On the probability of strain invasion in endemic settings: accounting for individual heterogeneity and control in multi-strain dynamics

Meehan, Michael T., Cope, Robert C., and McBryde, Emma S. (2020) On the probability of strain invasion in endemic settings: accounting for individual heterogeneity and control in multi-strain dynamics. Journal of Theoretical Biology, 487. 110109.

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Pathogen evolution is an imminent threat to global health that has warranted, and duly received, considerable attention within the medical, microbiological and modelling communities. Outbreaks of new pathogens are often ignited by the emergence and transmission of mutant variants descended from wild-type strains circulating in the community. In this work we investigate the stochastic dynamics of the emergence of a novel disease strain, introduced into a population in which it must compete with an existing endemic strain. In analogy with past work on single-strain epidemic outbreaks, we apply a branching process approximation to calculate the probability that the new strain becomes established. As expected, a critical determinant of the survival prospects of any invading strain is the magnitude of its reproduction number relative to that of the background endemic strain. Whilst in most circumstances this ratio must exceed unity in order for invasion to be viable, we show that differential control scenarios can lead to less-fit novel strains invading populations hosting a fitter endemic one. This analysis and the accompanying findings will inform our understanding of the mechanisms that have led to past instances of successful strain invasion, and provide valuable lessons for thwarting future drug-resistant strain incursions.

Item ID: 64755
Item Type: Article (Research - C1)
ISSN: 1095-8541
Keywords: pathogen evolution, anti-microbial drug resistance, multi-strain, strain invasion, epidemic control, branching process
Copyright Information: © 2019 Elsevier Ltd. All rights reserved.
Funders: NHMRC Centre of Research Excellence in Policy Relevant Infectious disease Simulation and Mathematical Modelling, Data to Decisions Cooperative Research Centre, ARC Centre of Excellence for Mathematical and Statistical Frontiers
Projects and Grants: ARC project no. CE140100049
Date Deposited: 09 Mar 2021 04:01
FoR Codes: 49 MATHEMATICAL SCIENCES > 4901 Applied mathematics > 490102 Biological mathematics @ 50%
31 BIOLOGICAL SCIENCES > 3103 Ecology > 310307 Population ecology @ 50%
SEO Codes: 92 HEALTH > 9204 Public Health (excl. Specific Population Health) > 920404 Disease Distribution and Transmission (incl. Surveillance and Response) @ 20%
97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 40%
97 EXPANDING KNOWLEDGE > 970106 Expanding Knowledge in the Biological Sciences @ 40%
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