Spatially clustered count data provide more efficient search strategies in invasion biology and disease control.
Bayesian parameter estimation
Dirichlet process
epidemiology
finite mixture model
mapping
mosquito
Journal
Ecological applications : a publication of the Ecological Society of America
ISSN: 1051-0761
Titre abrégé: Ecol Appl
Pays: United States
ID NLM: 9889808
Informations de publication
Date de publication:
07 2021
07 2021
Historique:
revised:
23
10
2020
received:
20
08
2020
accepted:
06
12
2020
pubmed:
23
3
2021
medline:
12
8
2021
entrez:
22
3
2021
Statut:
ppublish
Résumé
Geographic profiling, a mathematical model originally developed in criminology, is increasingly being used in ecology and epidemiology. Geographic profiling boasts a wide range of applications, such as finding source populations of invasive species or breeding sites of vectors of infectious disease. The model provides a cost-effective approach for prioritizing search strategies for source locations and does so via simple data in the form of the positions of each observation, such as individual sightings of invasive species or cases of a disease. In doing so, however, classic geographic profiling approaches fail to make the distinction between those areas containing observed absences and those areas where no data were recorded. Absence data are generated via spatial sampling protocols but are often discarded during the inference process. Here we construct a geographic profiling model that resolves these issues by making inferences via count data, analyzing a set of discrete sentinel locations at which the number of encounters has been recorded. Crucially, in our model this number can be zero. We verify the ability of this new model to estimate source locations and other parameters of practical interest via a Bayesian power analysis. We also measure model performance via real-world data in which the model infers breeding locations of mosquitoes in bromeliads in Miami-Dade County, Florida, USA. In both cases, our novel model produces more efficient search strategies by shifting focus from those areas containing observed absences to those with no data, an improvement over existing models that treat these areas equally. Our model makes important improvements upon classic geographic profiling methods, which will significantly enhance real-world efforts to develop conservation management plans and targeted interventions.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
e02329Subventions
Organisme : Medical Research Council
ID : MR/R015600/1
Pays : United Kingdom
Organisme : NCEZID CDC HHS
ID : U01 CK000510
Pays : United States
Organisme : CDC HHS
ID : 1U01CK000510-03
Pays : United States
Informations de copyright
© 2021 The Authors. Ecological Applications published by Wiley Periodicals LLC on behalf of Ecological Society of America.
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