Exploring how ecological and epidemiological processes shape multi-host disease dynamics using global sensitivity analysis
Exploring how ecological and epidemiological processes shape multi-host disease dynamics using global sensitivity analysis
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DOI：
10.1007/s00285-023-01912-w
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发表时间：
2023-05-01
影响因子：
1.9
通讯作者：
Cortez，Michael H.
中科院分区：
文献类型：
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作者：
Arachchilage，Kalpana Hanthanan;Hussaini，Mohammed Y.;Cortez，Michael H.
我们使用全局敏感性分析（特别是，部分秩相关系数），探讨生态和流行病学过程中塑造的时间动态的参数化SIR型模型的两个主机物种和环境传播的病原体的作用。我们计算了每个宿主物种的疾病流行率对模型参数的敏感性。敏感性排名计算，生物学解释，并对比的情况下，病原体被引入到一个无病的社区和情况下，第二宿主物种被引入到一个地方性的单宿主社区。在某些情况下，只有通过了解宿主物种的特征（即，它们的竞争能力和疾病能力），而在其他情况下，它们可以通过独立于物种特征的因素（具体而言，种内与种间过程或物种的入侵者与居民角色）来预测。例如，当病原体最初被引入无病社区时，两种宿主中的疾病流行对第一宿主的爆发大小比第二宿主更敏感。相比之下，每种宿主的疾病流行率对其自身的感染率比其他宿主物种的感染率更敏感。总的来说，这项研究表明，全球敏感性分析可以提供有用的洞察生态和流行病学过程如何塑造疾病动态，以及这些影响如何随时间和系统条件而变化。我们的研究结果表明，敏感性分析可以提供量化和方向时，探索生物假说。
We use global sensitivity analysis (specifically, Partial Rank Correlation Coefficients) to explore the roles of ecological and epidemiological processes in shaping the temporal dynamics of a parameterized SIR-type model of two host species and an environmentally transmitted pathogen. We compute the sensitivities of disease prevalence in each host species to model parameters. Sensitivity rankings are calculated, interpreted biologically, and contrasted for cases where the pathogen is introduced into a disease-free community and cases where a second host species is introduced into an endemic single-host community. In some cases the magnitudes and dynamics of the sensitivities can be predicted only by knowing the host species’ characteristics (i.e., their competitive abilities and disease competence) whereas in other cases they can be predicted by factors independent of the species’ characteristics (specifically, intraspecific versus interspecific processes or a species’ roles of invader versus resident). For example, when a pathogen is initially introduced into a disease-free community, disease prevalence in both hosts is more sensitive to the burst size of the first host than the second host. In comparison, disease prevalence in each host is more sensitive to its own infection rate than the infection rate of the other host species. In total, this study illustrates that global sensitivity analysis can provide useful insight into how ecological and epidemiological processes shape disease dynamics and how those effects vary across time and system conditions. Our results show that sensitivity analysis can provide quantification and direction when exploring biological hypotheses.