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.

该项目将使用有关COVID-19恢复和疫苗突破的数据来建立N传播动态，并随后对Casses，死亡和住院数量的影响。 -19随着时间的流逝和暴露于美国的各个州和世界各国的风险-19 US Covid-19预测枢纽，欧洲COVID-19的预测和场景建模中心，因此在全球范围内为决策者提供了信息项目还将通过高级帽子计划提供研究和培训机会模型将通过固定数据的“统一模型”来验证。在内，在给定时间，包括不完美的保护措施的所有感染，一剂量，两剂量和助推器。该项目与CDC合作提供了资金，以支持快速响应研究项目，以进一步促进联邦感染性疾病疾病建模能力。该奖项反映了NSF的飞行任务，并通过评估美国知识分子和更广泛的影响评论值得支持。标准。

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

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

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