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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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 |
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Directory of Open Access Journals: DOAJ Articles |
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English |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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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 |
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PLOS Neglected Tropical Diseases |
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17 |
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e0011047 |
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