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Article

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

1
Engineering Department, University of Sannio, 82100 Benevento, Italy
2
Department of Industrial Chemistry Toso Montanari, University of Bologna, 40126 Bologna, Italy
3
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2021, 10(1), 34; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010034
Received: 20 November 2020 / Revised: 21 December 2020 / Accepted: 11 January 2021 / Published: 14 January 2021
(This article belongs to the Collection Spatial Components of COVID-19 Pandemic)
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. View Full-Text
Keywords: COVID-19 counteractions; risk levels; artificial intelligence; long short term memory neural network; satellite remote sensing; sensor networks; pollutants; macroanalysis; microanalysis; air quality; environmental chemistry; anthropogenic activities COVID-19 counteractions; risk levels; artificial intelligence; long short term memory neural network; satellite remote sensing; sensor networks; pollutants; macroanalysis; microanalysis; air quality; environmental chemistry; anthropogenic activities
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MDPI and ACS Style

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. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010034

AMA Style

Sebastianelli A, Mauro F, Di Cosmo G, Passarini F, Carminati M, Ullo SL. AIRSENSE-TO-ACT: A Concept Paper for COVID-19 Countermeasures Based on Artificial Intelligence Algorithms and Multi-Source Data Processing. ISPRS International Journal of Geo-Information. 2021; 10(1):34. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010034

Chicago/Turabian Style

Sebastianelli, Alessandro, Francesco Mauro, Gianluca Di Cosmo, Fabrizio Passarini, Marco Carminati, and Silvia L. Ullo 2021. "AIRSENSE-TO-ACT: A Concept Paper for COVID-19 Countermeasures Based on Artificial Intelligence Algorithms and Multi-Source Data Processing" ISPRS International Journal of Geo-Information 10, no. 1: 34. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010034

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