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

Abstract

In managing COVID-19 with non-pharmaceutical interventions, occupancy of intensive care units (ICU) is often used as an indicator to inform when to intensify mitigation and thus reduce SARS-CoV-2 transmission, strain on ICUs, and deaths. However, ICU occupancy thresholds at which action should be taken are often selected arbitrarily. We propose a quantitative approach using mathematical modeling to identify ICU occupancy thresholds at which mitigation should be triggered to avoid exceeding the ICU capacity available for COVID-19 patients. We used a stochastic compartmental model to simulate SARS-CoV-2 transmission and disease progression, including critical cases that would require intensive care. We calibrated the model for the United States city of Chicago using daily COVID-19 ICU and hospital census data between March and August 2020. We projected ICU occupancies from September to May 2021 under two possible levels of transmission increase. The effect of combined mitigation measures was modeled as a decrease in the transmission rate that took effect when projected ICU occupancy reached a specified threshold. We found that mitigation did not immediately eliminate the risk of exceeding ICU capacity. Delaying action by 7 days increased the probability of exceeding ICU capacity by 10-60% and this increase could not be counteracted by stronger mitigation. Even under modest transmission increase, a threshold occupancy no higher than 60% was required when mitigation reduced the reproductive number Rt to just below 1. At higher transmission increase, a threshold of at most 40% was required with mitigation that reduced Rt below 0.75 within the first two weeks after mitigation. Our analysis demonstrates a quantitative approach for the selection of ICU occupancy thresholds that considers parameter uncertainty and compares relevant mitigation and transmission scenarios. An appropriate threshold will depend on the location, number of ICU beds available for COVID-19, available mitigation options, feasible mitigation strengths, and tolerated durations of intensified mitigation.

Design of effective outpatient sentinel surveillance for COVID-19 decision-making: a modeling study

“…We used a published stochastic SEIR compartmental model (33) to simulate SARS-CoV-2 transmission and COVID-19 disease states (Figure 1A). The model included multiple symptom statuses (asymptomatic, presymptomatic, mild, and severe), and multiple severe disease outcomes (requiring hospitalization, critical illness requiring intensive care unit (ICU) admission, and deaths).…”

“…We use the model parameters from the previous study (33) which set the hospital and ICU lengths-of-stay based on data from Chicago and from literature during the first COVID-19 wave beginning March 2020 (34,35). This previous study also fitted a time-varying transmission rate to daily ICU census, hospital census, and reported deaths in Chicago.…”

“…To improve the computation efficiency in a high performance computing environment, we rewrote and ran the model using a custom program written in C++ (38) available at (39). The model state in August 2020, when transmission was steady and low (33,40), was used as a starting point for all scenarios. We introduced a step increase in transmission (transmission hike) on 17 September 2020 to induce a new epidemic wave (Figure 1B).…”

“…Modeling has been used to inform decision-makers about optimal lockdown, reopening and mitigation strategies (26)(27)(28)(29). While mathematical models have been used to compare testing strategies (26,(30)(31)(32), few modeling studies have been conducted to compare surveillance designs for timely and appropriate imposition of COVID-19 mitigations (33).…”

Tracking changes in SARS-CoV-2 transmission with a novel outpatient sentinel surveillance system in Chicago, USA

“…Worldwide, policymakers have implemented staged regional mitigation systems, where the progression of a region from one stage of mitigation policy to another is contingent upon certain indicators surpassing a given threshold or relative rate of growth [5][6][7]. The timeliness of mitigation measures is a decisive factor in their efficacy; delaying the implementation of mitigation measures can drastically increase prevalence, mortality, and the probability that healthcare systems are overwhelmed amidst a surge in transmission [8][9][10][11][12]. Thus, it is crucial that the indicators that inform these mitigation measures represent a timely and accurate measure of trends in infection prevalence.…”