https://hdl.handle.net/20.500.12412/2554 Thu, 30 Apr 2026 11:08:59 GMT 2026-04-30T11:08:59Z https://hdl.handle.net/20.500.12412/6959 Analysis of Prevention Policies and Health Improvements Applied to the COVID-19 Pandemic Partida Hanon, Angélica Inés In November 2019, a respiratory virus known as SARS-CoV-2, which causes the COVID-19 disease was first detected in China, rapidly leading to a global lockdown with significant impacts on economies, health systems, and societal structures. Traditionally, research has shown seasonal drivers in respiratory viruses due to factors like UV radiation, temperature, humidity, and human immune response, adding complexity to the classical SEIR epidemic models. As well, behavioural patterns, age, and social contact intensity have been seen to impact on overall transmission rates. The pandemic highlighted the need for robust systems to manage health crises and beyond the immediate health-related response, it caused economic and social disruptions, requiring companies to balance operations and employee safety. A major response involved shifting to remote work, affecting mental and physical health, productivity, and workforce engagement. Additionally, the pandemic triggered an economic crisis due to demand shocks from confinement. Moreover, cultural factors might have an influence on virus transmission and vaccination acceptance. Studies on societal behaviour might have opposite findings, while some authors highlight that individualistic societies may accelerate virus spread due to lower adherence to collective interests compared to collectivistic ones. Others discuss that individualistic societies might be expected to present less transmission rates due to lower intensity of physical contacts. Also, besides cultural aspects have been seen to significantly impact on vaccination intentions, healthcare infrastructure, government policies, and economic factors also play crucial roles in actual vaccination rates. Throughout this doctoral thesis, we present the data-science based decision-making process that led to define key measures taken by an important international financial institution headquartered in Spain to manage the pandemic within their corporate headquarters to ensure a secure workplace for personnel on premises, offering a detailed analysis of the protocols and tools used, and how they can serve as a model for future health crises. In this regard, the present work focuses on Madrid, Spain, using Machine Learning algorithms to propose both a reactive and preventive measurements. Among the reactive measurements we include the detection of focal infection points, statistical analyses to test new diagnostic kits, the design of a "return to office" protocol and a continuous follow-up of the evolution of the pandemic. Within the preventive measurements. We include behavioural analyses considering worker nature, seasonal effects, and psychosocial demands. Professionals conducted regular COVID-19 diagnostic tests utilising a data-driven decisionmaking process to optimise resource deployment for early detection and isolation of COVIDpositive cases, resulting in a total of 55,789 tests. The sanitary team conducted individual follow-ups for all personnel and recorded the information in databases. Individualised control panels enabled real-time monitoring to adjust restrictive measures as necessary. This process ensured that actions were appropriate for the evolving situation, such as identifying hotspots for quarantine assignment when needed. A positive correlation was observed between the cumulative incidence reported by Madrid’s Ministry of Health and the headcount. Moreover, 1.7% of individuals continued to test positive for COVID-19 after completing a 14-day quarantine period, which justified the decision to maintain this quarantine duration. Six occupational outbreaks were identified between the second and sixth waves, exhibiting varying degrees of severity and impact. Despite the high community incidence rate, occupational infections within the bank remained notably low, representing only 1.9% of the total positive cases. The study revealed significant variations in COVID-19 transmission based on demographic, organisational, and seasonal factors. Higher infection rates were found among higher seniority levels and certain departments, suggesting the need for tailored interventions. Seasonal variations also played a role, with higher transmission rates noted during the winter months compared to the summer along with a higher subjective identification of associated symptoms in summer. A combined approach using medical and computational tools was implemented to ensure workplace safety during periods of high transmission. The study demonstrated that targeted action strategies, such as identifying high-risk individuals and implementing specific preventive measures, were more effective and cost-efficient compared to extensive screenings across the entire workforce. Algorithm-based medical screenings yielded higher detection rates, allowing for more accurate identification of potential cases. Additionally, the study highlighted the importance of adapting preventive measures to various demographic, organisational, and seasonal variables to maximise efficiency. In summary, the COVID-19 pandemic has highlighted the critical importance of preparedness and adaptability amidst health emergencies by integrating both biomedical and computational tools. The insights obtained from this study can inform future strategies for managing similar situations, emphasising the necessity for a comprehensive and data-centric approach for dynamic and responsive strategies, ensuring that interventions remain relevant and effective in different contexts. Finally, these approaches can serve as a model for other organisations facing future health crises.; En abierto se puede consultar la parte no embargada de la Tesis Doctoral Mon, 01 Dec 2025 00:00:00 GMT https://hdl.handle.net/20.500.12412/6959 2025-12-01T00:00:00Z https://hdl.handle.net/20.500.12412/6168 Estimación de las elasticidades de demanda del transporte de pasajeros en Estados Unidos en los diferentes modos en competencia Escañuela Romana, Ignacio El objeto de esta tesis es estimar empíricamente las elasticidades de la demanda del transporte de pasajeros en los Estados Unidos, en rutas domésticas en las que los pasajeros pueden elegir entre varios modos de transporte. El objetivo de la tesis se ha desarrollado en cuatro artículos publicados en revistas de relevancia internacional. La estimación de la elasticidad de la demanda tiene una relevancia teórica y práctica. Se trata de una cuestión clásica y central en la econometría. Además, la estimación de las elasticidades precio y renta de la demanda de transporte hace posible cuantificar los impactos de las modificaciones en el precio o en la renta disponible de los consumidores, lo que permite efectuar una predicción de la demanda. Predicción de la demanda que es central para conocer cómo responde la densidad de utilización de la ruta de transporte a las variaciones de precios y renta. Ésta es una de las variables necesarias para proyectar y desarrollar políticas eficientes de oferta de servicios de transporte y de inversiones a realizar en infraestructuras. Del mismo modo, posibilita la planificación y programación adecuadas de las empresas privadas que operan en el sector del transporte (aerolíneas, gestión aeroportuaria, etc.).; Tesis embargada (tesis por compendio de publicaciones) Sun, 01 Sep 2024 00:00:00 GMT https://hdl.handle.net/20.500.12412/6168 2024-09-01T00:00:00Z https://hdl.handle.net/20.500.12412/6005 Análisis de los Homicidios y Asesinatos Contra las Mujeres en las Relaciones de Pareja Mediante un Enfoque Computacional García Vergara, Esperanza Violence against women within relationships constitutes a grave social and public health problem that affects millions of women worldwide. This form of violence manifests itself in physical, sexual, psychological, economic, and social abuses perpetrated by men against women partners or ex-partners. The most severe form of this violence is Intimate Partner Femicide (IPF), which refers to the act of killing women in intimate partner relationships. Globally, IPF accounts for approximately 38.6 percent of all homicides, resulting in more than 30,000 women murdered each year. This lethal result is the leading cause of violent deaths among women and is estimated to continue in the future. The magnitude of the problem manifests the risks women face in the context of intimate relationships, stressing the urgent need for anticipate IPF based on scientific evidence to prevent it. Previous studies have identified several predictors of IPF, which include characteristics of the aggressor, the victim, and the context. These predictors are critical components of risk assessment instruments for IPF, which are essential to evaluate the potential risk of IPF and to utilize this information to implement appropriate interventions for aggressors and develop safety plans for victims. It is crucial for these instruments to accurately differentiate between potential lethal and nonlethal violence to ensure that cases of IPF are effectively identified. This capacity enables professionals to efficiently prioritize and optimize limited resources in high-risk cases, focusing efforts to provide rapid, extensive, and effective protection to victims who face the highest risk of IPF. Although existing risk assessment instruments have allowed the identification of high-risk cases of IPF and the implementation of actions to prevent fatal outcomes, there remains a margin of error in the predictions. There have been cases detected as low-risk that subsequently resulted in IPF. There is a need for further research to analyze these cases to detect new predictors from those identified in the scientific literature and incorporate those into risk assessment instruments. Enhancing the accuracy and effectiveness of identifying cases at risk of IPF is crucial for effective prevention. Mon, 01 Jul 2024 00:00:00 GMT https://hdl.handle.net/20.500.12412/6005 2024-07-01T00:00:00Z https://hdl.handle.net/20.500.12412/4820 Retos en la Evaluación de Ecosistemas de Salud Mental: Aplicación de Metodologías para el Análisis de Sistemas Complejos en su Contexto Díaz Milanés, Diego Se estima que una de cada ocho personas en el mundo sufre algún trastorno de salud mental y su prevalencia sigue aumentando, especialmente debido al impacto de la pandemia de COVID-19. Esto ha evidenciado la existencia de una gran brecha entre los recursos necesarios, la financiación destinada y las necesidades reales de los sistemas de atención a la salud mental de la población. También existe una gran brecha entre los avances en investigación y su aplicación en la atención y gestión sanitaria. Por tanto, muchos organismos internacionales demandan una adecuada distribución de recursos humanos y materiales, a la vez que promueven la búsqueda y aplicación de soluciones innovadoras para lograr una provisión efectiva y eficiente para cubrir las necesidades reales de la población. En la presente tesis doctoral, se presentan cuatro estudios centrados en metodologías para evaluar políticas y sistemas de salud mental, con el objetivo de contribuir a una mejor gestión de los recursos disponibles. La tesis se compone por un total de ocho capítulos. Los dos primeros presentan la introducción y los objetivos de la tesis, los capítulos del tres al seis muestran la justificación, las metodologías empleadas, los resultados y el impacto científico, social y político de cada estudio, finalmente los dos últimos capítulos presentan la discusión y las conclusiones. La presente tesis doctoral cumple con los requisitos necesarios para la obtención de la mención internacional. Fri, 01 Dec 2023 00:00:00 GMT https://hdl.handle.net/20.500.12412/4820 2023-12-01T00:00:00Z