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Geospatial distribution and predictive modeling of onchocerciasis in Ogun State, Nigeria

Onchocerciasis caused by infection with Onchocerca volvulus is a disease of public health importance and is highly associated with disability. As Nigeria is aiming at eliminating onchocerciasis by 2030, there is a need to develop newer tools to map disease prevalence and identify environmental facto...

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Autores principales: Surakat, Olabanji Ahmed, Babalola, Ayodele S., Adeleke, Monsuru A., Adeogun, Adedapo O., Idowu, Olufunmilayo A., Sam-Wobo, Sammy O.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9977021/
https://www.ncbi.nlm.nih.gov/pubmed/36857325
http://dx.doi.org/10.1371/journal.pone.0281624
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author Surakat, Olabanji Ahmed
Babalola, Ayodele S.
Adeleke, Monsuru A.
Adeogun, Adedapo O.
Idowu, Olufunmilayo A.
Sam-Wobo, Sammy O.
author_facet Surakat, Olabanji Ahmed
Babalola, Ayodele S.
Adeleke, Monsuru A.
Adeogun, Adedapo O.
Idowu, Olufunmilayo A.
Sam-Wobo, Sammy O.
author_sort Surakat, Olabanji Ahmed
collection PubMed
description Onchocerciasis caused by infection with Onchocerca volvulus is a disease of public health importance and is highly associated with disability. As Nigeria is aiming at eliminating onchocerciasis by 2030, there is a need to develop newer tools to map disease prevalence and identify environmental factors driving disease prevalence, even in places that have not been previously targeted for preventive chemotherapy. This study produced predictive risk-maps of onchocerciasis in Ogun State. Georeferenced onchocerciasis infection data obtained from a cross-sectional survey at 32 locations between March and July 2015 together with remotely-sensed environmental data were analyzed using Ecological Niche Models (ENM). A total of 107 field occurrence points for O. volvulus infection were recorded. A total of 43 positive occurrence points were used for modelling. ENMs were used to estimate the current geographic distribution of O. volvulus in Ogun State. Maximum Entropy distribution modeling (MaxEnt) was used for predicting the potential suitable habitats, using a portion of the occurrence records. A total of 19 environmental variables were used to model the potential geographical distribution area under current climatic conditions. Empirical prevalence of 9.3% was recorded in this study. The geospatial distribution of infection revealed that all communities in Odeda Local Government Area (a peri-urban LGA) showed remarkably high prevalence compared with other LGAs. The predicted high-risk areas (probability > 0.8) of O. volvulus infection were all parts of Odeda, Abeokuta South, and Abeokuta North, southern part of Imeko-Afon, a large part of Yewa North, some parts of Ewekoro and Obafemi-Owode LGAs. The estimated prevalence for these regions were >60% (between 61% and 100%). As predicted, O. volvulus occurrence showed a positive association with variables reflecting precipitation in Ogun State. Our predictive risk-maps has provided useful information for the elimination of onchocerciais, by identifying priority areas for delivery of intervention in Ogun State, Nigeria.
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spelling pubmed-99770212023-03-02 Geospatial distribution and predictive modeling of onchocerciasis in Ogun State, Nigeria Surakat, Olabanji Ahmed Babalola, Ayodele S. Adeleke, Monsuru A. Adeogun, Adedapo O. Idowu, Olufunmilayo A. Sam-Wobo, Sammy O. PLoS One Research Article Onchocerciasis caused by infection with Onchocerca volvulus is a disease of public health importance and is highly associated with disability. As Nigeria is aiming at eliminating onchocerciasis by 2030, there is a need to develop newer tools to map disease prevalence and identify environmental factors driving disease prevalence, even in places that have not been previously targeted for preventive chemotherapy. This study produced predictive risk-maps of onchocerciasis in Ogun State. Georeferenced onchocerciasis infection data obtained from a cross-sectional survey at 32 locations between March and July 2015 together with remotely-sensed environmental data were analyzed using Ecological Niche Models (ENM). A total of 107 field occurrence points for O. volvulus infection were recorded. A total of 43 positive occurrence points were used for modelling. ENMs were used to estimate the current geographic distribution of O. volvulus in Ogun State. Maximum Entropy distribution modeling (MaxEnt) was used for predicting the potential suitable habitats, using a portion of the occurrence records. A total of 19 environmental variables were used to model the potential geographical distribution area under current climatic conditions. Empirical prevalence of 9.3% was recorded in this study. The geospatial distribution of infection revealed that all communities in Odeda Local Government Area (a peri-urban LGA) showed remarkably high prevalence compared with other LGAs. The predicted high-risk areas (probability > 0.8) of O. volvulus infection were all parts of Odeda, Abeokuta South, and Abeokuta North, southern part of Imeko-Afon, a large part of Yewa North, some parts of Ewekoro and Obafemi-Owode LGAs. The estimated prevalence for these regions were >60% (between 61% and 100%). As predicted, O. volvulus occurrence showed a positive association with variables reflecting precipitation in Ogun State. Our predictive risk-maps has provided useful information for the elimination of onchocerciais, by identifying priority areas for delivery of intervention in Ogun State, Nigeria. Public Library of Science 2023-03-01 /pmc/articles/PMC9977021/ /pubmed/36857325 http://dx.doi.org/10.1371/journal.pone.0281624 Text en © 2023 Surakat et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Surakat, Olabanji Ahmed
Babalola, Ayodele S.
Adeleke, Monsuru A.
Adeogun, Adedapo O.
Idowu, Olufunmilayo A.
Sam-Wobo, Sammy O.
Geospatial distribution and predictive modeling of onchocerciasis in Ogun State, Nigeria
title Geospatial distribution and predictive modeling of onchocerciasis in Ogun State, Nigeria
title_full Geospatial distribution and predictive modeling of onchocerciasis in Ogun State, Nigeria
title_fullStr Geospatial distribution and predictive modeling of onchocerciasis in Ogun State, Nigeria
title_full_unstemmed Geospatial distribution and predictive modeling of onchocerciasis in Ogun State, Nigeria
title_short Geospatial distribution and predictive modeling of onchocerciasis in Ogun State, Nigeria
title_sort geospatial distribution and predictive modeling of onchocerciasis in ogun state, nigeria
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9977021/
https://www.ncbi.nlm.nih.gov/pubmed/36857325
http://dx.doi.org/10.1371/journal.pone.0281624
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