The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/0041578f-5dd9-46d2-ad41-19ac36d7f1a5

Baas, Stef (2024): Simulation data for paper Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/0041578f-5dd9-46d2-ad41-19ac36d7f1a5.v1

Dataset

• Applied Mathematics
• Mathematical Sciences

Bed occupancy, Corona, COVID-19, Load balancing, Patient allocation, Queueing theory, regions, Stochastic program

CC BY-NC-SA 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Stef Baas

Simulation data of patient reallocation over hospitals or regions. Research objective: determining the benefits of regional re-allocation of COVID-19 patients using stochastic approximation and stochastic programming, type of research: simulation study, method of data collection: simulations, type of data: R datasets

• 2024-06-24 first online, published, posted

4TU.ResearchData

.rds files

Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy

University of Twente, Center for Healthcare Operations Improvement and Research (CHOIR)