Our understanding of ecological processes is built on patterns inferred from data. Applying modern analytical tools such as machine learning to increasingly high dimensional data offers the potential to expand our perspectives on these processes, shedding new light on complex ecological phenomena such as pathogen transmission in wild populations. Here, we propose a novel approach that combines data mining with theoretical models of disease dynamics. Using rodents as an example, we incorporate statistical differences in the life history features of zoonotic reservoir hosts into pathogen transmission models, enabling us to bound the range of dynamical phenomena associated with hosts, based on their traits. We then test for associations between equilibrium prevalence, a key epidemiological metric and data on human outbreaks of rodent‐borne zoonoses, identifying matches between empirical evidence and theoretical predictions of transmission dynamics. We show how this framework can be generalized to other systems through a rubric of disease models and parameters that can be derived from empirical data. By linking life history components directly to their effects on disease dynamics, our mining‐modelling approach integrates machine learning and theoretical models to explore mechanisms in the macroecology of pathogen transmission and their consequences for spillover infection to humans.

中文翻译：

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将数据挖掘和传播理论整合到传染病生态学中。

我们对生态过程的理解是建立在从数据推断的模式上的。将现代的分析工具（例如机器学习）应用到越来越高的维度数据上，有可能扩展我们对这些过程的看法，为复杂的生态现象（例如病原体在野生种群中的传播）提供新的思路。在这里，我们提出了一种新颖的方法，将数据挖掘与疾病动力学的理论模型相结合。以啮齿动物为例，我们将人畜共患病宿主的生活史特征中的统计差异纳入病原体传播模型，从而使我们能够根据宿主的特征来界定与宿主相关的动态现象的范围。然后，我们测试平衡流行率（一种关键的流行病学指标）与鼠源性人畜共患病的人类暴发数据之间的关联，确定经验证据与传输动力学理论预测之间的匹配。我们展示了如何通过疾病模型和可从经验数据得出的参数将这个框架推广到其他系统。通过将生活史组成部分直接与其对疾病动力学的影响联系起来，我们的挖掘建模方法将机器学习和理论模型相结合，以探索病原体传播的宏观生态学机制及其对人类溢出感染的后果。