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

Gail E. Potter, Timo Smieszek, Kerstin Sailer

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)298-325
Number of pages28
JournalNetwork Science
Issue number3
Publication statusPublished - 2015

Bibliographical note

Funding Information:
We are grateful to Elena U. Burri and Robert Scherzinger, who helped with the data collection and Roland W. Scholz, who contributed to the diary design. We are grateful to Ira M. Longini Jr., M. Elizabeth Halloran, Mark S. Handcock, Steven Goodreau, and Martina Morris for providing comments on this work. The data was made available by ETH Zurich and its collection was funded partly by the Swiss National Science Foundation (Grant 32003B 127548), partly by ETH basic funding. This research was supported by a fellowship from the German Academic Exchange Service DAAD to Timo Smieszek (Grant D/10/52328) and by the NIH/NIGMS MIDAS Grant U01-GM070749. Epidemic simulations were performed on the Biostar computational cluster, made available by the Huck Institutes of the Life Sciences at Pennsylvania State University. The systems, software, and consulting services for Biostar were provided by ITS Research Computing and Cyber Infrastructure. Timo Smieszek thanks the UK National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Modelling Methodology at Imperial College London in partnership with Public Health England (PHE) for funding. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health, or Public Health England.


  • contact network
  • discordant reports
  • epidemic model
  • infectious disease
  • latent variable model
  • measurement error
  • reporting error
  • social network
  • space syntax
  • valued network


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