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A data-driven understanding of COVID-19 dynamics using sequential genetic algorithm based probabilistic cellular

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This study models COVID-19 spread using probabilistic cellular automata and genetic algorithms. The approach accurately predicts infection dynamics, aiding pandemic control strategies.

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Area of Science:

  • Computational epidemiology
  • Mathematical modeling of infectious diseases
  • Complex systems analysis

Background:

  • The COVID-19 pandemic poses significant global health and socioeconomic challenges.
  • Despite control measures, infection spread remains a critical concern, necessitating better understanding of dynamics.
  • Identifying key factors driving pandemic spread is crucial for mitigation and future preparedness.

Purpose of the Study:

  • To develop and validate a novel data-driven modeling approach for COVID-19 spread.
  • To utilize probabilistic cellular automata and genetic algorithms for accurate infection dynamics modeling.
  • To analyze COVID-19 spread across diverse countries and identify influencing factors.

Main Methods:

  • Probabilistic cellular automata (PCA) framework for simulating infection dynamics.
  • Sequential genetic algorithm (SGA) for efficient parameter estimation in the PCA model.
  • Application and analysis of the model to COVID-19 statistics from forty countries.

Main Results:

  • The PCA-SGA model demonstrates flexibility and robustness in predicting daily active cases, total infections, and deaths.
  • Analysis revealed significant variations in infection spread timelines across countries due to demographic and socioeconomic factors.
  • The model established substantial predictive power for understanding pandemic dynamics.

Conclusions:

  • Optimized cellular automata, driven by genetic algorithms, offer a powerful tool for data-driven pandemic modeling.
  • The methodology provides insights into the key drivers of COVID-19 spread.
  • This approach can inform strategies for managing current and future pandemics.