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Multiple models for outbreak decision support in the face of uncertainty

April 25, 2023

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.

Publication Year 2023
Title Multiple models for outbreak decision support in the face of uncertainty
DOI 10.1073/pnas.2207537120
Authors 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, YangQuan Chen, Lauren M. Childs, Carson C. Chow, Isabel Crooker, Sara Y. Del Valle, Guido España, Geoffrey Fairchild, Richard C. Gerkin, Timothy C. Germann, Quanquan Gu, Xiangyang Guan, Linhong 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 A. Meyers, John E. Mittler, Kunpeng Mu, Rafael C. Nunez, 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 C. Rael, Katherine Rosenfeld, Chrysm W. Ross, Julie A. Spencer, Arlin B. Stoltzfus, Kok B. Toh, Shashaank Vattikuti, Alessandro Vespignani, Lingxiao Wang, Lisa J. White, X. Pan, Yupeng Yang, Osman N. Yogurtcu, Weitong Zhang, Yanting Zhao, Difan Zou, Matthew J. Ferrari, David Pannell, Michael J. Tildesley, J. Seiferth, Elyse Johnson, Matthew Biggerstaff, Michael A Johansson, Rachel B. Slayton, John Levander, Jeff Stazer, J. Kerr, Michael C. Runge
Publication Type Article
Publication Subtype Journal Article
Series Title PNAS
Index ID 70243206
Record Source USGS Publications Warehouse
USGS Organization Eastern Ecological Science Center