More prevalent, less deadly? Bayesian inference of the COVID19 Infection Fatality Ratio from mortality data

Delius, Gustav W; Powell, Benedict J; Bees, Martin A; Constable, George W A; MacKay, Niall J; Pitchford, Jonathan W

We use an established semi-mechanistic Bayesian hierarchical model of the COVID-19 pandemic, driven by European mortality data, to estimate the prevalence of immunity. We allow the infection-fatality ratio (IFR) to vary, adapt the model's priors to better reflect emerging information, and re-evaluate the model fitting in the light of current mortality data. The results indicate that the IFR of COVID-19 may be an order of magnitude smaller than the current consensus, with the corollary that the virus is more prevalent than currently believed. These results emerge from a simple model and ought to be treated with caution. They emphasise the value of rapid community-scale antibody testing when this becomes available.