A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real-time monitoring and short-term forecasting of the main epidemiological indicators within the first outbreak of COVID-19 in Italy. Accurate short-term predictions, including the potential effect of exogenous or external variables are provided. This ensures to accurately predict important characteristics of the epidemic (e.g., peak time and height), allowing for a better allocation of health resources over time. Parameter estimation is carried out in a maximum likelihood framework. All computational details required to reproduce the approach and replicate the results are provided.
Alaimo Di Loro, P., Divino, F., Farcomeni, A., Jona Lasinio, G., Lovison, G., Maruotti, A., et al. (2021). Nowcasting COVID-19 incidence indicators during the Italian first outbreak. STATISTICS IN MEDICINE, 40(16), 3843-3864 [10.1002/sim.9004].
Nowcasting COVID-19 incidence indicators during the Italian first outbreak
Divino, Fabio;Farcomeni, Alessio;Jona Lasinio, Giovanna;Mingione, Marco
2021-01-01
Abstract
A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real-time monitoring and short-term forecasting of the main epidemiological indicators within the first outbreak of COVID-19 in Italy. Accurate short-term predictions, including the potential effect of exogenous or external variables are provided. This ensures to accurately predict important characteristics of the epidemic (e.g., peak time and height), allowing for a better allocation of health resources over time. Parameter estimation is carried out in a maximum likelihood framework. All computational details required to reproduce the approach and replicate the results are provided.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
