Replicating superspreader dynamics with compartmental models.

Autor: Meehan MT; Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, 4811, Australia. michael.meehan1@jcu.edu.au.; College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, 4811, Australia. michael.meehan1@jcu.edu.au., Hughes A; School of Public Health and Preventive Medicine, Monash University, Melbourne, 3800, Australia., Ragonnet RR; School of Public Health and Preventive Medicine, Monash University, Melbourne, 3800, Australia., Adekunle AI; Defence Science and Technology Group, Department of Defence, Melbourne, 3207, Australia., Trauer JM; School of Public Health and Preventive Medicine, Monash University, Melbourne, 3800, Australia., Jayasundara P; School of Public Health and Preventive Medicine, Monash University, Melbourne, 3800, Australia., McBryde ES; Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, 4811, Australia., Henderson AS; Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, 4811, Australia.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 Sep 15; Vol. 13 (1), pp. 15319. Date of Electronic Publication: 2023 Sep 15.
DOI: 10.1038/s41598-023-42567-3
Abstrakt: 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.
(© 2023. Springer Nature Limited.)
Databáze: MEDLINE
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