Evaluating Efficacy of Landsat-Derived Environmental Covariates for Predicting Malaria Distribution in Rural Villages of Vhembe District, South Africa
Malaria in South Africa is still a problem despite existing efforts to eradicate the disease. In the Vhembe District Municipality, malaria prevalence is still high, with a mean incidence rate of 328.2 per 100,0000 persons/year. This study aimed at evaluating environmental covariates, such as vegetation moisture and vegetation greenness, associated with malaria vector distribution for better predictability towards rapid and efficient disease management and control. The 2005 malaria incidence data combined with Landsat 5 ETM were used in this study. A total of nine remotely sensed covariates were derived, while pseudo-absences in the ratio of 1:2 (presence/absence) were generated at buffer distances of 0.5–20 km from known presence locations. A stepwise logistic regression model was applied to analyse the spatial distribution of malaria in the area. A buffer distance of 10 km yielded the highest classification accuracy of 82% at a threshold of 0.9. This model was significant (ρ < 0.05) and yielded a deviance (D2) of 36%. The significantly positive relationship (ρ < 0.05) between the soil-adjusted vegetation index and malaria distribution at all buffer distances suggests that malaria vector (Anopheles arabiensis) prefer productive and greener vegetation. The significant negative relationship between water/moisture index (a1 index) and malaria distribution in buffer distances of 0.5, 10, and 20 km suggest that malaria distribution increases with a decrease in shortwave reflectance signal. The study has shown that suitable habitats of malaria vectors are generally found within a radius of 10 km in semi-arid environments, and this insight can be useful to aid efforts aimed at putting in place evidence-based preventative measures against malaria infections. Furthermore, this result is important in understanding malaria dynamics under the current climate and environmental changes. The study has also demonstrated the use of Landsat data and the ability to extract environmental conditions which favour the distribution of malaria vector (An. arabiensis) such as the canopy moisture content in vegetation, which serves as a surrogate for rainfall.
KeywordsVhembe District Municipality Malaria SAVI Landsat 5
Akaike’s information criterion
Advanced Spaceborne Thermal Emission and Reflection Radiometer
Digital elevation model
Genetic Algorithm for Rule Set Production
Malaria information system
Modified normalized difference water index
Normalized difference vegetation index
Soil-adjusted vegetation index
Species distribution modelling
Stepwise logistic regression
Vhembe District Municipality
Authors would like to thank QGIS Development Team for the Quantum GIS software used in this study. We would like to also extent our gratitude to the R Development Team for making R software available for data analysis. Our gratitude to the USGS for free Landsat dataset used in the study. This work is funded by the South African National Space Agency under the Human Capital Development. Authors would also like to thank two anonymous reviewers who have helped improve the quality of this manuscript.
OM, JO, CA conceptualized the research. JO and CA participated in data analysis. OM drafted the manuscript. JO and CA supervised the entire work. All authors read and approved final manuscript.
Compliance with Ethical Standards
Conflict of interest
Authors declare that they have no conflict of interest.
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