Distributed Model Predictive Control of Epidemics via Transportation Flow Regulations

The COVID-19 pandemic highlights the necessity of epidemic control while minimizing societal and economic disruption. This paper formulates an optimal transportation flow restriction problem that offers a more direct and realistic representation of real-world policy design. A sequential distributed Lyapunov-based MPC (SDLMPC) approach is introduced, enabling asynchronous decision-making for subpopulations. The feasibility of applying SDLMPC is proven, and its effectiveness is validated through simulations using real COVID-19 data from Germany. The results demonstrate that SDLMPC effectively balances epidemic control with reduced negative impacts, offering a practical and implementable decision-making framework.