  We propose a novel data-driven framework for assessing the a-priori epidemic
risk of a geographical area and for identifying high-risk areas within a
country. Our risk index is evaluated as a function of three different
components: the hazard of the disease, the exposure of the area and the
vulnerability of its inhabitants. As an application, we discuss the case of
COVID-19 outbreak in Italy. We characterize each of the twenty Italian regions
by using available historical data on air pollution, human mobility, winter
temperature, housing concentration, health care density, population size and
age. We find that the epidemic risk is higher in some of the Northern regions
with respect to Central and Southern Italy. The corresponding risk index shows
correlations with the available official data on the number of infected
individuals, patients in intensive care and deceased patients, and can help
explaining why regions such as Lombardia, Emilia-Romagna, Piemonte and Veneto
have suffered much more than the rest of the country. Although the COVID-19
outbreak started in both North (Lombardia) and Central Italy (Lazio) almost at
the same time, when the first cases were officially certified at the beginning
of 2020, the disease has spread faster and with heavier consequences in regions
with higher epidemic risk. Our framework can be extended and tested on other
epidemic data, such as those on seasonal flu, and applied to other countries.
We also present a policy model connected with our methodology, which might help
policy-makers to take informed decisions.
