Superspreading and the evolution of virulence.

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
PLoS Computational Biology Pub Date : 2025-10-09 eCollection Date: 2025-10-01 DOI:10.1371/journal.pcbi.1013517
Xander O'Neill, Andy White, Graham R Northrup, Chadi M Saad-Roy, P Signe White, Mike Boots
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引用次数: 0

Abstract

Superspreading, where a small proportion of a population can cause a high proportion of infection transmission, is well known to be important to the epidemiology of a wide range of pathogens, including SARS-CoV-2. However, despite its ubiquity in important human and animal pathogens, the impact of superspreading on the evolution of pathogen virulence is not well understood. Using theory and both deterministic and stochastic simulations we examine the evolution of pathogen virulence under a range of different distributions of infection transmission for the host. Importantly, for many pathogens, superpreader events may be associated with increased tolerance to infection or asymptomatic infection and when we account for this superspreading selects for higher virulence. In contrast, in animal populations where highly connected individuals, that are linked to superspreader events, also have fitness benefits, superspreading may select for milder pathogens. In isolation, the transmission distribution of the host does not impact selection for pathogen virulence. However, superspreading reduces the rate of pathogen evolution and generates considerable variation in pathogen virulence. Therefore, the adaptation of an emerging infectious disease, that exhibits superspreading, is likely to be slowed and characterised by the maintenance of maladaptive variants. Taken as a whole, our results show that superspreading can have important impacts on the evolution of pathogens.

超扩散和毒力的进化。
众所周知,超级传播对包括SARS-CoV-2在内的多种病原体的流行病学很重要,在这种情况下,一小部分人群可能导致高比例的感染传播。然而,尽管它在重要的人类和动物病原体中普遍存在,但超扩散对病原体毒力进化的影响尚不清楚。利用理论和确定性和随机模拟,我们研究了在宿主感染传播的一系列不同分布下病原体毒力的演变。重要的是,对于许多病原体,超级捕食者事件可能与对感染或无症状感染的耐受性增加有关,当我们考虑到这种超级传播时,选择了更高的毒力。相比之下,在动物种群中,高度关联的个体(与超级传播事件有关)也有健康益处,超级传播可能会选择较温和的病原体。在孤立情况下,宿主的传播分布不影响病原体毒力的选择。然而,超扩散降低了病原体进化的速度,并在病原体的毒力上产生了相当大的变化。因此,一种表现出超传播的新发传染病的适应可能会减慢,其特征是维持不适应的变异。总的来说,我们的研究结果表明,超传播对病原体的进化有重要影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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