University of Cambridge > Talks.cam > Worms and Bugs > The COVID-19 Scenario Modeling Hub: a multi-model effort towards addressing uncertainty during the pandemic in the United States

The COVID-19 Scenario Modeling Hub: a multi-model effort towards addressing uncertainty during the pandemic in the United States

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If you have a question about this talk, please contact Dr Ciara Dangerfield.

The COVID -19 pandemic continues to present challenges for mitigation of public health burden. Intervention decisions often require multiple months of projections; accounting for uncertainties involved over such timescales is necessary to provide responsible and effective guidance. The COVID -19 Scenario Modeling Hub has been developed to meet the need for longer-term projections of COVID -19 burden in the United States. Rounds of the Scenario Modeling Hub (SMH) have evaluated combinations of intervention strategies involving different levels of vaccine administration and hesitancy, nonpharmaceutical intervention (NPI) reduction, and transmission intensity of emerging SARS -CoV-2 variants. Repeated rounds and extensive interaction with policymakers and public health professionals has facilitated the development of scenarios that are of greatest policy interest for United States COVID -19 mitigation efforts. The incorporation of multiple modeling groups also allows for structured analysis and visualization of key areas of uncertainty, such as the potential for immune escape and behavioral feedback in response to surges in cases and hospitalizations. Here, we will further motivate SMH and discuss aspects of its development. We will present projection results for state and national-level cases, hospitalizations, and deaths through our most recent rapid rounds addressing emergence of the Omicron variant, including how results vary across modeling groups and states. We will also discuss challenges of scenario modeling efforts for public health decision-making and highlight areas for improvement.

This talk is part of the Worms and Bugs series.

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