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Optimizing agent-based transmission models for infectious diseases.

Lander Willem1,2,3, Sean Stijven4,5, Engelbert Tijskens6

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Optimizing agent-based models (ABMs) for infectious disease transmission significantly reduces simulation time. Key strategies include organizing population data by social contacts and health status for faster disease propagation calculations.

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Area of Science:

  • Computational epidemiology
  • Infectious disease modeling
  • High-performance computing

Background:

  • Advancements in computational power enable large-scale agent-based models (ABMs).
  • Complex hardware necessitates optimized software designs for efficient ABM simulations on modern workstations.

Purpose of the Study:

  • To investigate performance optimization techniques for discrete-time agent-based models (ABMs) simulating close-contact disease transmission.
  • To identify software design adaptations that leverage high-performance computing for infectious disease modeling.

Main Methods:

  • Implemented data locality optimizations by sorting the population based on social contact clusters.
  • Restructured data storage by separating person attributes instead of using person objects.
  • Introduced sorting by health status before simulating disease transmission.
  • Evaluated the impact of parallel programming techniques, including scheduling and workload chunk size.

Main Results:

  • Sorting by social contact clusters reduced simulation time by a factor of two.
  • Separating person attributes improved data locality and model performance.
  • Sorting by health status decreased run time by 26% to over 70%, depending on parameters.
  • Parallel programming yielded significant speedups, but performance decreased with more cores.

Conclusions:

  • Performance optimization of ABM simulator code yields substantial run time reductions.
  • Key optimization steps involve data structure design and pre-simulation sorting by health status.
  • Findings are applicable to a broad range of infectious disease ABMs; future studies should explore data management, algorithms, and parallelization impacts.