Improvement of disease prediction and modeling through the use of meteorological ensembles: human plague in Uganda.

Climate and weather influence the occurrence, distribution, and incidence of infectious diseases, particularly those caused by vector-borne or zoonotic pathogens. Thus, models based on meteorological data have helped predict when and where human cases are most likely to occur. Such knowledge aids in...

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Autores principales:	Sean M Moore, Andrew Monaghan, Kevin S Griffith, Titus Apangu, Paul S Mead, Rebecca J Eisen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3757f3f37a334bcc88f6d53047a39ab5
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spelling	oai:doaj.org-article:3757f3f37a334bcc88f6d53047a39ab52021-11-18T07:05:33ZImprovement of disease prediction and modeling through the use of meteorological ensembles: human plague in Uganda.1932-620310.1371/journal.pone.0044431https://doaj.org/article/3757f3f37a334bcc88f6d53047a39ab52012-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0044431https://doaj.org/toc/1932-6203Climate and weather influence the occurrence, distribution, and incidence of infectious diseases, particularly those caused by vector-borne or zoonotic pathogens. Thus, models based on meteorological data have helped predict when and where human cases are most likely to occur. Such knowledge aids in targeting limited prevention and control resources and may ultimately reduce the burden of diseases. Paradoxically, localities where such models could yield the greatest benefits, such as tropical regions where morbidity and mortality caused by vector-borne diseases is greatest, often lack high-quality in situ local meteorological data. Satellite- and model-based gridded climate datasets can be used to approximate local meteorological conditions in data-sparse regions, however their accuracy varies. Here we investigate how the selection of a particular dataset can influence the outcomes of disease forecasting models. Our model system focuses on plague (Yersinia pestis infection) in the West Nile region of Uganda. The majority of recent human cases have been reported from East Africa and Madagascar, where meteorological observations are sparse and topography yields complex weather patterns. Using an ensemble of meteorological datasets and model-averaging techniques we find that the number of suspected cases in the West Nile region was negatively associated with dry season rainfall (December-February) and positively with rainfall prior to the plague season. We demonstrate that ensembles of available meteorological datasets can be used to quantify climatic uncertainty and minimize its impacts on infectious disease models. These methods are particularly valuable in regions with sparse observational networks and high morbidity and mortality from vector-borne diseases.Sean M MooreAndrew MonaghanKevin S GriffithTitus ApanguPaul S MeadRebecca J EisenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 9, p e44431 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sean M Moore Andrew Monaghan Kevin S Griffith Titus Apangu Paul S Mead Rebecca J Eisen Improvement of disease prediction and modeling through the use of meteorological ensembles: human plague in Uganda.
description	Climate and weather influence the occurrence, distribution, and incidence of infectious diseases, particularly those caused by vector-borne or zoonotic pathogens. Thus, models based on meteorological data have helped predict when and where human cases are most likely to occur. Such knowledge aids in targeting limited prevention and control resources and may ultimately reduce the burden of diseases. Paradoxically, localities where such models could yield the greatest benefits, such as tropical regions where morbidity and mortality caused by vector-borne diseases is greatest, often lack high-quality in situ local meteorological data. Satellite- and model-based gridded climate datasets can be used to approximate local meteorological conditions in data-sparse regions, however their accuracy varies. Here we investigate how the selection of a particular dataset can influence the outcomes of disease forecasting models. Our model system focuses on plague (Yersinia pestis infection) in the West Nile region of Uganda. The majority of recent human cases have been reported from East Africa and Madagascar, where meteorological observations are sparse and topography yields complex weather patterns. Using an ensemble of meteorological datasets and model-averaging techniques we find that the number of suspected cases in the West Nile region was negatively associated with dry season rainfall (December-February) and positively with rainfall prior to the plague season. We demonstrate that ensembles of available meteorological datasets can be used to quantify climatic uncertainty and minimize its impacts on infectious disease models. These methods are particularly valuable in regions with sparse observational networks and high morbidity and mortality from vector-borne diseases.
format	article
author	Sean M Moore Andrew Monaghan Kevin S Griffith Titus Apangu Paul S Mead Rebecca J Eisen
author_facet	Sean M Moore Andrew Monaghan Kevin S Griffith Titus Apangu Paul S Mead Rebecca J Eisen
author_sort	Sean M Moore
title	Improvement of disease prediction and modeling through the use of meteorological ensembles: human plague in Uganda.
title_short	Improvement of disease prediction and modeling through the use of meteorological ensembles: human plague in Uganda.
title_full	Improvement of disease prediction and modeling through the use of meteorological ensembles: human plague in Uganda.
title_fullStr	Improvement of disease prediction and modeling through the use of meteorological ensembles: human plague in Uganda.
title_full_unstemmed	Improvement of disease prediction and modeling through the use of meteorological ensembles: human plague in Uganda.
title_sort	improvement of disease prediction and modeling through the use of meteorological ensembles: human plague in uganda.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/3757f3f37a334bcc88f6d53047a39ab5
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Improvement of disease prediction and modeling through the use of meteorological ensembles: human plague in Uganda.

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