Alessandro Ingrosso — Politecnico di Torino #
The patient-zero problem: a bayesian perspective #
The patient-zero problem consists in finding the initial source of an epidemic outbreak given observations 
at a later time. In this seminar, I will describe a Bayesian method which is able to infer details on the 
past history of an epidemics based solely on the topology of the contact network and a single snapshot of 
partial and noisy observations. The method is built on a Bethe approximation for the posterior 
distribution, and is inherently exact on tree graphs. Moreover, it can be coupled to a set of equations, 
based on the variational expression of the Bethe free energy, to find the patient-zero along with 
maximum-likelihood epidemic parameters. I will describe the method and some results for simulated 
epidemics on random graphs, and briefly mention future directions of research in the discrete-time 
setting, as well as a new method that can perform inference on a continuous time spreading model and deal 
efficiently with real contact-time data.
</br></br>
References:</br>
<a href="http://dx.doi.org/10.1088/1742-5468/2013/09/P09011">F Altarelli et al J. Stat. Mech. (2013) P09011</a></br>
<a href="http://dx.doi.org/10.1088/1742-5468/2014/10/P10016">Fabrizio Altarelli et al J. Stat. Mech. (2014) P10016</a></br>
<a href="http://dx.doi.org/10.1103/PhysRevLett.112.118701">Fabrizio Altarelli et al Phys Rev Lett (2014) 112, 118701</a>