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
基本信息
- 批准号:2055251
- 负责人:
- 金额:$ 18.7万
- 依托单位:
- 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:
- 关键词:
项目摘要
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
期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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:
- 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:
- 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:
- 影响因子:0
- 作者:Zelner J;Riou J;Etzioni R;Gelman A
- 通讯作者:Gelman A
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Andrew Gelman其他文献
C3(H<sub>2</sub>O) – Generation, quantitation, and marker of human disease
- DOI:
10.1016/j.molimm.2018.06.058 - 发表时间:
2018-10-01 - 期刊:
- 影响因子:
- 作者:
Michelle Elvington;M. Kathryn Liszewski;Hrishikesh Kulkarni;Andrew Gelman;Alfred Kim;John Atkinson - 通讯作者:
John Atkinson
A default prior distribution for logistic and other regression models ∗
逻辑和其他回归模型的默认先验分布 *
- DOI:
- 发表时间:
2002 - 期刊:
- 影响因子:0
- 作者:
Andrew Gelman;Aleks Jakulin;M. G. Pittau;Yu - 通讯作者:
Yu
An improved BISG for inferring race from surname and geolocation
一种改进的 BISG,用于根据姓氏和地理位置推断种族
- DOI:
10.48550/arxiv.2310.15097 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
P. Greengard;Andrew Gelman - 通讯作者:
Andrew Gelman
Community prevalence of SARS-CoV-2 in England during April to September 2020: Results from the ONS Coronavirus Infection Survey
2020 年 4 月至 9 月英格兰 SARS-CoV-2 社区流行情况:ONS 冠状病毒感染调查结果
- DOI:
10.1101/2020.10.26.20219428 - 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
K. Pouwels;T. House;E. Pritchard;J. Robotham;Paul J. Birrell;Andrew Gelman;K. Vihta;N. Bowers;Ian Boreham;Heledd Thomas;James W Lewis;Iain Bell;J. Bell;J. Newton;J. Farrar;I. Diamond;P. Benton;A. Walker - 通讯作者:
A. Walker
Ethics and Statistics: It's Too Hard to Publish Criticisms and Obtain Data for Republication
伦理与统计学:发表批评和获取重发表数据太难了
- DOI:
- 发表时间:
2013 - 期刊:
- 影响因子:0
- 作者:
Andrew Gelman - 通讯作者:
Andrew Gelman
Andrew Gelman的其他文献
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{{ truncateString('Andrew Gelman', 18)}}的其他基金
Scalable Bayesian regression: Analytical and numerical tools for efficient Bayesian analysis in the large data regime
可扩展贝叶斯回归:在大数据领域进行高效贝叶斯分析的分析和数值工具
- 批准号:
2311354 - 财政年份:2023
- 资助金额:
$ 18.7万 - 项目类别:
Standard Grant
Collaborative Research: PPoSS: Planning: Scalable Systems for Probabilistic Programming
协作研究:PPoSS:规划:概率编程的可扩展系统
- 批准号:
2029022 - 财政年份:2020
- 资助金额:
$ 18.7万 - 项目类别:
Standard Grant
RIDIR: Collaborative Research: Bayesian analytical tools to improve survey estimates for subpopulations and small areas
RIDIR:协作研究:贝叶斯分析工具,用于改进亚人群和小区域的调查估计
- 批准号:
1926578 - 财政年份:2019
- 资助金额:
$ 18.7万 - 项目类别:
Standard Grant
CI-SUSTAIN: Stan for the Long Run
CI-SUSTAIN:长远发展
- 批准号:
1730414 - 财政年份:2017
- 资助金额:
$ 18.7万 - 项目类别:
Standard Grant
Collaborative Research: Multilevel Regression and Poststratification: A Unified Framework for Survey Weighted Inference
协作研究:多级回归和后分层:调查加权推理的统一框架
- 批准号:
1534414 - 财政年份:2015
- 资助金额:
$ 18.7万 - 项目类别:
Standard Grant
CI-ADDO-NEW: Stan, Scalable Software for Bayesian Modeling
CI-ADDO-NEW:Stan,用于贝叶斯建模的可扩展软件
- 批准号:
1205516 - 财政年份:2012
- 资助金额:
$ 18.7万 - 项目类别:
Standard Grant
CMG: Reconstructing Climate from Tree Ring Data
CMG:从树木年轮数据重建气候
- 批准号:
0934516 - 财政年份:2009
- 资助金额:
$ 18.7万 - 项目类别:
Standard Grant
Design and Analysis of "How many X's do you know" surveys for the study of polarization in social networks
用于研究社交网络极化的“你知道多少个 X”调查的设计和分析
- 批准号:
0532231 - 财政年份:2005
- 资助金额:
$ 18.7万 - 项目类别:
Standard Grant
Multilevel Modeling for the Study of Public Opinion and Voting
用于民意和投票研究的多层次建模
- 批准号:
0318115 - 财政年份:2003
- 资助金额:
$ 18.7万 - 项目类别:
Continuing Grant
Doctoral Dissertation Research: Estimating Congressional District-Level Opinions from National Surveys using a Bayesian Hierarchical Logistic Regression Model
博士论文研究:使用贝叶斯分层逻辑回归模型从全国调查中估计国会选区级意见
- 批准号:
0241709 - 财政年份:2003
- 资助金额:
$ 18.7万 - 项目类别:
Standard Grant
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