Global patterns, predictors, and their dynamical consequences in zoonotic diseases of mammals

在世界各地，新型传染病的爆发频率正在增加。在人类中出现的大多数传染病都是由动物宿主引起的。现在有数以千计的科学研究调查动物(人)传播的疾病威胁，但到目前为止，几乎没有方法提前预测新的传染病威胁最有可能出现在哪里。在这个项目中，研究人员试图了解为什么一些动物比其他动物拥有更多的人类病原体；哪些病原体最有可能对人类构成不可预见的疾病威胁；以及未来哪些环境可能会发生大规模的疾病爆发事件。为了做到这一点，研究人员将分析关于哺乳动物、它们的病原体和环境条件的大型数据库，以描述哪些特征最能预测人类的动物传播传染病。研究人员还将使用数学模型检验这些预测的理论后果，以了解疾病模式最有可能随着时间的推移而发生变化。这个项目开发的预测和方法进步不是在新的疾病出现后做出反应，而是通过告知我们如何预防未来的疾病威胁而立即造福社会。通过将机器学习和数学建模与越来越多的关于野生动物和人类中的传染病的数据相结合，这个项目将：(1)识别最好地预测人畜共患感染模式的宿主和病原体的内在特征；(2)识别可能是未来人畜共患病的未知来源的特定哺乳动物物种和病原体；(3)调查区分人类感染和人畜共患病的宿主、病原体和环境协变量，并结合这些因素来预测暴发规模；(4)将数学模型(理论)与经验观察的模式相匹配，以得出潜在的潜在过程；(5)研究通过经验观察的宿主、病原体和变化的环境的特征影响传播过程和疾病动态的典型生态流行病学机制。

Around the world, outbreaks of novel infectious diseases are increasing in frequency. The majority of infectious diseases emerging in humans originate from animal hosts. There are now thousands of scientific studies investigating animal-borne (zoonotic) disease threats, but to date there are few methods to predict in advance where new infectious disease threats are most likely to arise. In this project, researchers seek to understand why some animals harbor many more human pathogens compared to others; what kinds of pathogens are most likely to pose unforeseen disease threats to humans; and what kinds of environments may see large disease outbreak events in the future. To do this, researchers will analyze large databases on mammals, their pathogens, and environmental conditions to describe what features best predict animal-borne infectious diseases in humans. Researchers will also examine the theoretical consequences of these predictions using mathematical models, to understand how disease patterns are most likely to change over time. Instead of reacting to new diseases after they have emerged, the predictions and methodological advances developed in this project will immediately benefit society by informing us how future disease threats may be preempted.By combining machine learning and mathematical modeling with growing volumes of data on infectious diseases in wildlife and humans, this project will: (1) Identify intrinsic traits of hosts and pathogens and general environmental features that best predict patterns of zoonotic infection; (2) Identify particular mammal species and pathogenic agents likely to be undiscovered sources of future zoonoses; (3) Investigate the host, pathogen, and environmental covariates that distinguish infection from zoonotic disease in humans, and that combine to predict outbreak size; (4) Fit mathematical models (theory) to empirically observed patterns to derive potential underlying processes; (5) Investigate paradigmatic eco-epidemiological mechanisms through which empirically observed traits of hosts, pathogens, and changing environments influence transmission processes and disease dynamics.