RAPID: Optimal allocation of COVID-19 testing based on context-specific outbreak control objectives

RAPID：根据具体情况的疫情控制目标优化 COVID-19 检测分配

• 批准号: 2037885
• 负责人: Katriona Shea
• 金额: $ 18.02万
• 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: 2020-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2037885&HistoricalAwards=false
• 关键词: RAPID; Optimal; allocation; COVID; 19

The purpose of this project is to design a framework for objective-driven, context dependent disease surveillance strategies, designed to deal with sampling errors and biases. This framework will be applied to the allocation of COVID-19 testing based on multiple outbreak scenarios, employing the use of multiple models to improve decision-making for COVID-19 surveillance and control. This work will improve the response to the COVID-19 pandemic. Results will be presented to key federal agencies, so that they can be considered as part of the decision-making process for the COVID-19 outbreak. These methods will also be highly applicable to optimal vaccine allocation especially during the early stages of vaccine availability when supplies will be limited. In addition, it will provide a framework for future outbreak testing response. One postdoctoral researcher will be trained in the theory and methods of applied epidemiological research. With a growing, but still limited number of imperfect tests available, the way in which tests are allocated critically determines what we can learn and in turn, what inferences can be made with respect to managing the disease for individuals and populations. This poses an optimal allocation problem for limited resources. The context-dependent nature of allocating a limited number of tests introduces additional potential sources of error and bias, making the question of optimal testing allocation a unique challenge. Current modeling efforts focus necessarily on disease dynamics and the efficacy of intervention strategies, but few consider testing, contact tracing, and isolation strategies explicitly. Unlike shelter-in-place or social distancing mandates, the impact of test allocation on management strategy decision-making is density-dependent. While testing remains limited, it is critical to explicitly model test allocation and strategies that involve both monitoring and management. The key to successful surveillance is to actively design surveillance strategies for a specific objective. The project will use a principle of effective monitoring based on two steps. First, identify the objective of the monitoring? Second, tailor the sampling design to achieve that objective, in this case selecting groups of individuals in a nonrepresentative way and to separately estimate the probabilities that a randomly sampled individual would appear in these groups. Misclassification of disease state (e.g., false positives/negatives) due to the specificity and sensitivity of different tests, the performance of different test platforms and population-level incidence will also be addressed.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.

该项目的目的是为目标驱动的，依赖背景的疾病监视策略设计一个框架，旨在处理抽样错误和偏见。该框架将根据多个爆发场景应用于Covid-19测试的分配，并采用多种模型来改善COVID-19-19的监视和控制。这项工作将改善对19009年大流行的反应。结果将介绍给主要的联邦机构，以便可以将其视为COVID-19疫情决策过程的一部分。这些方法也将非常适用于最佳疫苗分配，尤其是在疫苗可用性的早期阶段，当供应受到限制时。此外，它将为将来的爆发测试响应提供一个框架。一名博士后研究人员将接受应用流行病学研究的理论和方法的培训。随着可用的不完美测试的增长但仍有限制的测试方式，对测试的分配方式可以确定我们可以学到的东西，而对个人和人群管理疾病的管理可以做出什么推论。这为有限资源带来了最佳分配问题。分配有限数量测试的上下文依赖性性质引入了其他潜在的错误和偏见来源，这使得最佳测试分配的问题成为独特的挑战。当前的建模工作必须集中于疾病动态和干预策略的功效，但很少有人考虑测试，接触跟踪和隔离策略。与现场庇护所或社会距离任务不同，测试分配对管理策略决策的影响取决于密度。尽管测试仍然有限，但要明确模型测试分配以及涉及监视和管理的策略至关重要。成功监视的关键是为特定目标积极设计监视策略。该项目将根据两个步骤使用有效监控的原则。首先，确定监视的目标？其次，量身定制采样设计以实现这一目标，在这种情况下，以非代表性的方式选择了个体组，并分别估计这些组中随机抽样的个体会出现在这些组中的概率。由于不同测试的特异性和敏感性，疾病状态的错误分类（例如，误报/负面因素）也将被解决不同的测试平台和人口水平的发病率的性能。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和广泛的影响来审查CRETERIA的评估，并被认为是值得通过评估的支持。

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