The rapid and often uncontrolled rural–urban migration in Sub-Saharan Africa is transforming urban landscapes expected to provide shelter for more than 50% of Africa’s population by 2030. Consequently, the burden of malaria is increasingly affecting the urban population, while socio-economic inequalities within the urban settings are intensified. Few studies, relying mostly on moderate to high resolution datasets and standard predictive variables such as building and vegetation density, have tackled the topic of modeling intra-urban malaria at the city extent. In this research, we investigate the contribution of very-high-resolution satellite-derived land-use, land-cover and population information for modeling the spatial distribution of urban malaria prevalence across large spatial extents. As case studies, we apply our methods to two Sub-Saharan African cities, Kampala and Dar es Salaam.
Malaria is frequently diagnosed in urban Kampala, despite low transmission intensity. To evaluate the association between recent travel out of Kampala and malaria, we conducted a matched case-control study. Cases were febrile outpatients with a positive malaria test; controls were febrile outpatients with a negative test. For every two cases, five controls were selected, matching on age.