@article{discovery1426495,
           month = {July},
         address = {Cambridge},
            year = {2015},
          number = {3},
           pages = {298--325},
           title = {Modeling workplace contact networks: the effects of organizational structure, architecture, and reporting errors on epidemic predictions},
       publisher = {Cambridge Journals},
          volume = {3},
            note = {{\copyright} Cambridge University Press 2015. This is an Open Access
article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.},
         journal = {Network Science},
        abstract = {Face-to-face social contacts are potential 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 via 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 research group membership, role, and shared projects are strongly predictive of contact patterns. Estimated reporting probabilities were low only for 0-5 minute) 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.},
          author = {Potter, G and Smieszek, T and Sailer, K},
             url = {http://dx.doi.org/10.1017/nws.2015.22},
        keywords = {contact network, space syntax, physical distance, epidemic model, social network, influenza}
}