  The COVID-19 pandemic has had far-reaching consequences, highlighting the
urgency for explanatory and predictive tools to track infection rates and
burden of care over time and space. However, the scarcity and inhomogeneity of
data is a challenge. In this research we develop a robust framework for
estimating and predicting the occupied beds of Intensive Care Units by
presenting an innovative Small Area Estimation methodology based on the
definition of mixed models with random regression coefficients. We applied it
to estimate and predict the daily occupancy of Intensive Care Unit beds by
COVID-19 in health areas of Castilla y Le\'on, from November 2020 to March
2022.
