DOI: 10.1111/ele.13520
论文题名: Integrating data mining and transmission theory in the ecology of infectious diseases
作者: Han B.A. ; O’Regan S.M. ; Paul Schmidt J. ; Drake J.M.
刊名: Ecology Letters
ISSN: 1461023X
出版年: 2020
卷: 23, 期: 8 起始页码: 1178
结束页码: 1188
语种: 英语
中文关键词: 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
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 Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/166636
Appears in Collections: 气候变化与战略
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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
Recommended Citation:
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]. Ecology Letters,2020-01-01,23(8)