Multiple models for outbreak decision support in the face of uncertainty

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multi model efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.SignificanceDuring infectious disease outbreaks, uncertainty hinders our ability to forecast dynamics and to make critical decisions about management. Motivated by the COVID-19 pandemic, leading agencies have initiated efforts to prepare for future outbreaks, for example, the US Centers for Disease Control and Prevention's National Center for Forecasting and Outbreak Analytics and the WHO's Hub for Pandemic and Epidemic Intelligence were recently inaugurated. Critically, such efforts need to inform policy as well as provide insight into expected disease dynamics. We present a validated case study from early in the pandemic, drawing on recommendations to minimize cognitive biases and incorporate decision theory, to illustrate how a policy-focused process could work for urgent, important, time sensitive outbreak decision making in the face of uncertainty.