RAPID: Fast COVID-19 Scenario Projections in Presence of Vaccines and Competing Variants

RAPID：在存在疫苗和竞争变​​种的情况下快速进行 COVID-19 情景预测

• 批准号: 2135784
• 负责人: Ajitesh Srivastava
• 金额: $ 18.68万
• 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: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2135784&HistoricalAwards=false
• 关键词: RAPID; Fast; COVID; 19; Scenario

After more than a year, COVID-19 remains a concern worldwide. While the United States is moving towards a fast reopening of economic activities, it is crucial to ensure that it can be done without an increased burden on the healthcare system. As a result, there is an urgency to produce reliable long-term scenario projections of cases, hospitalizations, and deaths to inform policymakers. New challenges in modeling and estimations are emerging due to emerging competing variants with transmissibility advantage, possible waning immunity and immune escape, vaccine hesitancy, and changes in non-pharmaceutical interventions. The project will address these emerging challenges in scenario projections at the state-level in the US. A key advantage of the modeling technique is that it can incorporate various complexities and learn from a changing epidemiological and social environment, and yet it can produce fast projections. The techniques developed in the project will not only be applicable to the US locations but also locations around the world where COVID-19 is still a severe disaster. The scenario modeling framework developed during the project will also set the foundations for quick scenario generation for better preparedness during future epidemics. This project provides training opportunities for a graduate student.The proposed project develops a discrete-time heterogeneous rate model that can incorporate various complexities of COVID-19 and yet produce long-term scenario projections quickly on commodity hardware (2-3 mins/scenario for all US states). The fast projections of cases, hospitalizations, and deaths are enabled by decoupling of the parameter estimations so that they can be learned independently using simple regression techniques. This also results in the elimination of over-fitting arising from simultaneously learning complex interdependent parameters and from high-dimensional machine learning approaches. Projections are generated as probabilistic quantiles for a given scenario, health outcome, week, and location; the quantiles are predicted based on an ensemble of projections resulting from the uncertainties in data inputs and estimations. The project will also develop a novel constrained optimization-based learning approach to estimate the temporal dynamics of competing variants from genomics data.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.

一年多后，COVID-19 仍然是全世界关注的焦点。尽管美国正在迅速重新开放经济活动，但确保在不增加医疗保健系统负担的情况下实现这一目标至关重要。因此，迫切需要对病例、住院和死亡情况进行可靠的长期情景预测，以便为政策制定者提供信息。由于具有传播优势的竞争性变异的出现、免疫力可能减弱和免疫逃逸、疫苗犹豫以及非药物干预措施的变化，建模和估计方面出现了新的挑战。该项目将解决美国州一级情景预测中出现的这些新挑战。建模技术的一个关键优点是它可以整合各种复杂性并从不断变化的流行病学和社会环境中学习，但它可以产生快速预测。 该项目开发的技术不仅适用于美国地区，也适用于全球 COVID-19 仍为严重灾难的地区。该项目期间开发的场景建模框架还将为快速场景生成奠定基础，以便为未来的流行病做好更好的准备。该项目为研究生提供培训机会。拟议的项目开发了一种离散时间异构速率模型，该模型可以整合 COVID-19 的各种复杂性，并在商品硬件上快速生成长期场景预测（每个场景 2-3 分钟）美国所有州）。通过参数估计的解耦，可以快速预测病例、住院和死亡情况，以便可以使用简单的回归技术独立学习它们。这也消除了因同时学习复杂的相互依赖的参数和高维机器学习方法而引起的过度拟合。预测是针对给定场景、健康结果、周和位置生成的概率分位数；分位数是根据数据输入和估计的不确定性产生的预测集合来预测的。该项目还将开发一种新颖的基于约束优化的学习方法，以估计基因组数据中竞争变异的时间动态。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值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

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

• DOI: 10.1073/pnas.2113561119
• 发表时间: 2022-04-12
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1

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

Projected resurgence of COVID-19 in the United States in July-December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination.

• DOI: 10.7554/elife.73584
• 发表时间: 2022-06-21
• 期刊: eLife
• 影响因子: 7.7
• 作者: Truelove S;Smith CP;Qin M;Mullany LC;Borchering RK;Lessler J;Shea K;Howerton E;Contamin L;Levander J;Kerr J;Hochheiser H;Kinsey M;Tallaksen K;Wilson S;Shin L;Rainwater-Lovett K;Lemairtre JC;Dent J;Kaminsky J;Lee EC;Perez-Saez J;Hill A;Karlen D;Chinazzi M;Davis JT;Mu K;Xiong X;Pastore Y Piontti A;Vespignani A;Srivastava A;Porebski P;Venkatramanan S;Adiga A;Lewis B;Klahn B;Outten J;Orr M;Harrison G;Hurt B;Chen J;Vullikanti A;Marathe M;Hoops S;Bhattacharya P;Machi D;Chen S;Paul R;Janies D;Thill JC;Galanti M;Yamana TK;Pei S;Shaman JL;Healy JM;Slayton RB;Biggerstaff M;Johansson MA;Runge MC;Viboud C
• 通讯作者: Viboud C

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