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Talk: 'A Decision Support System based on Artificial Intelligence for risk evaluation. A model to treat emergencies related to COVID pandemic.' by Silvia Liberata Ullo - 11/02/2021

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Lecture on 11/02/2021, Thursday 18:00 CET (Paris-Berlin-Rome) - 5:00 p.m. GMT (London)

Title: A Decision Support System based on Artificial Intelligence for risk evaluation. A model to treat emergencies related to COVID pandemic.

Speaker: Silvia Liberata Ullo

Abstract: Due to the spread of COVID-19 pandemic, many governments and local institutions (regions and municipalities) worldwide have applied severe lockdown measures, but have always made a posteriori decision: increasing levels of lockdown have been activated, based on the number of infected, hospitalized and dead, all the up to a generalized lockdown, like the one imposed in Italy from the beginning of March until almost the end of June, and in many other countries worldwide as well. However, the lockdown measures have proven to result in different outcomes in different countries, since several factors impact the efficiency of the lockdown measures. Moreover, these methods of intervention were not appropriate due to their negative implications for economic and social aspects.

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 is described. The tool is a cloud-based centralized system; a multi-user platform that relies on artificial intelligence (AI) algorithms for the processing of heterogeneous data, which can produce as an output the level of risk. The tool is realized in two steps: the first training phase 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 led to a decrease in the degree of risk. In this way, it is possible to deal with different scenarios and compare different restrictive strategies and the actual expected benefits, to adopt measures sized to the actual needs, adapted to the specific areas of analysis and useful for safeguarding human health; and trying to evaluate the economic and social impacts of the choices.

A project still in progress is here described [1], but some preliminary analyses are shown, and two different case studies presented, whose results have highlighted a correlation between NO2, mobility and COVID-19 data. However, given the complexity of the virus diffusion mechanism, linked to air pollutants but also to many other factors, these preliminary studies confirmed the need, on the one hand, to carry out more in-depth analyses, and on the other, to use AI algorithms to capture the hidden relationships among the huge amounts of involved data.

[1] Sebastianelli, A.; Mauro, F.; Di Cosmo, G.; Passarini, F.; Carminati, M.; Ullo, S.L. AIRSENSE-TO-ACT: A Concept Paper for COVID-19 Countermeasures Based on Artificial Intelligence Algorithms and Multi-Source Data Processing. ISPRS Int. J. Geo-Inf. 2021, 10, 34.

The COVID-19 Epidemiological Modelling Project presents a series of talks on the pandemic. Previous talks can be viewed again on YouTube:

SARA Systems Streaming

This channel is also used for live streaming of the talks, for participants who do not like to take part in the discussion. For participants who like to take part in the discussion this Thursday, use the Zoom URL

Meeting-ID: 843 1697 2876

Overview of all talks can be found at the Mathematical Modelling web pages.

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General Assembly Covid-19 Project 04/02/2021


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Tuesday, 29 November 2022