15 research outputs found
A State Feedback Controller for Mitigation of Continuous-Time Networked SIS Epidemics
The paper considers continuous-time networked
susceptible-infected-susceptible (SIS) diseases spreading over a population.
Each agent represents a sub-population and has its own healing rate and
infection rate; the state of the agent at a time instant denotes what fraction
of the said sub-population is infected with the disease at the said time
instant. By taking account of the changes in behaviors of the agents in
response to the infection rates in real-time, our goal is to devise a feedback
strategy such that the infection level for each agent strictly stays below a
pre-specified value. Furthermore, we are also interested in ensuring that the
closed-loop system converges either to the disease-free equilibrium or, when it
exists, to the endemic equilibrium. The upshot of devising such a strategy is
that it allows health administration officials to ensure that there is
sufficient capacity in the healthcare system to treat the most severe cases. We
demonstrate the effectiveness of our controller via numerical examples
Optimal policy design to mitigate epidemics on networks using an SIS model
Understanding how to effectively control an epidemic spreading on a network is a problem of paramount importance for the scientific community. The ongoing COVID-19 pandemic has highlighted the need for policies that mitigate the spread, without relying on pharmaceutical interventions, that is, without the medical assurance of the recovery process. These policies typically entail lockdowns and mobility restrictions, having thus nonnegligible socio-economic consequences for the population. In this paper, we focus on the problem of finding the optimum policies that "flatten the epidemic curve" while limiting the negative consequences for the society, and formulate it as a nonlinear control problem over a finite prediction horizon. We utilize the model predictive control theory to design a strategy to effectively control the disease, balancing safety and normalcy. An explicit formalization of the control scheme is provided for the susceptible--infected--susceptible epidemic model over a network. Its performance and flexibility are demonstrated by means of numerical simulations