RAPID: Harnessing the power of multiple models for outbreak management

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For many of the most damaging or worrying pathogens, such as the SARS-CoV-2 virus that causes COVID-19, multiple scientific groups develop quantitative models to forecast disease dynamics and assess possible interventions. These models often differ significantly in their projections and recommendations, reflecting different policy assumptions, as well as scientific, logistical, and other uncertainty about biological and management processes. Such uncertainty can be challenging for policymakers, hindering intervention planning and response. Policymakers may thus choose to rely on single trusted sources of advice, or on consensus where it appears, without confidence that decisions will be the best possible. However, less-than-optimal decisions mean more lives may be lost or more resources used than needed. In the face of biological, epidemiological, and operational uncertainties, systematic strategies to formalize the process of using multiple models to develop policy can improve the effectiveness and efficiency of policy responses to outbreaks. The COVID-19 pandemic outbreak is a major health issue for most countries in the world. This work is intended to directly address this current problem in real time. The work will also provide a framework for future outbreak response. Many models to address the COVID-19 pandemic are in development, or recently published. This project will develop multiple-model elicitation protocols, embedded in a strong framework for decision making, that formally acknowledges our uncertainty about this novel pathogen, and avoids known sources of bias. A full acknowledgment and accounting of uncertainty is critical both for decision making and for public communication. Nationally relevant objectives (e.g., minimizing deaths) and interventions (e.g., social distancing) will be assessed during this process. The project will merge formal expert elicitation methods (usually used to elicit opinions from individual experts) with modeling analyses from multiple research groups to enhance decision making for outbreak management. Groups will project disease dynamics under different interventions, and the ensemble of outputs will be analyzed using decision analysis to provide an evaluation of interventions against the policy makers’ objectives. The project will conduct this exercise to address key policy decision-making needs in the face of uncertainty during the COVID-19 pandemic.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.