Real Time Forecasting of Covid-19 Intensive Care Units demand - Paolo Berta; Pietro Giorgio Lovaglio; Paolo Paruolo; Stefano Verzillo (Publications Office of the European Union, 2020)
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 regions in Europe in Italy, France and Scotland, both at the onset and at later stages of the spread of the disease. This reasonable forecast performance suggests that the present approach may be useful also beyond the set of analysed regions.
Keywords: C53; C32; 330; SARS-CoV-2; Covid-19; Intensive Care Units; Cointegration; Error correctionmodels; Health forecasting; Multivariate time series; Vector Autoregression Models