Network infection source identification under the SIRI model

TL;DR

This paper introduces the HISS estimator, a novel algorithm for identifying the infection source in networks under the SIRI model, effectively utilizing partial observations and prior information.

Contribution

The paper proposes the HISS estimator, a new method that improves infection source identification under the SIRI model by incorporating side information and prior probabilities.

Findings

HISS outperforms existing estimators in simulations.

The method effectively handles unknown elapsed infection time.

Incorporates side information and prior probabilities.

Abstract

We study the problem of identifying a single infection source in a network under the susceptible-infected-recovered-infected (SIRI) model. We describe the infection model via a state-space model, and utilizing a state propagation approach, we derive an algorithm known as the heterogeneous infection spreading source (HISS) estimator, to infer the infection source. The HISS estimator uses the observations of node states at a particular time, where the elapsed time from the start of the infection is unknown. It is able to incorporate side information (if any) of the observed states of a subset of nodes at different times, and of the prior probability of each infected or recovered node to be the infection source. Simulation results suggest that the HISS estimator outperforms the dynamic message pass- ing and Jordan center estimators over a wide range of infection and reinfection rates.