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

Sander Dijkstra; Stef Baas; Aleida Braaksma; Richard J. Boucherie

doi:10.1016/j.omega.2022.102801

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

Sander Dijkstra, Stef Baas, Aleida Braaksma^*, Richard J. Boucherie

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

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Abstract

This paper introduces mathematical models that support dynamic fair balancing of COVID-19 patients over hospitals in a region and across regions. Patient flow is captured in an infinite server queueing network. The dynamic fair balancing model within a region is a load balancing model incorporating a forecast of the bed occupancy, while across regions, it is a stochastic program taking into account scenarios of the future bed surpluses or shortages. Our dynamic fair balancing models yield decision rules for patient allocation to hospitals within the region and reallocation across regions based on safety levels and forecast bed surplus or bed shortage for each hospital or region.

Input for the model is an accurate real-time forecast of the number of COVID-19 patients hospitalised in the ward and the Intensive Care Unit (ICU) of the hospitals based on the predicted inflow of patients, their Length of Stay and patient transfer probabilities among ward and ICU. The required data is obtained from the hospitals’ data warehouses and regional infection data as recorded in the Netherlands.

The algorithm is evaluated in Dutch regions for allocation of COVID-19 patients to hospitals within the region and reallocation across regions using data from the second COVID-19 peak.

Original language	English
Article number	102801
Pages (from-to)	1-21
Number of pages	21
Journal	Omega
Volume	116
Early online date	16 Nov 2022
DOIs	https://doi.org/10.1016/j.omega.2022.102801
Publication status	Published - 1 Apr 2023

Keywords

UT-Hybrid-D
Patient allocation
Queueing theory
Load balancing
Stochastic program
Bed occupancy
COVID-19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.omega.2022.102801Licence: CC BY

ArticleFinal published version, 1.85 MBLicence: CC BY

Code for paper Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy
Baas, S. (Creator), 4TU.Centre for Research Data, 24 Jun 2024
DOI: 10.4121/b3fd0a0c-4f32-45e3-b931-fd4bd76dc0e0, https://data.4tu.nl/datasets/b3fd0a0c-4f32-45e3-b931-fd4bd76dc0e0 and 3 more links, https://doi.org/10.4121/b3fd0a0c-4f32-45e3-b931-fd4bd76dc0e0, https://data.4tu.nl/datasets/b3fd0a0c-4f32-45e3-b931-fd4bd76dc0e0/1, https://doi.org/10.4121/b3fd0a0c-4f32-45e3-b931-fd4bd76dc0e0.v1 (show fewer)
Dataset
Simulation data for paper Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy
Baas, S. (Creator), 4TU.Centre for Research Data, 24 Jun 2024
DOI: 10.4121/0041578f-5dd9-46d2-ad41-19ac36d7f1a5, https://data.4tu.nl/datasets/0041578f-5dd9-46d2-ad41-19ac36d7f1a5 and 3 more links, https://doi.org/10.4121/0041578f-5dd9-46d2-ad41-19ac36d7f1a5, https://data.4tu.nl/datasets/0041578f-5dd9-46d2-ad41-19ac36d7f1a5/1, https://doi.org/10.4121/0041578f-5dd9-46d2-ad41-19ac36d7f1a5.v1 (show fewer)
Dataset

Cite this

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title = "Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy",

abstract = "This paper introduces mathematical models that support dynamic fair balancing of COVID-19 patients over hospitals in a region and across regions. Patient flow is captured in an infinite server queueing network. The dynamic fair balancing model within a region is a load balancing model incorporating a forecast of the bed occupancy, while across regions, it is a stochastic program taking into account scenarios of the future bed surpluses or shortages. Our dynamic fair balancing models yield decision rules for patient allocation to hospitals within the region and reallocation across regions based on safety levels and forecast bed surplus or bed shortage for each hospital or region.Input for the model is an accurate real-time forecast of the number of COVID-19 patients hospitalised in the ward and the Intensive Care Unit (ICU) of the hospitals based on the predicted inflow of patients, their Length of Stay and patient transfer probabilities among ward and ICU. The required data is obtained from the hospitals{\textquoteright} data warehouses and regional infection data as recorded in the Netherlands.The algorithm is evaluated in Dutch regions for allocation of COVID-19 patients to hospitals within the region and reallocation across regions using data from the second COVID-19 peak.",

keywords = "UT-Hybrid-D, Patient allocation, Queueing theory, Load balancing, Stochastic program, Bed occupancy, COVID-19",

author = "Sander Dijkstra and Stef Baas and Aleida Braaksma and Boucherie, {Richard J.}",

note = "Funding Information: The authors would like to thank Fieke J. Snijders, Lars Tiemessen and Plom van Rooij for providing patient record data of Leiden University Medical Center, HagaZiekenhuis, Groene Hart Ziekenhuis, Elisabeth-TweeSteden Ziekenhuis and Rijnstate and helpful discussions to clarify patient reallocation protocols in the Netherlands. Publisher Copyright: {\textcopyright} 2022 The Authors",

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doi = "10.1016/j.omega.2022.102801",

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AU - Baas, Stef

AU - Braaksma, Aleida

AU - Boucherie, Richard J.

N1 - Funding Information: The authors would like to thank Fieke J. Snijders, Lars Tiemessen and Plom van Rooij for providing patient record data of Leiden University Medical Center, HagaZiekenhuis, Groene Hart Ziekenhuis, Elisabeth-TweeSteden Ziekenhuis and Rijnstate and helpful discussions to clarify patient reallocation protocols in the Netherlands. Publisher Copyright: © 2022 The Authors

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N2 - This paper introduces mathematical models that support dynamic fair balancing of COVID-19 patients over hospitals in a region and across regions. Patient flow is captured in an infinite server queueing network. The dynamic fair balancing model within a region is a load balancing model incorporating a forecast of the bed occupancy, while across regions, it is a stochastic program taking into account scenarios of the future bed surpluses or shortages. Our dynamic fair balancing models yield decision rules for patient allocation to hospitals within the region and reallocation across regions based on safety levels and forecast bed surplus or bed shortage for each hospital or region.Input for the model is an accurate real-time forecast of the number of COVID-19 patients hospitalised in the ward and the Intensive Care Unit (ICU) of the hospitals based on the predicted inflow of patients, their Length of Stay and patient transfer probabilities among ward and ICU. The required data is obtained from the hospitals’ data warehouses and regional infection data as recorded in the Netherlands.The algorithm is evaluated in Dutch regions for allocation of COVID-19 patients to hospitals within the region and reallocation across regions using data from the second COVID-19 peak.

AB - This paper introduces mathematical models that support dynamic fair balancing of COVID-19 patients over hospitals in a region and across regions. Patient flow is captured in an infinite server queueing network. The dynamic fair balancing model within a region is a load balancing model incorporating a forecast of the bed occupancy, while across regions, it is a stochastic program taking into account scenarios of the future bed surpluses or shortages. Our dynamic fair balancing models yield decision rules for patient allocation to hospitals within the region and reallocation across regions based on safety levels and forecast bed surplus or bed shortage for each hospital or region.Input for the model is an accurate real-time forecast of the number of COVID-19 patients hospitalised in the ward and the Intensive Care Unit (ICU) of the hospitals based on the predicted inflow of patients, their Length of Stay and patient transfer probabilities among ward and ICU. The required data is obtained from the hospitals’ data warehouses and regional infection data as recorded in the Netherlands.The algorithm is evaluated in Dutch regions for allocation of COVID-19 patients to hospitals within the region and reallocation across regions using data from the second COVID-19 peak.

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KW - Queueing theory

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KW - Stochastic program

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

Abstract

Keywords

UN SDGs

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

Simulation data for paper Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy

Cite this