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Alfredo Esposito

Publications and source records attributed to Alfredo Esposito.

5 recordsLinked to original sources
arXiv2021

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'

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} (`$ε$'). 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 $ε$, 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 simple model. However we maintain that the number to be reported as `vaccine efficacy' should be the mean of the distribution, rather than the mode, as it was already very clear to Laplace about 250 years ago (its `rule of succession' follows from the simplest problem of the kind). This is particularly important in the case in which the number of successes equals the numbers of trials, as it happens with the efficacy against `severe forms' of infection, claimed by Moderna to be 100%. The implication of the various uncertainties on the predicted number of vaccinated infectees is also shown, using both MCMC and approximated formulae.

stat.AP↗
arXiv2020

Checking individuals and sampling populations with imperfect tests

In the last months, due to the emergency of Covid-19, questions related to the fact of belonging or not to a particular class of individuals (`infected or not infected'), after being tagged as `positive' or `negative' by a test, have never been so popular. Similarly, there has been strong interest in estimating the proportion of a population expected to hold a given characteristics (`having or having had the virus'). Taking the cue from the many related discussions on the media, in addition to those to which we took part, we analyze these questions from a probabilistic perspective (`Bayesian'), considering several effects that play a role in evaluating the probabilities of interest. The resulting paper, written with didactic intent, is rather general and not strictly related to pandemics: the basic ideas of Bayesian inference are introduced and the uncertainties on the performances of the tests are treated using the metrological concepts of `systematics', and are propagated into the quantities of interest following the rules of probability theory; the separation of `statistical' and `systematic' contributions to the uncertainty on the inferred proportion of infectees allows to optimize the sample size; the role of `priors', often overlooked, is stressed, however recommending the use of `flat priors', since the resulting posterior distribution can be `reshaped' by an `informative prior' in a later step; details on the calculations are given, also deriving useful approximated formulae, the tough work being however done with the help of direct Monte Carlo simulations and Markov Chain Monte Carlo, implemented in R and JAGS (relevant code provided in appendix).

q-bio.PE↗
arXiv2012

Debunking some myths about biometric authentication

Biometric authentication systems are presented as the best way to reach high security levels in controlling access to IT systems or sensitive infrastructures. But several issues are often not taken properly into account. In order for the implementation of those systems to be successful, the hidden risks and the related liabilities have to be carefully analyzed before biometrics can be used on a large scale for sensitive applications.

cs.CR↗
arXiv2012

Autenticazione biometrica: Realtà e fantasia

Biometrical authentication systems are often presented as the best and simplest way to reach higher security levels. But a deeper analysis shows that several risks are hidden and the service provider adopting those system has to carefully check its liabilities before deploying them.

cs.CR↗