Abstract
BACKGROUND: The requirement for critical care in even a modest fraction of SARS-CoV-2-infected individuals made critical care resources a key societal chokepoint during the COVID-19 pandemic. We previously developed a simple regression-based point score to forecast critical care occupancy in Ontario, Canada, using case numbers, mean age of cases, and testing volume. In this study, we aimed to validate and update this forecasting model to account for evolving population immunity, including the effects of widespread vaccination. METHODS: We obtained complete provincial SARS-CoV-2 case, testing, and vaccination data from March 2020 to September 2022, subdividing the pandemic into six waves. Our initial model was fitted using data from the first two waves; an updated model included wave 3, which was dominated by N501Y+ variants. We validated the models by comparing projections to waves not used for fitting. Predictive validity was assessed using Spearman's rho. Counterfactual scenarios without vaccination were modeled to estimate vaccine-attributable reductions in critical care admissions. FINDINGS: The initial model (waves 1-2) was well calibrated (rho = 0.85) but had modest predictive validity (rho = 0.46). Predictive validity improved with models fitted to waves 1-3, both without (rho = 0.60) and with vaccination (rho = 0.68); model fit improved significantly with vaccination (p = 0.013). Averted admissions attributable to vaccination were estimated at 144% (22,017 expected vs. 9020 observed). INTERPRETATION: Simple regression-based forecasting tools remain valuable for predicting SARS-CoV-2 critical care occupancy. However, models developed early in the pandemic should be recalibrated to account for evolving immunity, including widespread vaccination. FUNDING: Canadian Institutes of Health Research (OV4-170360); R. Howard Webster Foundation (via the University of Toronto Institute for Pandemics).