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Advances and Limitations of Disease Biogeography Using Ecological Niche Modeling
Mapping disease transmission risk is crucial in public and animal health for evidence based decision-making. Ecology and epidemiology are highly related disciplines that may contribute to improvements in mapping disease, which can be used to answer health related questions. Ecological niche modeling...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4974947/ https://www.ncbi.nlm.nih.gov/pubmed/27547199 http://dx.doi.org/10.3389/fmicb.2016.01174 |
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author | Escobar, Luis E. Craft, Meggan E. |
author_facet | Escobar, Luis E. Craft, Meggan E. |
author_sort | Escobar, Luis E. |
collection | PubMed |
description | Mapping disease transmission risk is crucial in public and animal health for evidence based decision-making. Ecology and epidemiology are highly related disciplines that may contribute to improvements in mapping disease, which can be used to answer health related questions. Ecological niche modeling is increasingly used for understanding the biogeography of diseases in plants, animals, and humans. However, epidemiological applications of niche modeling approaches for disease mapping can fail to generate robust study designs, producing incomplete or incorrect inferences. This manuscript is an overview of the history and conceptual bases behind ecological niche modeling, specifically as applied to epidemiology and public health; it does not pretend to be an exhaustive and detailed description of ecological niche modeling literature and methods. Instead, this review includes selected state-of-the-science approaches and tools, providing a short guide to designing studies incorporating information on the type and quality of the input data (i.e., occurrences and environmental variables), identification and justification of the extent of the study area, and encourages users to explore and test diverse algorithms for more informed conclusions. We provide a friendly introduction to the field of disease biogeography presenting an updated guide for researchers looking to use ecological niche modeling for disease mapping. We anticipate that ecological niche modeling will soon be a critical tool for epidemiologists aiming to map disease transmission risk, forecast disease distribution under climate change scenarios, and identify landscape factors triggering outbreaks. |
format | Online Article Text |
id | pubmed-4974947 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-49749472016-08-19 Advances and Limitations of Disease Biogeography Using Ecological Niche Modeling Escobar, Luis E. Craft, Meggan E. Front Microbiol Microbiology Mapping disease transmission risk is crucial in public and animal health for evidence based decision-making. Ecology and epidemiology are highly related disciplines that may contribute to improvements in mapping disease, which can be used to answer health related questions. Ecological niche modeling is increasingly used for understanding the biogeography of diseases in plants, animals, and humans. However, epidemiological applications of niche modeling approaches for disease mapping can fail to generate robust study designs, producing incomplete or incorrect inferences. This manuscript is an overview of the history and conceptual bases behind ecological niche modeling, specifically as applied to epidemiology and public health; it does not pretend to be an exhaustive and detailed description of ecological niche modeling literature and methods. Instead, this review includes selected state-of-the-science approaches and tools, providing a short guide to designing studies incorporating information on the type and quality of the input data (i.e., occurrences and environmental variables), identification and justification of the extent of the study area, and encourages users to explore and test diverse algorithms for more informed conclusions. We provide a friendly introduction to the field of disease biogeography presenting an updated guide for researchers looking to use ecological niche modeling for disease mapping. We anticipate that ecological niche modeling will soon be a critical tool for epidemiologists aiming to map disease transmission risk, forecast disease distribution under climate change scenarios, and identify landscape factors triggering outbreaks. Frontiers Media S.A. 2016-08-05 /pmc/articles/PMC4974947/ /pubmed/27547199 http://dx.doi.org/10.3389/fmicb.2016.01174 Text en Copyright © 2016 Escobar and Craft. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Microbiology Escobar, Luis E. Craft, Meggan E. Advances and Limitations of Disease Biogeography Using Ecological Niche Modeling |
title | Advances and Limitations of Disease Biogeography Using Ecological Niche Modeling |
title_full | Advances and Limitations of Disease Biogeography Using Ecological Niche Modeling |
title_fullStr | Advances and Limitations of Disease Biogeography Using Ecological Niche Modeling |
title_full_unstemmed | Advances and Limitations of Disease Biogeography Using Ecological Niche Modeling |
title_short | Advances and Limitations of Disease Biogeography Using Ecological Niche Modeling |
title_sort | advances and limitations of disease biogeography using ecological niche modeling |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4974947/ https://www.ncbi.nlm.nih.gov/pubmed/27547199 http://dx.doi.org/10.3389/fmicb.2016.01174 |
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