The zero-patient problem with noisy observations

TL;DR

This paper extends the zero-patient problem in epidemic modeling to scenarios with noisy, uncertain observations, proposing a variational Bethe free energy approach to infer the outbreak origin and epidemic parameters from limited data.

Contribution

It introduces a novel variational method to handle noisy observations and jointly infer the epidemic source and parameters, improving robustness over previous approaches.

Findings

Effective in inferring epidemic origin from noisy data

Can correct and complete partial observations

Demonstrates success on simulated epidemics

Abstract

A Belief Propagation approach has been recently proposed for the zero-patient problem in a SIR epidemics. The zero-patient problem consists in finding the initial source of an epidemic outbreak given observations at a later time. In this work, we study a harder but related inference problem, in which observations are noisy and there is confusion between observed states. In addition to studying the zero-patient problem, we also tackle the problem of completing and correcting the observations possibly finding undiscovered infected individuals and false test results. Moreover, we devise a set of equations, based on the variational expression of the Bethe free energy, to find the zero patient along with maximum-likelihood epidemic parameters. We show, by means of simulated epidemics, how this method is able to infer details on the past history of an epidemic outbreak based solely on the topology of the contact network and a single snapshot of partial and noisy observations.