Replicating superspreader dynamics with compartmental models

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 outbrea...

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Bibliographic Details
Published in:	Scientific reports Vol. 13; no. 1; p. 15319
Main Authors:	Meehan, Michael T., Hughes, Angus, Ragonnet, Romain R., Adekunle, Adeshina I., Trauer, James M., Jayasundara, Pavithra, McBryde, Emma S., Henderson, Alec S.
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 15-09-2023 Nature Publishing Group Nature Portfolio
Subjects:	631/114/2397 692/699/255 692/700/478/174 Binomial distribution Disease transmission Epidemic models Epidemics Health care Heterogeneity Humanities and Social Sciences Infectious diseases Middle East respiratory syndrome multidisciplinary Outbreaks Pathogens Preventive medicine Public health Random variables Science Science (multidisciplinary) Severe acute respiratory syndrome coronavirus 2
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Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2045-2322 2045-2322
DOI:	10.1038/s41598-023-42567-3