Modeling scenarios for mitigating outbreaks in congregate settings.

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• Congregate settings like prisons and nursing homes are highly vulnerable to explosive COVID-19 outbreaks.
• Effective prevention and mitigation strategies are critical for these high-risk environments.

Purpose of the Study:

• To analyze the impact of various control interventions on the expected number of symptomatic infections during outbreaks in congregate settings.
• To provide a quantitative framework for evaluating infection control policies in outbreak settings.

Main Methods:

• Modeled disease introduction as a stochastic point process coupled to a branching process.
• Used a deterministic compartmental model for within-resident transmission, accounting for susceptible depletion.
• Modeled control strategies as reductions in susceptible population, reproduction number, or symptomatic proportion.

Main Results:

• Vaccination or depopulation can yield greater than linear reductions in expected cases.
• A 20% preemptive reduction in susceptible population decreased disease burden by 47% (with R0=3.0 and density-dependent transmission).
• Optimizing residential unit apportionment and implementing timely reactive controls can further mitigate outbreak risk and burden.

Conclusions:

• Quantitative modeling offers a framework for understanding disease transmission and optimizing control in congregate settings.
• Strategic interventions, including population reduction and optimized spatial organization, are key to managing outbreaks.
• The findings provide a basis for policy decisions in infection control for vulnerable populations.