Year 2021
September 2021


Abstract Title
Estimating Intensive Care Unit Bed Capacity during the COVID-19 Pandemic using What-if Analysis



National Healthcare Group HQ1

Background & Hypothesis

Planning of critical healthcare resources such as intensive care unit (ICU) was important for management of COVID-19. However, in the early phase of the COVID-19 pandemic, data was limited. The objective of this study was to estimate surge demand on ICU beds from COVID-19 patients, based on patient data during the early phase of the pandemic in Singapore.


We estimated average number of cases using local admissions, and applied what-if analysis to assess ICU bed demand, which was calculated using Little’s Law in queuing theory. The values of the parameters of ICU admission rate and average length of stay (ALOS) were estimated from Singapore reports or references based on China data. In analysis, we assumed 5 to 20% of confirmed cases would be admitted to ICU with ALOS ranging from 7 to 15 days.


Based on the number of daily new cases, say 10 or 40, we estimated ICU bed demand for the different scenarios of ICU admission rates and ALOS. If 5% of new cases were admitted to the ICU, the hospital would have needed 4 to 30 ICU beds. Similarly, the hospital would require 14 to 120 beds for a 20% admission rate.

Discussion & Conclusion

We estimated ICU bed demand using what-if analysis by considering different scenarios of ICU admission rates and ALOS. The obtained results can potentially be helpful for healthcare providers to better plan and allocate critical hospital resources, such as ICU beds, during the COVID-19 pandemic.