AAAI Publications, Twenty-Sixth AAAI Conference on Artificial Intelligence

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Coupling Spatiotemporal Disease Modeling with Diagnosis
Martin Gordon Mubangizi, Caterine Ikae, Athina Spiliopoulou, John A. Quinn

Last modified: 2012-07-12


Modelling the density of an infectious disease in space and time is a task generally carried out separately from the diagnosis of that disease in individuals. These two inference problems are complementary, however: diagnosis of disease can be done more accurately if prior information from a spatial risk model is employed, and in turn a disease density model can benefit from the incorporation of rich symptomatic information rather than simple counts of presumed cases of infection. We propose a unifying framework for both of these tasks, and illustrate it with the case of malaria. To do this we first introduce a state space model of malaria spread, and secondly a computer vision based system for detecting plasmodium in microscopical blood smear images, which can be run on location-aware mobile devices. We demonstrate the tractability of combining both elements and the improvement in accuracy this brings about.


Computational Sustainability and AI::Natural resources and ecosystems ** Machine Learning::Time-series/Data Streams

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