Publication: AIC-Driven spatial hierarchical clustering: Case study for malaria prediction in northern Thailand
Issued Date
2017-01-01
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ISSN
16113349
03029743
03029743
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2-s2.0-85034221721
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Mahidol University
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SCOPUS
Bibliographic Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol.10607 LNAI, (2017), 106-111
Suggested Citation
Peter Haddawy, Myat Su Yin, Tanawan Wisanrakkit, Rootrada Limsupavanich, Promporn Promrat, Saranath Lawpoolsri AIC-Driven spatial hierarchical clustering: Case study for malaria prediction in northern Thailand. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol.10607 LNAI, (2017), 106-111. doi:10.1007/978-3-319-69456-6_9 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/42417
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Title
AIC-Driven spatial hierarchical clustering: Case study for malaria prediction in northern Thailand
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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.