Abstract
We describe the models we built for predicting hospital admissions and bed occupancy of COVID-19 patients in the Netherlands. These models were used to make short-term decisions about transfers of patients between regions and for long-term policy making. For forecasting admissions we developed a new technique using linear programming. To predict occupancy we fitted residual lengths of stay and used results from queueing theory. Our models increased the accuracy of and trust in the predictions and helped manage the pandemic, minimizing the impact in terms of beds and maximizing remaining capacity for other types of care.
| Original language | English |
|---|---|
| Pages (from-to) | 207-218 |
| Number of pages | 12 |
| Journal | European Journal of Operational Research |
| Volume | 304 |
| Issue number | 1 |
| Early online date | 5 Jan 2022 |
| DOIs | |
| Publication status | Published - 1 Jan 2023 |
Bibliographical note
Funding Information:Part of the work has been carried out during the period that we were affiliated with the LCPS. We would like to thank Marcel de Jong and the LCPS for providing us insight in the management of COVID-19 in the Netherlands and the pleasant and fruitful cooperation.
Publisher Copyright:
© 2022 The Authors
Funding
Part of the work has been carried out during the period that we were affiliated with the LCPS. We would like to thank Marcel de Jong and the LCPS for providing us insight in the management of COVID-19 in the Netherlands and the pleasant and fruitful cooperation.
Keywords
- Bed occupancy levels
- COVID-19 hospital admissions
- OR in health services
- Prediction
