Person:
García de Vicuña Bilbao, Daniel

Profile Picture

Email Address

person.page.identifierURI

https://academica-e.unavarra.es/handle/2454/49079

Birth Date

Research Projects

Organizational Units

Job Title

Last Name

García de Vicuña Bilbao

First Name

Daniel

person.page.departamento

Estadística, Informática y Matemáticas

person.page.instituteName

ORCID

0000-0001-7826-3060

person.page.upna

811267

Name

Filters

2020 - 20223
Reset filters

Settings

Start date: 2020 × Subject: Hospital resources planning ×

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    PublicationOpen 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.
  • Thumbnail Image
    PublicationOpen 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.
  • Thumbnail Image
    PublicationOpen Access
    Simulation models to support intensive care unit decision-making in pandemic and non-pandemic times
    (2022) García de Vicuña Bilbao, Daniel; Mallor Giménez, Fermín; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
    The aim of this thesis is the construction of simulation models to analyse and improve patient admission and inpatient discharge decisions in an Intensive Care Unit (ICU). These decisions are especially relevant in situations of high ICU occupancy because they can lead to the early discharge of an admitted patient or the redirection of a newcomer. Exceptional circumstances, such as the global COVID-19 pandemic that broke out in 2019, increase the need for ICU beds, making this type of study even more relevant. The development of two interactive simulators has enabled us to understand and support ICU decision-making, both in and out of pandemics.