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 var-iation 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

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