  Information of 1.6 million patients identified as SARS-CoV-2 positive in
Mexico is used to understand the relationship between comorbidities, symptoms,
hospitalizations and deaths due to the COVID-19 disease. Using the presence or
absence of these latter variables a clinical footprint for each patient is
created. The risk, expected mortality and the prediction of death outcomes,
among other relevant quantities, are obtained and analyzed by means of a
multivariate Bernoulli distribution. The proposal considers all possible
footprint combinations resulting in a robust model suitable for Bayesian
inference.
