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
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
Design exploration prior to blade multi-disciplinary optimisation
(IOP Publishing, 2018) Echeverría Durá, Fernando; Mallor Giménez, Fermín; San Miguel, Unai; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
The approach of designing blades as a multi-disciplinary, holistic optimisation implies significant challenges owing to the high complexity of the involved factors such as aerodynamics, elasticity, controller and loads. Moreover, the large number of design variables complicates the intuitive analysis of the relationship between the design variables and responses. This paper presents the design variable exploration prior to blade optimisation, which reveals certain design variable combinations that lead to undesirable dynamic load amplification. Statistical tools, such as multiple logistic regression and fast and frugal decision trees, are applied to identify the conditions causing the phenomenon and predict the possible appearance under new design variable combinations.
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.
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
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
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
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
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
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.