Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1111/ele.13520 |
Integrating data mining and transmission theory in the ecology of infectious diseases | |
Han, Barbara A.1; 39;Regan, Suzanne M.2 | |
2020-05-22 | |
发表期刊 | ECOLOGY LETTERS
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ISSN | 1461-023X |
EISSN | 1461-0248 |
出版年 | 2020 |
卷号 | 23期号:8页码:1178-1188 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
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. |
英文关键词 | Boosted regression disease dynamics disease macroecology pathogen transmission random forest statistical learning zoonosis zoonotic spillover |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000534646500001 |
WOS关键词 | ANIMAL MIGRATION ; MONKEYPOX VIRUS ; HOST ; DYNAMICS ; BIODIVERSITY ; HISTORY ; LIFE ; INFERENCE ; PATHWAYS ; BEHAVIOR |
WOS类目 | Ecology |
WOS研究方向 | Environmental Sciences & Ecology |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/270426 |
专题 | 资源环境科学 |
作者单位 | 1.Cary Inst Ecosyst Studies, Box AB Millbrook, Millbrook, NY 12571 USA; 2.North Carolina A&T State Univ, Dept Math & Stat, 1601 E Market St, Greensboro, NC 27411 USA; 3.Univ Georgia, Odum Sch Ecol, 140 E Green St, Athens, GA 30602 USA; 4.Univ Georgia, Ctr Ecol Infect Dis, 203 DW Brooks Dr, Athens, GA 30602 USA |
推荐引用方式 GB/T 7714 | Han, Barbara A.,39;Regan, Suzanne M.. Integrating data mining and transmission theory in the ecology of infectious diseases[J]. ECOLOGY LETTERS,2020,23(8):1178-1188. |
APA | Han, Barbara A.,&39;Regan, Suzanne M..(2020).Integrating data mining and transmission theory in the ecology of infectious diseases.ECOLOGY LETTERS,23(8),1178-1188. |
MLA | Han, Barbara A.,et al."Integrating data mining and transmission theory in the ecology of infectious diseases".ECOLOGY LETTERS 23.8(2020):1178-1188. |
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