Network infection source identification under the SIRI model

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.Comment: 5 pages, 3 figures; to present in ICASSP 201

Infection source estimation under the SIRI model

In this dissertation, we aim to study the spread of infection in the light of Susceptible Infected Recovered Infected (SIRI) model. In order to achieve the objective, an estimator by the name Heterogeneous Infection Spreading Source (HISS) was developed. The estimator does the task of emulating the spread of infection by defining state space variables and auxiliary variables. Thus the estimator tries to obtain a distribution which is similar to the observed state of nodes. It not only estimates the most likely origin of infection, but also computes the most probable snapshot time. The estimator also incorporates side information. Side information is defined as the prior knowledge of a certain fraction of nodes to be in one of the three states namely Susceptible (S), Infected (I) or Recovered (R). This is observed before the snapshot instance. It is implemented to observe the detection accuracy of the true source with different number of known side information. The simulations are run on random tree graphs of degree 4 and size 1000 and on facebook network of size 500. The performance of our estimator are compared with Dynamic message Passing (DMP) algorithm and Jordan centrality. HISS estimator outperforms both of the other estimators. It accurately identifies the true source over a wide range of infection and reinfection rates.Master of Science (Communications Engineering