More prevalent, less deadly? Bayesian inference of the COVID19 Infection Fatality Ratio from mortality data
Delius, G. W.; Powell, B. J.; Bees, M. A.; Constable, G. W. A.; MacKay, N. J.; Pitchford, J. W.
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We use an established semi-mechanistic Bayesian hierarchical model of the COVID-19 pandemic [1], driven by European mortality data, to estimate the prevalence of immunity. We allow the infection-fatality ratio (IFR) to vary, adapt the models 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.
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