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

dc.citation.doi10.1371/journal.pntd.0005733en_ZA
dc.citation.issue7en_ZA
dc.contributor.authorAjakaye, O.G.
dc.contributor.authorAdedeji, O.I.
dc.contributor.authorAjayi, P.O.
dc.date.accessioned2017-11-06T10:38:20Z
dc.date.available2017-11-06T10:38:20Z
dc.date.issued2017-07
dc.description.abstractSchistosomiasis 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.description.librarianEM2017en_ZA
dc.identifier.citationAjakaye, 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.issn1935-2727 (Print)
dc.identifier.issn1935-2735 (Online)
dc.identifier.urihttp://hdl.handle.net/10539/23383
dc.journal.titlePLoS Neglected Tropical Diseasesen_ZA
dc.journal.volume11en_ZA
dc.language.isoenen_ZA
dc.publisherPublic Library of Scienceen_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.subjectDisease transmissionen_ZA
dc.subjectAnimalen_ZA
dc.subjectEnvironmenten_ZA
dc.subjectGovernmenten_ZA
dc.subjectHumanen_ZA
dc.subjectNigeriaen_ZA
dc.subjectParasitologyen_ZA
dc.subjectPrevalenceen_ZA
dc.subjectRisk assessmenten_ZA
dc.subjectSchistosomiasisen_ZA
dc.subjectSpace flighten_ZA
dc.subjectSpatial analysisen_ZA
dc.subjectStatistical modelen_ZA
dc.subjectTransmissionen_ZA
dc.subjectUrineen_ZA
dc.subjectDisease Transmission, Infectiousen_ZA
dc.subjectLocal Governmenten_ZA
dc.subjectModels, Statisticalen_ZA
dc.titleModeling the risk of transmission of schistosomiasis in Akure North Local Government Area of Ondo State, Nigeria using satellite derived environmental dataen_ZA
dc.typeArticleen_ZA
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