AI Article Synopsis

  • Policymakers face challenges in making decisions with limited information and conflicting predictions from different models, especially during crises like the COVID-19 pandemic.
  • A study brought together multiple modeling teams to assess reopening strategies in a mid-sized U.S. county, revealing consistent rankings for interventions despite variations in projection magnitudes.
  • The findings indicated that reopening workplaces could lead to a significant increase in infections, while restrictions could greatly reduce cumulative infections, highlighting the trade-offs between public health and economic activity with no optimal reopening strategy identified.

Article Abstract

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 multimodel 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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10160947PMC
http://dx.doi.org/10.1073/pnas.2207537120DOI Listing

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