  More than ever COVID-19 is putting pressure on health systems all around the
world, especially in Brazil. In this study we propose an analytical approach
based on statistics and machine learning that uses lab exam data coming from
patients to predict whether patients are going to require special care
(hospitalisation in regular or special-care units). We also predict the number
of days the patients will stay under such care. The two-step procedure
developed uses Bayesian Optimisation to select the best model among several
candidates leads us to final models that achieve 0.94 area under ROC curve
performance for the first target and 1.87 root mean squared error for the
second target (which is a 77% improvement over the mean baseline), making our
model ready to be deployed as a decision system that could be available for
everyone interested. The analytical approach can be used in other diseases and
can help the planning hospital capacity.
