气候变化领域集成服务门户(CCPortal): Integrating data mining and transmission theory in the ecology of infectious diseases

CCPortal

DOI	10.1111/ele.13520
	Integrating data mining and transmission theory in the ecology of infectious diseases
	Han B.A.; O’Regan S.M.; Paul Schmidt J.; Drake J.M.
发表日期	2020
ISSN	1461023X
起始页码	1178
结束页码	1188
卷号	23 期号:8
英文摘要	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. © 2020 The Authors. Ecology Letters published by CNRS and John Wiley & Sons Ltd
关键词	Boosted regression disease dynamics disease macroecology pathogen transmission random forest statistical learning zoonosis zoonotic spillover
英文关键词	data mining; disease transmission; infectious disease; life history; machine learning; pathogenicity; theoretical study; wild population; Rodentia; animal; data mining; epidemic; human; rodent; theoretical model; zoonosis; Animals; Data Mining; Disease Outbreaks; Humans; Models, Theoretical; Rodentia; Zoonoses
语种	英语
来源期刊	Ecology Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/204235
作者单位	Cary Institute of Ecosystem Studies, Box AB Millbrook, NY 12571, United States; Department of Mathematics and Statistics, North Carolina A&T State University, 1601 E. Market St., Greensboro, NC 27411, United States; Odum School of Ecology, University of Georgia, 140 E. Green St., Athens, GA 30602, United States; Center for the Ecology of Infectious Diseases, University of Georgia, 203 D.W. Brooks Drive, Athens, GA 30602, United States
推荐引用方式 GB/T 7714	Han B.A.,O’Regan S.M.,Paul Schmidt J.,et al. Integrating data mining and transmission theory in the ecology of infectious diseases[J],2020,23(8).
APA	Han B.A.,O’Regan S.M.,Paul Schmidt J.,&Drake J.M..(2020).Integrating data mining and transmission theory in the ecology of infectious diseases.Ecology Letters,23(8).
MLA	Han B.A.,et al."Integrating data mining and transmission theory in the ecology of infectious diseases".Ecology Letters 23.8(2020).