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Early warning signal for dengue outbreaks and identification of high risk areas for dengue fever in Colombia using climate and non-climate datasets

BACKGROUND: Dengue has been prevalent in Colombia with high risk of outbreaks in various locations. While the prediction of dengue epidemics will bring significant benefits to the society, accurate forecasts have been a challenge. Given competing health demands in Colombia, it is critical to conside...

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Autores principales: Lee, Jung-Seok, Carabali, Mabel, Lim, Jacqueline K., Herrera, Victor M., Park, Il-Yeon, Villar, Luis, Farlow, Andrew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5504639/
https://www.ncbi.nlm.nih.gov/pubmed/28693483
http://dx.doi.org/10.1186/s12879-017-2577-4
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author Lee, Jung-Seok
Carabali, Mabel
Lim, Jacqueline K.
Herrera, Victor M.
Park, Il-Yeon
Villar, Luis
Farlow, Andrew
author_facet Lee, Jung-Seok
Carabali, Mabel
Lim, Jacqueline K.
Herrera, Victor M.
Park, Il-Yeon
Villar, Luis
Farlow, Andrew
author_sort Lee, Jung-Seok
collection PubMed
description BACKGROUND: Dengue has been prevalent in Colombia with high risk of outbreaks in various locations. While the prediction of dengue epidemics will bring significant benefits to the society, accurate forecasts have been a challenge. Given competing health demands in Colombia, it is critical to consider the effective use of the limited healthcare resources by identifying high risk areas for dengue fever. METHODS: The Climate Risk Factor (CRF) index was constructed based upon temperature, precipitation, and humidity. Considering the conditions necessary for vector survival and transmission behavior, elevation and population density were taken into account. An Early Warning Signal (EWS) model was developed by estimating the elasticity of the climate risk factor function to detect dengue epidemics. The climate risk factor index was further estimated at the smaller geographical unit (5 km by 5 km resolution) to identify populations at high risk. RESULTS: From January 2007 to December 2015, the Early Warning Signal model successfully detected 75% of the total number of outbreaks 1 ~ 5 months ahead of time, 12.5% in the same month, and missed 12.5% of all outbreaks. The climate risk factors showed that populations at high risk are concentrated in the Western part of Colombia where more suitable climate conditions for vector mosquitoes and the high population level were observed compared to the East. CONCLUSIONS: This study concludes that it is possible to detect dengue outbreaks ahead of time and identify populations at high risk for various disease prevention activities based upon observed climate and non-climate information. The study outcomes can be used to minimize potential societal losses by prioritizing limited healthcare services and resources, as well as by conducting vector control activities prior to experiencing epidemics. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12879-017-2577-4) contains supplementary material, which is available to authorized users.
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spelling pubmed-55046392017-07-12 Early warning signal for dengue outbreaks and identification of high risk areas for dengue fever in Colombia using climate and non-climate datasets Lee, Jung-Seok Carabali, Mabel Lim, Jacqueline K. Herrera, Victor M. Park, Il-Yeon Villar, Luis Farlow, Andrew BMC Infect Dis Research Article BACKGROUND: Dengue has been prevalent in Colombia with high risk of outbreaks in various locations. While the prediction of dengue epidemics will bring significant benefits to the society, accurate forecasts have been a challenge. Given competing health demands in Colombia, it is critical to consider the effective use of the limited healthcare resources by identifying high risk areas for dengue fever. METHODS: The Climate Risk Factor (CRF) index was constructed based upon temperature, precipitation, and humidity. Considering the conditions necessary for vector survival and transmission behavior, elevation and population density were taken into account. An Early Warning Signal (EWS) model was developed by estimating the elasticity of the climate risk factor function to detect dengue epidemics. The climate risk factor index was further estimated at the smaller geographical unit (5 km by 5 km resolution) to identify populations at high risk. RESULTS: From January 2007 to December 2015, the Early Warning Signal model successfully detected 75% of the total number of outbreaks 1 ~ 5 months ahead of time, 12.5% in the same month, and missed 12.5% of all outbreaks. The climate risk factors showed that populations at high risk are concentrated in the Western part of Colombia where more suitable climate conditions for vector mosquitoes and the high population level were observed compared to the East. CONCLUSIONS: This study concludes that it is possible to detect dengue outbreaks ahead of time and identify populations at high risk for various disease prevention activities based upon observed climate and non-climate information. The study outcomes can be used to minimize potential societal losses by prioritizing limited healthcare services and resources, as well as by conducting vector control activities prior to experiencing epidemics. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12879-017-2577-4) contains supplementary material, which is available to authorized users. BioMed Central 2017-07-10 /pmc/articles/PMC5504639/ /pubmed/28693483 http://dx.doi.org/10.1186/s12879-017-2577-4 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Lee, Jung-Seok
Carabali, Mabel
Lim, Jacqueline K.
Herrera, Victor M.
Park, Il-Yeon
Villar, Luis
Farlow, Andrew
Early warning signal for dengue outbreaks and identification of high risk areas for dengue fever in Colombia using climate and non-climate datasets
title Early warning signal for dengue outbreaks and identification of high risk areas for dengue fever in Colombia using climate and non-climate datasets
title_full Early warning signal for dengue outbreaks and identification of high risk areas for dengue fever in Colombia using climate and non-climate datasets
title_fullStr Early warning signal for dengue outbreaks and identification of high risk areas for dengue fever in Colombia using climate and non-climate datasets
title_full_unstemmed Early warning signal for dengue outbreaks and identification of high risk areas for dengue fever in Colombia using climate and non-climate datasets
title_short Early warning signal for dengue outbreaks and identification of high risk areas for dengue fever in Colombia using climate and non-climate datasets
title_sort early warning signal for dengue outbreaks and identification of high risk areas for dengue fever in colombia using climate and non-climate datasets
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5504639/
https://www.ncbi.nlm.nih.gov/pubmed/28693483
http://dx.doi.org/10.1186/s12879-017-2577-4
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