We present a time-dependent SIRD model for the spread of COVID-19 infection at a provincial (i.e.EUNUTS-3) level in Italy, using official data from the Italian Ministry of Health, integrated with data extracted from daily official press conferences of regional authorities and from local newspaper websites. This integration concerns COVID-19 death data which are not available at NUTS-3 level from open official data channels.The model is trained for improved forecasting performance with similarity techniques putting together data from time series most similar to that for which the forecast is performed.

A Heavily Trained Time-Dependent SIRD Model for Local Covid-19 Data in Italy / L. Ferrari, G. Gerardi, G. Manzi, A. Micheletti, F. Nicolussi, E. Biganzoli, S. Salini - In: Proceedings of the COVid-19 Empirical Research (COVER) Conference / [a cura di] E. Biganzoli, G. Manzi, A. Micheletti, F. Nicolussi, S. Salini. - Milano : Milano University Press, 2022 Feb. - ISBN 9791280325457. - pp. 19-24 (( convegno COVid-19 Empirical Research (COVER) tenutosi a Milano nel 2020 [10.54103/milanoup.73.41].

A Heavily Trained Time-Dependent SIRD Model for Local Covid-19 Data in Italy

Giuseppe Gerardi;Giancarlo Manzi;Alessandra Micheletti;Federica Nicolussi;Elia Biganzoli;Silvia Salini
2022-02

Abstract

We present a time-dependent SIRD model for the spread of COVID-19 infection at a provincial (i.e.EUNUTS-3) level in Italy, using official data from the Italian Ministry of Health, integrated with data extracted from daily official press conferences of regional authorities and from local newspaper websites. This integration concerns COVID-19 death data which are not available at NUTS-3 level from open official data channels.The model is trained for improved forecasting performance with similarity techniques putting together data from time series most similar to that for which the forecast is performed.
Settore SECS-S/01 - Statistica
Settore MAT/06 - Probabilita' e Statistica Matematica
Settore MED/01 - Statistica Medica
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/905343
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