Predicting malaria epidemics in Burkina Faso with machine learning.

Accurately forecasting the case rate of malaria would enable key decision makers to intervene months before the onset of any outbreak, potentially saving lives. Until now, methods that forecast malaria have involved complicated numerical simulations that model transmission through a community. Here...

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Autores principales:	David Harvey, Wessel Valkenburg, Amara Amara
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/cad02408f83d41498bde3809f8ecff53
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spelling	oai:doaj.org-article:cad02408f83d41498bde3809f8ecff532021-12-02T20:10:23ZPredicting malaria epidemics in Burkina Faso with machine learning.1932-620310.1371/journal.pone.0253302https://doaj.org/article/cad02408f83d41498bde3809f8ecff532021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253302https://doaj.org/toc/1932-6203Accurately forecasting the case rate of malaria would enable key decision makers to intervene months before the onset of any outbreak, potentially saving lives. Until now, methods that forecast malaria have involved complicated numerical simulations that model transmission through a community. Here we present the first data-driven malaria epidemic early warning system that can predict the 13-week case rate in a primary health facility in Burkina Faso. Using the extraordinarily high-fidelity data of infant consultations taken from the Integrated e-Diagnostic Approach (IeDA) system that has been rolled out throughout Burkina Faso, we train a combination of Gaussian Processes and Random Forest Regressors to estimate the weekly number of malaria cases over a 13 week period. We test our algorithm on historical epidemics and find that for our lowest threshold for an epidemic alert, our algorithm has 30% precision with > 99% recall at raising an alert. This rises to > 99% precision and 5% recall for the high alert threshold. Our two-tailed predictions have an average 1σ and 2σ precision of 5 cases and 30 cases respectively.David HarveyWessel ValkenburgAmara AmaraPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0253302 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q David Harvey Wessel Valkenburg Amara Amara Predicting malaria epidemics in Burkina Faso with machine learning.
description	Accurately forecasting the case rate of malaria would enable key decision makers to intervene months before the onset of any outbreak, potentially saving lives. Until now, methods that forecast malaria have involved complicated numerical simulations that model transmission through a community. Here we present the first data-driven malaria epidemic early warning system that can predict the 13-week case rate in a primary health facility in Burkina Faso. Using the extraordinarily high-fidelity data of infant consultations taken from the Integrated e-Diagnostic Approach (IeDA) system that has been rolled out throughout Burkina Faso, we train a combination of Gaussian Processes and Random Forest Regressors to estimate the weekly number of malaria cases over a 13 week period. We test our algorithm on historical epidemics and find that for our lowest threshold for an epidemic alert, our algorithm has 30% precision with > 99% recall at raising an alert. This rises to > 99% precision and 5% recall for the high alert threshold. Our two-tailed predictions have an average 1σ and 2σ precision of 5 cases and 30 cases respectively.
format	article
author	David Harvey Wessel Valkenburg Amara Amara
author_facet	David Harvey Wessel Valkenburg Amara Amara
author_sort	David Harvey
title	Predicting malaria epidemics in Burkina Faso with machine learning.
title_short	Predicting malaria epidemics in Burkina Faso with machine learning.
title_full	Predicting malaria epidemics in Burkina Faso with machine learning.
title_fullStr	Predicting malaria epidemics in Burkina Faso with machine learning.
title_full_unstemmed	Predicting malaria epidemics in Burkina Faso with machine learning.
title_sort	predicting malaria epidemics in burkina faso with machine learning.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/cad02408f83d41498bde3809f8ecff53
work_keys_str_mv	AT davidharvey predictingmalariaepidemicsinburkinafasowithmachinelearning AT wesselvalkenburg predictingmalariaepidemicsinburkinafasowithmachinelearning AT amaraamara predictingmalariaepidemicsinburkinafasowithmachinelearning
_version_	1718375024978558976

Predicting malaria epidemics in Burkina Faso with machine learning.

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