RAPID: Modeling and Analytics for COVID-19 Outbreak Response in India: A multi-institutional, US-India joint collaborative effort

RAPID：印度 COVID-19 疫情应对的建模和分析：美印多机构联合协作

• 批准号: 2142997
• 负责人: Madhav Marathe
• 金额: $ 20万
• 依托单位: University of Virginia Main Campus
• 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: 2021-10-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2142997&HistoricalAwards=false
• 关键词: RAPID; Modeling; Analytics; COVID; 19

This project includes study of three broad problems pertaining to pandemic science with a specific focus on the ongoing COVID-19 outbreak in India. Team members from U. Virginia, Princeton University, Center for Disease Dynamics, Economics & Policy, the Indian Institute of Science and the Indian Statistical Institute, Bengaluru will study three central problems: (i) biosurveillance, (ii) forecasting and (iii) vaccine allocation. The choice of tasks is based on current needs, the importance of the problem, and the likelihood that they can be solved in a timely fashion. The first topic is integrated active biosurveillance. During this pandemic the interplay between viral mutations, human behavior, vaccines, and public policies has been unprecedented. An integral element of managing such a pandemic is biosurveillance; this involves collecting samples, testing and sequencing viruses across space and time, and combining this information to assess the distribution and impact of the viral strains. This project will use an abductive framework for smart biosurveillance – budget constrained methods for testing, genomic sequencing and identifying new variants and their transmissibility and evolution.The second topic is forecasting COVID-19 dynamics at the district/state level. Forecasting COVID-19 dynamics has been challenging everywhere; in India it has been even more challenging for a number of reasons including noisy data, undercounting of the deceased, lack of information on compliance of NPIs implemented, etc. This project will leverage ongoing work by team members on this topic to develop innovative COVID-19 forecasting methods for the Indian context. It will explore the use of multiple data sources to combat the inconsistencies and incorporate publicly available forecasts from other modeling teams to obtain robust ensembled forecasts.The third topic is vaccine prioritization, allocation, and distribution. This project will develop models and analytical tools to study a range of questions related to vaccine prioritization, allocation and distribution. The significant second surge has highlighted widespread susceptibility in early 2021, due either to waning immunity and/or limited spread of the first wave. With the possibility of novel variants due to uncontrolled spread, and emerging possibilities in vaccine development, an expedited, effective, and equitable vaccine campaign remains the most important pathway to controlling COVID-19 in India and elsewhere. The project will lead to new methods that combine multi-scale simulations with recent techniques in AI and machine learning to obtain implementable solutions to the problems above. The methods will also be generalizable -- the goal is to develop the needed technical capability to respond to future pandemics. This innovative partnership between academic institutions in the US and India will form the basis of future joint collaborations on this important topical area of global importance.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.

该项目包括对与大流行科学有关的三个广泛问题的研究，并特别侧重于印度正在爆发的新冠肺炎疫情。来自弗吉尼亚大学、普林斯顿大学、疾病动力学、经济学和政策中心、印度科学研究所和印度统计研究所的团队成员将研究三个核心问题：(I)生物监测、(Ii)预测和(Iii)疫苗分配。任务的选择是基于当前的需求、问题的重要性以及及时解决这些问题的可能性。第一个主题是集成主动生物监控。在这场大流行期间，病毒变异、人类行为、疫苗和公共政策之间的相互作用是前所未有的。管理这种大流行的一个不可或缺的要素是生物监测；这涉及到收集样本、跨空间和跨时间对病毒进行测试和测序，并结合这些信息来评估病毒株的分布和影响。该项目将使用一个用于智能生物监控的诱导性框架-预算受限的方法来测试、基因组测序和识别新的变异及其传播性和进化。第二个主题是预测区/州层面的新冠肺炎动态。预测“新冠肺炎”的动态在任何地方都是具有挑战性的；在印度，由于多种原因，预测“新冠肺炎”的难度更大，包括数据嘈杂、对死者的统计不足、缺乏有关已实施的国家绩效指标合规性的信息等。该项目将利用团队成员在此主题上的持续工作，为印度背景开发创新的“新冠肺炎”预测方法。它将探索使用多个数据源来解决不一致问题，并整合来自其他建模团队的公开可用预测，以获得稳健的集成预测。第三个主题是疫苗的优先顺序、分配和分发。该项目将开发模型和分析工具，以研究与疫苗优先顺序、分配和分配有关的一系列问题。由于免疫力减弱和/或第一波传播有限，第二次激增突显了2021年初普遍存在的易感性。鉴于由于不受控制的传播可能产生新的变种，以及疫苗开发中出现的新可能性，快速、有效和公平的疫苗运动仍然是在印度和其他地方控制新冠肺炎的最重要途径。该项目将产生新的方法，将多尺度模拟与人工智能和机器学习中的最新技术相结合，以获得上述问题的可实施解决方案。这些方法也将是可推广的--目标是发展应对未来大流行所需的技术能力。美国和印度学术机构之间的这种创新伙伴关系将成为未来在这一具有全球重要性的重要主题领域进行联合合作的基础。该奖项反映了NSF的法定使命，并通过使用基金会的学术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

Phase-Informed Bayesian Ensemble Models Improve Performance of COVID-19 Forecasts

阶段信息贝叶斯集成模型提高了 COVID-19 预测的性能

• DOI: 10.1609/aaai.v37i13.26855
• 发表时间: 2023
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Adiga, Aniruddha;Kaur, Gursharn;Wang, Lijing;Hurt, Benjamin;Porebski, Przemyslaw;Venkatramanan, Srinivasan;Lewis, Bryan;Marathe, Madhav V.
• 通讯作者: Marathe, Madhav V.

Understanding the coevolution of mask wearing and epidemics: A network perspective.

• DOI: 10.1073/pnas.2123355119
• 发表时间: 2022-06-28
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1

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