RAPID: Flexible, Efficient, and Available Bayesian Computation for Epidemic Models

RAPID：灵活、高效、可用的流行病模型贝叶斯计算

• 批准号: 2055251
• 负责人: Andrew Gelman
• 金额: $ 18.7万
• 依托单位: Columbia University
• 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: 2020-11-15 至 2021-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2055251&HistoricalAwards=false
• 关键词: RAPID; Flexible; Efficient; Available; Bayesian

Decisions about coronavirus response are necessarily based on statistical models of prevalence, transmission risks, case fatality rate, projection of future spread of infection, and estimated effects of medical and social interventions. Much of this modeling and inference is being done using the Bayesian framework, an approach to statistics that is well suited to integration of information from different sources and accounting for uncertainty in predictions that can be input into decision analysis. This is a project to develop computing tools to make Bayesian methods more accessible to researchers in quantitative social science and public health who are studying COVID-19 and epidemic models more generally. This work promises to advance scientific knowledge by enabling researchers to fit more flexible and realistic models accounting for multiple sources of uncertainty in data, and to advance societal goals by facilitating more accurate and granular estimates of exposure, reproduction rate, and other aspects of epidemic spread that inform public and private decision making. This project also provides professional development opportunities for a post-doctoral researcher, as well as student training.The research will be done in the open-source programming language Stan, which has already been used in several influential COVID-19 models as well as in economics, political science, biology, political science, and many other application areas. Specifically, the project includes: documentation and language features to make Stan programs easier to write and evaluate; continuation and extensions of existing collaborations on mathematical models for epidemic spread, causal models for estimating policy effects, and survey adjustment; and improved implementations for differential-equation models, which serve as the core of models for disease transmission and other diffusive social and biological processes.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.

关于冠状病毒应对的决定必须基于流行率、传播风险、病死率、对未来感染传播的预测以及医疗和社会干预措施的估计效果的统计模型。 大部分建模和推理都是使用贝叶斯框架完成的，贝叶斯框架是一种非常适合整合不同来源信息的统计方法，并考虑到可以输入决策分析的预测中的不确定性。 这是一个开发计算工具的项目，使贝叶斯方法更容易为正在研究COVID-19和流行病模型的定量社会科学和公共卫生研究人员所用。 这项工作有望通过使研究人员能够适应更灵活和更现实的模型来解释数据中的多种不确定性来推进科学知识，并通过促进对暴露，繁殖率和流行病传播的其他方面进行更准确和粒度的估计来推进社会目标，为公共和私人决策提供信息。该项目还为博士后研究人员提供专业发展机会以及学生培训。该研究将使用开源编程语言Stan进行，该语言已用于多个有影响力的COVID-19模型以及经济学、政治学、生物学、政治学和许多其他应用领域。 具体而言，该项目包括：文档和语言功能，使Stan程序更容易编写和评估;继续和扩展现有的流行病传播数学模型、估计政策效果的因果模型和调查调整方面的合作;以及微分方程模型的改进实现，该奖项反映了NSF的法定使命，并被认为是值得通过评估使用的支持。基金会的学术价值和更广泛的影响审查标准。

项目成果

Routine Hospital-based SARS-CoV-2 Testing Outperforms State-based Data in Predicting Clinical Burden

在预测临床负担方面，基于医院的常规 SARS-CoV-2 检测优于基于州的数据

• DOI: 10.1097/ede.0000000000001396
• 发表时间: 2021
• 期刊: Epidemiology
• 影响因子: 5.4
• 作者: Covello, Leonard;Gelman, Andrew;Si, Yajuan;Wang, Siquan
• 通讯作者: Wang, Siquan

Bayesian workflow for disease transmission modeling in Stan

• DOI: 10.1002/sim.9164
• 发表时间: 2021-09-08
• 期刊: STATISTICS IN MEDICINE
• 影响因子: 2
• 作者: Grinsztajn, Leo;Semenova, Elizaveta;Riou, Julien
• 通讯作者: Riou, Julien

Accounting for uncertainty during a pandemic.

• DOI: 10.1016/j.patter.2021.100310
• 发表时间: 2021-08-13
• 期刊: Patterns (New York, N.Y.)
• 作者: Zelner J;Riou J;Etzioni R;Gelman A
• 通讯作者: Gelman A