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An open-data-driven agent-based model to simulate infectious disease outbreaks.

Elizabeth Hunter1, Brian Mac Namee2, John Kelleher1

  • 1Technological University Dublin, Dublin, Ireland.

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Summary
This summary is machine-generated.

Agent-based models simulate infectious disease outbreaks, revealing that town characteristics alone do not predict outbreak severity. Individual behaviors and factor interactions drive disease dynamics, as shown in an Irish measles outbreak simulation.

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Area of Science:

  • Epidemiology
  • Computational modeling
  • Public health

Background:

  • Infectious disease outbreaks are complex, influenced by vaccination, population density, and age structure.
  • Understanding outbreak dynamics requires models that capture individual behaviors and factor interactions.
  • Agent-based models (ABMs) can reveal emergent patterns not explicitly programmed.

Purpose of the Study:

  • To develop and validate a data-driven agent-based model for simulating airborne infectious disease spread.
  • To investigate how town characteristics and agent interactions influence outbreak size and severity.
  • To test the hypothesis that complex interactions, not just town features, dictate outbreak outcomes.

Main Methods:

  • Created a data-driven agent-based model using open data to simulate airborne disease spread.
  • Simulated a 2012 measles outbreak in Schull, Ireland, across 33 towns.
  • Correlated model results with town characteristics (population, area, vaccination rates, age structure).

Main Results:

  • Simulated outbreaks showed minimal correlation with individual town characteristics.
  • Model results indicated that interactions between factors and agent decisions significantly influenced outbreak dynamics.
  • The agent-based model successfully captured variations in outbreak patterns.

Conclusions:

  • Agent-based models are effective tools for understanding infectious disease outbreak complexity.
  • Outbreak severity is driven by intricate interactions and individual behaviors, not solely by demographic factors.
  • This modeling approach provides valuable insights for public health preparedness and intervention strategies.