In December 2019, a new virus appeared in Wuhan, China. In a matter of weeks, this virus spread to all global regions, causing a pandemic. The first affected were the health systems globally due to the increasing demand of patients requiring hospitalization caused by the virus infection. This situation demanded national authorities to develop complex strategies to diminish the impact. One of the concerns to deal with this situation is an effective and equitable vaccination plan, effectively by attending ICU occupation and equity by prioritizing high-risk population. For this, we present a 5-step data analysis methodology for the calculation of a vulnerability index; considering comorbidities in the patients that enhance the risk of hospitalization. Four Machine Learning algorithms (i.e., Artificial Neural Network, C.50 Decision Tree, Logistic Regression and Naïve Bayes Classifier) were trained, and tested to predict a patients¿ outcome. The Decision Tree resulted with best performance both in accuracy (>95%) and computational time (<15 s). Finally, we provide an example of its application.
