Abstract
The aim of this concept 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 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 model includes a specific neural network which is first trained to learn the correlations 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 led to a decrease in the degree of risk. In this way, we aimed to design different scenarios to 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 we compared the economic and social impacts of the choices. Although ours is a concept paper, some preliminary analyses have been shown, and two different case studies are 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 data to process.
Highlights
COVID-19 pandemic started from China towards the end of 2019, and when the consequences of its spreading became clear, the Chinese Government immediately took actions to protect its citizenship by activating restrictive measures on mobility, and on industrial and commercial activities, before turning to a total lockdown for the area of Wuhan, which had been identified as the most affected area in the Hubei region
Many other governments and local institutions worldwide have applied severe lockdown measures, but have always made a posteriori decisions: 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. These measures of generalized lockdown were adopted by imitating what was decided in the Wuhan area, to contain the spread of COVID-19, since they had proved to result in a positive impact in terms of the number of infections [1,2]
While we could find some examples of similar works aiming to realize a decision support system (DSS) to benefit decision makers, none of them are based on artificial intelligence (AI) algorithms able to work on huge amounts of data and to use the necessary granularity based on time unit of 24 h or less
Summary
COVID-19 pandemic started from China towards the end of 2019, and when the consequences of its spreading became clear, the Chinese Government immediately took actions to protect its citizenship by activating restrictive measures on mobility, and on industrial and commercial activities, before turning to a total lockdown for the area of Wuhan, which had been identified as the most affected area in the Hubei region. Many other governments and local institutions (regions and municipalities) worldwide have applied severe lockdown measures, but have always made a posteriori decisions: 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 These measures of generalized lockdown were adopted by imitating what was decided in the Wuhan area, to contain the spread of COVID-19, since they had proved to result in a positive impact in terms of the number of infections [1,2]. In [14] the affect of lockdowns on the increase of obesity in youths is shown, and in [15] the greater risk for damage to the people’s health, physical and mental, is presented, all due to inactivity and the long stay at home
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