| NSF Org: |
DEB Division Of Environmental Biology |
| Recipient: |
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| Initial Amendment Date: | April 20, 2021 |
| Latest Amendment Date: | April 20, 2021 |
| Award Number: | 2126278 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Andrea Porras-Alfaro
aporrasa@nsf.gov (703)292-2944 DEB Division Of Environmental Biology BIO Directorate for Biological Sciences |
| Start Date: | May 1, 2021 |
| End Date: | April 30, 2024 (Estimated) |
| Total Intended Award Amount: | $200,000.00 |
| Total Awarded Amount to Date: | $200,000.00 |
| Funds Obligated to Date: |
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| Recipient Sponsored Research Office: |
201 OLD MAIN UNIVERSITY PARK PA US 16802-1503 (814)865-1372 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
University Park PA US 16802-1503 |
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Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.074 |
ABSTRACT
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Long-term (multi-month) COVID-19 projections that combine insights from multiple independent models are needed to inform decision-makers, public health experts, and the general public. We help to coordinate the US Scenario Modeling Hub. Our open-door policy solicits state and national projections of future public health outcomes from multiple modeling teams. Our primary value comes from aligning multiple teams on shared questions that are directly informed by close, decision-focused relationships with public health partners. By coordinating teams' research efforts, outputs from individual models can then be aggregated into an ensemble, which is known to provide more reliable projections. Importantly, by adapting concepts from expert judgment to manage interactions between Hub collaborators, our Hub efforts focus on reducing linguistic uncertainty (e.g., about scenario descriptions) while gaining a fuller expression of scientific uncertainty (e.g., about immunity and transmission of new SARS-CoV-2 variants) and logistical uncertainty (e.g., about vaccine effects on transmission, or on compliance with non-pharmaceutical interventions). We evaluated multiple projection aggregation methods to identify and develop preferred methods for characterizing uncertainty for risk analysis informing decisions. We also advanced the theory of scenario design, demonstrated the added value of a multi-model ensemble for infectious disease scenario projections, and addressed how many models are really needed for such multi-model efforts to be successful. The original COVID-19 Scenario Modeling Hub has now expanded its efforts to project other respiratory infections, specifically Flu and RSV, and we interact with similar Hub efforts in Europe. We have completed eighteen rounds of COVID-19 scenario projections, four for Flu and one for RSV to date. Results from the Scenario Modeling Hubs have been presented to multiple local, state and federal end users, and have informed decisions about, for example, the timing and age-targets for vaccines in the USA throughout and since the pandemic. Additionally, results have been posted to our public-facing website.
Last Modified: 08/28/2024
Modified by: Katriona Shea
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