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Updated: Mar 15, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Temporal network structures controlling disease spreading.

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  • 1Department of Energy Science, Sungkyunkwan University, Suwon 440-746, Korea.

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|September 15, 2016
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Summary
This summary is machine-generated.

Disease spreading is more influenced by temporal network structures than static ones. Long-term temporal patterns, such as node and link turnover, are crucial for understanding disease dynamics.

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Area of Science:

  • Epidemiology
  • Network Science
  • Computational Biology

Background:

  • Understanding disease transmission relies on accurate models of human contact patterns.
  • Previous studies often simplify contact networks, potentially overlooking crucial dynamic aspects.
  • Empirical data offers a more realistic basis for studying epidemic spread.

Purpose of the Study:

  • To compare the impact of different network representations on disease spreading dynamics.
  • To determine whether temporal or static network structures are more influential.
  • To identify key network features driving epidemic behavior.

Main Methods:

  • Analysis of eight empirical human contact datasets.
  • Comparison of disease spreading simulations on temporal networks, static networks, and fully connected topologies.
  • Utilizing 32 network measures to analyze differences between representations.

Main Results:

  • Temporal network structures significantly impact disease spreading more than static or fully connected ones.
  • Differences in time to extinction and average outbreak size were smaller between static and fully connected networks compared to temporal and static networks.
  • Long-term temporal structures, specifically node and link turnover, were identified as most critical for spreading dynamics.

Conclusions:

  • Temporal dynamics are essential for accurately modeling disease spread.
  • Simplifying contact networks to static representations can lead to significant inaccuracies.
  • Network temporal structures, particularly turnover, are key determinants of epidemic outcomes.