title: Evaluating empirical contact networks as potential transmission pathways for infectious diseases.
abstract: Networks are often used to incorporate heterogeneity in contact patterns in mathematical models of pathogen spread. However, few tools exist to evaluate whether potential transmission pathways in a population are adequately represented by an observed contact network. Here, we describe a novel permutation-based approach, the network k-test, to determine whether the pattern of cases within the observed contact network are likely to have resulted from transmission processes in the network, indicating that the network represents potential transmission pathways between nodes. Using simulated data of pathogen spread, we compare the power of this approach to other commonly used analytical methods. We test the robustness of this technique across common sampling constraints, including undetected cases, unobserved individuals and missing interaction data. We also demonstrate the application of this technique in two case studies of livestock and wildlife networks. We show that the power of the k-test to correctly identify the epidemiologic relevance of contact networks is substantially greater than other methods, even when 50% of contact or case data are missing. We further demonstrate that the impact of missing data on network analysis depends on the structure of the network and the type of missing data.
authors.affiliation: Department of Veterinary Population Medicine, University of Minnesota, St Paul, MN, USA kvw@umn.edu.
authors.forename: Kimberly
authors.identifier: 0000-0002-5764-6430
authors.initials: K
authors.lastname: VanderWaal
authors.affiliation: Division of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
authors.forename: Eva A
authors.initials: EA
authors.lastname: Enns
authors.affiliation: Department of Veterinary Population Medicine, University of Minnesota, St Paul, MN, USA.
authors.forename: Catalina
authors.initials: C
authors.lastname: Picasso
authors.affiliation: Department of Ecology, Evolution, and Behavior, University of Minnesota, St Paul, MN, USA.
authors.forename: Craig
authors.initials: C
authors.lastname: Packer
authors.affiliation: Department of Veterinary Population Medicine, University of Minnesota, St Paul, MN, USA.
authors.forename: Meggan E
authors.initials: ME
authors.lastname: Craft
mesh_terms: D003198:Computer Simulation; D006801:Humans; D007239:Infections / Q000453:epidemiology* / Q000635:transmission*; D008954:Models, Biological
keywords: clustering; livestock movement; missing data; pathogen transmission; social network analysis; wildlife epidemiology