Quantifying the risk of spillover reduction programs for human health

Reducing spillover of zoonotic pathogens is an appealing approach to preventing human disease and minimizing the risk of future epidemics and pandemics. Although the immediate human health benefit of reducing spillover is clear, over time, spillover reduction could lead to counterintuitive negative consequences for human health. Here, we use mathematical models and computer simulations to explore the conditions under which unanticipated consequences of spillover reduction can occur in systems where the severity of disease increases with age at infection. Our results demonstrate that, because the average age at infection increases as spillover is reduced, programs that reduce spillover can actually increase population-level disease burden if the clinical severity of infection increases sufficiently rapidly with age. If, however, immunity wanes over time and reinfection is possible, our results reveal that negative health impacts of spillover reduction become substantially less likely. When our model is parameterized using published data on Lassa virus in West Africa, it predicts that negative health outcomes are possible, but likely to be restricted to a small subset of populations where spillover is unusually intense. Together, our results suggest that adverse consequences of spillover reduction programs are unlikely but that the public health gains observed immediately after spillover reduction may fade over time as the age structure of immunity gradually re-equilibrates to a reduced force of infection.

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Publication Details

Journal
PLoS Computational Biology
Published
2024-08-15
DOI
https://doi.org/10.1371/journal.pcbi.1012358
Citations
2
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
0.79

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article

Quantifying the risk of spillover reduction programs for human health

Courtney Schreiner, Andrew J. Basinski, Scott L. Nuismer, Christopher H. Remien et al.
2 citations
PLoS Computational Biology
COVID-19 epidemiological studies
0.79
article

Quantifying the risk of spillover reduction programs for human health

Courtney Schreiner, Andrew J. Basinski, Scott L. Nuismer, Christopher H. Remien, Élisabeth Fichet-Calvet, Evan A. Eskew
article en
2 citations

Abstract

Reducing spillover of zoonotic pathogens is an appealing approach to preventing human disease and minimizing the risk of future epidemics and pandemics. Although the immediate human health benefit of reducing spillover is clear, over time, spillover reduction could lead to counterintuitive negative consequences for human health. Here, we use mathematical models and computer simulations to explore the conditions under which unanticipated consequences of spillover reduction can occur in systems where the severity of disease increases with age at infection. Our results demonstrate that, because the average age at infection increases as spillover is reduced, programs that reduce spillover can actually increase population-level disease burden if the clinical severity of infection increases sufficiently rapidly with age. If, however, immunity wanes over time and reinfection is possible, our results reveal that negative health impacts of spillover reduction become substantially less likely. When our model is parameterized using published data on Lassa virus in West Africa, it predicts that negative health outcomes are possible, but likely to be restricted to a small subset of populations where spillover is unusually intense. Together, our results suggest that adverse consequences of spillover reduction programs are unlikely but that the public health gains observed immediately after spillover reduction may fade over time as the age structure of immunity gradually re-equilibrates to a reduced force of infection.

PLoS Computational BiologyVol. 20(8)
University of Idaho (US), University of Tennessee at Knoxville (US), Bernhard Nocht Institute for Tropical Medicine (DE)
National Science Foundation, National Institutes of Health, Directorate for Biological Sciences, National Institute of General Medical Sciences
Good health and well-being
Openalex Percentile: Top 31%
COVID-19 epidemiological studies
0.79
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