RAPID: COVID-19 Scenario Modeling Hub to harness multiple models for long-term projections and decision support

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The ongoing COVID-19 pandemic has been accompanied by many difficult management decisions for policymakers. This project supports the COVID-19 Scenario Modeling Hub, which brings together multiple modeling groups from different scientific backgrounds to help inform decisions about the long-term potential impact of control measures on SARS-CoV-2 infections, hospitalizations, and deaths. By considering projections of these outcomes under different assumptions about the upcoming course of the pandemic, researchers will help inform decisions by providing timely information to government officials and the public to inform response efforts in the United States. The need to consider multiple potential scenarios and involve input from multiple modeling teams is particularly important when the conditions under which the pandemic will continue are uncertain. This includes effects on pathogen transmissibility and disease severity that may accompany novel variants. The environment in which the pathogen spreads also varies greatly in often unpredictable ways, depending on human behavior and interventions, such as social distancing and vaccine administration. Epidemic projections generated from this research will help inform decisions about how to manage COVID-19 interventions under rapidly changing circumstances.Approaches from decision analysis, expert elicitation, and model aggregation will be used to collect model projections from multiple groups and then synthesize these results into a unified ensemble projection. This synthesis will be particularly useful as timely management decisions need to be made in order to reduce devastating effects on public health while also accounting for uncertainty and limited resources. Updates will be provided directly to stakeholders, such as the United States Centers for Disease Control and the White House COVID-19 Data Team. Progress will also be shared with other interested parties (e.g., the World Health Organization). Visualizations of the individual model projections and the ensemble projection will be made accessible to the public via a web interface and scientific insights will be made accessible through open access publishing. The development of this framework will also benefit future endeavors to quickly establish collaborations across modeling groups to help inform decisions to limit public health and economic burden in the face of other emerging and endemic pathogens.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.