Optimal Control for Economic Development During the Pandemic

Abstract

This research introduces a compartmental model designed to address the critical challenge of cost-effective pandemic modeling and analysis. The proposed framework extends the Susceptible, Exposed, Infectious, and Recovered (SEIR) model by incorporating environmental pathogen dynamics and a mortality factor within the compartmental model. To identify an optimal strategy for disease control, we formulate an optimization problem. To expedite the solution of this nonlinear optimization problem, we leverage Geometric Programming (GP), which is well-suited for handling convex optimization problems related to parameter control within the compartmental model. Additionally, we employ Particle Swarm Optimization (PSO) to explore potential solutions. Our simulation results underscore a key finding: the optimal disease control strategy is a dynamic function of time. This insight highlights the need to go beyond conventional tactics like managed isolation and quarantine, thereby improving our approach to pandemic management.

Keywords

• Evolutionary algorithm
• data science
• compartmental models
• convex optimization
• mathematical epidemiology
• artificial intelligence