Design of COVID-19 staged alert systems to ensure healthcare capacity with minimal closures

Abstract

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 proof-of-concept, we describe the optimization and maintenance of the staged alert system that has guided COVID-19 policy in a large US city (Austin, Texas) since May 2020. 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.

Real-time pandemic surveillance using hospital admissions and mobility data

“…Although this increases coverage—the proportion of observed values falling within prediction intervals—it significantly reduces the informativeness and public health utility of the forecasts. Since May 2020, our model projections have informed numerous time-sensitive policy decisions and response actions, including resource planning by local hospitals, urgent requests to state and federal agencies for additional surge resources, the launch and dismantling of alternative care sites to provide additional healthcare capacity, and numerous changes in the Austin-area COVID-19 alert stage to communicate and manage rising and declining risks ( 43 ).…”

“…COVID-19 healthcare data including hospital admissions, census, and ICU usage offer the fidelity of mortality data with a shorter lag, while also providing an immediate indication of healthcare resource needs. For example, COVID-19 hospitalizations have been used to estimate the impact of nonpharmaceutical interventions ( 37 ), provide healthcare demand forecasts ( 38 – 41 ), and guide mitigation policies ( 42 , 43 ). However, such data can be biased by shifting demographics of COVID-19 patients, changes in admission criteria during surges, and the availability of post–acute care facilities ( 44 , 45 ).…”

Modeling Approaches for Addressing Simple Unrelaxable Constraints with Unconstrained Optimization Methods

“…We conjecture that analogous warpings exist for decision sets defined by general bounded polyhedron (polytopes) and convex sets, with future research addressing settings where such warpings open up avenues of applying specialized algorithms that may otherwise ignore these types of constraints. Such approaches are needed in sciences where problems with simple linear unrelaxable constraints, such as xi ≤ xi+1, naturally appear; examples range from ordering particle accelerator elements [24] to pandemic alert staging [25]. We hope researchers extend this work to develop smooth modeling approaches for optimization problems with general nonlinear and convex unrelaxable constraints.…”

Epidemiologic Information Discovery from Open-Access COVID-19 Case Reports Via Pretrained Language Model

“…Analyses using open-access data have contributed key insights to help understand the epidemiological and pathological characteristics of COVID-19 (United States of America, 2021; Freunde von GISAID, 2021; Xu et al, 2020a;Du et al, 2020a;Ali et al, 2020;Xu et al, 2020b) to estimate the infection and disease burdens (O'Driscoll et al, 2021;Salje et al, 2020), characterize population behavioral changes (Zhang et al, 2020;Du et al, 2020b;Tian et al, 2020), and optimize control measures (Hale et al, 2021;Yang et al, 2021;World Health Organization, 2022). However, publicly disclosed case reports obtained from epidemiological investigations were often written in natural language without a standardized structure (e.g., different writers may use different words to express the same information).…”