  Aim of this paper is the description of a new tool to support institutions in
the implementation of targeted countermeasures, based on quantitative and
multi-scale elements, for the fight and prevention of emergencies, such as the
current COVID-19 pandemic. The tool is a centralized system (web application),
single multi-user platform, which relies on Artificial Intelligence (AI)
algorithms for the processing of heterogeneous data, and which can produce an
output level of risk. The model includes a specific neural network which will
be first trained to learn the correlation between selected inputs, related to
the case of interest: environmental variables (chemical-physical, such as
meteorological), human activity (such as traffic and crowding), level of
pollution (in particular the concentration of particulate matter), and
epidemiological variables related to the evolution of the contagion. The tool
realized in the first phase of the project will serve later both as a decision
support system (DSS) with predictive capacity, when fed by the actual measured
data, and as a simulation bench performing the tuning of certain input values,
to identify which of them lead to a decrease in the degree of risk. In this
way, the authors aim to design different scenarios to compare different
restrictive strategies and the actual expected benefits, to adopt measures
sized to the actual need, and adapted to the specific areas of analysis, useful
to safeguard human health, but also the economic and social impact of the
choices.
