Mapping, bayesian geostatistical analysis and spatial prediction of lymphatic filariasis prevalence in Africa.

There is increasing interest to control or eradicate the major neglected tropical diseases. Accurate modelling of the geographic distributions of parasitic infections will be crucial to this endeavour. We used 664 community level infection prevalence data collated from the published literature in co...

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Autores principales:	Hannah Slater, Edwin Michael
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e006cdac17c940879c1aacd10303e980
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spelling	oai:doaj.org-article:e006cdac17c940879c1aacd10303e9802021-11-18T09:00:08ZMapping, bayesian geostatistical analysis and spatial prediction of lymphatic filariasis prevalence in Africa.1932-620310.1371/journal.pone.0071574https://doaj.org/article/e006cdac17c940879c1aacd10303e9802013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23951194/?tool=EBIhttps://doaj.org/toc/1932-6203There is increasing interest to control or eradicate the major neglected tropical diseases. Accurate modelling of the geographic distributions of parasitic infections will be crucial to this endeavour. We used 664 community level infection prevalence data collated from the published literature in conjunction with eight environmental variables, altitude and population density, and a multivariate Bayesian generalized linear spatial model that allows explicit accounting for spatial autocorrelation and incorporation of uncertainty in input data and model parameters, to construct the first spatially-explicit map describing LF prevalence distribution in Africa. We also ran the best-fit model against predictions made by the HADCM3 and CCCMA climate models for 2050 to predict the likely distributions of LF under future climate and population changes. We show that LF prevalence is strongly influenced by spatial autocorrelation between locations but is only weakly associated with environmental covariates. Infection prevalence, however, is found to be related to variations in population density. All associations with key environmental/demographic variables appear to be complex and non-linear. LF prevalence is predicted to be highly heterogenous across Africa, with high prevalences (>20%) estimated to occur primarily along coastal West and East Africa, and lowest prevalences predicted for the central part of the continent. Error maps, however, indicate a need for further surveys to overcome problems with data scarcity in the latter and other regions. Analysis of future changes in prevalence indicates that population growth rather than climate change per se will represent the dominant factor in the predicted increase/decrease and spread of LF on the continent. We indicate that these results could play an important role in aiding the development of strategies that are best able to achieve the goals of parasite elimination locally and globally in a manner that may also account for the effects of future climate change on parasitic infection.Hannah SlaterEdwin MichaelPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 8, p e71574 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hannah Slater Edwin Michael Mapping, bayesian geostatistical analysis and spatial prediction of lymphatic filariasis prevalence in Africa.
description	There is increasing interest to control or eradicate the major neglected tropical diseases. Accurate modelling of the geographic distributions of parasitic infections will be crucial to this endeavour. We used 664 community level infection prevalence data collated from the published literature in conjunction with eight environmental variables, altitude and population density, and a multivariate Bayesian generalized linear spatial model that allows explicit accounting for spatial autocorrelation and incorporation of uncertainty in input data and model parameters, to construct the first spatially-explicit map describing LF prevalence distribution in Africa. We also ran the best-fit model against predictions made by the HADCM3 and CCCMA climate models for 2050 to predict the likely distributions of LF under future climate and population changes. We show that LF prevalence is strongly influenced by spatial autocorrelation between locations but is only weakly associated with environmental covariates. Infection prevalence, however, is found to be related to variations in population density. All associations with key environmental/demographic variables appear to be complex and non-linear. LF prevalence is predicted to be highly heterogenous across Africa, with high prevalences (>20%) estimated to occur primarily along coastal West and East Africa, and lowest prevalences predicted for the central part of the continent. Error maps, however, indicate a need for further surveys to overcome problems with data scarcity in the latter and other regions. Analysis of future changes in prevalence indicates that population growth rather than climate change per se will represent the dominant factor in the predicted increase/decrease and spread of LF on the continent. We indicate that these results could play an important role in aiding the development of strategies that are best able to achieve the goals of parasite elimination locally and globally in a manner that may also account for the effects of future climate change on parasitic infection.
format	article
author	Hannah Slater Edwin Michael
author_facet	Hannah Slater Edwin Michael
author_sort	Hannah Slater
title	Mapping, bayesian geostatistical analysis and spatial prediction of lymphatic filariasis prevalence in Africa.
title_short	Mapping, bayesian geostatistical analysis and spatial prediction of lymphatic filariasis prevalence in Africa.
title_full	Mapping, bayesian geostatistical analysis and spatial prediction of lymphatic filariasis prevalence in Africa.
title_fullStr	Mapping, bayesian geostatistical analysis and spatial prediction of lymphatic filariasis prevalence in Africa.
title_full_unstemmed	Mapping, bayesian geostatistical analysis and spatial prediction of lymphatic filariasis prevalence in Africa.
title_sort	mapping, bayesian geostatistical analysis and spatial prediction of lymphatic filariasis prevalence in africa.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/e006cdac17c940879c1aacd10303e980
work_keys_str_mv	AT hannahslater mappingbayesiangeostatisticalanalysisandspatialpredictionoflymphaticfilariasisprevalenceinafrica AT edwinmichael mappingbayesiangeostatisticalanalysisandspatialpredictionoflymphaticfilariasisprevalenceinafrica
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Mapping, bayesian geostatistical analysis and spatial prediction of lymphatic filariasis prevalence in Africa.

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