A multicriteria approach for risk assessment of Covid-19 in urban district lockdown. (October 2020)

• Record Type: Journal Article
• Authors: Sangiorgio, Valentino; Parisi, Fabio
• Is Part Of: Safety science. Volume 130(2020)
• Publication Date: 2020-10
• Subjects: Risk of Covid-19 contagion -- Multicriteria analysis -- Calibration -- Mathematical programming problem -- Artificial neural networks -- Apulian Region (Italy); Industrial accidents -- Periodicals; Accident Prevention -- Periodicals; Safety -- Periodicals; Travail -- Accidents -- Périodiques; 363.11
• DOI: 10.1016/j.ssci.2020.104862
• Languages: English
• ISSNs: 0925-7535

Abstract

Highlights: A multicriteria-based approach is used to analyse Risk of Covid-19 contagion. Data of 257 Apulian urban districts (Italy) support an optimization-based calibration. The parameters involved in the spread of the Covid-19 in urban districts are quantified. Artificial Neural Networks are used to study the non-linearity of the phenomenon. Three different forecasting scenarios are evaluated for the Apulian Covid-19 risk. Abstract: At the beginning of 2020, the spread of a new strand of Coronavirus named SARS-CoV-2 (COVID-19) raised the interest of the scientific community about the risk assessment related to the viral infection. The contagion became pandemic in few months forcing many Countries to declare lockdown status. In this context of quarantine, all commercial and productive activities are suspended, and many Countries are experiencing a serious crisis. To this aim, the understanding of risk of contagion in every urban district is fundamental for governments and administrations to establish reopening strategies. This paper proposes the calibration of an index able to predict the risk of contagion in urban districts in order to support the administrations in identifying the best strategies to reduce or restart the local activities during lockdown conditions. The objective regards the achievement of a useful tool to predict the risk of contagion by considering socio-economic data such as the presence of activities, companies, institutions and number of infections in urban districts. The proposed index is based on a factorial formula, simple and easy to be applied by practitioners, calibrated by using an optimization-based procedure and exploiting data of 257 urban districts of Apulian region (Italy). Moreover, a comparison with a more refined analysis, based on the training of Artificial Neural Networks, is performed in order to take into account the non-linearity of the phenomenon. The investigation quantifies the influence of each considered parameter in the risk of contagion useful to obtain risk analysis and forecast scenarios.