Multiple models for outbreak decision support in the face of uncertainty

Katriona Shea, Rebecca K. Borchering, William J.M. Probert, Emily Howerton, Tiffany L. Bogich, Shou Li Li, Willem G. van Panhuis, Cecile Viboud, Ricardo Aguás, Artur A. Belov, Sanjana H. Bhargava, Sean M. Cavany, Joshua C. Chang, Cynthia Chen, Jinghui Chen, Shi Chen, Yang Quan Chen, Lauren M. Childs, Carson C. Chow, Isabel CrookerSara Y. Del Valle, Guido España, Geoffrey Fairchild, Richard C. Gerkin, Timothy C. Germann, Quanquan Gu, Xiangyang Guan, Lihong Guo, Gregory R. Hart, Thomas J. Hladish, Nathaniel Hupert, Daniel Janies, Cliff C. Kerr, Daniel J. Klein, Eili Y. Klein, Gary Lin, Carrie Manore, Lauren Ancel Meyers, John E. Mittler, Kunpeng Mu, Rafael C. Núñez, Rachel J. Oidtman, Remy Pasco, Ana Pastore Y Piontti, Rajib Paul, Carl A.B. Pearson, Dianela R. Perdomo, T. Alex Perkins, Kelly Pierce, Alexander N. Pillai, Rosalyn Cherie Rael, Katherine Rosenfeld, Chrysm Watson Ross, Julie A. Spencer, Arlin B. Stoltzfus, Kok Ben Toh, Shashaank Vattikuti, Alessandro Vespignani, Lingxiao Wang, Lisa J. White, Pan Xu, Yupeng Yang, Osman N. Yogurtcu, Weitong Zhang, Yanting Zhao, Difan Zou, Matthew J. Ferrari, David Pannell, Michael J. Tildesley, Jack Seifarth, Elyse Johnson, Matthew Biggerstaff, Michael A. Johansson, Rachel B. Slayton, John D. Levander, Jeff Stazer, Jessica Kerr, Michael C. Runge

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

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.
Original languageEnglish
Article numbere2207537120
JournalProceedings of the National Academy of Sciences of the United States of America
Volume120
Issue number118
DOIs
Publication statusPublished - 2 May 2023

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