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Applying fuzzy logic to assess the biogeographical risk of dengue in South America

BACKGROUND: Over the last decade, reports about dengue cases have increase worldwide, which is particularly worrisome in South America due to the historic record of dengue outbreaks from the seventeenth century until the first half of the twentieth century. Dengue is a viral disease that involves in...

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Autores principales: Romero, David, Olivero, Jesús, Real, Raimundo, Guerrero, José Carlos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6727500/
https://www.ncbi.nlm.nih.gov/pubmed/31488198
http://dx.doi.org/10.1186/s13071-019-3691-5
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author Romero, David
Olivero, Jesús
Real, Raimundo
Guerrero, José Carlos
author_facet Romero, David
Olivero, Jesús
Real, Raimundo
Guerrero, José Carlos
author_sort Romero, David
collection PubMed
description BACKGROUND: Over the last decade, reports about dengue cases have increase worldwide, which is particularly worrisome in South America due to the historic record of dengue outbreaks from the seventeenth century until the first half of the twentieth century. Dengue is a viral disease that involves insect vectors, namely Aedes aegypti and Ae. albopictus, which implies that, to prevent and combat outbreaks, it is necessary to understand the set of ecological and biogeographical factors affecting both the vector species and the virus. METHODS: We contribute with a methodology based on fuzzy logic that is helpful to disentangle the main factors that determine favorable environmental conditions for vectors and diseases. Using favorability functions as fuzzy logic modelling technique and the fuzzy intersection, union and inclusion as fuzzy operators, we were able to specify the territories at biogeographical risk of dengue outbreaks in South America. RESULTS: Our results indicate that the distribution of Ae. aegypti mostly encompasses the biogeographical framework of dengue in South America, which suggests that this species is the principal vector responsible for the geographical extent of dengue cases in the continent. Nevertheless, the intersection between the favorability for dengue cases and the union of the favorability for any of the vector species provided a comprehensive map of the biogeographical risk for dengue. CONCLUSIONS: Fuzzy logic is an appropriate conceptual and operational tool to tackle the nuances of the vector-illness biogeographical interaction. The application of fuzzy logic may be useful in decision-making by the public health authorities to prevent, control and mitigate vector-borne diseases.
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spelling pubmed-67275002019-09-12 Applying fuzzy logic to assess the biogeographical risk of dengue in South America Romero, David Olivero, Jesús Real, Raimundo Guerrero, José Carlos Parasit Vectors Research BACKGROUND: Over the last decade, reports about dengue cases have increase worldwide, which is particularly worrisome in South America due to the historic record of dengue outbreaks from the seventeenth century until the first half of the twentieth century. Dengue is a viral disease that involves insect vectors, namely Aedes aegypti and Ae. albopictus, which implies that, to prevent and combat outbreaks, it is necessary to understand the set of ecological and biogeographical factors affecting both the vector species and the virus. METHODS: We contribute with a methodology based on fuzzy logic that is helpful to disentangle the main factors that determine favorable environmental conditions for vectors and diseases. Using favorability functions as fuzzy logic modelling technique and the fuzzy intersection, union and inclusion as fuzzy operators, we were able to specify the territories at biogeographical risk of dengue outbreaks in South America. RESULTS: Our results indicate that the distribution of Ae. aegypti mostly encompasses the biogeographical framework of dengue in South America, which suggests that this species is the principal vector responsible for the geographical extent of dengue cases in the continent. Nevertheless, the intersection between the favorability for dengue cases and the union of the favorability for any of the vector species provided a comprehensive map of the biogeographical risk for dengue. CONCLUSIONS: Fuzzy logic is an appropriate conceptual and operational tool to tackle the nuances of the vector-illness biogeographical interaction. The application of fuzzy logic may be useful in decision-making by the public health authorities to prevent, control and mitigate vector-borne diseases. BioMed Central 2019-09-05 /pmc/articles/PMC6727500/ /pubmed/31488198 http://dx.doi.org/10.1186/s13071-019-3691-5 Text en © The Author(s) 2019 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
Romero, David
Olivero, Jesús
Real, Raimundo
Guerrero, José Carlos
Applying fuzzy logic to assess the biogeographical risk of dengue in South America
title Applying fuzzy logic to assess the biogeographical risk of dengue in South America
title_full Applying fuzzy logic to assess the biogeographical risk of dengue in South America
title_fullStr Applying fuzzy logic to assess the biogeographical risk of dengue in South America
title_full_unstemmed Applying fuzzy logic to assess the biogeographical risk of dengue in South America
title_short Applying fuzzy logic to assess the biogeographical risk of dengue in South America
title_sort applying fuzzy logic to assess the biogeographical risk of dengue in south america
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6727500/
https://www.ncbi.nlm.nih.gov/pubmed/31488198
http://dx.doi.org/10.1186/s13071-019-3691-5
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