Since February 2020, the COVID-19 epidemic has rapidly spread throughout Italy. Some studies showed an association of environmental factors, such as PM10, PM2.5, NO2, temperature, relative humidity, wind speed, solar radiation and mobility with the spread of the epidemic. In this work, we aimed to predict via Deep Learning the real-time transmission of SARS-CoV-2 in the province of Reggio Emilia, Northern Italy, in a grid with a small resolution (12 km × 12 km), including satellite information.

A deep learning approach for Spatio-Temporal forecasting of new cases and new hospital admissions of COVID-19 spread in Reggio Emilia, Northern Italy

Sciannameo, Veronica;Berchialla, Paola
2022

Abstract

Since February 2020, the COVID-19 epidemic has rapidly spread throughout Italy. Some studies showed an association of environmental factors, such as PM10, PM2.5, NO2, temperature, relative humidity, wind speed, solar radiation and mobility with the spread of the epidemic. In this work, we aimed to predict via Deep Learning the real-time transmission of SARS-CoV-2 in the province of Reggio Emilia, Northern Italy, in a grid with a small resolution (12 km × 12 km), including satellite information.
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COVID-19; ConvLSTM; Deep learning; Forecasting; SARS-CoV-2; Spatio-temporal; Hospitals; Humans; Italy; SARS-CoV-2; COVID-19; Deep Learning
Sciannameo, Veronica; Goffi, Alessia; Maffeis, Giuseppe; Gianfreda, Roberta; Jahier Pagliari, Daniele; Filippini, Tommaso; Mancuso, Pamela; Giorgi-Rossi, Paolo; Alberto Dal Zovo, Leonardo; Corbari, Angela; Vinceti, Marco; Berchialla, Paola
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1873141
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