  The impact of mitigation or control measures on an epidemics can be estimated
by fitting the parameters of a compartmental model to empirical data, and
running the model forward with modified parameters that account for a specific
measure. This approach has several drawbacks, stemming from biases or lack of
availability of data and instability of parameter estimates. Here we take the
opposite approach -- that we call reverse epidemiology. Given the data, we
reconstruct backward in time an ensemble of networks of contacts, and we assess
the impact of measures on that specific realization of the contagion process.
This approach is robust because it only depends on parameters that describe the
evolution of the disease within one individual (e.g. latency time) and not on
parameters that describe the spread of the epidemics in a population. Using
this method, we assess the impact of preventive quarantine on the ongoing
outbreak of Covid-19 in Italy. This gives an estimate of how many infected
could have been avoided had preventive quarantine been enforced at a given
time.
