title: The importance of location in contact networks: Describing early epidemic spread using spatial social network analysis.
abstract: This paper explores methods for describing the dynamics of early epidemic spread and the clustering of infected cases in space and time when an underlying contact network structure is influencing disease spread. A novel method of describing an epidemic is presented that applies social network analysis to characterise the importance of both spatial location and contact network position. This method enables the development of a model of how these clusters formed, incorporating spatial clustering and contact network topology. Based on data from the first 30 days of the 2007 equine influenza outbreak in Australia, clusters of infected premises (IPs) were identified and delineated using social network analysis to integrate contact-tracing and spatial relationships. Clusters identified by this approach were compared to those detected using the space-time scan statistic to analyse the same data. To further investigate the importance of network and spatial location in epidemic spread, kriging by date of onset of clinical signs was used to model the spatio-temporal spread without reference to underlying contact network structure. Leave-one-out cross-validation was used to derive a summary estimate of the accuracy of the kriged surface. Observations poorly explained by the kriged surface were identified, and their position within the contact network was explored to determine if they could be better explained through analysis of the underlying contact network. The contact network was found to lie at the core of a combined network model of spread, with infected horse movements connecting spatial clusters of infected premises. Kriging was imprecise in describing the pattern of spread during this early phase of the outbreak (explaining only 13% of the variation in date of onset of IPs), because early dissemination was dominated by network-based spread. Combined analysis of spatial and contact network data demonstrated that over the first 30 days of this outbreak local spread emanated outwards from the small number of infected premises in the contact network, up to a distance of around 15km. Consequently, when a contact network structure underlies epidemic spread, we recommend applying a method of spatial analysis that incorporates the network component of disease spread. Linking the spatial and network analysis of epidemics facilitates inference of the methods of disease transmission, providing a description of the sequence of spatial cluster formation.
authors.affiliation: Faculty of Veterinary Science, The University of Sydney, 425 Werombi Road, Camden, NSW 2570, Australia. simon.firestone@sydney.edu.au
authors.forename: Simon M
authors.initials: SM
authors.lastname: Firestone
authors.forename: Michael P
authors.initials: MP
authors.lastname: Ward
authors.forename: Robert M
authors.initials: RM
authors.lastname: Christley
authors.forename: Navneet K
authors.initials: NK
authors.lastname: Dhand
mesh_terms: D000818:Animals; D016000:Cluster Analysis; D016358:Contact Tracing / Q000379:methods* / Q000662:veterinary; D058872:Epidemics / Q000662:veterinary*; D006734:Horse Diseases / Q000453:epidemiology* / Q000635:transmission; D006736:Horses; D053123:Influenza A Virus, H3N8 Subtype; D008954:Models, Biological; D009517:New South Wales / Q000453:epidemiology; D009976:Orthomyxoviridae Infections / Q000453:epidemiology / Q000635:transmission / Q000662:veterinary*; D011793:Queensland / Q000453:epidemiology