Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach

  • Sami Ullah | sami.khan3891@gmail.com Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia. http://orcid.org/0000-0001-7371-7297
  • Hanita Daud Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia.
  • Sarat C. Dass Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia.
  • Habib Nawaz Khan Department of Economics and Management Sciences, University of Science and Technology, Bannu, Pakistan.
  • Alamgir Khalil Department of Statistics, University of Peshawar, Pakistan.

Abstract

Ability to detect potential space-time clusters in spatio-temporal data on disease occurrences is necessary for conducting surveillance and implementing disease prevention policies. Most existing techniques use geometrically shaped (circular, elliptical or square) scanning windows to discover disease clusters. In certain situations, where the disease occurrences tend to cluster in very irregularly shaped areas, these algorithms are not feasible in practise for the detection of space-time clusters. To address this problem, a new algorithm is proposed, which uses a co-clustering strategy to detect prospective and retrospective space-time disease clusters with no restriction on shape and size. The proposed method detects space-time disease clusters by tracking the changes in space–time occurrence structure instead of an in-depth search over space. This method was utilised to detect potential clusters in the annual and monthly malaria data in Khyber Pakhtunkhwa Province, Pakistan from 2012 to 2016 visualising the results on a heat map. The results of the annual data analysis showed that the most likely hotspot emerged in three sub-regions in the years 2013-2014. The most likely hotspots in monthly data appeared in the month of July to October in each year and showed a strong periodic trend.

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Published
2017-11-06
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Original Articles
Keywords:
Space-time disease clusters, Co-clustering algorithm, Likelihood ratio, Pakistan
Statistics
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How to Cite
Ullah, S., Daud, H., Dass, S. C., Khan, H. N., & Khalil, A. (2017). Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach. Geospatial Health, 12(2). https://doi.org/10.4081/gh.2017.567