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Aim of this paper is the description of a new tool to support institutions inthe implementation of targeted countermeasures, based on quantitative andmulti-scale elements, for the fight and prevention of emergencies, such as thecurrent 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 anoutput level of risk. The model includes a specific neural network which willbe first trained to learn the correlation between selected inputs, related tothe case of interest: environmental variables (chemical-physical, such asmeteorological), human activity (such as traffic and crowding), level ofpollution (in particular the concentration of particulate matter), andepidemiological variables related to the evolution of the contagion. The toolrealized in the first phase of the project will serve later both as a decisionsupport system (DSS) with predictive capacity, when fed by the actual measureddata, 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 thisway, the authors aim to design different scenarios to compare differentrestrictive strategies and the actual expected benefits, to adopt measuressized to the actual need, and adapted to the specific areas of analysis, usefulto safeguard human health, but also the economic and social impact of thechoices.

AIRSENSE-TO-ACT: A Concept Paper for COVID-19 Countermeasures based on Artificial Intelligence algorithms and multi-sources Data Processing