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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/42653
Title: Spatiotemporal Bayesian networks for malaria prediction: Case study of northern Thailand
Authors: Peter Haddawy
Rangwan Kasantikul
A. H.M.Imrul Hasan
Chunyanuch Rattanabumrung
Pichamon Rungrun
Natwipa Suksopee
Saran Tantiwaranpant
Natcha Niruntasuk
Mahidol University
Keywords: Engineering;Health Professions
Issue Date: 1-Jan-2017
Citation: Studies in Health Technology and Informatics. Vol.228, (2017), 773-777
Abstract: ©2016 European Federation for Medical Informatics (EFMI) and IOS Press. 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.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85033486170&origin=inward
http://repository.li.mahidol.ac.th/dspace/handle/123456789/42653
ISSN: 18798365
09269630
Appears in Collections:Scopus 2016-2017

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