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Dettaglio pubblicazione

2022, J-READING-JOURNAL OF RESEARCH AND DIDACTICS IN GEOGRAPHY, Pages 27-48 (volume: 1)

The ECO4CO project among retrospective data, improving of algorithms, predictive hypothesis and future perspectives to tackle health emergencies (01a Articolo in rivista)

Pesaresi Cristiano, Atek Sofiane, De Vito Corrado, Cardinale Vincenzo, Bianchini Filippo, Novelli Simone, Eugeni Marco, Mecella Massimo, Rescio Antonello, Petronzio Luca, Vincenzi Aldo, Pistillo Pasquale, Giusto Gianfranco, Pasquali Giorgio, Alvaro Domenico, Gaudenzi Paolo, Mancini Marco, Villari Paolo

International literature has evidenced how GIS, geotechnologies and Artificial Intelligence (AI) can provide very useful supports and indications to tackle COVID-19 and health emergencies. This paper starts by giving some synthetic geographical considerations regarding the consequences of the COVID-19 pandemic in different fields of daily life and then provides a literary review concerning the actual state of the art and inputs for future perspectives using GIS, geotechnologies and AI to tackle infectious diseases and global emergencies, starting from the numerous studies conducted in the context of COVID-19. In fact, the current pandemic has also been an event capable of providing notable new indications for the profitable use of GIS applications, simulation and geolocalisation models, AI and satellite images. The paper then focuses the attention on the ECO4CO – Earth Cognitive System 4 Covid-19 project, co-funded by the European Space Agency (ESA) under its Business Applications programme and essentially aimed at advancing innovative proposals, combining geotechnological know-how and interdisciplinary approaches in the healthcare, epidemiological, engineering and geographical fields, through the use of geoinformatics models and algorithms and satellite resources, for ad hoc data elaboration and mapping. In particular, the ECO4CO service concept and service pillars are presented and the work is focused on Logistic Planning, a crucial service used to assist clinical teams and healthcare authorities in the organization of activities useful to tackle health emergencies. Specific applied examples are shown to underline how it is possible to pass from retrospective data to predictive hypothesis through conceptual models based on interdisciplinary synergies and refining and educating algorithms.
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