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Related Experiment Video

Updated: Jun 14, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Exploratory topological data analysis for spatio-temporal knowledge discovery in epidemiology.

Cheng Jie Ooi1, Nur Fariha Syaqina Zulkepli1, R U Gobithaasan1

  • 1School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Gelugor, Penang, Malaysia.

Spatial and Spatio-Temporal Epidemiology
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel topological framework using the Mapper algorithm to analyze dengue fever patterns in Malaysia. The method effectively identifies distinct spatial severity levels, aiding public health strategies.

Keywords:
DengueEpidemiologyMapper algorithmSpatio-temporal

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Area of Science:

  • Epidemiology
  • Network Science
  • Public Health

Background:

  • Dengue poses a significant public health challenge in Malaysia, straining healthcare resources.
  • Understanding complex spatio-temporal dengue patterns is crucial for effective control but difficult with traditional methods.

Purpose of the Study:

  • To propose and evaluate a topological-based framework using the Mapper algorithm for analyzing dengue dynamics in Malaysian districts.
  • To reveal distinct spatial severity regimes and improve the understanding of dengue transmission patterns.

Main Methods:

  • Applied the Mapper algorithm to create topological graphs of dengue dynamics across Malaysian districts.
  • Utilized network metrics like component density, path structures, and modularity to analyze the graphs.
  • Examined geographical and spatio-temporal characteristics of Mapper components.

Main Results:

  • Mapper graphs revealed distinct spatial severity regimes, with network topology correlating to dengue transmission intensity.
  • Weakly connected components indicated low-severity areas, while dense or isolated components signified moderate-to-high severity.
  • Modularity analysis decomposed districts into structurally distinct variability groups, and component analysis filtered noise while preserving temporal structures.

Conclusions:

  • Topological-based methods, specifically the Mapper algorithm, offer a powerful approach for exploratory epidemiological analysis.
  • This framework enhances the data-driven understanding of heterogeneous dengue patterns at the district level in Malaysia.
  • The approach aids in differentiating normal seasonal trends from anomalous dengue dynamics.