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Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment
Infectious diseases are changing due to the environment and altered interactions among hosts, reservoirs, vectors, and pathogens. This is particularly true for zoonotic diseases that infect humans, agricultural animals, and wildlife. Within the subset of zoonoses, vector-borne pathogens are changing...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6632117/ https://www.ncbi.nlm.nih.gov/pubmed/31064099 http://dx.doi.org/10.3390/vetsci6020040 |
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author | Bartlow, Andrew W. Manore, Carrie Xu, Chonggang Kaufeld, Kimberly A. Del Valle, Sara Ziemann, Amanda Fairchild, Geoffrey Fair, Jeanne M. |
author_facet | Bartlow, Andrew W. Manore, Carrie Xu, Chonggang Kaufeld, Kimberly A. Del Valle, Sara Ziemann, Amanda Fairchild, Geoffrey Fair, Jeanne M. |
author_sort | Bartlow, Andrew W. |
collection | PubMed |
description | Infectious diseases are changing due to the environment and altered interactions among hosts, reservoirs, vectors, and pathogens. This is particularly true for zoonotic diseases that infect humans, agricultural animals, and wildlife. Within the subset of zoonoses, vector-borne pathogens are changing more rapidly with climate change, and have a complex epidemiology, which may allow them to take advantage of a changing environment. Most mosquito-borne infectious diseases are transmitted by mosquitoes in three genera: Aedes, Anopheles, and Culex, and the expansion of these genera is well documented. There is an urgent need to study vector-borne diseases in response to climate change and to produce a generalizable approach capable of generating risk maps and forecasting outbreaks. Here, we provide a strategy for coupling climate and epidemiological models for zoonotic infectious diseases. We discuss the complexity and challenges of data and model fusion, baseline requirements for data, and animal and human population movement. Disease forecasting needs significant investment to build the infrastructure necessary to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions. These investments can contribute to building a modeling community around the globe to support public health officials so as to reduce disease burden through forecasts with quantified uncertainty. |
format | Online Article Text |
id | pubmed-6632117 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-66321172019-08-19 Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment Bartlow, Andrew W. Manore, Carrie Xu, Chonggang Kaufeld, Kimberly A. Del Valle, Sara Ziemann, Amanda Fairchild, Geoffrey Fair, Jeanne M. Vet Sci Review Infectious diseases are changing due to the environment and altered interactions among hosts, reservoirs, vectors, and pathogens. This is particularly true for zoonotic diseases that infect humans, agricultural animals, and wildlife. Within the subset of zoonoses, vector-borne pathogens are changing more rapidly with climate change, and have a complex epidemiology, which may allow them to take advantage of a changing environment. Most mosquito-borne infectious diseases are transmitted by mosquitoes in three genera: Aedes, Anopheles, and Culex, and the expansion of these genera is well documented. There is an urgent need to study vector-borne diseases in response to climate change and to produce a generalizable approach capable of generating risk maps and forecasting outbreaks. Here, we provide a strategy for coupling climate and epidemiological models for zoonotic infectious diseases. We discuss the complexity and challenges of data and model fusion, baseline requirements for data, and animal and human population movement. Disease forecasting needs significant investment to build the infrastructure necessary to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions. These investments can contribute to building a modeling community around the globe to support public health officials so as to reduce disease burden through forecasts with quantified uncertainty. MDPI 2019-05-06 /pmc/articles/PMC6632117/ /pubmed/31064099 http://dx.doi.org/10.3390/vetsci6020040 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Bartlow, Andrew W. Manore, Carrie Xu, Chonggang Kaufeld, Kimberly A. Del Valle, Sara Ziemann, Amanda Fairchild, Geoffrey Fair, Jeanne M. Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment |
title | Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment |
title_full | Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment |
title_fullStr | Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment |
title_full_unstemmed | Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment |
title_short | Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment |
title_sort | forecasting zoonotic infectious disease response to climate change: mosquito vectors and a changing environment |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6632117/ https://www.ncbi.nlm.nih.gov/pubmed/31064099 http://dx.doi.org/10.3390/vetsci6020040 |
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