After group discussion and individual model refinement, the models are combined into an overall projection for each management strategy, which can be used to help guide risk analysis and policy deliberation. At this stage, methods from the field of decision analysis can allow the decision maker, for example a public health agency, to understand the merits of different management options in the face of the existing uncertainty.
Additionally, the combined results can help identify which uncertainty — what pieces of missing information — are most critical to learn about in order to improve models and thus improve decision making, providing a way to prioritize research directions.
“This process allows us to embrace uncertainty, rather than hastening to a premature consensus that could derail or deflect management efforts,” said Shea. “The process encourages a healthy conversation between scientists and decision makers, enabling policy agencies to more effectively achieve their management goals.”
Even after initial decisions are made, the process can continue as new information about the outbreak and management becomes available. This “adaptive management” strategy can allow researchers to refine their models and make new predictions as the outbreak progresses. For COVID-19, this process might inform how and when isolation and travel bans are lifted, and if these or other measures might be necessary again in the future.
The research team plans to implement this process immediately for COVID-19. By taking advantage of the many research groups already producing models for the current outbreak, the strategy should be easy to implement while producing more robust results from the existing process. The team will share results with the U.S. Centers for Disease Control and Prevention as they are generated.
“We hope this process actively feeds into policy for the COVID-19 response in the United States,” said Shea. “It also provides a framework for future outbreak settings, including emerging diseases and agricultural pest species, and management of endemic infectious diseases, including vaccination strategies and disease surveillance.”
In addition to Shea and Runge, the research team includes David Pannell at the University of Western Australia, William Probert at the University of Oxford in the United Kingdom, Shou-Li Li at Lanzhou University in China, Michael Tildesley at the University of Warwick in the United Kingdom, and Matthew Ferrari at Penn State.
This work was supported by the National Science Foundation and the Penn State Huck Institutes of the Life Sciences though the Coronavirus Research Seed Fund.