Hybrid Optimal Control of Homogeneous Epidemiological Compartmental Models with Regime Switching
For policymakers and epidemiologists, this work provides a framework for optimizing multi-phase intervention strategies, though the improvement is demonstrated only in numerical examples.
This paper formulates optimal intervention design for epidemiological compartmental models as a hybrid optimal control problem, incorporating work-from-home policies and vaccination protocols across multiple phases. Numerical results show that coordinating these policies provides improved mitigation of disease spread compared to single-phase interventions.
Optimal intervention design is formulated as a hybrid optimal control problem for multiphase homogeneous epidemiological systems. The system extends a foundational compartmental model through intermediate phases that incorporate work-from-home (WFH) policies and a vaccination protocol, yielding a four-phase hybrid system that captures policy escalation and relaxation. Key characteristics of the resulting hybrid system include (i) phase-dependent continuous dynamics and running costs that respectively capture distinct disease transmission mechanisms and shifting public health socioeconomic trade-offs, (ii) a combination of autonomous and controlled switchings for intervention policies, whose times are co-optimized - whether indirectly via state thresholds or directly as decision variables alongside continuous inputs to minimize the overall cost, and (iii) nontrivial state jump maps that govern transitions between phases with differing state and control space dimensions. The Hybrid Minimum Principle (HMP) is invoked to obtain the optimal solutions. Numerical results demonstrate that coordinating WFH policies with vaccination efforts provides improved mitigation of disease spread compared to single-phase policy interventions.