ABSTRACT
While a diversity of modeling technique have been used to create predictive models of malaria, no work has made use of Bayesian networks. Bayes nets are attractive due to their ability to represent uncertainty, model time lagged and nonlinear relations, and provide explanations of inferences. This paper explores the use of Bayesian networks to model malaria, demonstrating the approach by creating a village level model with weekly temporal resolution for Tha Song Yang district in northern Thailand. The network is learned using data on cases and environmental covariates. The network models incidence over time as well as evolution of the environmental variables, and captures time lagged and nonlinear effects. Out of sample evaluation shows the model to have high accuracy for one and two week predictions.