tetano
Editor, Senior Moderator
Infect Dis Model
. 2021 Jan 26;6:362-369.
doi: 10.1016/j.idm.2021.01.003. Online ahead of print.
Forecasting the number of confirmed new cases of COVID-19 in Italy for the period from 19 May to 2 June 2020
Marco Triacca[SUP] 1 [/SUP], Umberto Triacca[SUP] 2 [/SUP]
Affiliations
Abstract
In this paper we forecast the spread of the coronavirus disease 2019 outbreak in Italy in the time window from May 19 to June 2, 2020. In particular, we consider the forecast of the number of new daily confirmed cases. A forecast procedure combining a log-polynomial model together with a first-order integer-valued autoregressive model is proposed. An out-of-sample comparison with forecasts from an autoregressive integrated moving average (ARIMA) model is considered. This comparison indicates that our procedure outperforms the ARIMA model. The Root Mean Square Error (RMSE) of the ARIMA is always greater than that of the our procedure and generally more than twice as high as the our procedure RMSE. We have also conducted Diebold and Mariano (1995) tests of equal mean square error (MSE). The tests results confirm that forecasts from our procedure are significantly more accurate at all horizons. We think that the advantage of our approach comes from the fact that it explicitly takes into account the number of swabs.
Keywords: COVID-19; Real-time forecasts; Time series.
. 2021 Jan 26;6:362-369.
doi: 10.1016/j.idm.2021.01.003. Online ahead of print.
Forecasting the number of confirmed new cases of COVID-19 in Italy for the period from 19 May to 2 June 2020
Marco Triacca[SUP] 1 [/SUP], Umberto Triacca[SUP] 2 [/SUP]
Affiliations
- PMID: 33521404
- PMCID: PMC7835080
- DOI: 10.1016/j.idm.2021.01.003
Abstract
In this paper we forecast the spread of the coronavirus disease 2019 outbreak in Italy in the time window from May 19 to June 2, 2020. In particular, we consider the forecast of the number of new daily confirmed cases. A forecast procedure combining a log-polynomial model together with a first-order integer-valued autoregressive model is proposed. An out-of-sample comparison with forecasts from an autoregressive integrated moving average (ARIMA) model is considered. This comparison indicates that our procedure outperforms the ARIMA model. The Root Mean Square Error (RMSE) of the ARIMA is always greater than that of the our procedure and generally more than twice as high as the our procedure RMSE. We have also conducted Diebold and Mariano (1995) tests of equal mean square error (MSE). The tests results confirm that forecasts from our procedure are significantly more accurate at all horizons. We think that the advantage of our approach comes from the fact that it explicitly takes into account the number of swabs.
Keywords: COVID-19; Real-time forecasts; Time series.