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COVID-19 healthcare demand projections: Arizona.
Gel, Esma S; Jehn, Megan; Lant, Timothy; Muldoon, Anna R; Nelson, Trisalyn; Ross, Heather M.
  • Gel ES; School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States of America.
  • Jehn M; School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, United States of America.
  • Lant T; Arizona State University, Tempe, AZ, United States of America.
  • Muldoon AR; School for the Future of Innovation in Society, Arizona State University, Tempe, AZ, United States of America.
  • Nelson T; School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, United States of America.
  • Ross HM; School for the Future of Innovation in Society, Arizona State University, Tempe, AZ, United States of America.
PLoS One ; 15(12): e0242588, 2020.
Article in English | MEDLINE | ID: covidwho-954386
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ABSTRACT
Beginning in March 2020, the United States emerged as the global epicenter for COVID-19 cases with little to guide policy response in the absence of extensive data available for reliable epidemiological modeling in the early phases of the pandemic. In the ensuing weeks, American jurisdictions attempted to manage disease spread on a regional basis using non-pharmaceutical interventions (i.e., social distancing), as uneven disease burden across the expansive geography of the United States exerted different implications for policy management in different regions. While Arizona policymakers relied initially on state-by-state national modeling projections from different groups outside of the state, we sought to create a state-specific model using a mathematical framework that ties disease surveillance with the future burden on Arizona's healthcare system. Our framework uses a compartmental system dynamics model using a SEIRD framework that accounts for multiple types of disease manifestations for the COVID-19 infection, as well as the observed time delay in epidemiological findings following public policy enactments. We use a compartment initialization logic coupled with a fitting technique to construct projections for key metrics to guide public health policy, including exposures, infections, hospitalizations, and deaths under a variety of social reopening scenarios. Our approach makes use of X-factor fitting and backcasting methods to construct meaningful and reliable models with minimal available data in order to provide timely policy guidance in the early phases of a pandemic.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 / Health Services Needs and Demand Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Humans Country/Region as subject: North America Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2020 Document Type: Article Affiliation country: Journal.pone.0242588

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 / Health Services Needs and Demand Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Humans Country/Region as subject: North America Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2020 Document Type: Article Affiliation country: Journal.pone.0242588