  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.
