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
Dual staining for p 167Ki67 is a more specific test than cytology for triage of HPV-positive women
(Springer, 2018) Areán Cuns, Carolina; Mercado Gutiérrez, María R.; Paniello Alastruey, Irene; Mallor Giménez, Fermín; Córdoba Iturriagagoitia, Alicia; Lozano Escario, María; Santamaría Martínez, Mercedes; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Open Access
Dynamic mean absolute error as new measure for assessing forecasting errors
(Elsevier, 2018-02-14) Frías Paredes, Laura; Mallor Giménez, Fermín; Gastón Romeo, Martín; León, Teresa; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
Accurate wind power forecast is essential for grid integration, system planning, and electricity trading in certain electricity markets. Therefore, analyzing prediction errors is a critical task that allows a comparison of prediction models and the selection of the most suitable model. In this work, the temporal error and absolute magnitude error are simultaneously considered to assess the forecast error. The trade-off between both types of errors is computed, analyzed, and interpreted. Moreover, a new index, the dynamic mean absolute error, DMAE, is defined to measure the prediction accuracy. This index accounts for both error components: temporal and absolute. Real cases of wind energy forecasting are used to illustrate the use of the new DMAE index and show the advantages of this new index over other error indices.
Open Access
Early detection of new pandemic waves: control chart and a new surveillance index
(Public Library of Science, 2024) Cildoz Esquíroz, Marta; Gastón Romeo, Martín; Frías Paredes, Laura; García de Vicuña Bilbao, Daniel; Azcárate Camio, Cristina; Mallor Giménez, Fermín; Institute of Smart Cities - ISC
The COVID-19 pandemic highlights the pressing need for constant surveillance, updating of the response plan in post-peak periods and readiness for the possibility of new waves of the pandemic. A short initial period of steady rise in the number of new cases is sometimes followed by one of exponential growth. Systematic public health surveillance of the pandemic should signal an alert in the event of change in epidemic activity within the community to inform public health policy makers of the need to control a potential outbreak. The goal of this study is to improve infectious disease surveillance by complementing standardized metrics with a new surveillance metric to overcome some of their difficulties in capturing the changing dynamics of the pandemic. At statistically-founded threshold values, the new measure will trigger alert signals giving early warning of the onset of a new pandemic wave. We define a new index, the weighted cumulative incidence index, based on the daily new-case count. We model the infection spread rate at two levels, inside and outside homes, which explains the overdispersion observed in the data. The seasonal component of real data, due to the public surveillance system, is incorporated into the statistical analysis. Probabilistic analysis enables the construction of a Control Chart for monitoring index variability and setting automatic alert thresholds for new pandemic waves. Both the new index and the control chart have been implemented with the aid of a computational tool developed in R, and used daily by the Navarre Government (Spain) for virus propagation surveillance during post-peak periods. Automated monitoring generates daily reports showing the areas whose control charts issue an alert. The new index reacts sooner to data trend changes preluding new pandemic waves, than the standard surveillance index based on the 14-day notification rate of reported COVID-19 cases per 100,000 population.
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
Simulation of household electricity consumption by using functional data analysis
(Taylor & Francis, 2018) Mallor Giménez, Fermín; Moler Cuiral, José Antonio; Urmeneta Martín-Calero, Henar; Estadística e Investigación Operativa; Estatistika eta Ikerketa Operatiboa
Open Access
Introducing the Temporal Distortion Index to perform a bidimensional analysis of renewable energy forecast
(Elsevier, 2015-11-21) Frías Paredes, Laura; Mallor Giménez, Fermín; León, Teresa; Gastón Romeo, Martín; Estadística e Investigación Operativa; Estatistika eta Ikerketa Operatiboa; Institute of Smart Cities - ISC
Wind has been the largest contributor to the growth of renewal energy during the early 21st century. However, the natural uncertainty that arises in assessing the wind resource implies the occurrence of wind power forecasting errors which perform a considerable role in the impacts and costs in the wind energy integration and its commercialization. The main goal of this paper is to provide a deeper insight in the analysis of timing errors which leads to the proposal of a new methodology for its control and measure. A new methodology, based on Dynamic TimeWarping, is proposed to be considered in the estimation of accuracy as attribute of forecast quality. A new dissimilarity measure, the Temporal Distortion Index, among time series is introduced to complement the traditional verication measures found in the literature. Furthermore we provide a bi-criteria perspective to the problem of comparing different forecasts. The methodology is illustrated with several examples including a real case.
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
A management flight simulator of an intensive care unit
(IEEE, 2019) García de Vicuña Bilbao, Daniel; Mallor Giménez, Fermín; Esparza, Laida; Mateo, Pedro; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Management Flight Simulators (MFS) supply a simulated environment in which managers can learn from experience in a controlled setting. Although its use is usual in other areas, no such software has been developed to learn about the complexity of the Intensive Care Unit (ICU) management. This paper describes an MFS of ICUs which includes main features that distinguish it from other simulators such as the evolution of patients' health status and the recreation of real discharge and admission processes. The mathematical model is a discrete event simulation model in which different types of patients arrive at the ICU (emergency and scheduled patients). The user manages the simulated ICU by deciding about their admission or diversion and which inpatients are discharged. The analysis of recorded data is used to detect controversial scenarios and to understand how physicians' decisions are made.
Open Access
Accumulating priority queues versus pure priority queues for managing patients in emergency departments
(Elsevier, 2019) Cildoz Esquíroz, Marta; Ibarra, Amaia; Mallor Giménez, Fermín; Institute of Smart Cities - ISC
Improving the quality of healthcare in emergency departments (EDs) is at the forefront of many hospital managers’ efforts, as they strive to plan and implement better patient flow strategies. In this paper, a new approach to manage the patient flow in EDs after triage is proposed. The new queue discipline, named accumulative priority queue with finite horizon and denoted by APQ-h, is an extension of the accumulative priority queue (APQ) discipline that considers not only the acuity level of patients and their waiting time but also the stage of the healthcare treatment. APQ disciplines have been studied in the literature from a queueing theory point of view, which requires assumptions rarely found in real EDs, such as homogeneity in the patient arrival pattern and only one service stage. The APQ-h discipline accumulates priority from the point of waiting for the first physician consultation until the moment the waiting time exceeds the upper time limit set to access the physician after the patient's arrival. A recent study shows that a management strategy of this type is applied in practice in several Canadian EDs. The main aim of this paper is to explore the implementation of APQ-h managing policies in a real ED. For this purpose, a simulation model replicating a real ED is developed. This simulation model is also used to obtain the optimal APQ type polices through a simulation-based optimization method that solves a multi-objective and stochastic optimization problem. Arrival to provider time and total waiting time in the ED are considered to be the key ED performance indicators. An extensive computational analysis shows the flexibility of the APQ-h and APQ discipline and their superiority over other pure priority disciplines in a real setting and in a variety of ED scenarios. In addition, no superiority over the APQ discipline is demonstrated. © 2019 The Authors