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

TL;DR

This study models workplace contact networks using contact diaries, architectural data, and organizational info, revealing how structure influences epidemic spread and how reporting errors affect contact estimates.

Contribution

It introduces a latent variable model to correct reporting errors in workplace contact data and demonstrates the impact of organizational structure on epidemic predictions.

Findings

Contact probability decreases with distance.

Organizational factors like research group influence contact patterns.

Adjusting for reporting errors affects contact duration estimates.

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 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.