An ensemble approach to short‐term forecast of COVID‐19 intensive care occupancy in Italian regions. Issue 3 (30th November 2020)

• Record Type: Journal Article
• Authors: Farcomeni, Alessio; Maruotti, Antonello; Divino, Fabio; Jona‐Lasinio, Giovanna; Lovison, Gianfranco
• Is Part Of: Biometrical journal. Volume 63:Issue 3(2021)
• Page Start: 503
• Page End: 513
• Publication Date: 2020-11-30
• Subjects: clustered data -- COVID‐19 -- generalized linear mixed model -- integer autoregressive -- integer autoregressive model -- panel data -- SARS‐CoV‐2 -- weighted ensemble; Biometry -- Periodicals; Medical statistics -- Periodicals; 570.15195
• DOI: 10.1002/bimj.202000189
• Languages: English
• ISSNs: 0323-3847

Abstract

Abstract: The availability of intensive care beds during the COVID‐19 epidemic is crucial to guarantee the best possible treatment to severely affected patients. In this work we show a simple strategy for short‐term prediction of COVID‐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 area‐specific nonstationary integer autoregressive methodology. Optimal weights are estimated using a leave‐last‐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.