Modeling the risk of transmission of schistosomiasis in Akure North Local Government Area of Ondo State, Nigeria using satellite derived environmental data

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dc.contributor.author Ajakaye, O.G.
dc.contributor.author Adedeji, O.I.
dc.contributor.author Ajayi, P.O.
dc.date.accessioned 2017-11-06T10:38:20Z
dc.date.available 2017-11-06T10:38:20Z
dc.date.issued 2017-07
dc.identifier.citation Ajakaye, O.G., Adedeji, O.I. and Ajayi, P.O. 2017. Modeling the risk of transmission of schistosomiasis in Akure North Local Government Area of Ondo State, Nigeria using satellite derived environmental data. PLoS Neglected Tropical Diseases 11 (7), Article number e0005733. en_ZA
dc.identifier.issn 1935-2727 (Print)
dc.identifier.issn 1935-2735 (Online)
dc.identifier.uri http://hdl.handle.net/10539/23383
dc.description.abstract Schistosomiasis is a parasitic disease and its distribution, in space and time, can be influenced by environmental factors such as rivers, elevation, slope, land surface temperature, land use/cover and rainfall. The aim of this study is to identify the areas with suitable conditions for schistosomiasis transmission on the basis of physical and environmental factors derived from satellite imagery and spatial analysis for Akure North Local Government Area (LGA) of Ondo State. Nigeria. This was done through methodology multicriteria evaluation (MCE) using Saaty’s analytical hierarchy process (AHP). AHP is a multi-criteria decision method that uses hierarchical structures to represent a problem and makes decisions based on priority scales. In this research AHP was used to obtain the mapping weight or importance of each individual schistosomiasis risk factor. For the purpose of identifying areas of schistosomiasis risk, this study focused on temperature, drainage, elevation, rainfall, slope and land use/land cover as the factors controlling schistosomiasis incidence in the study area. It is by reclassifying and overlaying these factors that areas vulnerable to schistosomiasis were identified. The weighted overlay analysis was done after each factor was given the appropriate weight derived through the analytical hierarchical process. The prevalence of urinary schistosomiasis in the study area was also determined by parasitological analysis of urine samples collected through random sampling. The results showed varying risk of schistosomiasis with a larger portion of the area (82%) falling under the high and very high risk category. The study also showed that one community (Oba Ile) had the lowest risk of schistosomiasis while the risk increased in the four remaining communities (Iju, Igoba, Ita Ogbolu and Ogbese). The predictions made by the model correlated strongly with observations from field study. The high risk zones corresponded to known endemic communities. This study revealed that environmental factors can be used in identifying and predicting the transmission of schistosomiasis as well as effective monitoring of disease risk in newly established rural and agricultural communities. en_ZA
dc.language.iso en en_ZA
dc.publisher Public Library of Science en_ZA
dc.rights © 2017 Ajakaye, O.G. et al. This is an open access article distributed under the terms of the Creative Commons Attribution License. en_ZA
dc.subject Disease transmission en_ZA
dc.subject Animal en_ZA
dc.subject Environment en_ZA
dc.subject Government en_ZA
dc.subject Human en_ZA
dc.subject Nigeria en_ZA
dc.subject Parasitology en_ZA
dc.subject Prevalence en_ZA
dc.subject Risk assessment en_ZA
dc.subject Schistosomiasis en_ZA
dc.subject Space flight en_ZA
dc.subject Spatial analysis en_ZA
dc.subject Statistical model en_ZA
dc.subject Transmission en_ZA
dc.subject Urine en_ZA
dc.subject Disease Transmission, Infectious en_ZA
dc.subject Local Government en_ZA
dc.subject Models, Statistical en_ZA
dc.title Modeling the risk of transmission of schistosomiasis in Akure North Local Government Area of Ondo State, Nigeria using satellite derived environmental data en_ZA
dc.type Article en_ZA
dc.journal.volume 11 en_ZA
dc.journal.title PLoS Neglected Tropical Diseases en_ZA
dc.description.librarian EM2017 en_ZA
dc.citation.doi 10.1371/journal.pntd.0005733 en_ZA
dc.citation.issue 7 en_ZA


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