  We study the impact of parameter estimation and state measurement errors on a
control framework for optimally mitigating the spread of epidemics. We capture
the epidemic spreading process using a susceptible-infected-removed (SIR)
epidemic model and consider isolation as the control strategy. We use a control
strategy to remove (isolate) a portion of the infected population. Our goal is
to maintain the daily infected population below a certain level, while
minimizing the resource captured by the isolation rate. Distinct from existing
works on leveraging control strategies in epidemic spreading, we propose a
parameter estimation strategy and further characterize the parameter estimation
error bound. In order to deal with uncertainties, we propose a robust control
strategy by overestimating the seriousness of the epidemic and study the
feasibility of the system. Compared to the optimal control strategy, we
establish that the proposed strategy under parameter estimation and measurement
errors will sacrifice optimality to flatten the curve.
