ArticleBMC health services research2021
Prediction of hospital bed capacity during the COVID- 19 pandemic.
Article in BMC health services research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A modular approach to forecasting COVID-19 hospital bed occupancy.Communications medicine · 2025Article
- Predicting COVID-19 progression in hospitalized patients in Kurdistan Province using a multi-state model.Journal of diabetes and metabolic disorders · 2025Article
- Review
- Social health gradient and risk factors among patients hospitalized for COVID-19 and pre-pandemic respiratory infections. A linked national individual case-control study in Belgium.Frontiers in public health · 2024Article
- A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis.Healthcare analytics (New York, N.Y.) · 2023Article
- COVID-19 cluster identification and support vector machine classifier model construction using global healthcare and socio-economic features.Epidemiology and infection · 2023Article
- POST-OPERATIVE INTENSIVE CARE UNIT ADMISSION FOR ELECTIVE BRAIN TUMOUR SURGERIES: A NIGERIAN NEUROSURGICAL UNIT EXPERIENCE.Annals of Ibadan postgraduate medicine · 2023Article
- Wastewater-based epidemiology predicts COVID-19-induced weekly new hospital admissions in over 150 USA counties.Nature communications · 2023Article
- Resilience in keeping the balance between demand and capacity in the COVID-19 pandemic, a case study at a Swedish middle-sized hospital.BMC health services research · 2023Article
- A multistate model and its standalone tool to predict hospital and ICU occupancy by patients with COVID-19.Heliyon · 2023Article
- Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves.PloS one · 2023Article
- Article
- Mortality trends and length of stays among hospitalized patients with COVID-19 in Ontario and Québec (Canada): a population-based cohort study of the first three epidemic waves.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2022Article
- The Effects of the COVID-19 Pandemic on Penetrating Neurotrauma at a Level 1 Trauma Center.World neurosurgery · 2022Article
- Associations between the COVID-19 Pandemic and Hospital Infrastructure Adaptation and Planning-A Scoping Review.International journal of environmental research and public health · 2022Article
- The disruption of elective procedures due to COVID-19 in Brazil in 2020.Scientific reports · 2022Article
- Predicting COVID-19 progression in hospitalized patients in Belgium from a multi-state model.Frontiers in medicine · 2022Article
- Who should receive treatment? Healthcare professionals' perspectives surrounding the medical management of patients with COVID-19.Risk management and healthcare policy · 2021Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrediction of the necessary capacity of beds by ward type (e.g. ICU) is essential for planning purposes during epidemics, such as the COVID- 19 pandemic. The COVID- 19 taskforce within the Ghent University hospital made use of ten-day forecasts on the required number of beds for COVID- 19 patients across different wards.
methodsThe planning tool combined a Poisson model for the number of newly admitted patients on each day with a multistate model for the transitions of admitted patients to the different wards, discharge or death. These models were used to simulate the required capacity of beds by ward type over the next 10 days, along with worst-case and best-case bounds.
resultsOverall, the models resulted in good predictions of the required number of beds across different hospital wards. Short-term predictions were especially accurate as these are less sensitive to sudden changes in number of beds on a given ward (e.g. due to referrals). Code snippets and details on the set-up are provided to guide the reader to apply the planning tool on one's own hospital data.
conclusionsWe were able to achieve a fast setup of a planning tool useful within the COVID- 19 pandemic, with a fair prediction on the needed capacity by ward type. This methodology can also be applied for other epidemics.
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