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Phylogeography as a Proxy for Population Connectivity for Spatial Modeling of Foot-and-Mouth Disease Outbreaks in Vietnam

Bayesian space–time regression models are helpful tools to describe and predict the distribution of infectious disease outbreaks and to delineate high-risk areas for disease control. In these models, structured and unstructured spatial and temporal effects account for various forms of non-independen...

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Autores principales: Gunasekara, Umanga, Bertram, Miranda R., Van Long, Nguyen, Minh, Phan Quang, Chuong, Vo Dinh, Perez, Andres, Arzt, Jonathan, VanderWaal, Kimberly
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9958845/
https://www.ncbi.nlm.nih.gov/pubmed/36851602
http://dx.doi.org/10.3390/v15020388
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author Gunasekara, Umanga
Bertram, Miranda R.
Van Long, Nguyen
Minh, Phan Quang
Chuong, Vo Dinh
Perez, Andres
Arzt, Jonathan
VanderWaal, Kimberly
author_facet Gunasekara, Umanga
Bertram, Miranda R.
Van Long, Nguyen
Minh, Phan Quang
Chuong, Vo Dinh
Perez, Andres
Arzt, Jonathan
VanderWaal, Kimberly
author_sort Gunasekara, Umanga
collection PubMed
description Bayesian space–time regression models are helpful tools to describe and predict the distribution of infectious disease outbreaks and to delineate high-risk areas for disease control. In these models, structured and unstructured spatial and temporal effects account for various forms of non-independence amongst case counts across spatial units. Structured spatial effects capture correlations in case counts amongst neighboring provinces arising from shared risk factors or population connectivity. For highly mobile populations, spatial adjacency is an imperfect measure of connectivity due to long-distance movement, but we often lack data on host movements. Phylogeographic models inferring routes of viral dissemination across a region could serve as a proxy for patterns of population connectivity. The objective of this study was to investigate whether the effects of population connectivity in space–time regressions of case counts were better captured by spatial adjacency or by inferences from phylogeographic analyses. To compare these two approaches, we used foot-and-mouth disease virus (FMDV) outbreak data from across Vietnam as an example. We identified that accounting for virus movement through phylogeographic analysis serves as a better proxy for population connectivity than spatial adjacency in spatial–temporal risk models. This approach may contribute to design surveillance activities in countries lacking movement data.
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spelling pubmed-99588452023-02-26 Phylogeography as a Proxy for Population Connectivity for Spatial Modeling of Foot-and-Mouth Disease Outbreaks in Vietnam Gunasekara, Umanga Bertram, Miranda R. Van Long, Nguyen Minh, Phan Quang Chuong, Vo Dinh Perez, Andres Arzt, Jonathan VanderWaal, Kimberly Viruses Article Bayesian space–time regression models are helpful tools to describe and predict the distribution of infectious disease outbreaks and to delineate high-risk areas for disease control. In these models, structured and unstructured spatial and temporal effects account for various forms of non-independence amongst case counts across spatial units. Structured spatial effects capture correlations in case counts amongst neighboring provinces arising from shared risk factors or population connectivity. For highly mobile populations, spatial adjacency is an imperfect measure of connectivity due to long-distance movement, but we often lack data on host movements. Phylogeographic models inferring routes of viral dissemination across a region could serve as a proxy for patterns of population connectivity. The objective of this study was to investigate whether the effects of population connectivity in space–time regressions of case counts were better captured by spatial adjacency or by inferences from phylogeographic analyses. To compare these two approaches, we used foot-and-mouth disease virus (FMDV) outbreak data from across Vietnam as an example. We identified that accounting for virus movement through phylogeographic analysis serves as a better proxy for population connectivity than spatial adjacency in spatial–temporal risk models. This approach may contribute to design surveillance activities in countries lacking movement data. MDPI 2023-01-29 /pmc/articles/PMC9958845/ /pubmed/36851602 http://dx.doi.org/10.3390/v15020388 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gunasekara, Umanga
Bertram, Miranda R.
Van Long, Nguyen
Minh, Phan Quang
Chuong, Vo Dinh
Perez, Andres
Arzt, Jonathan
VanderWaal, Kimberly
Phylogeography as a Proxy for Population Connectivity for Spatial Modeling of Foot-and-Mouth Disease Outbreaks in Vietnam
title Phylogeography as a Proxy for Population Connectivity for Spatial Modeling of Foot-and-Mouth Disease Outbreaks in Vietnam
title_full Phylogeography as a Proxy for Population Connectivity for Spatial Modeling of Foot-and-Mouth Disease Outbreaks in Vietnam
title_fullStr Phylogeography as a Proxy for Population Connectivity for Spatial Modeling of Foot-and-Mouth Disease Outbreaks in Vietnam
title_full_unstemmed Phylogeography as a Proxy for Population Connectivity for Spatial Modeling of Foot-and-Mouth Disease Outbreaks in Vietnam
title_short Phylogeography as a Proxy for Population Connectivity for Spatial Modeling of Foot-and-Mouth Disease Outbreaks in Vietnam
title_sort phylogeography as a proxy for population connectivity for spatial modeling of foot-and-mouth disease outbreaks in vietnam
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9958845/
https://www.ncbi.nlm.nih.gov/pubmed/36851602
http://dx.doi.org/10.3390/v15020388
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