2021
DOI: 10.1038/s41467-021-23989-x
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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 o… Show more

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Cited by 34 publications
(32 citation statements)
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“…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 ).…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…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 ).…”
Section: Discussionmentioning
confidence: 99%
“…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 ).…”
mentioning
confidence: 99%
“…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.…”
Section: Discussionmentioning
confidence: 99%
“…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).…”
Section: Introductionmentioning
confidence: 99%