Center for Pandemic Decision Science

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Browsing Center for Pandemic Decision Science by Subject "alert systems"

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    Design of COVID-19 Staged Alert Systems to Ensure Healthcare Capacity with Minimal Closures
    (2020-12) Yang, Haoxiang; Sürer, Özge; Duque, Daniel; Morton, David P.; Singh, Bismark; Fox, Spencer J.; Pasco, Remy; Pierce, Kelly; Rathouz, Paul; Du, Zhanwei; Pignone, Michael; Escott, Mark E.; Adler, Stephen I.; Johnston, S. Clairborne; Meyers, Lauren Ancel
    Community mitigation strategies to combat COVID-19, ranging from healthy hygiene to shelter-in-place orders, exact substantial socioeconomic costs. Judicious implementation and relaxation of restrictions amplify their public health benefits while reducing costs. We derive optimal strategies for toggling between mitigation stages using daily COVID-19 hospital admissions. With public compliance, the policy triggers ensure adequate intensive care unit capacity with high probability while minimizing the duration of strict mitigation measures. In comparison, we show that other sensible COVID-19 staging policies, including France's ICU-based thresholds and a widely adopted indicator for reopening schools and businesses, require overly restrictive measures or trigger strict stages too late to avert catastrophic surges. As cities worldwide face future pandemic waves, our findings provide a robust strategy for tracking COVID-19 hospital admissions as an early indicator of hospital surges and enacting staged measures to ensure integrity of the health system, safety of the health workforce, and public confidence.
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    Projections for Variants of Concern under Austin's COVID-19 Staged-Alert System
    (2021-10) Arslan, Nazlican; Morton, David P.; Walkes, Desmar; Meyers, Lauren Ancel
    To support public health decision-making in Austin, Texas, we use a data-driven model of COVID-19 transmission in the five-county Austin-Round Rock Metropolitan Statistical Area to project hospital demand under plausible scenario for future COVID-19 transmission. This model integrates Austin's COVID-19 staged-alert system, which informs the city's adaptive risk-based guidelines. Given the emergence of SARS-CoV-2 variants and the ongoing roll-out of vaccines, we apply the model to evaluate the robustness of the thresholds governing changes between alert stages. The projections consider several scenarios for the future transmission of the Delta variant and the emergence of other variants of concern. We assume that Delta is 1.65 times more transmissible than previous variants, has a higher hospitalization rate among symptomatic individuals, has a shorter incubation period, and leads to longer ICU stays. The hypothesized variants of concern are identical to Delta, except that they are instead 2.0 and 2.5 times more transmissible than pre-Delta variants. The results presented here are based on multiple assumptions about the transmission rate, age-specific severity of COVID-19, efficacy of vaccines, waning immunity following infection or vaccination, and uptake of initial two-dose vaccination as well as boosters. They do not represent the full range of uncertainty that the City of Austin may encounter. The projections suggest that the current threshold for transitioning from Stage 2 (blue) to Stage 1 (green) may fail to guard against future variants of concern. Reducing the threshold from a rolling-average of five new COVID-19 admissions to zero would be expected to reduce risks of rapid resurgences. We are posting these results prior to peer review to provide intuition for both policy makers and the public regarding the near-term threat of COVID-19.