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Spatial Epidemics Prediction by a Random Forest Classifier and Bayesian (309984)*Salha Qahl, University of Calgary
Keywords: Infectious disease, inference, Bayesian
This talk is concerned with the application of two major approaches in statistical inference of infectious disease dynamics. We describe the fitting of a class of epidemic models using Bayesian inference and Machine learning. We apply the two methods to real data from Measles outbreak. Our results suggest that both inference methods are computationally feasible in this context, and show a trade-off between statistical efficiency versus computational speed. The latter appears particularly relevant for real-time applications.