RAPID: Data-driven Understanding of Imperfect Protection for Long-term COVID-19 Projections

RAPID：数据驱动的对长期 COVID-19 预测不完美保护的理解

• 批准号: 2223933
• 负责人: Ajitesh Srivastava
• 金额: $ 19.92万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-15 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2223933&HistoricalAwards=false
• 关键词: RAPID; Data; driven; Understanding; Imperfect

This project will use data on COVID-19 reinfections and vaccine breakthroughs to build a model of how imperfect immunity affects SARS-CoV-2 pathogen transmission dynamics and subsequent effects on numbers of cases, deaths, and hospitalizations. A key factor dictating the long-term dynamics of COVID-19 is how population immunity against COVID-19 changes over time and exposure. Data on vaccination breakthroughs and reinfections in various states of the US and countries around the world create a unique opportunity to study immunity waning dynamics at the population level. The project will help understand the long-term risks of resurgence and severity of COVID-19, contribute to the US COVID-19 Scenario Modeling Hub, the US COVID-19 Forecast Hub, and the European COVID-19 Forecast and Scenario Modeling Hubs, and thus inform policymakers worldwide. The PI will integrate the lessons learned in an undergraduate on programming and a graduate-level class on Machine Learning for health. The project will also provide research and training opportunities through a senior capstone program and a minority-serving program. The model will represent a class of imperfect protection in the presence of multiple variants. Popular models such as all or nothing, leaky, and time-dependent waning will be considered along with interpretable machine learning models. The models will be validated by their “generalizability” on held-out data. The unified model of immunity will be developed in a way that it can be integrated with various epidemiological models. As a demonstration, it will be integrated with a model that tracks various states an individual can be in, including all permutations of infections, reinfections, one-dose, two-doses, and boosters. Having these states over time, age groups, and variants, for a given model of imperfect protection allows for precise computation of immunity in the population at a given time. The overall approach will also be evaluated by the accuracy of US state-level cases, deaths, and hospitalization forecasts it produces. This project was funded in collaboration with the CDC to support rapid-response research projects to further advance federal infectious disease modeling capabilities.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.

这个项目将使用新冠肺炎再感染和疫苗突破的数据来建立一个模型，了解免疫缺陷如何影响SARS-CoV-2病原体的传播动力学，以及随后对病例数量、死亡和住院的影响。决定新冠肺炎长期动态的一个关键因素是人群对新冠肺炎的免疫力如何随着时间和接触情况的变化而变化。美国各州和世界各国疫苗接种突破和再感染的数据为在人口层面研究免疫力减弱的动态创造了独特的机会。该项目将有助于了解“新冠肺炎”死灰复燃和严重程度的长期风险，为美国“新冠肺炎”情景建模中心、美国“新冠肺炎”预测中心以及欧洲“新冠肺炎”预测和情景建模中心做出贡献，从而为世界各地的政策制定者提供信息。PI将整合在本科生编程课程和研究生水平的健康机器学习课程中学到的课程。该项目还将通过高级顶峰计划和少数群体服务计划提供研究和培训机会。该模型将代表一类在存在多个变种的情况下的不完全保护。流行的模型，如全有或全无、泄漏和依赖时间的衰减，将与可解释的机器学习模型一起被考虑。这些模型将通过它们对搁置数据的“概括性”进行验证。统一的免疫模式将以一种可以与各种流行病学模式相结合的方式发展。作为演示，它将与一个模型集成，该模型跟踪个人可能处于的各种状态，包括感染、再感染、单剂、双剂和加强剂的所有组合。对于给定的不完美保护模型，具有随时间、年龄组和变种的这些状态允许精确计算给定时间的人群中的免疫力。整体方法还将通过其产生的美国州级病例、死亡和住院预测的准确性进行评估。该项目是与疾控中心合作资助的，以支持快速反应研究项目，以进一步推进联邦传染病建模能力。该奖项反映了NSF的法定使命，并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

Detection of Delays and Feedthroughs in Dynamic Networked Systems

动态网络系统中的延迟和馈通检测

• DOI: 10.1109/lcsys.2022.3233123
• 发表时间: 2023
• 期刊: IEEE Control Systems Letters
• 影响因子: 3
• 作者: Jahandari, Sina;Srivastava, Ajitesh
• 通讯作者: Srivastava, Ajitesh

Shape-based Evaluation of Epidemic Forecasts

基于形状的疫情预测评估

• DOI: 10.1109/bigdata55660.2022.10020895
• 发表时间: 2022
• 期刊: 2022 IEEE International Conference on Big Data (Big Data
• 作者: Srivastava, Ajitesh;Singh, Satwant;Lee, Fiona
• 通讯作者: Lee, Fiona

The variations of SIkJalpha model for COVID-19 forecasting and scenario projections

用于 COVID-19 预测和情景预测的 SIkJalpha 模型的变化

• DOI: 10.1016/j.epidem.2023.100729
• 发表时间: 2023
• 期刊: Epidemics
• 影响因子: 3.8
• 作者: Srivastava, Ajitesh

Adjusting for Unmeasured Confounding Variables in Dynamic Networks

调整动态网络中未测量的混杂变量

• DOI: 10.1109/lcsys.2022.3233701
• 发表时间: 2023
• 期刊: IEEE Control Systems Letters
• 影响因子: 3
• 作者: Jahandari, Sina;Srivastava, Ajitesh
• 通讯作者: Srivastava, Ajitesh