Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques.

Dengue fever is a vector-borne disease affecting millions yearly, mostly in tropical and subtropical countries. Driven mainly by social and environmental factors, dengue incidence and geographical expansion have increased in recent decades. Therefore, understanding how climate variables drive dengue...

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Published in:PLOS Neglected Tropical Diseases
Main Authors: Luis A Barboza, Shu-Wei Chou-Chen, Paola Vásquez, Yury E García, Juan G Calvo, Hugo G Hidalgo, Fabio Sanchez
Format: Article in Journal/Newspaper
Language:English
Published: Public Library of Science (PLoS) 2023
Subjects:
Online Access:https://doi.org/10.1371/journal.pntd.0011047
https://doaj.org/article/82bd4f342f054df48e89717879ee8d41
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spelling ftdoajarticles:oai:doaj.org/article:82bd4f342f054df48e89717879ee8d41 2023-05-15T15:04:16+02:00 Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques. Luis A Barboza Shu-Wei Chou-Chen Paola Vásquez Yury E García Juan G Calvo Hugo G Hidalgo Fabio Sanchez 2023-01-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0011047 https://doaj.org/article/82bd4f342f054df48e89717879ee8d41 EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pntd.0011047 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0011047 https://doaj.org/article/82bd4f342f054df48e89717879ee8d41 PLoS Neglected Tropical Diseases, Vol 17, Iss 1, p e0011047 (2023) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2023 ftdoajarticles https://doi.org/10.1371/journal.pntd.0011047 2023-03-05T01:33:15Z Dengue fever is a vector-borne disease affecting millions yearly, mostly in tropical and subtropical countries. Driven mainly by social and environmental factors, dengue incidence and geographical expansion have increased in recent decades. Therefore, understanding how climate variables drive dengue outbreaks is challenging and a problem of interest for decision-makers that could aid in improving surveillance and resource allocation. Here, we explore the effect of climate variables on relative dengue risk in 32 cantons of interest for public health authorities in Costa Rica. Relative dengue risk is forecast using a Generalized Additive Model for location, scale, and shape and a Random Forest approach. Models use a training period from 2000 to 2020 and predicted climatic variables obtained with a vector auto-regressive model. Results show reliable projections, and climate variables predictions allow for a prospective instead of a retrospective study. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 17 1 e0011047
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic Arctic medicine. Tropical medicine
RC955-962
Public aspects of medicine
RA1-1270
spellingShingle Arctic medicine. Tropical medicine
RC955-962
Public aspects of medicine
RA1-1270
Luis A Barboza
Shu-Wei Chou-Chen
Paola Vásquez
Yury E García
Juan G Calvo
Hugo G Hidalgo
Fabio Sanchez
Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques.
topic_facet Arctic medicine. Tropical medicine
RC955-962
Public aspects of medicine
RA1-1270
description Dengue fever is a vector-borne disease affecting millions yearly, mostly in tropical and subtropical countries. Driven mainly by social and environmental factors, dengue incidence and geographical expansion have increased in recent decades. Therefore, understanding how climate variables drive dengue outbreaks is challenging and a problem of interest for decision-makers that could aid in improving surveillance and resource allocation. Here, we explore the effect of climate variables on relative dengue risk in 32 cantons of interest for public health authorities in Costa Rica. Relative dengue risk is forecast using a Generalized Additive Model for location, scale, and shape and a Random Forest approach. Models use a training period from 2000 to 2020 and predicted climatic variables obtained with a vector auto-regressive model. Results show reliable projections, and climate variables predictions allow for a prospective instead of a retrospective study.
format Article in Journal/Newspaper
author Luis A Barboza
Shu-Wei Chou-Chen
Paola Vásquez
Yury E García
Juan G Calvo
Hugo G Hidalgo
Fabio Sanchez
author_facet Luis A Barboza
Shu-Wei Chou-Chen
Paola Vásquez
Yury E García
Juan G Calvo
Hugo G Hidalgo
Fabio Sanchez
author_sort Luis A Barboza
title Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques.
title_short Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques.
title_full Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques.
title_fullStr Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques.
title_full_unstemmed Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques.
title_sort assessing dengue fever risk in costa rica by using climate variables and machine learning techniques.
publisher Public Library of Science (PLoS)
publishDate 2023
url https://doi.org/10.1371/journal.pntd.0011047
https://doaj.org/article/82bd4f342f054df48e89717879ee8d41
geographic Arctic
geographic_facet Arctic
genre Arctic
genre_facet Arctic
op_source PLoS Neglected Tropical Diseases, Vol 17, Iss 1, p e0011047 (2023)
op_relation https://doi.org/10.1371/journal.pntd.0011047
https://doaj.org/toc/1935-2727
https://doaj.org/toc/1935-2735
1935-2727
1935-2735
doi:10.1371/journal.pntd.0011047
https://doaj.org/article/82bd4f342f054df48e89717879ee8d41
op_doi https://doi.org/10.1371/journal.pntd.0011047
container_title PLOS Neglected Tropical Diseases
container_volume 17
container_issue 1
container_start_page e0011047
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