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Modeling epidemics using cellular automata
S Hoya White1, A Martín Del Rey2, G Rodríguez Sánchez3
1Department of Applied Mathematics, E.T.S.I.I., Universidad de Salamanca, Avda. Fernández Ballesteros 2, 37700-Béjar, Salamanca, Spain.
Applied Mathematics and Computation
|April 15, 2020
Summary
This study introduces a cellular automata model to simulate epidemic spreading. The model tracks susceptible, infected, and recovered individuals, incorporating vaccination effects for enhanced disease modeling.
Area of Science:
- Computational epidemiology
- Mathematical modeling of infectious diseases
Background:
- Epidemic simulation is crucial for public health planning.
- Existing models may not fully capture population dynamics or intervention effects.
Purpose of the Study:
- To present a novel theoretical model for simulating epidemic spreading using cellular automata.
- To incorporate population vaccination as a key factor in disease transmission dynamics.
Main Methods:
- Development of a cellular automata model.
- Categorization of the population into susceptible, infected, and recovered states.
- Simulation of disease spread over time steps, considering vaccination.
Main Results:
- The model successfully simulates epidemic dynamics based on defined population classes.
- The impact of vaccination on disease spread can be analyzed within the model framework.
Conclusions:
- The proposed cellular automata model provides a flexible framework for epidemic simulation.
- This model can be a foundation for developing more sophisticated algorithms using real-world data.
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