MACHINE LEARNING TO FORECAST ZOONOTIC DISEASE EMERGENCE

机器学习预测人畜共患疾病的出现

• 批准号: 8061158
• 负责人: Barbara A. Han
• 金额: $ 5.13万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-11 至 2014-07-10
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8061158
• 关键词: Address; Algorithms; Animals; Area; Automobile Driving; Award; Biological; Characteristics; Climate; Communicable Diseases; Complex; Computers; Computing Methodologies; Data; Data Set; Databases; Decision Making; Disease; Disease Outbreaks; Ecology; Emerging Communicable Diseases; Environment; Environmental Risk Factor; Evolution; Future; Geographic Locations; Goals; Health; Human; Infection; Infectious Agent; Location; Lyme Disease; Machine Learning; Mammals; Methods; Nature; Output; Parasites; Parasitic Diseases; Pattern; Pattern Recognition; Population; Precipitation; Predisposition; Primates; Public Health; Publications; Publishing; Rabies; Recording of previous events; Research; Research Personnel; Resources; Sampling Biases; Series; Signal Transduction; Source; Training; Ungulate; Vertebrates; West Nile virus; Zoonoses; Zoonotic Infection; anthropogenesis; base; career development; comparative; computer science; disease transmission; global environment; global health; innovation; land use; pathogen; trait; transmission process

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.

描述(申请人提供)：由于超过70%的新发传染病是由寄生虫或从动物传播给人类的病原体(导致人畜共患传染病)引起的，公共卫生的一个基本问题是确定导致人畜共患疾病的驱动因素。感染性病原体的跨物种传播取决于宿主的许多特征、其感染性病原体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局限于在有限的空间尺度上对单一传染病(如狂犬病、莱姆病)的关注，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、自相关性和抽样偏差，无法进行跨越众多宿主物种和感染源的大规模分析。拟议的研究通过创新应用机器学习算法来面对这些计算限制。具体地说，分析将解决全球卫生中三个相互关联的悬而未决的问题：(1)哺乳动物宿主物种容易成为人畜共患病宿主的特征；(2)感染病原体中的哪些特征预测其可能导致人畜共患病？(3)全球人畜共患疾病暴发的最重要的环境和人为预测因素是什么？分析将把一系列监督、非监督和半监督机器学习算法应用到新的全球规模的数据库中，这些数据库包含三组哺乳动物宿主(灵长类、食肉动物和有蹄类)及其人畜传染病病原体的生物、生态、环境和人类数据。这项研究的一个长期目标是通过强调哺乳动物宿主的关键特征、传染病病原体以及描述近代史上人畜共患病暴发的环境和人类因素，对人畜共患病制定经验性的“经验法则”。最终，本文提出的研究将为预测未来将出现人畜共患病的地理位置、传染病病原体和动物宿主提供基础。 公共卫生相关性：该项目建议通过创新地应用机器学习算法来研究人畜共患病暴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病病原体、它们的哺乳动物宿主物种、人类种群和全球环境。归根结底，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。