Spatiotemporal Bayesian networks for malaria prediction

Publication date: Available online 11 December 2017 Source:Artificial Intelligence in Medicine Author(s): Peter Haddawy, A.H.M. Imrul Hasan, Rangwan Kasantikul, Saranath Lawpoolsri, Patiwat Sa-angchai, Jaranit Kaewkungwal, Pratap Singhasivanon Targeted intervention and resource allocation are essential for effective malaria control, particularly in remote areas, with predictive models providing important information for decision making. 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. This paper explores the use of Bayesian networks to model malaria, demonstrating the approach by creating village level models with weekly temporal resolution for Tha Song Yang district in northern Thailand. The networks are learned using data on cases and environmental covariates. Three types of networks are explored: networks for numeric prediction, networks for outbreak prediction, and networks that incorporate spatial autocorrelation. Evaluation of the numeric prediction network shows that the Bayes net has prediction accuracy in terms of mean absolute error of about 1.4 cases for 1 week prediction and 1.7 cases for 6 week prediction. The network for outbreak prediction has an ROC AUC above 0.9 for all prediction horizons. Comparison of prediction accuracy...
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research