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

哺乳动物人畜共患疾病的全球模式、预测因素及其动态后果

基本信息

批准号：
1717282
负责人：
Barbara Han
金额：
$ 200万
依托单位：
Cary Institute of Ecosystem Studies, Inc.
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717282&HistoricalAwards=false
关键词：
Global patterns predictors their dynamical

项目摘要

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.

在世界范围内，新型传染病的爆发频率正在增加。人类出现的大多数传染病都源自动物宿主。现在有数千项科学研究调查动物传播（人畜共患）疾病的威胁，但迄今为止，几乎没有方法可以提前预测新的传染病威胁最有可能出现的地方。在这个项目中，研究人员试图了解为什么某些动物比其他动物携带更多的人类病原体。哪种病原体最有可能对人类造成不可预见的疾病威胁；以及未来什么样的环境可能会发生大规模的疾病暴发事件。为此，研究人员将分析有关哺乳动物、其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动物传播的传染病。研究人员还将使用数学模型检查这些预测的理论后果，以了解疾病模式最有可能如何随着时间的推移而变化。该项目中开发的预测和方法进步将立即造福社会，告诉我们如何预防未来的疾病威胁，而不是在新疾病出现后对其做出反应。通过将机器学习和数学模型与不断增长的传染病数据相结合在野生动物和人类中，该项目将：（1）确定宿主和病原体的内在特征以及最能预测人畜共患感染模式的一般环境特征； (2) 确定可能是未来人畜共患病的未被发现的来源的特定哺乳动物物种和病原体； (3) 研究区分人类感染和人畜共患疾病的宿主、病原体和环境协变量，并结合起来预测爆发规模； (4) 将数学模型（理论）与经验观察到的模式相拟合，以得出潜在的潜在过程； (5) 研究典型的生态流行病学机制，通过该机制，经验观察到的宿主、病原体和不断变化的环境特征影响传播过程和疾病动态。