Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding...

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health Policy

Background:

• Intensive care unit (ICU) occupancy is a key metric for triggering COVID-19 mitigation strategies.
• Current thresholds for intensifying public health interventions are often arbitrary, potentially leading to overwhelmed healthcare systems.

Purpose of the Study:

• To develop and demonstrate a quantitative, model-based approach for determining optimal ICU occupancy thresholds for COVID-19 mitigation.
• To assess the impact of delayed mitigation actions on the risk of exceeding ICU capacity.

Main Methods:

• Utilized a stochastic compartmental model to simulate SARS-CoV-2 transmission and critical care needs.
• Calibrated the model with COVID-19 ICU and hospital census data from Chicago (March-August 2020).
• Projected future ICU occupancy under varying transmission scenarios and mitigation strategies triggered by occupancy thresholds.

Main Results:

• Delaying mitigation by 7 days increased the probability of exceeding ICU capacity by 10-60%, a risk not fully offset by stronger measures.
• A threshold of 60% or lower was required for effective mitigation under modest transmission increases.
• Higher transmission increases necessitated thresholds of 40% or lower with rapid, strong mitigation.

Conclusions:

• A quantitative, data-driven approach is essential for setting ICU occupancy thresholds to manage COVID-19.
• Early and decisive mitigation, triggered by lower occupancy thresholds, is critical to prevent healthcare system overload.
• Optimal thresholds are location-specific, depending on ICU bed availability, mitigation effectiveness, and desired intervention duration.