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Trop Med Int Health. 2016 May;21(5):675-86. doi: 10.1111/tmi.12680. Epub 2016 Mar 3.

Environmental factors and population at risk of malaria in Nkomazi municipality, South Africa.

Author information

Centre for Geoinformation Science, Department of Geography, Geoinformation and Meteorology, University of Pretoria, Hatfield, South Africa.
Earth Observation Directorate, South African National Space Agency, Pretoria, South Africa.
Department of Geography, University of Ibadan, Ibadan, Nigeria.
Centre for Environmental Study, Department of Geography, Geoinformation and Meteorology, University of Pretoria, Hatfield, South Africa.



Nkomazi local municipality of South Africa is a high-risk malaria region with an incidence rate of about 500 cases per 100 000. We examined the influence of environmental factors on population (age group) at risk of malaria.


r software was used to statistically analyse data. Using remote sensing technology, a Landsat 8 image of 4th October 2015 was classified using object-based classification and a 5-m resolution. Spot height data were used to generate a digital elevation model of the area.


A total of 60 718 malaria cases were notified across 48 health facilities in Nkomazi municipality between January 1997 and August 2015. Malaria incidence was highly associated with irrigated land (P = 0.001), water body (P = 0.011) and altitude ≤400 m (P = 0.001). The multivariate model showed that with 10% increase in the extent of irrigated areas, malaria risk increased by almost 39% in the entire study area and by almost 44% in the 2-km buffer zone of selected villages. Malaria incidence is more pronounced in the economically active population aged 15-64 and in males. Both incidence and case fatality rate drastically declined over the study period.


A predictive model based on environmental factors would be useful in the effort towards malaria elimination by fostering appropriate targeting of control measures and allocating of resources.


LULC; Landsat; Malaria; clasificación basada en objetos; classification basée sur l'objet; datos de uso y cobertura del suelo; elevación; elevation; environment; environnement; land use/land cover; malaria; medio ambiente; object-based classification; paludisme; remote sensing; sensores remotos; télédétection; élévation

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