New Method Uses Data-Driven Models to Inform COVID-19 Policymaking

May 08, 2020 - An international team of researchers has developed a new method to leverage multiple data-driven models for COVID-19 outbreak management.

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When a disease outbreak strikes, many different research groups will independently generate data-driven models. These models may project how the disease will spread, which groups will be most severely affected, or how certain interventions could impact these dynamics. Models like these can help leaders gain more insight into a disease, as well as inform public policy for managing the outbreak.

"While most models have strong scientific underpinnings, they often differ greatly in their projections and policy recommendation," said Katriona Shea, professor of biology and Alumni Professor in the Biological Sciences, Penn State.

"This means that policymakers are forced to rely on consensus when it appears, or on a single trusted source of advice, without confidence that their decisions will be the best possible."

At the onset of an outbreak, especially for a new disease like COVID-19, there is often a large amount of data that is unknown or unavailable to researchers. This leaves them to make decisions about how to incorporate this uncertainty into their models, leading to different projections.

This has been especially true in the COVID-19 pandemic. Lawmakers and researchers are faced with uncertainty in a number of areas, including infection rate, transmission details, and the capacity of healthcare systems. The teams that develop each model bring their own approach and perspective to address these uncertainties.

"In order to improve modeling and analysis of epidemic disease, it is essential to develop protocols that deliberately generate and evaluate valuable individual ideas from across the modeling community," said Michael Runge, a research ecologist at the US Geological Survey's Patuxent Wildlife Research Center who specializes in decision analysis for wildlife management.

"We have identified best practices that allow the synthesis and evaluation of input from multiple modeling groups in an efficient and timely manner."

To improve the accuracy and effectiveness of data-driven disease outbreak models, the research team established a new method of model development. In the three-part process, multiple research groups first create models for specified management scenarios. This could include addressing how caseload could be affected if social distancing measures were lifted this summer, or how the outbreak would change if students returned to school in the fall.

During this step, the research groups work independently to encourage a wide range of ideas. Then, the modeling groups formally discuss their models with each other, allowing them to examine why their models might disagree.

Finally, the groups again work independently to refine their models, based on the insights they’ve gained from the discussion and comparison stage.

After group discussion and individual model refinement, the models are combined into an overall projection for each management strategy, which can help inform risk analysis and policy deliberation. This stage involves methods from the field of decision analysis, which can allow the decision maker to understand the different management options in the face of existing uncertainty.

The combined results can help identify which uncertainty, or what missing pieces of information, are most critical to learn about in order to improve models and decision-making.

"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."

This process can continue even after initial decisions are made, as new information about the outbreak and management become available. This adaptive management strategy can help researchers refine their models and make new predictions as the outbreak progresses.

The team plans to implement this method immediately for COVID-19. The strategy can leverage the many research groups already producing models for the current outbreak, and produce more robust results from the existing process. As results are generated, the team will share their findings with the CDC.

"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."