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Toolbox to simulate and mitigate COVID-19 propagation.

Zahrate El Oula Frihi¹, Julian Barreiro-Gomez^2,3, Salah Eddine Choutri^2,3

¹Lab. of Probability and Statistics (LaPS), Department of Mathematics, Badji-Mokhtar University, B.P.12, 23000, Annaba, Algeria.

Softwarex

|February 24, 2021

Summary

This study introduces an age-and-migration-dependent Markov chain model to simulate COVID-19 spread. The model, with a user-friendly MATLAB toolbox, allows for international and local virus propagation simulations.

Keywords:

60-04 60J27 COVID-19 Markov chain Simulation Toolbox

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Area of Science:

Epidemiology
Mathematical Modeling
Computational Biology

Background:

The COVID-19 pandemic highlighted the need for robust epidemiological models.
Understanding virus propagation dynamics, considering demographic factors like age and migration, is crucial for effective public health strategies.

Purpose of the Study:

To develop and present an age-and-migration-dependent Markov-chain-based model for simulating COVID-19 propagation.
To provide a user-friendly MATLAB-based toolbox to facilitate the application of this model.
To demonstrate the model's utility through international and local simulation examples.

Main Methods:

Development of a Markov-chain model incorporating age structure and migration patterns.
Implementation of the model within a MATLAB-based toolbox for accessibility.
Simulation of COVID-19 propagation scenarios at both international and country-specific levels.

Main Results:

The presented model effectively simulates COVID-19 propagation by accounting for age and migration.
The MATLAB toolbox offers a practical tool for researchers and policymakers to explore different scenarios.
Simulations illustrate the potential impact of age demographics and migration on virus spread.

Conclusions:

The age-and-migration-dependent Markov chain model provides a valuable framework for understanding and predicting COVID-19 transmission.
The accompanying MATLAB toolbox enhances the accessibility and applicability of sophisticated epidemiological modeling.
This approach supports informed decision-making in public health interventions and resource allocation.