1) Finding a scientific and plausible way of adding human mobility information to disease models. Movement of an infected person from one area, can greatly contribute to a disease risk of another area more than proximity of the two locations. We are using call data records,CDR, for mining human mobility. One of the intermediate results in this work is shown in the figure below.
2) Processing of satellite imagery for both climatic proxies (like rainfall from Rainfall Estimates and normalized difference vegetation index - NDVI, temperature from land surface temperature - LST) and environmental factors (like topology from the digital elevation model - DEM. As part of this, we are also trying to visually understand the distribution of these factors to gain an insight into how well to model the Malaria disease over Uganda. Taking just a portion of Uganda in the figure below
mTrac, a project supported by UNICEF, is a simple SMS data collection service, which allows health centers to text their weekly reports.
This work is a joint collaboration between the Machine Learning Lab at the University of Sheffield and AI Lab at Makerere University.