Real Time Forecasting of Covid-19 Intensive Care Units demand
Paolo Paruolo and
Health, Econometrics and Data Group (HEDG) Working Papers from HEDG, c/o Department of Economics, University of York
Response management to the SARS-CoV-2 outbreak requires to answer several forecasting tasks. For hospital managers, a major one is to anticipate the likely needs of beds in intensive care in a given catchment area one or two weeks ahead, starting as early as possible in the evolution of the epidemic. This paper proposes to use a bivariate Error Correction model to forecast the needs of beds in intensive care, jointly with the number of patients hospitalised with Covid-19 symptoms. Error Correction models are found to provide reliable forecasts that are tailored to the local characteristics both of epidemic dynamics and of hospital practice for various in Europe in Italy, France and Scotland, both at the onset and at later stages of the spread of the disease. The forecast performance is encouraging for all analysed regions, suggesting that the present approach may be useful also beyond the analysed cases.
Keywords: SARS-CoV-2; Covid-19; Intensive Care Units; Forecasting; Vector error correction model; VAR (search for similar items in EconPapers)
JEL-codes: C53 C32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for, nep-hea and nep-ore
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Working Paper: Real Time Forecasting of Covid-19 Intensive Care Units demand (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:yor:hectdg:20/16
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