RAPID: Harnessing the power of multiple models for outbreak management

RAPID：利用多种模型的力量进行疫情管理

• 批准号: 2028301
• 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: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2022-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028301&HistoricalAwards=false
• 关键词: RAPID; Harnessing; power; multiple; models

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.

对于许多最具破坏性或最令人担忧的病原体，比如导致新冠肺炎的SARS-CoV-2病毒，多个科学小组开发了定量模型，以预测疾病动态并评估可能的干预措施。这些模型的预测和建议往往有很大不同，反映了不同的政策假设，以及关于生物和管理过程的科学、后勤和其他不确定性。这种不确定性可能会给政策制定者带来挑战，阻碍干预规划和应对。因此，政策制定者可能会选择依赖单一可信的建议来源，或者在看起来像是共识的情况下，而不相信决策将是最好的。然而，不是最优的决定意味着可能会失去更多的生命或使用比所需更多的资源。面对生物、流行病学和操作上的不确定性，系统化的战略使使用多种模型制定政策的过程正规化，可以提高应对疫情的政策的效力和效率。新冠肺炎大流行疫情是世界上大多数国家面临的重大健康问题。这项工作旨在直接实时地解决当前的问题。这项工作还将为未来的疫情应对提供一个框架。许多应对新冠肺炎大流行的模型正在开发中，或最近发布。该项目将开发多模式诱导协议，嵌入一个强大的决策框架中，正式承认我们对这种新病原体的不确定性，并避免已知的偏见来源。充分认识和解释不确定性对于决策和公共交流都是至关重要的。在这一过程中，将评估与国家有关的目标(例如，尽量减少死亡)和干预措施(例如，社会距离)。该项目将把正式的专家引诱方法(通常用于征求个别专家的意见)与来自多个研究小组的建模分析相结合，以加强疫情管理的决策。小组将预测不同干预措施下的疾病动态，并将使用决策分析来分析总体产出，以提供针对政策制定者目标的干预措施评估。该项目将进行这项工作，以满足在新冠肺炎大灾难期间面临不确定性的关键政策决策需求。该奖项反映了美国国家科学基金会的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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