Quantifying the risk of spillover reduction programs for human health.

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS PLoS Computational Biology Pub Date : 2024-08-15 eCollection Date: 2024-08-01 DOI:10.1371/journal.pcbi.1012358
Scott L Nuismer, Andrew J Basinski, Courtney L Schreiner, Evan A Eskew, Elisabeth Fichet-Calvet, Christopher H Remien
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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.

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量化减少溢出计划对人类健康造成的风险。
减少人畜共患病病原体的外溢是预防人类疾病和最大限度降低未来流行病和大流行风险的一种有吸引力的方法。虽然减少外溢对人类健康的直接益处显而易见,但随着时间的推移,减少外溢可能会给人类健康带来反直觉的负面影响。在这里,我们利用数学模型和计算机模拟,探讨了在疾病严重程度随感染年龄增加而增加的系统中,减少溢出效应在什么条件下会产生意想不到的后果。我们的研究结果表明,由于感染的平均年龄会随着外溢效应的减少而增加,如果感染的临床严重程度随着年龄的增长而迅速增加,那么减少外溢效应的计划实际上会增加人群的疾病负担。但是,如果免疫力随着时间的推移而减弱,并且有可能再次感染,那么我们的结果表明,减少溢出效应对健康产生负面影响的可能性就会大大降低。当我们使用已公布的西非拉沙病毒数据对模型进行参数化时,我们预测负面健康结果是可能的,但可能仅限于溢出异常强烈的一小部分人群。总之,我们的研究结果表明,减少外溢计划不太可能产生不良后果,但减少外溢后立即观察到的公共卫生收益可能会随着时间的推移而消失,因为免疫力的年龄结构会逐渐重新适应感染力的降低。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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