Geospatial Modeled Analysis and Laboratory Based Technology for Determination of Malaria Risk and Burden in a Rural Community
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
International Journal of TROPICAL DISEASE & Health
Abstract
Introduction: Geographical Information System (GIS) has proven to be very useful for large scale
mapping of ecosystems, land use and cover, disease prevalence, risk mapping and forecasting.
GIS establish relationship or link between vector borne diseases and associated environmental
factors thereby providing explanation for spatial distribution pattern, possible causes of diseases
outbreak and implications on the community.
Aims and Objectives: Our approach in this study was to define and identify areas and places that
are exposed to Malaria risk through proximity analysis and to compare geospatial risk with
laboratory diagnosed malaria epidemiology.
Methodology: Garmin GPS was used to capture the geographic coordinates of six (6) selected
settlements and overlaid with georeferenced and processed satellite images in the study area. GIS
modeling was performed on risk factors using weighted overlay technique to produce malaria risk
map. A total of One hundred and thirty-five (135) vulnerable individuals were diagnosed for Malaria
with light Olympus microscope and rapid diagnostic kit (RDT). Data were entered and analyzed
using R-Package for Statistical Computing and Graphics.
Results: Proximity to malaria risk follows relatively the order Apodu > Central Malete > Elemere >
KWASU Campus > Gbugudu. Apodu being the largest place with proximity to malaria risk, within
500 m radius. The risk index increases as one move away from the center of the settlement. The
possible explanation for this high risk could be the presence of pond / lake in Apodu. This is a good
breeding site for mosquito couple with dense vegetation as one move away from the centre of the
settlements. Unlike Apodu, Gbugudu was at medium risk at 100 m buffer (60%) but the risk index
decreases as one move away from the settlement centre. The absence of thick vegetation and
presence of numerous open farms and partly cultivated farmlands on the eastern part could have
been responsible for reduction in risk index. Dense vegetation and ponds were observed within
Apodu, while Central Malete was built up with dense vegetation are possible reasons for the high risk index, while settlements within 1 km radius around KWASU campus recorded lower risk index
possibly due to low vegetation. The geospatial malaria risk analysis correlates with the laboratory based test results. RDT kits and light microscopy results showed Apodu having the highest malaria
prevalence with 46% and 58.7% followed by Elemere 41% and 30.3% respectively. When
calculating prevalence by aggregating results across all communities, Apodu still had the highest
malaria prevalence for the whole region. RDT and light microscopy results combined for all
communities had Apodu with malaria prevalence of 21.48% and 27.4% followed by Elemere with
11.85% and 12.5% respectively. Gbugudu had the least malaria prevalence within the region with
3.7% and 7.4% respectively.
Discussion and Conclusion: Findings of this study showed dense vegetation and ponds within
Apodu, Elemere and Central Malete served as good breeding site for mosquitoes and were
responsible for the high-risk index at these areas. Settlements within 1 km radius around KWASU
campus recorded lower index possibly due to low vegetation. Results from this study indicate that
the degree of malaria parasitaemia in the three major settlements correlates directly with the
remote sensing data.