  We introduce a methodology to guarantee safety against the spread of
infectious diseases by viewing epidemiological models as control systems and by
considering human interventions (such as quarantining or social distancing) as
control input. We consider a generalized compartmental model that represents
the form of the most popular epidemiological models and we design
safety-critical controllers that formally guarantee safe evolution with respect
to keeping certain populations of interest under prescribed safe limits.
Furthermore, we discuss how measurement delays originated from incubation
period and testing delays affect safety and how delays can be compensated via
predictor feedback. We demonstrate our results by synthesizing active
intervention policies that bound the number of infections, hospitalizations and
deaths for epidemiological models capturing the spread of COVID-19 in the USA.
