title: Supervised learning and prediction of spatial epidemics.
abstract: Parameter estimation for mechanistic models of infectious disease can be computationally intensive. Nsoesie et al. (2011) introduced an approach for inference on infectious disease data based on the idea of supervised learning. Their method involves simulating epidemics from various infectious disease models, and using classifiers built from the epidemic curve data to predict which model were most likely to have generated observed epidemic curves. They showed that the classification approach could fairly identify underlying characteristics of the disease system, without fitting various transmission models via, say, Bayesian Markov chain Monte Carlo. We extend this work to the case where the underlying infectious disease model is inherently spatial. Our goal is to compare the use of global epidemic curves for building the classifier, with the use of spatially stratified epidemic curves. We demonstrate these methods on simulated data and apply the method to analyze a tomato spotted wilt virus epidemic dataset.
authors.affiliation: Department of Mathematics and Statistics, University of Guelph, ON N1G2W1, Canada. Electronic address: gpokhare@uoguelph.ca.
authors.forename: Gyanendra
authors.initials: G
authors.lastname: Pokharel
authors.affiliation: Department of Mathematics and Statistics, University of Guelph, ON N1G2W1, Canada. Electronic address: rdeardon@uoguelph.ca.
authors.forename: Rob
authors.initials: R
authors.lastname: Deardon
mesh_terms: D001499:Bayes Theorem; D003141:Communicable Diseases / Q000453:epidemiology*; D003198:Computer Simulation; D058872:Epidemics / Q000706:statistics & numerical data*; D006801:Humans; D008390:Markov Chains; D008962:Models, Theoretical; D009010:Monte Carlo Method; D012044:Regression Analysis; D015203:Reproducibility of Results; D062206:Spatial Analysis; D017865:Tospovirus
keywords: Random forests; Spatial epidemic; Spatial stratification; Supervised learning