A flatter curve affords hospitals greater time to prepare for a pandemic surge

Abstract

This paper quantifies the benefits of flattening the curve (with a constant total patient load over the study period) on the risk of a hospital bed shortage in a pandemic. Using discrete-event simulation of patient care paths in hospitals, synthetic data that eliminates issues of confounding affects from the simultaneous occurrence of regional response actions and/or changes in resources, treatments or other situational circumstances, is produced for estimating hospital capacity for pandemic response. Results from systematically designed numerical experiments produced several findings. These include that the higher the acceleration in pandemic patient demand growth, the greater the impact of the intervention. Cutting this acceleration by 75% from the greatest studied rate created over four additional weeks to prepare for an 80% risk of running out of intensive care beds. Additionally, the greater the acceleration in growth, the fewer the days with a high risk of running out of beds, but the greater the total number of critical patients that could not be served with existing resources. Finally, the lower this acceleration, the fewer resources or modifications needed to cope with the surge, but the longer they are needed. The findings further show how hospitals can benefit from analytical tools that exploit digital health information to predict and plan for need levels and time to onset of these levels. These tools can be embedded within a real-time framework in which automated and early warnings can inform the selection of strategies for managing or coping with expected increases in demand for emergency hospital services.

Keywords

• Discrete-event simulation
• COVID-19
• Hospital capacity management
• Intensive care
• Digital health solution
• Bed shortage prediction