  No, they can't. Epidemic spread is characterized by exponentially growing
dynamics, which are intrinsically unpredictable. The time at which the growth
in the number of infected individuals halts and starts decreasing cannot be
calculated with certainty before the turning point is actually attained;
neither can the end of the epidemic after the turning point. An SIR model with
confinement (SCIR) illustrates how lockdown measures inhibit infection spread
only above a threshold that we calculate. The existence of that threshold has
major effects in predictability: A Bayesian fit to the COVID-19 pandemic in
Spain shows that a slow-down in the number of newly infected individuals during
the expansion phase allows to infer neither the precise position of the maximum
nor whether the measures taken will bring the propagation to the inhibition
regime. There is a short horizon for reliable prediction, followed by a
dispersion of the possible trajectories that grows extremely fast. The
impossibility to predict in the mid-term is not due to wrong or incomplete
data, since it persists in error-free, synthetically produced data sets, and
does not necessarily improve by using larger data sets. Our study warns against
precise forecasts of the evolution of epidemics based on mean-field, effective
or phenomenological models, and supports that only probabilities of different
outcomes can be confidently given.
