title: Modeling workplace contact networks: The effects of organizational structure, architecture, and reporting errors on epidemic predictions.
abstract: Face-to-face social contacts are potentially important transmission routes for acute respiratory infections, and understanding the contact network can improve our ability to predict, contain, and control epidemics. Although workplaces are important settings for infectious disease transmission, few studies have collected workplace contact data and estimated workplace contact networks. We use contact diaries, architectural distance measures, and institutional structures to estimate social contact networks within a Swiss research institute. Some contact reports were inconsistent, indicating reporting errors. We adjust for this with a latent variable model, jointly estimating the true (unobserved) network of contacts and duration-specific reporting probabilities. We find that contact probability decreases with distance, and that research group membership, role, and shared projects are strongly predictive of contact patterns. Estimated reporting probabilities were low only for 0-5 min contacts. Adjusting for reporting error changed the estimate of the duration distribution, but did not change the estimates of covariate effects and had little effect on epidemic predictions. Our epidemic simulation study indicates that inclusion of network structure based on architectural and organizational structure data can improve the accuracy of epidemic forecasting models.
authors.affiliation: California Polytechnic State University, San Luis Obispo, CA, USA; Center for Statistics and Quantitative Infectious Disease, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
authors.forename: Gail E
authors.initials: GE
authors.lastname: Potter
authors.affiliation: Center for Infectious Disease Dynamics, Pennsylvania State University; Modelling and Economics Unit, Centre for Infectious Disease Surveillance and Control, Public Health England, London, UK; MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College School of Public Health, London, UK; NIHR Health Protection Research Unit in Modelling Methodology, Department of Infectious Disease Epidemiology, Imperial College School of Public Health, London, UK.
authors.forename: Timo
authors.initials: T
authors.lastname: Smieszek
authors.affiliation: The Bartlett School of Graduate Studies, University College London.
authors.forename: Kerstin
authors.initials: K
authors.lastname: Sailer
keywords: contact network; discordant reports; epidemic model; infectious disease; latent variable model; measurement error; reporting error; social network; space syntax; valued network