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

RAPID：COVID-19 场景建模中心，利用多个模型进行长期预测和决策支持

• 批准号: 2126278
• 负责人: Katriona Shea
• 金额: $ 20万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126278&HistoricalAwards=false
• 关键词: RAPID; COVID; 19; Scenario; Modeling

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.

持续的COVID-19疫情为决策者带来了许多困难的管理决策。该项目支持COVID-19情景建模中心，该中心汇集了来自不同科学背景的多个建模小组，以帮助就控制措施对SARS-CoV-2感染、住院和死亡的长期潜在影响做出决策。通过考虑在对即将到来的大流行过程的不同假设下对这些结果的预测，研究人员将通过向政府官员和公众提供及时的信息来帮助为美国的应对工作提供信息。当疫情持续的条件不确定时，考虑多个潜在情景并涉及多个建模团队的投入尤为重要。这包括可能伴随新变体的对病原体传播性和疾病严重程度的影响。病原体传播的环境也因人类行为和干预措施（如社交距离和疫苗接种）的不同而存在很大差异，这些差异往往是不可预测的。本研究所产生的疫情预测将有助于在快速变化的情况下如何管理COVID-19干预措施的决策。将使用决策分析、专家启发和模型聚合的方法来收集来自多个群体的模型预测，然后将这些结果合成为统一的集合预测。这一综合报告将特别有用，因为需要及时作出管理决定，以减少对公共卫生的破坏性影响，同时还要考虑到不确定性和有限的资源。更新将直接提供给利益相关者，如美国疾病控制中心和白宫COVID-19数据团队。还将与其他感兴趣的各方分享进展情况（例如，世界卫生组织）。个人模型预测和整体预测的可视化将通过网络界面向公众开放，科学见解将通过开放获取出版物提供。这一框架的发展也将有利于未来的努力，以快速建立跨建模组的合作，以帮助通知决策，以限制公共卫生和经济负担，面对其他新兴和地方性病原体。这一奖项反映了NSF的法定使命，并已被认为是值得支持的评估使用基金会的知识价值和更广泛的影响审查标准。

项目成果

Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty.

• DOI: 10.1038/s41467-023-42680-x
• 发表时间: 2023-11-20
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Howerton E;Contamin L;Mullany LC;Qin M;Reich NG;Bents S;Borchering RK;Jung SM;Loo SL;Smith CP;Levander J;Kerr J;Espino J;van Panhuis WG;Hochheiser H;Galanti M;Yamana T;Pei S;Shaman J;Rainwater-Lovett K;Kinsey M;Tallaksen K;Wilson S;Shin L;Lemaitre JC;Kaminsky J;Hulse JD;Lee EC;McKee CD;Hill A;Karlen D;Chinazzi M;Davis JT;Mu K;Xiong X;Pastore Y Piontti A;Vespignani A;Rosenstrom ET;Ivy JS;Mayorga ME;Swann JL;España G;Cavany S;Moore S;Perkins A;Hladish T;Pillai A;Ben Toh K;Longini I Jr;Chen S;Paul R;Janies D;Thill JC;Bouchnita A;Bi K;Lachmann M;Fox SJ;Meyers LA;Srivastava A;Porebski P;Venkatramanan S;Adiga A;Lewis B;Klahn B;Outten J;Hurt B;Chen J;Mortveit H;Wilson A;Marathe M;Hoops S;Bhattacharya P;Machi D;Cadwell BL;Healy JM;Slayton RB;Johansson MA;Biggerstaff M;Truelove S;Runge MC;Shea K;Viboud C;Lessler J
• 通讯作者: Lessler J