The case for using flexible healthcare capacity constraints to optimize pandemic control strategies

Abstract

Abstract During the COVID-19 pandemic, a key challenge was designing control strategies that balance the benefits and costs of public health measures while preventing healthcare systems from becoming overwhelmed. This was often addressed by epidemiological modellers by implementing a binding (hard) constraint based on the maximum number of hospital beds that could be occupied at any given time. However, experience from the pandemic demonstrated that healthcare capacity depends not only on bed availability, but also on staffing and the availability of other resources. We argue that defining healthcare capacity using a single number (bed availability) does not adequately capture long-term pressures in healthcare settings as high occupancy is sustained. We therefore introduce a framework for implementing flexible (soft) constraints on healthcare capacity by allowing the cost of control strategies to depend continuously on intensive care unit occupancy. We illustrate scenarios where a soft constraint captures pressures on the healthcare system that are neglected when a hard constraint is used. Additionally, we highlight that explicitly accounting for uncertainty is essential to choosing a robust strategy. For the most useful evidence to be provided to policy advisors during future pandemics, modellers should consider how healthcare capacity constraints are implemented in epidemiological models.