Estimation of the Epidemic Branching Factor in Noisy Contact Networks

TL;DR

This paper investigates how errors in contact network data affect the estimation of the epidemic branching factor and introduces a method-of-moments estimator to improve accuracy in noisy conditions.

Contribution

It characterizes the impact of network noise on branching factor estimation and proposes a new estimator for more accurate results in noisy contact networks.

Findings

Network noise causes bias and variance in branching factor estimates.

The proposed estimator outperforms naive methods in simulations.

Application to real contact networks demonstrates practical utility.

Abstract

Many fundamental concepts in network-based epidemic modeling depend on the branching factor, which captures a sense of dispersion in the network connectivity and quantifies the rate of spreading across the network. Moreover, contact network information generally is available only up to some level of error. We study the propagation of such errors to the estimation of the branching factor. Specifically, we characterize the impact of network noise on the bias and variance of the observed branching factor for arbitrary true networks, with examples in sparse, dense, homogeneous and inhomogeneous networks. In addition, we propose a method-of-moments estimator for the true branching factor. We illustrate the practical performance of our estimator through simulation studies and with contact networks observed in British secondary schools and a French hospital.