2020/02/13 by Wenrui Li, Daniel L. Sussman, Li, Wenrui +3
Mathematics · Physics and Astronomy · #COVID-19 epidemiological studies #Complex Network Analysis Techniques #FOS: Computer and information sciences #Methodology (stat.ME) #Opinion Dynamics and Social Influence #stat.ME
paper · pdf · doi:10.48550/arxiv.2002.05763
44 pages, 4 figures
openalex publication_date 2020/02/13 · arxiv created 2020/10/12 · arxiv updated 2020/10/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.