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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.

AIRSENSE-TO-ACT: A Concept Paper for COVID-19 Countermeasures Based on Artificial Intelligence Algorithms and Multi-Source Data Processing