Evaluating COVID-19 vaccination policy in Québec (Canada) using a data-driven dynamic transmission model.

During the COVID-19 pandemic, decision-makers had imperfect information and faced resource constraints (i.e., vaccine availability). Public health decisions at the time may not have been optimal for minimizing disease burden. Here, we perform counterfactual evaluations of the impact of various vaccination strategies in Québec (Canada) on the COVID-19 burden from March 2020 to November 2021. In particular, we evaluate the effect of alternative age-specific prioritization sequences of vaccine dose roll-out and assess the impacts of vaccine hesitancy. To achieve this, we develop and calibrate a deterministic, compartmental dynamic transmission model, stratified according to age, susceptibility to infection, viral variant, and outcome-specific immunity. The initial conditions and parameters of the model are obtained through a combination of population-based surveillance data and Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) parameter estimation methods. Using our calibrated model, we find that the vaccination prioritization policies implemented at the start of the pandemic, where age groups at highest risk were sequentially prioritized, was only outperformed by prioritizing the vaccination of younger, more socially connected groups together with higher-risk individuals aged 50+ (3% fewer hospitalizations compared to the baseline strategy). These results hold when we account for vaccine hesitancy. Specifically, we generally observe the fewest hospitalizations for the optimal strategies at the highest uptake rates (i.e. with the least vaccine refusal). However, certain sub-optimal strategies show higher hospitalization rates for higher vaccine uptake as a result of reduced vaccine dose redistribution to more interconnected age groups. Overall, our findings illustrate how the impact of vaccination strategies depends on population factors (e.g. contact patterns, vaccine uptake, and degree of immunity), age-specific risk of severe disease and transmission dynamics. Understanding these dependencies are important for guiding future decision-making related to priority vaccine administration in the face of known and emerging pathogens and potential shortages of doses.