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dc.contributor.authorPeter Haddawyen_US
dc.contributor.authorMyat Su Yinen_US
dc.contributor.authorTanawan Wisanrakkiten_US
dc.contributor.authorRootrada Limsupavanichen_US
dc.contributor.authorPromporn Promraten_US
dc.contributor.authorSaranath Lawpoolsrien_US
dc.contributor.otherMahidol Universityen_US
dc.contributor.otherUniversity of Bremenen_US
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol.10607 LNAI, (2017), 106-111en_US
dc.description.abstract© Springer International Publishing AG 2017. Targeted intervention and resource allocation are essential in effective control of infectious diseases, particularly those like malaria that tend to occur in remote areas. Disease prediction models can help support targeted intervention, particularly if they have fine spatial resolution. But there is typically a tradeoff between spatial resolution and predictability of the time series of infection. In this paper we present a systematic method to quantify the relationship between spatial resolution and predictability of disease and to help provide guidance in selection of appropriate spatial resolution. We introduce a complexity-based approach to spatial hierarchical clustering. We show that use of reduction in Akaike Information Criterion (AIC) as a clustering criterion leads to significantly more rapid improvement in predictability than spatial clustering alone. We evaluate our approach with two years of malaria case data from northern Thailand.en_US
dc.rightsMahidol Universityen_US
dc.subjectComputer Scienceen_US
dc.titleAIC-Driven spatial hierarchical clustering: Case study for malaria prediction in northern Thailanden_US
dc.typeConference Paperen_US
Appears in Collections:Scopus 2016-2017

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