—which constitute the most common transmission dynamic modeling framework. In this study we propose different classes of compartmental epidemic models that incorporate transmission heterogeneity, fit them to a number of real outbreak datasets, and benchmark their performance against the canonical superspreader model (i.e., the negative binomial branching process model). We find that properly constructed compartmental models can capably reproduce observed superspreader dynamics and we provide the pathogen-specific parameter settings required to do so. As a consequence, we also show that compartmental models parameterized according to a binary clinical classification have limited support.
| Item ID: | 80820 |
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| Item Type: | Article (Research - C1) |
| ISSN: | 2045-2322 |
| Copyright Information: | © The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Funders: | Australian Research Council (ARC) |
| Projects and Grants: | ARC DE210101344, ARC DE230100730 |
| Date Deposited: | 16 Feb 2024 00:32 |
| FoR Codes: | 42 HEALTH SCIENCES > 4202 Epidemiology > 420205 Epidemiological modelling @ 100% |
| SEO Codes: | 20 HEALTH > 2004 Public health (excl. specific population health) > 200404 Disease distribution and transmission (incl. surveillance and response) @ 100% |
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