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Computational Model on COVID-19 Pandemic Using Probabilistic Cellular Automata.

Sayantari Ghosh1, Saumik Bhattacharya2

  • 1Department of Physics, National Institute of Technology Durgapur, Durgapur, India.

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|April 28, 2021
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Summary
This summary is machine-generated.

This study introduces a novel probabilistic cellular automata (PCA) model to analyze COVID-19 spread. The model incorporates spatial dynamics and behavioral factors to understand epidemic control strategies.

Keywords:
Lattice epidemicMathematical model of epidemiologyProbabilistic cellular automata

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Area of Science:

  • Epidemiology
  • Computational modeling
  • Public health

Background:

  • Coronavirus disease (COVID-19) became a global pandemic in March 2020, necessitating interventions like lockdowns and testing.
  • Understanding the effectiveness of these measures is crucial for epidemic control.

Purpose of the Study:

  • To develop and analyze a data-driven probabilistic cellular automata (PCA) model for COVID-19 spread.
  • To investigate the spatial and temporal dynamics of the epidemic under various mitigation strategies.

Main Methods:

  • Proposed a probabilistic cellular automata (PCA) based epidemiological model.
  • Incorporated data on virus chronology, symptoms, pathogenesis, and transmissivity.
  • Performed computational analyses considering spatial dynamics, population density, and testing efficiency.

Main Results:

  • The PCA model captures epidemic dynamics and fluctuations, including spatial social distancing effects.
  • Analysis revealed insights into the variability of epidemic data across different countries.
  • Identified key factors influencing contrasting epidemic trajectories.

Conclusions:

  • This is the first known attempt to model COVID-19 spread using PCA, offering both spatial and temporal insights.
  • The model provides a framework for understanding the impact of different infection parameters and mitigation strategies.
  • Highlights the importance of data-driven modeling for effective public health interventions.