Coordinated Strategy for a Model-Based Decision Support Tool for Coronavirus Disease, Utah, USA

Abstract

The coronavirus disease pandemic has highlighted the key role epidemiologic models play in supporting public health decision-making. In particular, these models provide estimates of outbreak potential when data are scarce and decision-making is critical and urgent. We document the integrated modeling response used in the US state of Utah early in the coronavirus disease pandemic, which brought together a diverse set of technical experts and public health and healthcare officials and led to an evidence-based response to the pandemic. We describe how we adapted a standard epidemiologic model; harmonized the outputs across modeling groups; and maintained a constant dialogue with policymakers at multiple levels of government to produce timely, evidence-based, and coordinated public health recommendations and interventions during the first wave of the pandemic. This framework continues to support the state's response to ongoing outbreaks and can be applied in other settings to address unique public health challenges.

Keywords: COVID-19; SARS-CoV-2; Utah; coronavirus disease; modeling; respiratory infections; severe acute respiratory syndrome coronavirus 2; viruses; zoonoses.

Figure 1

Schematic of the modeling process used as a decision support tool for coronavirus disease, Utah, USA. The epidemiologic model produces outputs of disease impact and key health outcomes that are used by the post–acute-care model. All model results are incorporated into the report, which is generated weekly and shared with policymakers who then make decisions on which interventions to implement. Those interventions impact the reproductive number, which is then used as an input to the epidemiologic model. The color of the box represents the time input was added, with dark blue for earliest and light blue for most recent. Policymakers and interventions are gray to indicate that although they are a critical component of our modeling process, they are external to our inputs to the process. R_t, real-time effective reproduction number.

Figure 2

Example epidemiologic model output presented to stakeholders as part of decision support tool for coronavirus disease, Utah, USA. Model results compare daily incidence across 3 planning scenarios: no interventions, social distancing only, and comprehensive testing only. Bold lines represent the median daily incidence (cases/100,000 population) calculated from 1,000 simulations, whereas the lighter lines represent 15 random example simulations.

Figure 3

Example of a consensus model figure from a decision support tool for coronavirus disease, Utah, USA. Model results compare the number of new reported infections (daily) across the 4 modeling groups presented to Utah stakeholders on September 9, 2020. Light gray line represents reported infections, black line represents the consensus model (i.e., the average of the 4 individual group models), green line represents the results from modeling group 1, yellow line represents the results from the UDOH, blue line represents the results from the Intermountain Healthcare model, and red line represents the results from the University of Utah model. UDOH, Utah Department of Health.

Figure 4

Sample model outputs from additional model components for a decision support tool for coronavirus disease, Utah, USA. Solid lines indicate the average daily occupancy, and shaded areas represent 95% CIs. A, B) Estimates of R_t for the entire state of Utah (A) and for 4 counties (B). The dashed blue line at the end of each time course represents the period within 1 serial interval from the end of the available data, where estimates of R_t are not accurate; dashed black line depicts R_t = 1, below which the disease will disappear and above which the disease will spread. C) Post–acute-care occupancy for each of 3 care types: home healthcare, hospice care, and skilled nursing facility. R_t, real-time effective reproduction number.