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Background <p>Understanding disease transmission through human contact networks is critical for public health decision-making. Traditional epidemic models often assume homogeneous mixing or static contact patterns, misrepresenting real-world infection dynamics. The increasing availability of empirical human mobility data presents an opportunity to overcome these limitations. However, working with temporal network data introduces computational challenges, particularly for large populations.</p> Results <p>We introduce <span>Replay</span>, a GPU-accelerated framework for efficiently simulating infectious disease spread over temporal contact networks. <span>Replay</span> transforms timestamped empirical contact data into duration-weighted contact graphs and employs sparse matrix operations to account for each exposure event that occurs. By literally “replaying” observed contacts across various scenarios, it enables realistic, data-driven epidemic simulations. This matrix-based approach facilitates large-scale Monte Carlo simulations, executing thousands of parallel runs per second on commodity hardware. Benchmarks demonstrate substantial GPU-based performance gains compared to equivalent CPU-based algorithms. Crucially, <span>Replay</span> responds to localized outbreaks and superspreader events without requiring reparameterization.</p> Conclusions <p><span>Replay</span> offers a practical solution for modeling disease transmission using empirical contact data in an efficient and realistic way. It is especially valuable for public health planning, hospital infection control, and retrospective outbreak analysis.</p>

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Replay: a GPU-accelerated tool for temporal contact network epidemiology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月22日

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  • Harrison J. Greenlee,
  • Assefaw H. Gebremedhin,
  • Eric T. Lofgren

摘要

Background

Understanding disease transmission through human contact networks is critical for public health decision-making. Traditional epidemic models often assume homogeneous mixing or static contact patterns, misrepresenting real-world infection dynamics. The increasing availability of empirical human mobility data presents an opportunity to overcome these limitations. However, working with temporal network data introduces computational challenges, particularly for large populations.

Results

We introduce Replay, a GPU-accelerated framework for efficiently simulating infectious disease spread over temporal contact networks. Replay transforms timestamped empirical contact data into duration-weighted contact graphs and employs sparse matrix operations to account for each exposure event that occurs. By literally “replaying” observed contacts across various scenarios, it enables realistic, data-driven epidemic simulations. This matrix-based approach facilitates large-scale Monte Carlo simulations, executing thousands of parallel runs per second on commodity hardware. Benchmarks demonstrate substantial GPU-based performance gains compared to equivalent CPU-based algorithms. Crucially, Replay responds to localized outbreaks and superspreader events without requiring reparameterization.

Conclusions

Replay offers a practical solution for modeling disease transmission using empirical contact data in an efficient and realistic way. It is especially valuable for public health planning, hospital infection control, and retrospective outbreak analysis.