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Analysis of a discrete mathematical COVID-19 model
Thanin Sitthiwirattham1, Anwar Zeb2, Saowaluck Chasreechai3
1Mathematics Department, Faculty of Science and Technology, Suan Dusit University, Bangkok, Thailand.
Results in Physics
|August 17, 2021
Summary
This study uses a discrete SEIR model to understand COVID-19 spread and control. Numerical examples with real data from India and Algeria illustrate disease dynamics for susceptible and infected individuals.
Area of Science:
- Epidemiology
- Mathematical Biology
- Infectious Disease Modeling
Background:
- The COVID-19 pandemic necessitates understanding viral disease propagation.
- Effective control strategies require accurate modeling of disease spread.
- Previous models may not fully capture real-world dynamics.
Purpose of the Study:
- To present a discrete Susceptible-Exposed-Infectious-Recovered (SEIR) model for COVID-19.
- To analyze the fundamental properties of disease spread curves.
- To provide a simplified framework for understanding viral transmission.
Main Methods:
- Development of a discrete-time SEIR mathematical model.
- Analysis of model dynamics related to susceptible and infected populations.
- Application of the model using real-world COVID-19 data.
Main Results:
- The discrete SEIR model effectively describes COVID-19 propagation patterns.
- Analysis reveals key characteristics of susceptible and infected curves.
- Numerical simulations provide insights into disease dynamics in specific regions.
Conclusions:
- Discrete SEIR modeling offers a viable approach for studying COVID-19.
- Understanding disease curves is crucial for implementing control measures.
- The model's application to India and Algeria demonstrates its practical utility.
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