No abstract

For a discrete-time vector linear stationary process, {X(t)}, admitting forward and backward autoregressive representations, the variance matrix of an optimal linear interpolator of X(t), based on a knowledge of {X(t-j), j≠0}, is known to be given by Ri (0) The relationship between the matrix A and the corresponding matrix, P, obtained by considering only an optimal one-step linear predictor of X(t) from a knowledge of its infinite past, {X(t-j),j>0}, is also discussed. The possible role the inverse correlation function may have for model specification of a vector ARMA model is explored. Close parallels between the problem of interpolation for a stationary univariate two-dimensional Gaussian random field and time series are examined and an index of linear determinism for the latter class of processes is also defined. An application of this index for model specification and diagnostic testing of a Gaussian Markov Random Field is investigated together with the question of its estimation from observed data. Results are illustrated by a simulation study.

Background and aimsAcute neurological symptoms are responsible for 5-15% of referral to the Emergency Department (ED). Since the COVID-19 outbreak, our stroke network shifted toward a mothership model with direct transport of stroke patients to the Comprehensive Stroke Center ("M. Bufalini" hospital, Cesena, Italy). To handle the increase in referrals due to mothership and guarantee a neurological fasttrack in the ED, the Neurology service opened a four-bed Short-Stay Neurological Observation Unit (SSNO or "OBI Neuro"). We retrospectively analyzed the performances of SSNO after one year of activity. MethodsWe evaluated one-year (1st April 2020-31st March 2021) SSNO patients' admissions, discharge destinations, diagnosis and mean length of stay. ResultsWe managed 556 patients in the SSNO. Of these, 280 (50.4%) were discharged at home, 248 (44.6%) were admitted as in-patients, 20 (3.6%) were transferred to other hospitals, 8 (1.4%) selfdischarged. Among patients admitted as in-patients, 91 (36.7%) were admitted in the Neurology ward, 135 (54.4%) were admitted in the Stroke Unit, the remaining (8.9%) in other wards of the hospital. The most common diagnoses were stroke (32.3%), seizure (20.4%), TIA (13%) and headache (7.4%); all other diagnoses individually represented less than 5% of the total. The mean stay of length was 11.5 h (14.4 h for discharged patients and 7.3 h for admitted as inpatients). ConclusionsThe SSNO was feasible and strategic in managing stroke referrals in a mothership model and lifting the burden of neurological patients from the ED during the COVID-19 pandemic.