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Found 2 papers, 1 papers with code
Variational Learning on Aggregate Outputs with Gaussian Processes
While a typical supervised learning framework assumes that the inputs and the outputs are measured at the same levels of granularity, many applications, including global mapping of disease, only have access to outputs at a much coarser level than that of the inputs.
Improved prediction accuracy for disease risk mapping using Gaussian Process stacked generalisation
Maps of infectious disease---charting spatial variations in the force of infection, degree of endemicity, and the burden on human health---provide an essential evidence base to support planning towards global health targets.
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