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

16 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)298-325
Number of pages28
JournalNetwork Science
Volume3
Issue number3
DOIs
Publication statusPublished - 2015

Keywords

  • 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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