Information on 4.1 million patients identified as COVID-19 positive in Mexico is used to understand the relationship between comorbidities, symptoms, hospitalisations and deaths due to the COVID-19 disease. Using the presence or absence of these 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 analysed by means of a multivariate Bernoulli distribution. The proposal considers all possible footprint combinations resulting in a robust model suitable for Bayesian inference. The analysis is carried out considering the information on the monthly COVID-19 cases, from March 2020 to the first days of January 2022. This allows one to appreciate the evolution of the mortality risk over time and the effect the strategies of the health authorities have had on it. Supporting information for this article, containing code and the dataset used for the analysis, is available online.
Keywords: Bayesian modelling; COVID‐19 footprint; correlation; mortality risk; prediction.
© 2022 The Authors. Journal of the Royal Statistical Society: Series A (Statistics in Society) published by John Wiley & Sons Ltd on behalf of Royal Statistical Society.