INTRODUCTION: Supply network design is a long-studied topic that has evolved to address disruptive situations. The risk of supply chain disruption leads to the development of resilient supply chains that are capable of reacting effectively.OBJECTIVES: In the context of public health, drug supply networks face shortage challenges in many situations, such as current epidemic outbreaks such as COVID-19. Drug shortages can occur due to manufacturing problems, lack of infrastructure, and immediate reaction mechanisms.METHODS: The case study is solved with anyLogistix optimization and simulation software.RESULTS: We present the results of a hypothetical study on the impact of COVID-19 on a regional supply network. The results of this research are intended to be the basis for the design of resilient supply chains in epidemic outbreaks.CONCLUSION: Drug providers should consider strategies to prevent or reduce the impact of shortages as well as disruption spreads.

INTRODUCTION: Two major technological paradigms have been developed in recent years, digital twins and the multiparadigm simulation. In the Health Sector, the enormous potential of both approaches for the management of public health emergencies is envisioned.OBJECTIVES: This study aims to develop the conceptual framework for the development of a Digital Public Health Emergency System.METHODS: The integration of the digital twins in health with the multi-paradigm simulation for the design of a digital system of public health emergencies is proposed.RESULTS: The proposal establishes the conceptual framework for the integration of a digital public health emergency system, incorporating multi-paradigm simulation in the construction of a digital twin.CONCLUSION: The use of cutting-edge technologies for public health emergencies will allow for a better response to contingencies as well as optimally managing the resources available in a health system.

According to the literature review performed, there are few methods focused on the study of qualitative and quantitative variables when making demand projections by using fuzzy logic and artificial neural networks. The purpose of this research is to build a hybrid method for integrating demand forecasts generated from expert judgements and historical data and application in the automotive industry. Demand forecasts through the integration of variables; expert judgements and historical data using fuzzy logic and neural network. The methodology includes the integration of expert and historical data applying the Delphi method as a means of collecting fuzzy date. The result according to proposed methodology shows how fuzzy logic and neural networks is an alternative for demand planning activity. Machine learning techniques are techniques that generate alternatives for the tools development for demand forecasting. In this study, qualitative and quantitative variables are integrated through the implementation of fuzzy logic and time series artificial neural networks. The study aims to focus in manufacturing industry factors in conjunction time series data.

The maternal mortality situation is analyzed in México as an indicator that reflects the social development level of the country and was one of the millennial development objectives. The effect of a maternal death in the related social group has multiplier effects, since it involves family dislocation, economic impact and disruption of the orphans' normal social development. Two perspectives that causes of maternal mortality were analyzed, on one hand, their relationship with social determinants and on the other, factors directly related to the health system. Evidence shows that comparing populations based on group of selected variables according to social conditions and health care access, statistically significant differences prevail according to education and marginalization levels, and access to medical care. In addition, the Age-Period-Cohort model raised, shows significant progress in terms of a downward trend in maternal mortality in a generational level. Those women born before 1980 had a greater probability of maternal death in relation to recent generations, which is a reflection of the improvement in social determinants and in the Health System. The age effect shows a problem in maternal mortality in women under 15 years old, so teen pregnancy is a priority in health and must be addressed in short term. There is no clear evidence of a period effect.

This paper describes the use of Chambers-Mallows-Stuck method for simulating stable random variables in the generation of test systems for economic analysis in power systems. A study that focused on generating test electrical systems through fat tail model for unit commitment problem in electrical power systems is presented. Usually, the instances of test systems in Unit Commitment are generated using normal distribution, but in this work, simulations data are based on a new method. For simulating, we used three original systems to obtain the demand behavior and thermal production costs. The estimation of stable parameters for the simulation of stable random variables was based on three generally accepted methods: (a) regression, (b) quantiles, and (c) maximum likelihood, choosing one that has the best fit of the tails of the distribution. Numerical results illustrate the applicability of the proposed method by solving several unit commitment problems.