Evaluating efficacy of landsat-derived environmental covariates for predicting malaria distribution in rural villages of Vhembe District, South Africa

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dc.contributor.author Malahlela, Oupa E.
dc.contributor.author Olwoch, Jane Mukarugwiza
dc.contributor.author Adjorlolo, Clement
dc.date.accessioned 2018-02-14T09:39:38Z
dc.date.issued 2018-03
dc.description.abstract 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. en_ZA
dc.description.department Geography, Geoinformatics and Meteorology en_ZA
dc.description.embargo 2019-03-01
dc.description.librarian hj2018 en_ZA
dc.description.sponsorship The South African National Space Agency under the Human Capital Development. en_ZA
dc.description.uri http://link.springer.com/journal/10393 en_ZA
dc.identifier.citation Malahlela, O.E., Olwoch, J.M. & Adjorlolo, C. Evaluating Efficacy of Landsat-Derived Environmental Covariates for Predicting Malaria Distribution in Rural Villages of Vhembe District, South Africa. EcoHealth (2018) 15: 23-40. https://doi.org/10.1007/s10393-017-1307-0. en_ZA
dc.identifier.issn 1612-9202 (print)
dc.identifier.issn 1612-9210 (online)
dc.identifier.other 10.1007/s10393-017-1307-0
dc.identifier.uri http://hdl.handle.net/2263/63951
dc.language.iso en en_ZA
dc.publisher Springer en_ZA
dc.rights © 2018 EcoHealth Alliance. The original publication is available at : http://link.springer.comjournal/10393. en_ZA
dc.subject Vhembe District Municipality en_ZA
dc.subject Malaria en_ZA
dc.subject Landsat 5 en_ZA
dc.subject Soil-adjusted vegetation index (SAVI) en_ZA
dc.title Evaluating efficacy of landsat-derived environmental covariates for predicting malaria distribution in rural villages of Vhembe District, South Africa en_ZA
dc.type Postprint Article en_ZA


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