Dynamic stability and optimal control of SIS I RS epidemic network
1School of Mathematical and Statistics, Guizhou University, Guiyang 550025, Guizhou, China.
Chaos, Solitons, and Fractals
|September 5, 2022
Summary
This study introduces a network model for infectious disease spread, finding that combined isolation and vaccination strategies minimize disease scale and cost. The model was validated using COVID-19 data.
Area of Science:
- Epidemiology
- Network Science
- Mathematical Modeling
Background:
- Infectious disease transmission is a complex process influenced by population interactions.
- Effective control strategies are crucial for mitigating disease outbreaks.
- Network-based models offer a powerful framework for understanding disease dynamics.
Purpose of the Study:
- To develop and analyze a complex network-based Susceptible-Infected-Recovered-Susceptible (SIRS) model.
- To incorporate and evaluate the impact of three control measures: isolation of susceptible individuals, isolation of infected individuals, and vaccination.
- To determine the optimal control strategies for minimizing disease scale and cost.
Main Methods:
- Development of a network-based SIRS epidemiological model.
- Application of optimal control theory to identify time-varying control strategies.
- Sensitivity analysis of model parameters and optimal control interventions.
- Numerical simulations to analyze model stability and control effectiveness.
- Fitting the model to real-world COVID-19 data.
Main Results:
- The study calculated the threshold for infectious disease transmission within the network model.
- Optimal control strategies were derived, demonstrating the existence and solution for minimizing disease impact.
- Simulations confirmed that simultaneous application of isolation and vaccination yields the minimal disease scale and cost.
- The model showed good agreement when fitted to COVID-19 data.
Conclusions:
- Integrated control strategies, combining isolation and vaccination, are highly effective in managing infectious diseases.
- Network-based modeling provides valuable insights into disease dynamics and optimal intervention planning.
- The developed model serves as a robust tool for analyzing and predicting disease spread and evaluating control measures.
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