John Quinn

AAAI Conference 2012 Conference Paper

Coupling Spatiotemporal Disease Modeling with Diagnosis

• Martin Mubangizi
• Caterine Ikae
• Athina Spiliopoulou
• John Quinn

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.

AAAI Conference 2011 Conference Paper

Modeling and Monitoring Crop Disease in Developing Countries

• John Quinn
• Kevin Leyton-Brown
• Ernest Mwebaze

NeurIPS Conference 2005 Conference Paper

Factorial Switching Kalman Filters for Condition Monitoring in Neonatal Intensive Care

• Christopher Williams
• John Quinn
• Neil McIntosh

The observed physiological dynamics of an infant receiving intensive care are affected by many possible factors, including interventions to the baby, the operation of the monitoring equipment and the state of health. The Factorial Switching Kalman Filter can be used to infer the presence of such factors from a sequence of observations, and to estimate the true values where these observations have been corrupted. We apply this model to clinical time series data and show it to be effective in identifying a number of artifactual and physiological patterns.