Speaker
Description
Effectively assessing outbreak control strategies in real-time remains a substantial public health challenge. During the COVID-19 pandemic, public health and social measures (PHSMs) were often implemented and relaxed under a "tiered" system to balance the benefits and costs associated with broad societal measures. To guide decision making, modelling studies designed frameworks for tiered PHSMs which typically assumed the movement between tiers was triggered by an incidence or prevalence metric (for example, the number of COVID-19 patients occupying beds in hospital). However, the epidemiological delay from transmission events to clinical outcomes leads to a substantial epidemic peak overshoot upon imposing "lockdown", which is difficult to infer from measurements of current incidence or prevalence alone. We therefore introduce a framework that combines scenario modelling of tiered PHSMs with explicit short-term forecasts to estimate the epidemic peak in real-time. As we show, optimal hospital occupancy thresholds for imposing lockdown can be inferred using this framework, accounting for a range of sources of uncertainty. We demonstrate that metrics characterising the epidemic speed (in particular, the growth rate and the serial interval) are crucial for imposing a timely and effective lockdown to prevent healthcare systems from becoming overwhelmed.
