title: Statistical inference to advance network models in epidemiology.
abstract: Contact networks are playing an increasingly important role in the study of epidemiology. Most of the existing work in this area has focused on considering the effect of underlying network structure on epidemic dynamics by using tools from probability theory and computer simulation. This work has provided much insight on the role that heterogeneity in host contact patterns plays on infectious disease dynamics. Despite the important understanding afforded by the probability and simulation paradigm, this approach does not directly address important questions about the structure of contact networks such as what is the best network model for a particular mode of disease transmission, how parameter values of a given model should be estimated, or how precisely the data allow us to estimate these parameter values. We argue that these questions are best answered within a statistical framework and discuss the role of statistical inference in estimating contact networks from epidemiological data.
authors.affiliation: Department of Statistics, The Pennsylvania State University, University Park, 16802, USA.
authors.forename: David
authors.initials: D
authors.lastname: Welch
authors.forename: Shweta
authors.initials: S
authors.lastname: Bansal
authors.forename: David R
authors.initials: DR
authors.lastname: Hunter
mesh_terms: D001699:Biometry; D003198:Computer Simulation; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D018562:Disease Transmission, Infectious / Q000706:statistics & numerical data*; D006801:Humans; D008954:Models, Biological; D012931:Social Environment