Dynamic Control of Modern, Network-Based Epidemic Models
In this paper we make the first steps to bridge the gap between classic control theory and modern, network-based epidemic models. In particular, we apply nonlinear model predictive control (NMPC) to a pairwise ODE model which we use to model a susceptible-infectious-susceptible (SIS) epidemic on nontrivial contact structures. While classic control of epidemics concentrates on aspects such as vaccination, quarantine, and fast diagnosis, our novel setup allows us to deliver control by altering the contact network within the population. Moreover, the ideal outcome of control is to eradicate the disease while keeping the network well connected. The paper gives a thorough and detailed numerical investigation of the impact and interaction of system and control parameters on the controllability of the system. For a certain combination of parameters, we used our method to identify the critical control bounds above which the system is controllable. We foresee that our approach can be extended to even more realistic or simulation-based models with the aim of applying these to real-world situations.