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Environmental suitability for lymphatic filariasis in Nigeria

BACKGROUND: Lymphatic filariasis (LF) is a mosquito-borne parasitic disease and a major cause of disability worldwide. It is one of the neglected tropical diseases identified by the World Health Organization for elimination as a public health problem by 2020. Maps displaying disease distribution are...

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Autores principales: Eneanya, Obiora A., Cano, Jorge, Dorigatti, Ilaria, Anagbogu, Ifeoma, Okoronkwo, Chukwu, Garske, Tini, Donnelly, Christl A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6142334/
https://www.ncbi.nlm.nih.gov/pubmed/30223860
http://dx.doi.org/10.1186/s13071-018-3097-9
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author Eneanya, Obiora A.
Cano, Jorge
Dorigatti, Ilaria
Anagbogu, Ifeoma
Okoronkwo, Chukwu
Garske, Tini
Donnelly, Christl A.
author_facet Eneanya, Obiora A.
Cano, Jorge
Dorigatti, Ilaria
Anagbogu, Ifeoma
Okoronkwo, Chukwu
Garske, Tini
Donnelly, Christl A.
author_sort Eneanya, Obiora A.
collection PubMed
description BACKGROUND: Lymphatic filariasis (LF) is a mosquito-borne parasitic disease and a major cause of disability worldwide. It is one of the neglected tropical diseases identified by the World Health Organization for elimination as a public health problem by 2020. Maps displaying disease distribution are helpful tools to identify high-risk areas and target scarce control resources. METHODS: We used pre-intervention site-level occurrence data from 1192 survey sites collected during extensive mapping surveys by the Nigeria Ministry of Health. Using an ensemble of machine learning modelling algorithms (generalised boosted models and random forest), we mapped the ecological niche of LF at a spatial resolution of 1 km(2). By overlaying gridded estimates of population density, we estimated the human population living in LF risk areas on a 100 × 100 m scale. RESULTS: Our maps demonstrate that there is a heterogeneous distribution of LF risk areas across Nigeria, with large portions of northern Nigeria having more environmentally suitable conditions for the occurrence of LF. Here we estimated that approximately 110 million individuals live in areas at risk of LF transmission. CONCLUSIONS: Machine learning and ensemble modelling are powerful tools to map disease risk and are known to yield more accurate predictive models with less uncertainty than single models. The resulting map provides a geographical framework to target control efforts and assess its potential impacts.
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spelling pubmed-61423342018-09-20 Environmental suitability for lymphatic filariasis in Nigeria Eneanya, Obiora A. Cano, Jorge Dorigatti, Ilaria Anagbogu, Ifeoma Okoronkwo, Chukwu Garske, Tini Donnelly, Christl A. Parasit Vectors Research BACKGROUND: Lymphatic filariasis (LF) is a mosquito-borne parasitic disease and a major cause of disability worldwide. It is one of the neglected tropical diseases identified by the World Health Organization for elimination as a public health problem by 2020. Maps displaying disease distribution are helpful tools to identify high-risk areas and target scarce control resources. METHODS: We used pre-intervention site-level occurrence data from 1192 survey sites collected during extensive mapping surveys by the Nigeria Ministry of Health. Using an ensemble of machine learning modelling algorithms (generalised boosted models and random forest), we mapped the ecological niche of LF at a spatial resolution of 1 km(2). By overlaying gridded estimates of population density, we estimated the human population living in LF risk areas on a 100 × 100 m scale. RESULTS: Our maps demonstrate that there is a heterogeneous distribution of LF risk areas across Nigeria, with large portions of northern Nigeria having more environmentally suitable conditions for the occurrence of LF. Here we estimated that approximately 110 million individuals live in areas at risk of LF transmission. CONCLUSIONS: Machine learning and ensemble modelling are powerful tools to map disease risk and are known to yield more accurate predictive models with less uncertainty than single models. The resulting map provides a geographical framework to target control efforts and assess its potential impacts. BioMed Central 2018-09-17 /pmc/articles/PMC6142334/ /pubmed/30223860 http://dx.doi.org/10.1186/s13071-018-3097-9 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Eneanya, Obiora A.
Cano, Jorge
Dorigatti, Ilaria
Anagbogu, Ifeoma
Okoronkwo, Chukwu
Garske, Tini
Donnelly, Christl A.
Environmental suitability for lymphatic filariasis in Nigeria
title Environmental suitability for lymphatic filariasis in Nigeria
title_full Environmental suitability for lymphatic filariasis in Nigeria
title_fullStr Environmental suitability for lymphatic filariasis in Nigeria
title_full_unstemmed Environmental suitability for lymphatic filariasis in Nigeria
title_short Environmental suitability for lymphatic filariasis in Nigeria
title_sort environmental suitability for lymphatic filariasis in nigeria
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6142334/
https://www.ncbi.nlm.nih.gov/pubmed/30223860
http://dx.doi.org/10.1186/s13071-018-3097-9
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