Am Nat. 2021 Dec;198(6):661-677. doi: 10.1086/716914. Epub 2021 Oct 4.
AbstractInfection intensity can dictate disease outcomes but is typically ignored when modeling infection dynamics of microparasites (e.g., bacteria, virus, and fungi). However, for a number of pathogens of wildlife typically categorized as microparasites, accounting for infection intensity and within-host infection processes is critical for predicting population-level responses to pathogen invasion. Here, we develop a modeling framework we refer to as reduced-dimension host-parasite integral projection models (reduced IPMs) that we use to explore how within-host infection processes affect the dynamics of pathogen invasion and virulence evolution. We find that individual-level heterogeneity in pathogen load-a nearly ubiquitous characteristic of host-parasite interactions that is rarely considered in models of microparasites-generally reduces pathogen invasion probability and dampens virulence-transmission trade-offs in host-parasite systems. The latter effect likely contributes to widely predicted virulence-transmission trade-offs being difficult to observe empirically. Moreover, our analyses show that intensity-dependent host mortality does not always induce a virulence-transmission trade-off, and systems with steeper than linear relationships between pathogen intensity and host mortality rate are significantly more likely to exhibit these trade-offs. Overall, reduced IPMs provide a useful framework to expand our theoretical and data-driven understanding of how within-host processes affect population-level disease dynamics.
摘要
感染强度可以决定疾病的结果,但在模拟微观寄生虫(如细菌、病毒和真菌)的感染动态时通常被忽略。然而,对于通常被归类为微观寄生虫的野生动物的许多病原体,考虑感染强度和宿主内感染过程对于预测病原体入侵对种群水平的反应至关重要。在这里,我们开发了一种建模框架,我们称之为简化维度宿主-寄生虫积分投影模型(简化 IPM),我们用它来探索宿主内感染过程如何影响病原体入侵和毒力进化的动态。我们发现,病原体负荷的个体间异质性——宿主-寄生虫相互作用中几乎普遍存在的特征,在微观寄生虫模型中很少被考虑——通常会降低病原体入侵的概率,并减弱宿主-寄生虫系统中毒力-传播的权衡。后一种效应可能导致广泛预测的毒力-传播权衡难以在经验上观察到。此外,我们的分析表明,依赖于强度的宿主死亡率并不总是诱导毒力-传播权衡,并且病原体强度与宿主死亡率之间呈非线性关系的系统更有可能表现出这些权衡。总体而言,简化 IPM 为扩展我们对宿主内过程如何影响种群水平疾病动态的理论和数据驱动理解提供了一个有用的框架。