A Prediction Model to Determine a COVID-19 Patient's Outcome Based on Its Risk Factors

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. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Impact of Mexican Public Policies in the Development of COVID-19 Pandemic

The COVID-19 pandemic has challenged the entire world's health, social and economic systems. More than a year after the declaration of the pandemic, the current situation of the different countries is defined by the actions taken towards containing the spread of the virus and reducing the number of deaths.Mexico has had one of the highestCOVID-19 death tolls in theworld and the policies implemented by the government have been questioned by some international agencies. This chapter seeks to statistically analyze the effect of the policies proposed by theMexican federal government in the number of weekly COVID-19 cases and deaths. The results show that the effects vary from state to state, this depends on the real enforcement of the policies. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Practice-Based Education in Engineering Addressing Real-business Problems amid the Covid-19 Crisis

This qualitative research uses the content analysis of written reports that account for the solution of problematic situations that occurred in small and medium-sized companies in Mexico. The real-world problems addressed by students of Industrial and Systems Engineering at the Tecnologico de Monterrey arose from the new reality imposed by the Covid-19 pandemic, whose fingerprint is the high degree of uncertainty in the social and economic fields. The analysis presented here shows the level of integration of the categories of content learning, social learning, and learning itself, under the practice-based education approach. The success detected in the development of the various projects is due to the social interactions that the students have with the company's training partner, as well as the interaction they have with the teacher in the conceptual and procedural development of specific disciplinary content. In other words, in the transgression of school and workspaces. Correctly, discrete event simulation tools were used for decision making. In this way, the projects studied in this report evaluate and propose alternative solutions that conclude in recommendations with a mathematical simulation basis for companies to solve, to a certain extent, the impact suffered for the pandemic. © 2021 ACM.