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Competitive dual-strain SIS epidemiological models with awareness programs in heterogeneous networks: two modeling
1Department of Mathematics, Shanghai University, Shanghai, 200444, China. mengfeng_sun@shu.edu.cn.
Journal of Mathematical Biology
|June 19, 2023
Summary
This study introduces two mathematical models for multi-strain epidemic diseases, incorporating media-driven awareness campaigns. The research reveals how awareness programs can control epidemic spread and identifies optimal strategies for their implementation.
Area of Science:
- Mathematical Epidemiology
- Network Science
- Public Health Interventions
Background:
- Epidemic diseases often involve multiple pathogen strains.
- Media campaigns play a crucial role in public health responses to epidemics.
Purpose of the Study:
- To propose and analyze two novel multi-strain SIS epidemic models in heterogeneous networks.
- To investigate the impact of media-driven awareness programs on epidemic dynamics.
- To determine conditions for strain extinction, coexistence, and dominance, and to find optimal control strategies.
Main Methods:
- Development of two multi-strain SIS epidemic models with varying awareness program incorporation.
- Analytical derivation of basic reproductive numbers and conditions for strain dynamics.
- Formulation and solution of an optimal control problem for awareness campaigns.
- Numerical simulations to explore complex dynamics and validate theoretical findings.
Main Results:
- Analytical conditions for strain extinction, coexistence, and dominance were established.
- Hopf bifurcations and periodic solutions were identified in the first model.
- Multistability was observed in the second model, showing a multistage impact of awareness growth rates on epidemic size.
- Optimal control strategies for awareness campaigns were identified.
Conclusions:
- Awareness programs significantly influence multi-strain epidemic dynamics.
- Effective control of epidemics can be achieved by optimizing awareness transmission, reducing memory fading, and ensuring rapid program growth.
- The study highlights new phenomena like multistability and Hopf bifurcations in epidemic modeling.
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