Forecasting local hospital bed demand for COVID-19 using on-request simulations

Author:

Kociurzynski Raisa,D’Ambrosio Angelo,Papathanassopoulos Alexis,Bürkin Fabian,Hertweck Stephan,Eichel Vanessa M.,Heininger Alexandra,Liese Jan,Mutters Nico T.,Peter Silke,Wismath Nina,Wolf Sophia,Grundmann Hajo,Donker Tjibbe

Abstract

AbstractAccurate forecasting of hospital bed demand is crucial during infectious disease epidemics to avoid overwhelming healthcare facilities. To address this, we developed an intuitive online tool for individual hospitals to forecast COVID-19 bed demand. The tool utilizes local data, including incidence, vaccination, and bed occupancy data, at customizable geographical resolutions. Users can specify their hospital’s catchment area and adjust the initial number of COVID-19 occupied beds. We assessed the model’s performance by forecasting ICU bed occupancy for several university hospitals and regions in Germany. The model achieves optimal results when the selected catchment area aligns with the hospital’s local catchment. While expanding the catchment area reduces accuracy, it improves precision. However, forecasting performance diminishes during epidemic turning points. Incorporating variants of concern slightly decreases precision around turning points but does not significantly impact overall bed occupancy results. Our study highlights the significance of using local data for epidemic forecasts. Forecasts based on the hospital’s specific catchment area outperform those relying on national or state-level data, striking a better balance between accuracy and precision. These hospital-specific bed demand forecasts offer valuable insights for hospital planning, such as adjusting elective surgeries to create additional bed capacity promptly.

Funder

Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg

Universitätsklinikum Freiburg

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference23 articles.

1. Li, R. et al. The demand for inpatient and ICU beds for COVID-19 in the US: Lessons from Chinese cities. medRxiv 2020.03.09.20033241. https://www.medrxiv.org/content/10.1101/2020.03.09.20033241v2 (2020).

2. Emanuel, E. J. et al. Fair allocation of scarce medical resources in the time of covid-19 (2020).

3. Dong, E., Du, H. & Gardner, L. An interactive web-based dashboard to track covid-19 in real time. Lancet Infect. Dis 20, 533–534 (2020).

4. Salehi, M. et al. A synergetic r-shiny portal for modeling and tracking of covid-19 data. Front. Public Health 8, 623624 (2021).

5. Covid-19 dashboard by the center for systems science and engineering (csse) at johns hopkins university (jhu). https://www.arcgis.com/apps/dashboards/bda7594740fd40299423467b48e9ecfi. Accessed: 2023-07-06.

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