A Data-Driven Hybrid Model Predictive Control Framework for Managing Epidemics Using 3DoF-KF HMPC

This paper describes a systematic approach for epidemic control using control-relevant identification coupled with a multi-input, multi-output 3-Degree-of-Freedom Kalman filter-based Hybrid Model Predictive Control (MIMO 3DoF-KF HMPC) featuring online controller reconfiguration. The combined data-driven modeling and control strategy is evaluated on a Susceptible-Infected-Recovered (SIR) model involving vaccination and loss of immunity (i.e., reinfection). "Zippered" multisine input signals and ARX estimation are applied to obtain a multivariable dynamic model that is the basis for an HMPC algorithm featuring both continuous and categorical (i.e., discrete level) actions through a Mixed Integer Quadratic Programming (MIQP) formulation. The goal is to reduce the infected population while balancing societal impacts. The hybrid formulation with reconfiguration dynamically adjusts health intervention policies, such as categorical lockdown levels and continuous vaccination rates. This approach enables operational goals such as reducing the infected population to a desired interval or relaxing lockdown to a designated setpoint; furthermore, the 3DoF formulation enables independent tuning for setpoint tracking and measured and unmeasured disturbance rejection, allowing scalable solutions across diverse epidemiological settings. The framework is demonstrated through two demanding case studies involving 90% infection reduction under time-varying recovery and loss of immunity. The resulting closed-loop model provides a practical tool for guiding government policy and public decisions during pandemics.