Author: Farcomeni, Alessio; Maruotti, Antonello; Divino, Fabio; Jonaâ€Lasinio, Giovanna; Lovison, Gianfranco
Title: An ensemble approach to shortâ€term forecast of COVIDâ€19 intensive care occupancy in Italian regions Cord-id: omyyqge1 Document date: 2020_11_30
ID: omyyqge1
Snippet: The availability of intensive care beds during the COVIDâ€19 epidemic is crucial to guarantee the best possible treatment to severely affected patients. In this work we show a simple strategy for shortâ€term prediction of COVIDâ€19 intensive care unit (ICU) beds, that has proved very effective during the Italian outbreak in February to May 2020. Our approach is based on an optimal ensemble of two simple methods: a generalized linear mixed regression model, which pools information over differe
Document: The availability of intensive care beds during the COVIDâ€19 epidemic is crucial to guarantee the best possible treatment to severely affected patients. In this work we show a simple strategy for shortâ€term prediction of COVIDâ€19 intensive care unit (ICU) beds, that has proved very effective during the Italian outbreak in February to May 2020. Our approach is based on an optimal ensemble of two simple methods: a generalized linear mixed regression model, which pools information over different areas, and an areaâ€specific nonstationary integer autoregressive methodology. Optimal weights are estimated using a leaveâ€lastâ€out rationale. The approach has been set up and validated during the first epidemic wave in Italy. A report of its performance for predicting ICU occupancy at regional level is included.
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