Evaluating Efficacy of Landsat-Derived Environmental Covariates for Predicting Malaria Distribution in Rural Villages of Vhembe District, South Africa

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 (D) 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 (a 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.

Investigating the Resurgence of Malaria Prevalence in South Africa Between 2015 and 2018: A Scoping Review

“…arabiensis) population dynamics on malaria transmission was investigated over 26,28,40,43], Limpopo [28] and Mpumalanga provinces [11]. Other studies also confirm that the vector population correlates with the resurgence [5,42,44]. More importantly, the resurgence has been traced to the arrival of new mosquito species across the epidemic regions [18,45].…”

“…A high correlation was reported between malaria cases and derived environmental metrics such as the Normalized Difference Water Index (NDVI) [5,31] Normalized Difference Water Index (NDWI) and altitude, water body and irrigated land [5,60] in Mpumalanga province. Furthermore, Malahlela [42] concluded that malaria resurgence in Limpopo province is traceable to vegetation moisture and vegetation greenness, which are proxy/surrogate for rainfall. The availability of an adequate amount of rainfall for the provision of ideal environmental conditions (such as open water, green vegetation) provides suitable breeding and resting places for the major vector.…”

Investigating the resurgence of malaria prevalence in South Africa between 2015 and 2018: A scoping review

“…Limpopo [24] and Mpumalanga provinces [8]. With statistical approach, other studies confirm mosquitos' impact on the resurgence [5,38,40]. More importantly, the resurgence has been traced to arrival of new mosquito species across the epidemic regions [15].…”

“…Half of the 24 papers highlight the importance of mosquito abundance on malaria resurgence across the epidemic regions in South Africa [5,8,15,21,[22][23][24][36][37][38][39][40]. For example, using climate-based mathematical models, the impact of mosquito (mainly An.…”

“…The resurgence in Mpumalanga province has been associated with normalized difference vegetation index (NDVI) [5,53] normalized difference water index (NDWI), altitude, water body and irrigated land [5,54]. Malahlela et al (2018) concluded that the resurgence in Limpopo province is traceable to vegetation moisture and vegetation greenness. We found no study in this regard over KwaZulu-Natal within the study period [38].…”