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Face-to-face social contacts are potentially important transmission routesfor acute respiratory infections, and understanding the contact network canimprove our ability to predict, contain, and control epidemics. Althoughworkplaces are important settings for infectious disease transmission, fewstudies have collected workplace contact data and estimated workplace contactnetworks. We use contact diaries, architectural distance measures, andinstitutional structures to estimate social contact networks within a Swissresearch institute. Some contact reports were inconsistent, indicatingreporting errors. We adjust for this with a latent variable model, jointlyestimating the true (unobserved) network of contacts and duration-specificreporting probabilities. We find that contact probability decreases withdistance, and research group membership, role, and shared projects are stronglypredictive of contact patterns. Estimated reporting probabilities were low onlyfor 0-5 minute contacts. Adjusting for reporting error changed the estimate ofthe duration distribution, but did not change the estimates of covariateeffects and had little effect on epidemic predictions. Our epidemic simulationstudy indicates that inclusion of network structure based on architectural andorganizational structure data can improve the accuracy of epidemic forecastingmodels.

Modelling workplace contact networks: the effects of organizational structure, architecture, and reporting errors on epidemic predictions