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A deep learning based surrogate model for the parameter identification problem in probabilistic cellular automaton

F H Pereira1, P H T Schimit2, F E Bezerra3

  • 1Universidade Nove de Julho, Informatics and Knowledge Management Graduate Program, PPGI-UNINOVE, São Paulo, SP, Brazil; Universidade Nove de Julho, Industrial Engineering Graduate Program, PPGEP-UNINOVE, São Paulo, SP, Brazil.

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Machine Learning

Background:

  • Accurate estimation of epidemiological model coefficients is crucial for understanding disease spread.
  • Traditional probabilistic cellular automaton (PCA) models require extensive computational time for simulation and fine-tuning.

Purpose of the Study:

  • To develop a deep learning-based surrogate model to accelerate PCA simulations.
  • To maintain estimation precision while reducing computational demands.

Main Methods:

  • Trained a deep learning surrogate model using PCA data from regular lattices of varying sizes.
  • Input variables included individual movement and disease infectivity parameters.
  • Output variables comprised steady-state percentages of susceptible and infected individuals, R0, and peak infection time.

Main Results:

  • The surrogate model accurately predicts all output variables with low relative error.
  • Training time is independent of lattice size.
  • Evaluation time is minimal and independent of lattice size.

Conclusions:

  • The surrogate model offers fast simulation times for Susceptible-Infected-Removed (SIR) models within PCA frameworks.
  • It aids in model tuning, parameter estimation for inverse problems, and provides accurate estimates for large populations.