Many statistical modeling and simulation-based methods have been proposed to detect, simulate and predict disease in space and time. However, these models either make use of unprecedented amount of domain-related parameters, or suffer from issues of global-generality and local over fitting. In this work, a methodology is proposed for detection, simulation and prediction of disease in a region based on an aggregated idea of spatio-temporal zoning. Specifically, a novel method is developed to model meso-scale processes by capturing spatio-temporal correlation leveraging both potential dependencies among local coefficients, and the potential incongruities within global models for prediction of trends in the context of disease management. The method is able to infer causality, and simulate the spread given the initial sources derived primarily from the spatio-temporal history of the disease. Illustrative case study of Salmonellosis disease in USA is presented to demonstrate utility of the method. The prediction trends mimic the observed event data better than the standard methodology even though magnitudinal predictions need to be improved. It is evident that such a methodology will help prioritize decision-making process for better risk assessment and management including disease outbreak.
['Vipul Raheja', 'K. S. Rajan']
Disease Occurrence Prediction Based on Spatio-temporal Characterization --- A Mesoscale Study for Knowledge and Pattern Discovery