N. Diz-Rosales, M. J. Lombardía, D. Morales
COVID-19 has shown the need for spatio-temporal monitoring of the saturation of healthcare capacity in epidemic contexts. However, the scarcity and lack of homogeneity of data is a challenge. Under a random regression coefficient Poisson model, this work derives area-level predictors of occupied bed counts and occupancy ratios in intensive care units, and introduces bootstrap estimators of mean squared errors. Maximum likelihood estimators of model parameters and mode predictors of random effects are calculated using a Laplace approximation algorithm. Simulation experiments are conducted to investigate the behaviour of the fitting algorithm, the predictors and the mean squared error estimators. The new statistical methodology is applied to data from the Autonomous Community of Castilla y León. The target is to estimate the proportions of beds occupied by COVID-19 patients in intensive care units by health areas and days.
Keywords: small area estimation, random coefficient Poisson regression models, bootstrap, intensive care units occupancy rate estimation, COVID-19.
Scheduled
Mixed models I
November 8, 2023 5:20 PM
HC1: Canónigos Room 1