Age-structured estimation of COVID-19 ICU demand from low quality data
This work addresses ICU capacity planning for healthcare systems during the COVID-19 pandemic, but it is incremental as it builds on existing logistic models and data correction methods.
The authors tackled the problem of projecting COVID-19 ICU bed demand by estimating subnotification factors from age-structured data and logistic fits, resulting in projections that account for low-quality data and plateau scenarios.
We sample aggravated cases following age-structured probabilities from confirmed cases and use ICU occupation data to find a subnotification factor. A logistic fit is then employed to project the progression of the COVID-19 epidemic with plateau scenarios taken from locations that have reached this stage. Finally, the logistic curve found is corrected by the subnotification factor and sampled to project the future demand for ICU beds.