AI-DEV group paper entitled "Coupling Spatiotemporal Disease Modeling with Diagnosis" in July this year (2012) won the Community Computing Consortium Outstanding Student Paper Award in the Computational Sustainability and AI track, AAAI-12 Conference in Toronto, Canada.
The idea in the paper can be summarized using the figure above. Disease density modeling (that may result in a risk map) and disease diagnosis are important tasks in biosurveillance. These tasks are always performed separately but can complement each other. For example, if the location of an individual to be diagnosed is known, the risk at that location can be used as a prior in the diagnosis and in turn, the map can be updated with the result of the diagnosis.
In this paper, we present a general framework of combining these two tasks and we use malaria as a case study to demonstrate the tractability of combining both tasks and the improvement in accuracy this brings about.Avaliable as [PDF] [BiBTeX]