A deep learning approach for Spatio-Temporal forecasting of new cases and new hospital admissions of COVID-19 spread in Reggio Emilia, Northern Italy
Articolo
Data di Pubblicazione:
2022
Citazione:
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; Goffi, Alessia; Maffeis, Giuseppe; Gianfreda, Roberta; Jahier Pagliari, Daniele; Filippini, Tommaso; Mancuso, Pamela; Giorgi Rossi, Paolo; Dal Zovo, Leonardo Alberto; Corbari, Angela; Vinceti, Marco; Berchialla, Paola. - In: JOURNAL OF BIOMEDICAL INFORMATICS. - ISSN 1532-0464. - 132:(2022), pp. 1-9. [10.1016/j.jbi.2022.104132]
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.
Tipologia CRIS:
Articolo su rivista
Keywords:
COVID-19; ConvLSTM; Deep learning; Forecasting; SARS-CoV-2; Spatio-temporal
Elenco autori:
Sciannameo, Veronica; Goffi, Alessia; Maffeis, Giuseppe; Gianfreda, Roberta; Jahier Pagliari, Daniele; Filippini, Tommaso; Mancuso, Pamela; Giorgi Rossi, Paolo; Dal Zovo, Leonardo Alberto; Corbari, Angela; Vinceti, Marco; Berchialla, Paola
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