Using remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya

Abstract Malaria surveillance data provide opportunity to develop forecasting models. Seasonal variability in environmental factors correlate with malaria transmission, thus the identification of transmission patterns is useful in developing prediction models. However, with changing seasonal transmi...

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Autores principales:	Maquins Odhiambo Sewe, Yesim Tozan, Clas Ahlm, Joacim Rocklöv
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/aca57109d9bb4340a8df10ac5865201b
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spelling	oai:doaj.org-article:aca57109d9bb4340a8df10ac5865201b2021-12-02T12:32:05ZUsing remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya10.1038/s41598-017-02560-z2045-2322https://doaj.org/article/aca57109d9bb4340a8df10ac5865201b2017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-02560-zhttps://doaj.org/toc/2045-2322Abstract Malaria surveillance data provide opportunity to develop forecasting models. Seasonal variability in environmental factors correlate with malaria transmission, thus the identification of transmission patterns is useful in developing prediction models. However, with changing seasonal transmission patterns, either due to interventions or shifting weather seasons, traditional modelling approaches may not yield adequate predictive skill. Two statistical models,a general additive model (GAM) and GAMBOOST model with boosted regression were contrasted by assessing their predictive accuracy in forecasting malaria admissions at lead times of one to three months. Monthly admission data for children under five years with confirmed malaria at the Siaya district hospital in Western Kenya for the period 2003 to 2013 were used together with satellite derived data on rainfall, average temperature and evapotranspiration(ET). There was a total of 8,476 confirmed malaria admissions. The peak of malaria season changed and malaria admissions reduced overtime. The GAMBOOST model at 1-month lead time had the highest predictive skill during both the training and test periods and thus can be utilized in a malaria early warning system.Maquins Odhiambo SeweYesim TozanClas AhlmJoacim RocklövNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Maquins Odhiambo Sewe Yesim Tozan Clas Ahlm Joacim Rocklöv Using remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya
description	Abstract Malaria surveillance data provide opportunity to develop forecasting models. Seasonal variability in environmental factors correlate with malaria transmission, thus the identification of transmission patterns is useful in developing prediction models. However, with changing seasonal transmission patterns, either due to interventions or shifting weather seasons, traditional modelling approaches may not yield adequate predictive skill. Two statistical models,a general additive model (GAM) and GAMBOOST model with boosted regression were contrasted by assessing their predictive accuracy in forecasting malaria admissions at lead times of one to three months. Monthly admission data for children under five years with confirmed malaria at the Siaya district hospital in Western Kenya for the period 2003 to 2013 were used together with satellite derived data on rainfall, average temperature and evapotranspiration(ET). There was a total of 8,476 confirmed malaria admissions. The peak of malaria season changed and malaria admissions reduced overtime. The GAMBOOST model at 1-month lead time had the highest predictive skill during both the training and test periods and thus can be utilized in a malaria early warning system.
format	article
author	Maquins Odhiambo Sewe Yesim Tozan Clas Ahlm Joacim Rocklöv
author_facet	Maquins Odhiambo Sewe Yesim Tozan Clas Ahlm Joacim Rocklöv
author_sort	Maquins Odhiambo Sewe
title	Using remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya
title_short	Using remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya
title_full	Using remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya
title_fullStr	Using remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya
title_full_unstemmed	Using remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya
title_sort	using remote sensing environmental data to forecast malaria incidence at a rural district hospital in western kenya
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/aca57109d9bb4340a8df10ac5865201b
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Using remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya

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