NDLI: Complexity-Based Spatial Hierarchical Clustering for Malaria Prediction.

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Complexity-Based Spatial Hierarchical Clustering for Malaria Prediction.

Content Provider	Europe PMC
Author	Haddawy, Peter Yin, Myat Su Wisanrakkit, Tanawan Limsupavanich, Rootrada Promrat, Promporn Lawpoolsri, Saranath Sa-angchai, Patiwat
Abstract	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, choosing an appropriate resolution is a difficult problem since choice of spatial scale can have a significant impact on accuracy of predictive models. In this paper, we introduce a new approach to spatial clustering for disease prediction we call complexity-based spatial hierarchical clustering. The technique seeks to find spatially compact clusters that have time series that can be well characterized by models of low complexity. We evaluate our approach with 2 years of malaria case data from Tak Province in northern Thailand. We show that the technique’s use of reduction in Akaike information criterion (AIC) and Bayesian information criterion (BIC) as clustering criteria leads to rapid improvement in predictability and significantly better predictability than clustering based only on minimizing spatial intra-cluster distance for the entire range of cluster sizes over a variety of predictive models and prediction horizons.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC8982740&blobtype=pdf
ISSN	25094971
Volume Number	2
DOI	10.1007/s41666-018-0031-z
PubMed Central reference number	PMC8982740
Issue Number	4
PubMed reference number	35415412
Journal	Journal of Healthcare Informatics Research [J Healthc Inform Res]
e-ISSN	2509498X
Language	English
Publisher	Springer International Publishing
Publisher Date	2018-08-21
Publisher Place	Gewerbestrasse 11, Cham, Ch 6330, Switzerland
Access Restriction	Open
Rights License	© Springer Nature Switzerland AG 2018
Subject Keyword	Malaria prediction Spatial epidemiology Spatial clustering Akaike information criterion Bayesian information criterion
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Information Systems Health Informatics Computer Science Applications

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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