TITLE:
Investigation of Spatial Risk Factors for RVF Disease Occurrence Using Remote Sensing & GIS—A Case Study: Sinnar State, Sudan
AUTHORS:
Kowther Mohamed Saeed Ahmed, Amna Ahmed Hamid, Abbas Doka
KEYWORDS:
Rift Valley Fever, Vector-Borne Diseases, Spatial Risk Factors, Normalized Difference Vegetation Index (NDVI)
JOURNAL NAME:
Journal of Geographic Information System,
Vol.7 No.2,
April
22,
2015
ABSTRACT: Rift Valley Fever (RVF) is
an emerging, mosquito-borne disease with serious economical and negative
implications on human and animal health. This study was conducted to verify the
factors which influenced the spatial pattern of Rift Valley Fever occurrence
and identified the high risk areas for the occurrence of the disease at Sinner
State, Sudan. The normalized difference vegetation index (NDVI) derived from
Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and rainfall
data in addition to the point data of RVF clinical cases in humans were used in
this study. In order to identify the RVF high risk areas, remote sensing data
and rainfall data were integrated in a GIS with other information including,
soil type, water body, DEM (Digital Elevation Model), and animal routes and
analyzed using Spatial Analysis tools. The information on clinical cases was
used for verification. The Normalized Difference Vegetation Index (NDVI) was
used to describe vegetation patterns of the study area by calculating the mean
NDVI. The results of the study showed that, RVF risk increased with the
increase in vegetation cover (high NDVI values), and increase in rainfall,
which both provided suitable conditions for disease vectors breeding and a good
indicator for RVF epizootics. The study concluded that, identification of high
risk area for RVF disease improved the understanding of the spatial
distribution of the disease and helped in locating the areas where disease was
likely to be endemic and therefore preparedness measures should be taken. The
identification represents the first step of prospective predictions of RVF
outbreaks and provides a baseline for improved early warning, control, response
planning, and mitigation. Further detailed studies are recommended in this
domain.