Application of a composite, multi-scale COVID-19 mitigation framework: US border use-case

Area of Science:

• Epidemiology
• Public Health
• Mathematical Modeling

Background:

• Crowded facilities pose significant risks for airborne pathogen transmission.
• Managing infectious disease spread, such as COVID-19, in these settings presents complex challenges.

Purpose of the Study:

• To develop and apply a composite model for airborne pathogen spread in a specific crowded facility.
• To evaluate the effectiveness of proposed interventions in reducing infection rates.
• To provide quantitative decision-making options for disease management.

Main Methods:

• Combined analytical models incorporating human movement, airflow dynamics, and aerosol dispersion.
• Simulated pathogen spread in a crowded immigrant processing center.
• Assessed the impact of proposed recommendations on infection rates.

Main Results:

• Current COVID-19 infection rate of 11% could be reduced to 6.16% with proposed interventions.
• Under surge conditions (10-day processing), infection rates could decrease from a worst-case 31.14% to 6.35%.

Conclusions:

• The study highlights challenges in managing COVID-19 in crowded environments.
• Quantitative recommendations can effectively slow disease spread and reduce economic burden.
• Interventions offer a cost-effective approach to enhance public health in high-density settings.