Using deliberate-delay decentralized controllers to stop spread dynamics in canonical network models
We introduce a deliberate-delay feedback paradigm for mitigating infection spreads in two canonical network models, namely the multi-group susceptible-infected-recovered and the multi-group susceptible-infected-exposed-recovered model, through placement of control resources such as quarantine or treatment capabilities. We apply a recently-developed methodology for dynamical-network controller design to acheive high-performance controls. This design methodology yields simple spread-control schemes that use trends in measured infection counts (as obtained from deliberately-delayed and current observations) to allocate control resources. The developed methodologies hold promise for both public-health and genetic-epidemiology applications.