title: Spatiotemporal Bayesian Networks for Malaria Prediction: Case Study of Northern Thailand.
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.
authors: Faculty of Information and Communication Technology, Mahidol University.
authors: Peter
authors: P
authors: Haddawy
authors: Faculty of Information and Communication Technology, Mahidol University.
authors: Rangwan
authors: R
authors: Kasantikul
authors: Faculty of Information and Communication Technology, Mahidol University.
authors: A H M Imrul
authors: AH
authors: Hasan
authors: Faculty of Information and Communication Technology, Mahidol University.
authors: Chunyanuch
authors: C
authors: Rattanabumrung
authors: Faculty of Information and Communication Technology, Mahidol University.
authors: Pichamon
authors: P
authors: Rungrun
authors: Faculty of Information and Communication Technology, Mahidol University.
authors: Natwipa
authors: N
authors: Suksopee
authors: Faculty of Information and Communication Technology, Mahidol University.
authors: Saran
authors: S
authors: Tantiwaranpant
authors: Faculty of Information and Communication Technology, Mahidol University.
authors: Natcha
authors: N
authors: Niruntasuk
mesh_terms: D001499:Bayes Theorem; D004777:Environment; D006801:Humans; D015994:Incidence; D008288:Malaria / Q000453:epidemiology*; D015233:Models, Statistical; D013785:Thailand / Q000453:epidemiology