In this paper, we develop a forecasting model for the spread of COVID-19 infection at a provincial (i.e., EU NUTS-3) level in Italy by using official data from the Italian Ministry of Health integrated with data extracted from daily official press conferences of regional authorities and local newspaper websites. This data integration is needed as COVID-19 death data are not available at the NUTS-3 level from official open data channels. An adjusted time-dependent SIRD model is used to predict the behavior of the epidemic; specifically, the number of susceptible, infected, deceased, recovered people and epidemiological parameters. Predictive model performance is evaluated using comparison with real data.
We present a time-dependent SIRD model for the spread of COVID-19 infection at a provincial (i.e. EU NUTS-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.
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