We consider a class of epidemiological models in which a compartmental linear system, including various categories of infected individuals (e.g. asymptomatic, symptomatic, quarantined), is fed back by a positive feedback, representing contagion. The positive feedback gain decreases (in a sort of negative feedback) as the epidemic evolves, due to the decrease in the number of susceptible individuals. We first propose a convergence result based on a special copositive Lyapunov function. Then, we address a major problem for this class of systems: the deep uncertainty affecting parameter values. We face the problem adopting techniques from optimal and robust control theory to assess the sensitivity of the model. For this class of systems, the optimal control solution has a peculiar decoupling property that no shooting procedure is required. Finally, we exploit the obtained bounds to assess the effectiveness of possible epidemic control strategies, including intermittent restrictions adopted during the COVID-19 pandemic.
