What is the probability that a vaccinated person is shielded from Covid-19? A Bayesian MCMC based reanalysis of published data with emphasis on what should be reported as 'efficacy'
Giulio D'Agostini, Alfredo Esposito

TL;DR
This paper reanalyzes Covid-19 vaccine efficacy data using Bayesian MCMC methods, emphasizing that the reported efficacy should be based on the mean of the posterior distribution rather than the mode, to better reflect uncertainty.
Contribution
It introduces a Bayesian model for vaccine efficacy estimation from trial data and advocates for reporting the mean of the posterior distribution instead of the mode.
Findings
The Bayesian model produces stable efficacy estimates consistent with company reports.
The mean of the posterior distribution is recommended over the mode for reporting efficacy.
Uncertainty analysis shows implications for predicting vaccinated infection numbers.
Abstract
Based on the information communicated in press releases, and finally published towards the end of 2020 by Pfizer, Moderna and AstraZeneca, we have built up a simple Bayesian model, in which the main quantity of interest plays the role of {\em vaccine efficacy} (`'). The resulting Bayesian Network is processed by a Markov Chain Monte Carlo (MCMC), implemented in JAGS interfaced to R via rjags. As outcome, we get several probability density functions (pdf's) of , each conditioned on the data provided by the three pharma companies. The result is rather stable against large variations of the number of people participating in the trials and it is `somehow' in good agreement with the results provided by the companies, in the sense that their values correspond to the most probable value (`mode') of the pdf's resulting from MCMC, thus reassuring us about the validity of our…
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Taxonomy
TopicsCOVID-19 epidemiological studies · Advanced Statistical Process Monitoring · Advanced Statistical Methods and Models
