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

Estimation of the Epidemic Branching Factor in Noisy Contact Networks