MACHINE LEARNING TO FORECAST ZOONOTIC DISEASE EMERGENCE

描述（由申请人提供）：由于超过70%的新发传染病是由动物传播给人类的寄生虫或病原体引起的（导致“人畜共患病”感染），公共卫生的一个基本问题是确定导致人类人畜共患病的驱动因素。感染因子的跨物种传播取决于宿主的许多特征、其感染因子以及定义疾病外部环境的环境因素。以前确定跨物种传播预测因子的研究受到了单一传染病（例如，狂犬病、莱姆病），部分原因是由于在多变量、高维数据中常见的许多复杂的相互作用、自相关和采样偏差，排除了跨越众多宿主物种和感染因子的大规模分析。所提出的研究通过机器学习算法的创新应用来面对这些计算限制。具体而言，分析将解决全球卫生中三个突出和相互关联的问题：（1）哪些特征标志着哺乳动物宿主物种易成为人畜共患病的宿主？(2)传染源的哪些特征预示着它们有可能引起人畜共患传染病？(3)全球人畜共患病暴发的最重要的环境和人为预测因子是什么？分析将应用一系列监督，无监督和半监督机器学习算法到新的，全球规模的数据库，包含生物，生态，环境和人为数据的三组哺乳动物宿主（灵长类动物，食肉动物和有蹄类动物）和他们的人畜共患传染病病原体。本研究的长期目标是通过突出哺乳动物宿主的关键特征，传染性病原体，以及描述近年来人畜共患病爆发的环境和人为因素，以经验为基础制定关于人畜共患病的“经验法则”。最终，本文提出的研究将为预测未来人畜共患病将出现的地理位置、传染源和动物宿主提供基础。 公共卫生相关性：该项目旨在通过将机器学习算法创新应用于新发布的数据，研究驱动人畜共患病爆发和从野生哺乳动物到人类的跨物种传播的因素，这些数据描述了数百种传染性病原体，其哺乳动物宿主物种，人类种群和全球环境。最终，该项目旨在预测未来疾病将出现的地点和物种，因此与改善人类健康直接相关。

DESCRIPTION (provided by applicant): As over 70% of emerging infectious diseases are caused by parasites or pathogens transmitted from animals to humans (leading to 'zoonotic' infections), a fundamental issues for public health is identifying the drivers leading to zoonotic diseases in humans. Cross-species transmission of infectious agents depends on numerous traits of hosts, their infectious agents, and environmental factors defining the external context of disease. Previous studies identifying predictors of cross-species transmission have been limited by a focus on single infectious diseases (e.g., rabies, Lyme disease) at restricted spatial scales, in part because large-scale analyses spanning numerous host species and infectious agents are precluded by the many complex interactions, autocorrelations, and sampling biases common in multivariate, high-dimensional data. The proposed research confronts these computational limitations through the innovative application of machine learning algorithms. Specifically, analyses will address three outstanding and interrelated questions in global health: (1) What characteristics signal a predisposition of mammalian host species to be reservoirs of zoonotic disease?; (2) What traits among infectious agents predict their potential to cause zoonotic infection?; (3) What are the most important environmental and anthropogenic predictors of zoonotic outbreaks globally? Analyses will apply a series of supervised, unsupervised and semi-supervised machine learning algorithms to new, global-scale databases containing biological, ecological, environmental, and anthropogenic data for three groups of mammalian hosts (primates, carnivores, and ungulates) and their zoonotic infectious agents. A long-term goal of this research is to empirically develop "rules of thumb" about zoonotic diseases by highlighting the key traits of mammalian hosts, infectious agents, and the environmental and human factors describing zoonotic outbreaks in recent history. Ultimately, research proposed herein will provide a basis for predicting the geographic locations, infectious agents, and animal reservoirs from which future zoonoses will emerge. PUBLIC HEALTH RELEVANCE: This project proposes to investigate the factors driving zoonotic disease outbreaks and cross-species transmission from wild mammals into humans through the innovative application of machine learning algorithms to newly published data describing hundreds of infectious agents, their mammalian host species, human populations, and the global environment. Ultimately, this project aims to predict the locations and species from which future diseases will emerge, and is therefore directly relevant for the improvement of human health.