# Comprehensively Profiling Social Mixing Patterns in Workplace Settings to Model Pandemic Influenza Transmission

> **NIH ALLCDC U01** · YALE UNIVERSITY · 2020 · $750,000

## Abstract

PROJECT SUMMARY/ABSTRACT
Dynamic transmission models of infectious diseases are increasingly influential for
developing interventions and informing policy. Infectious disease transmissibility and
hence, the effectiveness of control strategies, is strongly influenced by social interactions.
Consequently, accurate data on social contact rates and mixing patterns are fundamental
parameters in the calculation of the force of infection (i.e. the rate of susceptible
individuals becoming infected). Despite the strong role social mixing patterns play in the
accurate parameterization of mathematical models, these data remain limited, particularly
in workplace settings. There are also limited data on the social interactions among those
who telework or the diversity in patterns among different occupations and business
sectors.
We propose the first multi-site study with the overall goal to use standardized methods to
collect social contact data from workplace settings in the United States. Data will be
rigorously collected from four large companies in the United States. We will use
standardized social contact diaries to characterize the patterns of social contacts and
mixing across workplace environments (i.e., when an individual is performing in-office
work and when an individual is teleworking). We will also comprehensively profile the
social contacts within a company by collecting and analyzing high resolution
measurements collected using wearable proximity-sensing devices. Using these data, we
will develop contact matrices and aggregate contact networks that will inform an agent-
based model of pandemic influenza transmission. The agent-based model will assess the
effectiveness of various workplace social distancing strategies in reducing or slowing the
transmission of pandemic influenza.
Moreover, through this project, we will create a database of social mixing data from
workplace settings. We will make this database, as well as the transmission model spatial
simulation code, publicly available using contemporary standards in Open Access data
sharing and documentation. These data can be used by infectious disease modelers and
other researchers in the biomedical and social science communities.

## Key facts

- **NIH application ID:** 9988300
- **Project number:** 5U01CK000572-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Saad B. Omer
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** ALLCDC
- **Fiscal year:** 2020
- **Award amount:** $750,000
- **Award type:** 5
- **Project period:** 2019-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9988300

## Citation

> US National Institutes of Health, RePORTER application 9988300, Comprehensively Profiling Social Mixing Patterns in Workplace Settings to Model Pandemic Influenza Transmission (5U01CK000572-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9988300. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*