Adaptive Sentinel Testing in Workplace for COVID-19 Pandemic

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• Workplace safety during pandemics requires effective testing and isolation strategies.
• Existing models for testing frequency are often unsuitable for unique workplace dynamics.
• Workplace transmission is influenced by community rates, internal measures, and employee behavior.

Purpose of the Study:

• To develop an adaptive testing frequency model for workplaces.
• To create an R-shiny tool for estimating optimal workplace testing schedules.
• To account for workplace-specific factors in pandemic modeling.

Main Methods:

• Development of a bi-modal SEIR (Susceptible, Exposed, Infectious, Removed) model.
• Integration of workplace-specific parameters: community incidence, workplace/community risk, workforce size, and test sensitivity.
• Simulation analysis to evaluate factors influencing testing frequency.

Main Results:

• Employee behavior and onsite workforce size are key determinants of testing frequency.
• Workplace mitigation protocols and test sensitivity have a lesser, but significant, impact.
• Sentinel testing demonstrates cost-effectiveness with minimal infection increase, even at high community rates.

Conclusions:

• The bi-modal SEIR model and R-shiny tool provide adaptive testing frequency estimation for workplaces.
• The model is best suited for moderate-sized organizations with controlled internal transmission.
• Findings support sentinel testing as a viable strategy for future pandemic preparedness.