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 / Ferrari, Luisa; Gerardi, Giuseppe; Manzi, Giancarlo; Micheletti, Alessandra; Nicolussi, Federica; Biganzoli, Elia; Salini, Silvia. - (2022), pp. 19-24. (Intervento presentato al convegno COVid-19 Empirical Research (COVER) tenutosi a Milano) [10.54103/milanoup.73.41].
A Heavily Trained Time-Dependent SIRD Model for Local Covid-19 Data in Italy
Giancarlo Manzi;Silvia Salini
2022
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
