Forecasting local hospital bed demand for COVID-19 using on-request simulations
Abstract Accurate 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 in...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-48601-8 |
| _version_ | 1797398090841325568 |
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| author | Raisa Kociurzynski Angelo D’Ambrosio Alexis Papathanassopoulos Fabian Bürkin Stephan Hertweck Vanessa M. Eichel Alexandra Heininger Jan Liese Nico T. Mutters Silke Peter Nina Wismath Sophia Wolf Hajo Grundmann Tjibbe Donker |
| author_facet | Raisa Kociurzynski Angelo D’Ambrosio Alexis Papathanassopoulos Fabian Bürkin Stephan Hertweck Vanessa M. Eichel Alexandra Heininger Jan Liese Nico T. Mutters Silke Peter Nina Wismath Sophia Wolf Hajo Grundmann Tjibbe Donker |
| author_sort | Raisa Kociurzynski |
| collection | DOAJ |
| description | Abstract Accurate 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. |
| first_indexed | 2024-03-09T01:19:43Z |
| format | Article |
| id | doaj.art-9fd9b71cb558444baa52fba29122820f |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T01:19:43Z |
| publishDate | 2023-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-9fd9b71cb558444baa52fba29122820f2023-12-10T12:13:55ZengNature PortfolioScientific Reports2045-23222023-12-0113111510.1038/s41598-023-48601-8Forecasting local hospital bed demand for COVID-19 using on-request simulationsRaisa Kociurzynski0Angelo D’Ambrosio1Alexis Papathanassopoulos2Fabian Bürkin3Stephan Hertweck4Vanessa M. Eichel5Alexandra Heininger6Jan Liese7Nico T. Mutters8Silke Peter9Nina Wismath10Sophia Wolf11Hajo Grundmann12Tjibbe Donker13Institute for Infection Prevention and Hospital Hygiene, Freiburg University HospitalInstitute for Infection Prevention and Hospital Hygiene, Freiburg University HospitalInstitute for Infection Prevention and Hospital Hygiene, Freiburg University HospitalInstitute for Infection Prevention and Hospital Hygiene, Freiburg University HospitalInstitute for Infection Prevention and Hospital Hygiene, Freiburg University HospitalSection for Hospital Hygiene and Environmental Health, Center for Infectious Diseases, Heidelberg University HospitalUnit of Hospital Hygiene, Mannheim University HospitalInstitute of Medical Microbiology and Hygiene, Tübingen University HospitalInstitute for Hygiene and Public Health, Medical Faculty University of BonnInstitute of Medical Microbiology and Hygiene, Tübingen University HospitalUnit of Hospital Hygiene, Mannheim University HospitalInstitute of Medical Microbiology and Hygiene, Tübingen University HospitalInstitute for Infection Prevention and Hospital Hygiene, Freiburg University HospitalInstitute for Infection Prevention and Hospital Hygiene, Freiburg University HospitalAbstract Accurate 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.https://doi.org/10.1038/s41598-023-48601-8 |
| spellingShingle | Raisa Kociurzynski Angelo D’Ambrosio Alexis Papathanassopoulos Fabian Bürkin Stephan Hertweck Vanessa M. Eichel Alexandra Heininger Jan Liese Nico T. Mutters Silke Peter Nina Wismath Sophia Wolf Hajo Grundmann Tjibbe Donker Forecasting local hospital bed demand for COVID-19 using on-request simulations Scientific Reports |
| title | Forecasting local hospital bed demand for COVID-19 using on-request simulations |
| title_full | Forecasting local hospital bed demand for COVID-19 using on-request simulations |
| title_fullStr | Forecasting local hospital bed demand for COVID-19 using on-request simulations |
| title_full_unstemmed | Forecasting local hospital bed demand for COVID-19 using on-request simulations |
| title_short | Forecasting local hospital bed demand for COVID-19 using on-request simulations |
| title_sort | forecasting local hospital bed demand for covid 19 using on request simulations |
| url | https://doi.org/10.1038/s41598-023-48601-8 |
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