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A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles

Transmission of dengue fever depends on a complex interplay of human, climate and mosquito dynamics, which often change in time and space. It is well known that its disease dynamics are highly influenced by multiple factors including population susceptibility to infection as well as by microclimates...

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Detalles Bibliográficos
Autores principales: McGough, Sarah F., Clemente, Leonardo, Kutz, J. Nathan, Santillana, Mauricio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8205538/
https://www.ncbi.nlm.nih.gov/pubmed/34129785
http://dx.doi.org/10.1098/rsif.2020.1006
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author McGough, Sarah F.
Clemente, Leonardo
Kutz, J. Nathan
Santillana, Mauricio
author_facet McGough, Sarah F.
Clemente, Leonardo
Kutz, J. Nathan
Santillana, Mauricio
author_sort McGough, Sarah F.
collection PubMed
description Transmission of dengue fever depends on a complex interplay of human, climate and mosquito dynamics, which often change in time and space. It is well known that its disease dynamics are highly influenced by multiple factors including population susceptibility to infection as well as by microclimates: small-area climatic conditions which create environments favourable for the breeding and survival of mosquitoes. Here, we present a novel machine learning dengue forecasting approach, which, dynamically in time and space, identifies local patterns in weather and population susceptibility to make epidemic predictions at the city level in Brazil, months ahead of the occurrence of disease outbreaks. Weather-based predictions are improved when information on population susceptibility is incorporated, indicating that immunity is an important predictor neglected by most dengue forecast models. Given the generalizability of our methodology to any location or input data, it may prove valuable for public health decision-making aimed at mitigating the effects of seasonal dengue outbreaks in locations globally.
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spelling pubmed-82055382021-06-17 A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles McGough, Sarah F. Clemente, Leonardo Kutz, J. Nathan Santillana, Mauricio J R Soc Interface Life Sciences–Physics interface Transmission of dengue fever depends on a complex interplay of human, climate and mosquito dynamics, which often change in time and space. It is well known that its disease dynamics are highly influenced by multiple factors including population susceptibility to infection as well as by microclimates: small-area climatic conditions which create environments favourable for the breeding and survival of mosquitoes. Here, we present a novel machine learning dengue forecasting approach, which, dynamically in time and space, identifies local patterns in weather and population susceptibility to make epidemic predictions at the city level in Brazil, months ahead of the occurrence of disease outbreaks. Weather-based predictions are improved when information on population susceptibility is incorporated, indicating that immunity is an important predictor neglected by most dengue forecast models. Given the generalizability of our methodology to any location or input data, it may prove valuable for public health decision-making aimed at mitigating the effects of seasonal dengue outbreaks in locations globally. The Royal Society 2021-06-16 /pmc/articles/PMC8205538/ /pubmed/34129785 http://dx.doi.org/10.1098/rsif.2020.1006 Text en © 2021 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Life Sciences–Physics interface
McGough, Sarah F.
Clemente, Leonardo
Kutz, J. Nathan
Santillana, Mauricio
A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles
title A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles
title_full A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles
title_fullStr A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles
title_full_unstemmed A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles
title_short A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles
title_sort dynamic, ensemble learning approach to forecast dengue fever epidemic years in brazil using weather and population susceptibility cycles
topic Life Sciences–Physics interface
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8205538/
https://www.ncbi.nlm.nih.gov/pubmed/34129785
http://dx.doi.org/10.1098/rsif.2020.1006
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