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