Open Access
Including learning and forgetting processes in agent-based simulation models: application to police intervention in out-of-hospital cardiac arrests
(Elsevier, 2025-01-01) Baigorri Iguzquiaguirre, Miguel; Mallor Giménez, Fermín; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Agent-based modeling has become increasingly popular in recent decades; however, defining agents that accurately depict human behavior remains a significant challenge. This paper contributes to the precise definition of human-like agents by incorporating learning and forgetting processes from the medical and psychological literature into agent-based simulation models. Specifically, the mathematical model for forgetting is developed to be compatible with empirical findings. The empirical evidence also supports the decomposition of the learning process into training sessions and the application of skills in real situations, as followed in this model. The resulting model of learning agents is then applied to study police intervention in out-of-hospital cardiac arrests. In numerous urban areas, there's ongoing discussion regarding the provision of defibrillators in patrol cars and CPR training for police officers. The results demonstrate that including learning and forgetting processes in simulation models provide a more accurate understanding of the benefits of using local police to attend cardiac arrests.
Open Access
Gestión de camas hospitalarias durante la pandemia en Navarra con el apoyo de métodos matemáticos de predicción
(Departamento de Salud del Gobierno de Navarra, 2023) Rodrigo Rincón, Isabel; García de Vicuña Bilbao, Daniel; Esparza Artanga, Laida; Santana-Domínguez, Sergio; Martínez-Larrea, Jesús Alfredo; Mallor Giménez, Fermín; Institute of Smart Cities - ISC
Durante la pandemia por coronavirus, en Navarra se utilizaron modelos matemáticos de predicción para estimar las camas necesarias, convencionales y de críticos, para atender a los pacientes COVID-19. Las seis ondas pandémicas presentaron distinta incidencia en la población, ocasionando variabilidad en los ingresos hospitalarios y en la ocupación hospitalaria. La respuesta a la enfermedad de los pacientes no fue constante en cada onda, por lo que, para la predicción de cada una, se utilizaron los datos correspondientes de esa onda. El método de predicción constó de dos partes: una describió la entrada de pacientes al hospital y la otra su estancia dentro del mismo. El modelo requirió de la alimentación a tiempo real de los datos actualizados. Los resultados de los modelos de predicción fueron posteriormente volcados al sistema de información corporativo tipo Business Intelligence. Esta información fue utilizada para planificar el recurso cama y las necesidades de profesionales asociadas a la atención de estos pacientes en el ámbito hospitalario. En la cuarta onda se realizó un análisis para cuantificar el grado de acierto de los modelos predictivos. Los modelos predijeron adecuadamente el pico, la meseta y el cambio de tendencia, pero sobreestimaron los recursos necesarios para la atención de los pacientes en la parte descendente de la curva. El principal punto fuerte de la sistemática utilizada para la construcción de modelos predictivos fue proporcionar modelos en tiempo real con datos recogidos con precisión por los sistemas de información que consiguieron un grado de acierto aceptable permitiendo una utilización inmediata.
Open Access
The optimal container selection problem for parts transportation in the automotive sector
(Elsevier, 2024) Cildoz Esquíroz, Marta; Mateo, Pedro; Alonso, María Teresa; Parreño, Francisco; Alvarez-Valdes, Ramon; Mallor Giménez, Fermín; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
Today's automotive factories are essentially assembly plants that receive parts from a vast network of suppliers around the world. Transporting thousands of part types over very long distances is a major logistic problem whose solution is a critical factor in the factory management. In this study we have developed a statistical and optimization methodology implemented in a software tool to help the decision makers select the most appropriate container for each part. A key element is to determine the number of parts that fit in a given container. Two optimization procedures have been developed, depending on the type of part, and used to calculate costs of each container. These costs include not only transporting parts from supplier to factory, but also the costs of handling parts within the factory and returning the empty containers to the supplier.
Open Access
A GRASP-based algorithm for solving the emergency room physician scheduling problem
(Elsevier, 2021) Cildoz Esquíroz, Marta; Mallor Giménez, Fermín; Mateo, Pedro; Institute of Smart Cities - ISC
This paper addresses a physician scheduling problem in an Emergency Room (ER) requiring a long-term work calendar to allocate work days and types of shift among all the doctors. The mathematical model is created without simplifications, using the real calendar, including holidays. This precludes the possibility of cyclic-type solutions, and involves numerous and varied constraints (demand, workload, ergonomics, fairness, etc.). An effective solution to this very difficult practical problem cannot be obtained, for large instances, with exact solution methods. We formulate a mathematical representation of a real-world ER physician scheduling problem featuring a hybrid algorithm combining continuous linear programming with a greedy randomized adaptive search procedure (GRASP). Linear programming is used to model a general physician-demand covering problem, where the solution is used to guide the construction phase of the GRASP, to obtain initial full schedules for subsequent improvement by iterative application of Variable Neighborhood Descent Search (VNDS) and Network Flow Optimization (NFO). A computational study shows the superiority of our approach over the Integer Linear Programming method in a set of instances of varying size and difficulty inspired by a real setting. The methodology is embedded in a software tool for generating one-year-ahead physician schedules for a local ER. These solutions, which are now in use, outperform the manually-created schedules used previously. © 2021 Elsevier B.V.
Open Access
Police as first reponders improve out-of-hospital cardiac arrest survival
(BMC, 2023) Jean Louis, Clint; Cildoz Esquíroz, Marta; Echarri Sucunza, Alfredo; Beaumont, Carlos; Mallor Giménez, Fermín; Greif, Robert; Baigorri Iguzquiaguirre, Miguel; Reyero Díez, Diego; Ciencias de la Salud; Osasun Zientziak; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Background: Police forces are abundant circulating and might arrive before the emergency services to Out-of-Hospital-Cardiac-Arrest victims. If properly trained, they can provide basic life support and early defibrillation within minutes, probably increasing the survival of the victims. We evaluated the impact of local police as first responders on the survival rates of out-of-hospital cardiac arrest victims in Navarra, Spain, over 7 years. Methods: A retrospective analysis of an ongoing Out-of-Hospital Cardiac registry to compare the characteristics and survival of Out-of-Hospital-Cardiac-Arrest victims attended to in first place by local police, other first responders, and emergency ambulance services between 2014 and 2020. Results: Of 628 cases, 73.7% were men (aged 68.9 ± 15.8), and 26.3% were women (aged 65,0 ± 14,7 years, p < 0.01). Overall survival of patients attended to by police in the first place was 17.8%, other first responders 17.4% and emergency services 13.5% with no significant differences (p > 0.1). Time to initiating cardiopulmonary resuscitation is significant for survival. When police arrived first and started CPR before the emergency services, they arrived at a mean of 5.4 ± 3 min earlier (SD = 3.10). This early police intervention showed an increase in the probability of survival by 10.1%. Conclusions: The privileged location and the sole amount of personnel of local police forces trained in life support and their fast delivery of defibrillators as first responders can improve the survival of out-of-hospital cardiac arrest victims.
Open Access
Hospital preparedness during epidemics using simulation: the case of COVID-19
(Springer, 2021) García de Vicuña Bilbao, Daniel; Esparza, Laida; Mallor Giménez, Fermín; Institute of Smart Cities - ISC; Gobierno de Navarra / Nafarroako Gobernua
This paper presents a discrete event simulation model to support decision-making for the short-term planning of hospital resource needs, especially Intensive Care Unit (ICU) beds, to cope with outbreaks, such as the COVID-19 pandemic. Given its purpose as a short-term forecasting tool, the simulation model requires an accurate representation of the current system state and high fidelity in mimicking the system dynamics from that state. The two main components of the simulation model are the stochastic modeling of patient admission and patient flow processes. The patient arrival process is modelled using a Gompertz growth model, which enables the representation of the exponential growth caused by the initial spread of the virus, followed by a period of maximum arrival rate and then a decreasing phase until the wave subsides. We conducted an empirical study concluding that the Gompertz model provides a better fit to pandemic-related data (positive cases and hospitalization numbers) and has superior prediction capacity than other sigmoid models based on Richards, Logistic, and Stannard functions. Patient flow modelling considers different pathways and dynamic length of stay estimation in several healthcare stages using patient-level data. We report on the application of the simulation model in two Autonomous Regions of Spain (Navarre and La Rioja) during the two COVID-19 waves experienced in 2020. The simulation model was employed on a daily basis to inform the regional logistic health care planning team, who programmed the ward and ICU beds based on the resulting predictions.
Open Access
Un modelo para predecir cuántas camas UCI harán falta durante cada oleada
(Asociacion the Conversation España, 2021) Mallor Giménez, Fermín; García de Vicuña Bilbao, Daniel; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
La crisis financiera mundial de 2008 puso de moda en España el término económico “prima de riesgo”, hasta entonces desconocido. Del mismo modo, la pandemia ha popularizado expresiones y términos como “doblar la curva”, “incidencia acumulada” e incluso conceptos epidemiológicos más específicos como “el número efectivo de reproducción R₀”. Ocupan portadas de periódicos, así como espacios en noticiarios televisivos y radiofónicos. Constituyen una muestra del uso de las matemáticas para describir la evolución de la pandemia y para proporcionar indicadores con los que las autoridades políticas pueden fundamentar una toma de decisiones informada sobre medidas de distanciamiento social y restricciones a la movilidad. Sin embargo, los modelos matemáticos no solo sirven para describir qué ha pasado o el estado actual de la pandemia, sino que pueden facilitar predicciones muy útiles sobre cómo va a evolucionar. Estas son útiles para la planificación de los recursos sanitarios necesarios para atender a paciente covid-19, como las camas UCI. La planificación facilita la utilización eficiente de recursos y, en consecuencia, proporcionar una mejor atención a todos los pacientes, covid y no covid. Los modelos matemáticos más útiles para predecir variables relacionadas con la evolución de la pandemia son los de simulación. Estos modelos son capaces de representar características complejas de la realidad pandémica, como su aleatoriedad e incertidumbre, así como la variabilidad en el impacto que la enfermedad puede tener en distintas personas
Open Access
I Congreso Salud, Desastres y Desarrollo Sostenible: libro congreso
(2022) Azcárate Camio, Cristina; Cildoz Esquíroz, Marta; Frías Paredes, Laura; Ibarra, Amaia; Galbete Jiménez, Arkaitz; García de Vicuña Bilbao, Daniel; Gastón Romeo, Martín; Moler Cuiral, José Antonio; Mallor Giménez, Fermín; Jean Louis, Clint; Institute of Smart Cities - ISC
El congreso se plantea como un foro de encuentro de investigadores del área de Investigación Operativa con interés en aplicaciones a la salud, los desastres y el desarrollo sostenible, y los profesionales de la toma de decisiones concernientes a los ámbitos anteriores. Este encuentro promueve el intercambio de conocimiento y experiencias entre Universidad y Servicios de Salud para afrontar retos asociados al acceso de la población a unos servicios de salud de calidad y a la gestión del riesgo creciente de desastres naturales o provocados por el ser humano. El envejecimiento de la población y el desarrollo tecnológico plantean nuevos entornos para la provisión de los servicios de salud, en los que su correcta planificación y gestión debe contribuir a garantizar su eficiencia y sostenibilidad. El creciente impacto en términos de vidas humanas y daños económicos causados por desastres naturales y no naturales, como incendios, inundaciones, terremotos, fugas industriales, pandemias, etc. precisa de su comprensión para desarrollar estrategias de prevención y elaborar planes efectivos de respuesta.
Open Access
Operations research helps public health services managers planning resources in the COVID-19 crisis
(Sociedad de Estadística e Investigación Operativa, 2020) García de Vicuña Bilbao, Daniel; Cildoz Esquíroz, Marta; Gastón Romeo, Martín; Azcárate Camio, Cristina; Mallor Giménez, Fermín; Esparza, Laida; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y Matemáticas
This article presents the usefulness of operational research models tosupport the decision-making in management problems on the COVID-19 pandemic. The work describes a discrete event simulation model combined with population growth models, which has been used to provide daily predictions of the needs of ward and intensive care unit beds during the COVID-19 outbreak in the Autonomous Community of Navarre, in Spain. This work also discusses the use of the simulation model in non-acutephases of the pandemic to support decision-making during the return to the normal operation of health services or as a resource management learning tool for health logistic planners.
Open Access
Coping with stress in emergency department physicians through improved patient-flow management
(Elsevier, 2020) Cildoz Esquíroz, Marta; Ibarra, Amaia; Mallor Giménez, Fermín; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y Matemáticas
This paper provides a method for the real-time monitoring of job stress in emergency department (ED) physicians. It is implemented in a Decision Support System (DSS) designed for patient-to-physician assignment after triage. Our concept of job stress includes not only the workload but also time pressure and uncertainty. A job stress function is estimated based on the consensus views of ED physicians obtained through a novel methodology involving stress factor analysis, questionnaire design, and the statistical analysis of expert opinions. The resulting stress score enables the assessment of job stress using workload data from the ED physicians’ whiteboard. These data can be used for the real-time measurement and monitoring of ED physician job stress in a stochastic and dynamic environment, which is the main novelty of this method as compared to previous workload and stress measurement proposals. A further advantage of this methodology is that it is general enough to be adapted to physician job stress monitoring in any ED. The use of the DSS for ED patient-flow management reduces job stress and spreads it more evenly among the whole team of physicians, while also improving other important ED performance measures such as arrival-to-provider time and the percentage of compliance with patient waiting time targets. A case study illustrates the application of the methodology for the construction of a stress-score, the monitoring of physician stress levels, and ED patient-flow management.