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. These policies typically entail lockdowns and mobility restrictions, having thus nonnegligible socio-economic consequences for the population. 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.