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Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning

Malaria is the cause of nearly half a million deaths worldwide each year, posing a great socioeconomic burden. Despite recent progress in understanding the influence of climate on malaria infection rates, climatic sources of predictability remain poorly understood and underexploited. Local weather v...

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Autores principales: Martineau, Patrick, Behera, Swadhin K., Nonaka, Masami, Jayanthi, Ratnam, Ikeda, Takayoshi, Minakawa, Noboru, Kruger, Philip, Mabunda, Qavanisi E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9453600/
https://www.ncbi.nlm.nih.gov/pubmed/36091554
http://dx.doi.org/10.3389/fpubh.2022.962377
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author Martineau, Patrick
Behera, Swadhin K.
Nonaka, Masami
Jayanthi, Ratnam
Ikeda, Takayoshi
Minakawa, Noboru
Kruger, Philip
Mabunda, Qavanisi E.
author_facet Martineau, Patrick
Behera, Swadhin K.
Nonaka, Masami
Jayanthi, Ratnam
Ikeda, Takayoshi
Minakawa, Noboru
Kruger, Philip
Mabunda, Qavanisi E.
author_sort Martineau, Patrick
collection PubMed
description Malaria is the cause of nearly half a million deaths worldwide each year, posing a great socioeconomic burden. Despite recent progress in understanding the influence of climate on malaria infection rates, climatic sources of predictability remain poorly understood and underexploited. Local weather variability alone provides predictive power at short lead times of 1–2 months, too short to adequately plan intervention measures. Here, we show that tropical climatic variability and associated sea surface temperature over the Pacific and Indian Oceans are valuable for predicting malaria in Limpopo, South Africa, up to three seasons ahead. Climatic precursors of malaria outbreaks are first identified via lag-regression analysis of climate data obtained from reanalysis and observational datasets with respect to the monthly malaria case count data provided from 1998–2020 by the Malaria Institute in Tzaneen, South Africa. Out of 11 sea surface temperature sectors analyzed, two regions, the Indian Ocean and western Pacific Ocean regions, emerge as the most robust precursors. The predictive value of these precursors is demonstrated by training a suite of machine-learning classification models to predict whether malaria case counts are above or below the median historical levels and assessing their skills in providing early warning predictions of malaria incidence with lead times ranging from 1 month to a year. Through the development of this prediction system, we find that past information about SST over the western Pacific Ocean offers impressive prediction skills (~80% accuracy) for up to three seasons (9 months) ahead. SST variability over the tropical Indian Ocean is also found to provide good skills up to two seasons (6 months) ahead. This outcome represents an extension of the effective prediction lead time by about one to two seasons compared to previous prediction systems that were more computationally costly compared to the machine learning techniques used in the current study. It also demonstrates the value of climatic information and the prediction framework developed herein for the early planning of interventions against malaria outbreaks.
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spelling pubmed-94536002022-09-09 Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning Martineau, Patrick Behera, Swadhin K. Nonaka, Masami Jayanthi, Ratnam Ikeda, Takayoshi Minakawa, Noboru Kruger, Philip Mabunda, Qavanisi E. Front Public Health Public Health Malaria is the cause of nearly half a million deaths worldwide each year, posing a great socioeconomic burden. Despite recent progress in understanding the influence of climate on malaria infection rates, climatic sources of predictability remain poorly understood and underexploited. Local weather variability alone provides predictive power at short lead times of 1–2 months, too short to adequately plan intervention measures. Here, we show that tropical climatic variability and associated sea surface temperature over the Pacific and Indian Oceans are valuable for predicting malaria in Limpopo, South Africa, up to three seasons ahead. Climatic precursors of malaria outbreaks are first identified via lag-regression analysis of climate data obtained from reanalysis and observational datasets with respect to the monthly malaria case count data provided from 1998–2020 by the Malaria Institute in Tzaneen, South Africa. Out of 11 sea surface temperature sectors analyzed, two regions, the Indian Ocean and western Pacific Ocean regions, emerge as the most robust precursors. The predictive value of these precursors is demonstrated by training a suite of machine-learning classification models to predict whether malaria case counts are above or below the median historical levels and assessing their skills in providing early warning predictions of malaria incidence with lead times ranging from 1 month to a year. Through the development of this prediction system, we find that past information about SST over the western Pacific Ocean offers impressive prediction skills (~80% accuracy) for up to three seasons (9 months) ahead. SST variability over the tropical Indian Ocean is also found to provide good skills up to two seasons (6 months) ahead. This outcome represents an extension of the effective prediction lead time by about one to two seasons compared to previous prediction systems that were more computationally costly compared to the machine learning techniques used in the current study. It also demonstrates the value of climatic information and the prediction framework developed herein for the early planning of interventions against malaria outbreaks. Frontiers Media S.A. 2022-08-25 /pmc/articles/PMC9453600/ /pubmed/36091554 http://dx.doi.org/10.3389/fpubh.2022.962377 Text en Copyright © 2022 Martineau, Behera, Nonaka, Jayanthi, Ikeda, Minakawa, Kruger and Mabunda. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Martineau, Patrick
Behera, Swadhin K.
Nonaka, Masami
Jayanthi, Ratnam
Ikeda, Takayoshi
Minakawa, Noboru
Kruger, Philip
Mabunda, Qavanisi E.
Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning
title Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning
title_full Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning
title_fullStr Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning
title_full_unstemmed Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning
title_short Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning
title_sort predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in limpopo, south africa, using machine learning
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9453600/
https://www.ncbi.nlm.nih.gov/pubmed/36091554
http://dx.doi.org/10.3389/fpubh.2022.962377
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