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Spatially clustered count data provide more efficient search strategies in invasion biology and disease control.

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第一作者： Michael C A,Stevens

第一单位： School of Biological and Chemical Sciences, Queen Mary University of London, London, E1 4NS, UK.;Centre for Advanced Spatial Analysis, University College London, London, W1T 4TJ, UK.

作者： Michael C A,Stevens ^[1] ; Sally C,Faulkner ^[2] ; André B B,Wilke ^[3] ; John C,Beier ^[3] ; Chalmers,Vasquez ^[4] ; William D,Petrie ^[4] ; Hannah,Fry ^[5] ; Richard A,Nichols ^[2] ; Robert,Verity ^[6] ; Steven C,Le Comber ^[2]

作者单位： School of Biological and Chemical Sciences, Queen Mary University of London, London, E1 4NS, UK.;Centre for Advanced Spatial Analysis, University College London, London, W1T 4TJ, UK. ^[1] School of Biological and Chemical Sciences, Queen Mary University of London, London, E1 4NS, UK. ^[2] Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, Florida, 33136, USA. ^[3] Miami-Dade County Mosquito Control Division, Miami, Florida, 33178, USA. ^[4] Centre for Advanced Spatial Analysis, University College London, London, W1T 4TJ, UK. ^[5] Department of Infectious Disease Epidemiology, MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, W2 1PG, UK. ^[6]

关键词 Bayesian parameter estimation Dirichlet process epidemiology finite mixture model mapping mosquito

医学主题词动物(Animals);贝叶斯定理(Bayes Theorem);蚊科(Culicidae);生态学(Ecology);佛罗里达(Florida)

DOI 10.1002/eap.2329

PMID 33752255

发布时间 2025-05-30

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