A Rapid Assessment of Covid-19 Vaccine Averted Mortality Modelling

Introduction: The ubiquitous use of COVID-19 vaccination during the pandemic makes it challenging to quantify its effect. While comparisons can be made over time (comparing outcomes by vaccination rate), most estimates rely on modeling using vaccine efficacy taken from clinical trials. Estimates from averted mortality (AM) models impact policy decisions, and their assumptions must be replicable. Aim: To assess the accuracy of model assumptions for estimates of AM due to COVID-19 vaccines. Recognizing the need for simplifications and assumptions in model building. Methods: The study employs a thorough analysis of existing models that quantify the impact of mass vaccination on AM, both globally and in specific countries/regions. The research scrutinizes the assumptions made by these models and identifies areas where they might overstate the degree of AM due to vaccination. This study also makes inter-model comparisons to find outlier models. Results: Several assumptions in existing models tend to overstate the level of AM from COVID-19 vaccines significantly. This correlation raises questions about the accuracy of estimates regarding positive AM due to mass vaccination. This investigation finds a notable outlier for AM modeling, a Canadian study. Conclusion: We highlight the need for improved epidemiological modeling in assessing the impact of vaccination. Assumptions tend to overstate AM, motivating the importance of grounding public health responses to infectious diseases in robust and rigorous analysis. The research contributes to refining our understanding of the consequences of mass vaccination during the COVID-19 pandemic and encourages a more nuanced approach to policy decisions.