Evaluating Efficacy of Landsat-Derived Environmental Covariates for Predicting Malaria Distribution in Rural Villages of Vhembe District, South Africa
Bibliographic Data
| ID | 19528163 |
|---|---|
| Authors | Oupa E Malahlela (0000-0002-3416-9213, South African National Space Agency, corresponding author), Jane Mukarugwiza Olwoch (University of Pretoria), Clement Adjorlolo (South African National Space Agency) |
| Year | 2018 |
| Volume | 15 |
| Issue | 1 |
| Pages | 23-40 |
| Publication date | 2018-03-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | EcoHealth (JOURNAL) |
| Journal identifiers | ISSN: 1612-9202 • E-ISSN: 1612-9210 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s10393-017-1307-0 |
| PMID | 29330677 |
| OpenAlex | W2783701285 |
| Language | EN |
| References cited | 57 |
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 2 ) 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 1 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
Anopheles · Biology · Climate change · Geography · Malaria · Normalized Difference Vegetation Index · Veterinary medicine · Environmental Science · Malaria Research and Control · Medicine · Mosquito-borne diseases and control · Species Distribution and Climate Change · Ecology
Selecting pseudo‐absences for species distribution models
NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space
A soil-adjusted vegetation index (Savi)
Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery
Use of normalized difference built-up index in automatically mapping urban areas from TM imagery
Maximum entropy modeling of species geographic distributions
Sample selection bias and presence‐only distribution models
Could Malaria Control Programmes be Timed to Coincide with Onset of Rainfall
Long-Run Relative Importance of Temperature as the Main Driver to Malaria Transmission in Limpopo Province, South Africa
| Highly cited | No |
|---|