ISABB Journal of
Health and Environmental Sciences

OFFICIAL PUBLICATION OF THE INTERNATIONAL SOCIETY OF AFRICAN BIOTECHNOLOGISTS AND BIOSCIENTISTS
  • Abbreviation: ISABB J. Health Environ. sci.
  • Language: English
  • ISSN: 1937-3236
  • DOI: 10.5897/ISABB-JHE
  • Start Year: 2011
  • Published Articles: 13

Full Length Research Paper

Modeling schistosomiasis infection using Kriging interpolation method in Osun State, South west, Nigeria

Oladejo S. O.
  • Oladejo S. O.
  • Department of Remote Sensing and GIS, School of Earth and Mineral Science, Federal University of Technology, Akure, Ondo State, Nigeria.
  • Google Scholar
Morenikeji O. A.
  • Morenikeji O. A.
  • Department of Zoology, Faculty of Science, University of Ibadan, Oyo State, Nigeria.
  • Google Scholar


  •  Received: 04 December 2018
  •  Accepted: 06 February 2019
  •  Published: 28 February 2019

Abstract

Schistosomiasis, an environmentally-mediated disease, contracted by swimming or wading in freshwater bodies harbouring the snail intermediate hosts of the genus Schistosoma consisting of five human species: S. haematobium S. mansoni, S. japonicum,   S. intercalatum and S. mekongi. Transmission is on the increase with consequent adverse effects on children’s health and school attendance. There is a dearth of information in mapping and prediction of the disease transmission and distribution in the  study area. This study made use of Geographic Information System and Remote Sensing (GIS/RS) in mapping and prediction of the disease in some affected areas of the state. There was a high prevalence of urinary schistosomiasis in the study communities. There is considerable interest in the literature on disease mapping to interpolate the occurrence, risk and probability from prevalence disease data. There is the need for continuous mapping using GIS/RS,  and develop further risk model maps for the whole state using this approach to further understand the spatial pattern of urinary schistosomiasis in Nigeria. SPM is based on the number of interventions. The higher the number of interventions is the lower the prevalence of infection. This study will serve as a reliable baseline data for intervention planning in the State.

 

Key words: Urinary schistosomiasis, remote sensing and geographical information system, Kriging interpolation, mapping, predictive risk models and simple proportion model.