COVID-19 healthcare demand projections: Arizona

• Published in: PloS one Vol. 15; no. 12; p. e0242588
• Main Authors: Gel, Esma S, Jehn, Megan, Lant, Timothy, Muldoon, Anna R, Nelson, Trisalyn, Ross, Heather M
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.12.2020; Public Library of Science (PLoS)
• Subjects: Arizona - epidemiology; Biology and Life Sciences; Control; Coronaviruses; COVID-19; COVID-19 - epidemiology; COVID-19 - mortality; COVID-19 - therapy; Disease control; Disease spread; Disease transmission; Earth Sciences; Epidemic models; Epidemics; Epidemiology; Geography; Health care; Health care industry; Health policy; Health Services Needs and Demand - statistics & numerical data; Hospitalization; Hospitals - statistics & numerical data; Humans; Industry forecasts; Infections; Mathematical models; Medicine and Health Sciences; Methods; Models, Statistical; Pandemics; People and places; Policy; Population; Public health; Public policy; Quarantine - statistics & numerical data; Sentinel surveillance; Severe acute respiratory syndrome coronavirus 2; Shelter in place; Social distancing; Social Sciences; Statistics; Supply and demand; System dynamics; Viral diseases; Arizona; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0242588

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.