In this article, we propose an application of humanitarian logistics theory to build a supportive framework for economic reactivation and pandemic management based on province vulnerability against COVID-19. The main research question is which factors are related to COVID-19 mortality between Peruvian provinces? We conduct a spatial regression analysis to explore which factors determine the differences in COVID-19 cumulative mortality rates for 189 Peruvian provinces up to December 2020. The most vulnerable provinces are characterized by having low outcomes of long-run poverty and high population density. Low poverty means high economic activity, which leads to more deaths due to COVID-19. There is a lack of supply in the set of relief goods defined as Pandemic Response and Recovery Supportive Goods and Services (PRRSGS). These goods must be delivered in order to mitigate the risk associated with COVID-19. A supportive framework for economic reactivation can be built based on regression results and a delivery strategy can be discussed according to the spatial patterns that we found for mortality rates.

This work studies the health status of two populations similar in most social and environmental interactions but one: the individuals from one population are victims of an internal armed conflict. Both populations are located in the Risaralda province, Colombia and the data for this study results from a combination of administrative records from the health system, between 2011 and 2016. We implemented a methodology based on graph theory that defines the system as a set of heterogeneous social actors, including individuals as well as organizations, embedded in a biological environment. The model of analysis uses the diagnoses in medical records to detect morbidity and mortality patterns for each individual (ego-networks), and assumes that these patterns contain relevant information about the effects of the actions of social actors, in a given environment, on the status of health. The analysis of the diagnoses and causes of specific mortality, following the Social Network Analysis framework, shows similar morbidity and mortality rates for both populations. However, the diagnoses’ patterns show that victims portray broader interactions between diagnoses, including mental and behavioral disorders, due to the hardships of this population.

Micro and small enterprises (MSEs) play a significant role in developing countries such as Ecuador. The food and beverage industry is essential for Ecuador’s economy, contributing approximately 5% to its GDP. Focusing on the impact of COVID-19 on the industry, this mixed research takes a qualitative and quantitative approach involving four stages: foundation, prefield, field, and reporting. The fieldwork was carried out when Ecuador’s economy reopened in September 2020, which saw demand increase gradually, production capacity recovering, mobility return to normal levels, and curfew restrictions reduced. As far as biosecurity protocols were concerned, communities were allowed to resume pre-pandemic activities, provided they complied with social distancing, permanent mask wearing, and strict cleaning procedures. The effect on each company’s process depends on the activities they adopted to mitigate the risk of the COVID-19 pandemic risk, e.g., service companies experienced, on average, a 22% cost increase due to the purchase of cleaning supplies, while manufacturing companies were more likely to have related measures in place and so the effect on them was on a comparatively smaller scale.

This paper explores the application of machine learning techniques and statistical analysis to identify the patterns of victimization and the risk of gender-based violence in San Andrés de Tumaco, Nariño, Colombia. Models were developed to classify women according to their vulnerability and risk of suffering various forms of violence, which were integrated into a decision-making tool for local authorities. The algorithms employed include K-means for clustering, artificial neural networks, random forests, decision trees, and multiclass classification algorithms combined with fuzzy classification techniques to handle the incomplete data. Implemented in Python and R, the models were statistically validated to ensure their reliability. Analysis based on health data revealed the key victimization patterns and risks associated with gender-based violence in the region. This study presents a data science model that uses a social determinant approach to assess the characteristics and patterns of violence against women in the Pacific region of Nariño. This research was conducted within the framework of the Orquídeas Program of the Colombian Ministry of Science, Technology, and Innovation.

In this article we propose an application of humanitarian logistics theory to build a supportive framework for economic reactivation and pandemic management based on province vulnerability against COVID-19. The main research question is: which factors are related to COVID-19 mortality between Peruvian provinces? We conduct a spatial regression analysis to explore which factors determines the differences in COVID-19 cumulative mortality rates for 189 Peruvian provinces up to December 2020. The most vulnerable provinces are characterized by having low outcomes of long-run poverty and high population density. Low poverty means a high economic activity that leads to more deaths of COVID-19. There is a lack of supply of a set of relief goods defined as Pandemic Response and Recovery Supportive Goods and Services (PRRSGS). These goods must be delivered in order to mitigate the risk associated to COVID-19. A supportive framework for economic reactivation can be built based on regression results and a delivery strategy can be discussed according to the spatial patterns that we found for mortality rates.

Health differences across socioeconomic strata have always pointed out that poorer and minorities have higher mortality and morbidity than richer and majorities. This difference is exacerbated for particular populations such as the victims of ongoing armed conflict, who are also much harder to quantify due to the conflict itself. This study uses complex network analysis applied to a combination of three large administrative records for the health system and mortality records in the province of Risaralda (Colombia) between 2011 and 2016. We estimate the most common causes of morbi-mortality for both victims of violence and the poorest inhabitants of Risaralda, defined as those who qualify as recipients of subsidies from the Colombian welfare program, called SISBEN, in the categories of those with the highest need, levels I and II. Both populations show high morbidity frequencies for non-communicable diseases such as Type II diabetes, hypertension and hyperglyceridaemia, mostly associated with exposure to unhealthy lifestyles. However, these mortality outcomes reflect the different treatments and lifestyles of both subpopulations. While the poorest replicate the same causes identified for morbidity, the victims of armed conflict die of additional causes including Type I diabetes, which reflects the even worse conditions they face.