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Complexity-Based Spatial Hierarchical Clustering for Malaria Prediction.

Peter Haddawy1,2, Myat Su Yin1, Tanawan Wisanrakkit1

  • 1Faculty of ICT, Mahidol University, Nakhon Pathom, Thailand.

Journal of Healthcare Informatics Research
|April 13, 2022
PubMed
Summary

This study introduces complexity-based spatial hierarchical clustering for infectious disease prediction. This method improves malaria prediction accuracy in remote areas by finding optimal spatial resolutions.

Keywords:
Akaike information criterionBayesian information criterionMalaria predictionSpatial clusteringSpatial epidemiology

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Area of Science:

  • Epidemiology
  • Spatial analysis
  • Computational biology

Background:

  • Effective infectious disease control, especially for malaria in remote regions, requires targeted interventions and resource allocation.
  • Disease prediction models are crucial for supporting these interventions, but their accuracy is sensitive to spatial resolution.
  • Selecting an appropriate spatial scale for predictive models presents a significant challenge.

Purpose of the Study:

  • To introduce a novel spatial clustering technique, complexity-based spatial hierarchical clustering, for enhancing disease prediction.
  • To identify spatially compact clusters with time series data that can be modeled with low complexity.
  • To evaluate the effectiveness of this new clustering approach using malaria case data.

Main Methods:

  • Developed a complexity-based spatial hierarchical clustering algorithm.
  • Utilized Akaike information criterion (AIC) and Bayesian information criterion (BIC) reduction as clustering criteria.
  • Applied the method to two years of malaria case data from Tak Province, Thailand.
  • Compared the new technique against clustering based solely on minimizing spatial intra-cluster distance.

Main Results:

  • The complexity-based clustering approach demonstrated rapid improvement in prediction accuracy.
  • The technique significantly outperformed traditional spatial clustering methods across various cluster sizes.
  • Improved predictability was observed for multiple predictive models and prediction horizons.

Conclusions:

  • Complexity-based spatial hierarchical clustering offers a robust method for determining optimal spatial resolutions in disease prediction.
  • This approach enhances the accuracy and effectiveness of infectious disease control strategies, particularly for diseases like malaria in resource-limited settings.
  • The use of AIC and BIC reduction provides a data-driven criterion for optimizing spatial clustering in epidemiological modeling.