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.

该项目包括研究与大流行科学有关的三个广泛问题，特别关注印度正在进行的Covid-19爆发。来自美国弗吉尼亚大学，普林斯顿大学，疾病动态，经济学与政策中心，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）生物监视，（ii）预测和（iii）疫苗分配。任务的选择是基于当前需求，问题的重要性以及可以及时解决的可能性。第一个主题是集成的主动生物监视。在这种大流行期间，病毒突变，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视。这涉及收集样品，测试和测序在空间和时间之间进行测试，并结合这些信息以评估病毒菌株的分布和影响。该项目将使用绑架框架来用于智能生物监视 - 预算约束方法，用于测试，基因组测序和识别新变体及其传播和进化。第二个主题是预测地区/州级别的Covid-19动力学。预测Covid-19动力学到处都是挑战。在印度，由于多种原因，包括嘈杂数据，降低死者的估计，缺乏有关实施NPI的遵守信息等的许多原因，这一挑战更加挑战。该项目将利用团队成员在此主题上正在进行的工作来开发创新的COVID-19预测方法，以供印度背景使用。它将探索使用多个数据源来消除不一致之处的使用，并结合其他建模团队的公开森林人，以获得强大的结合森林人。第三个主题是疫苗的优先级，分配和分布。该项目将开发模型和分析工具，以研究与疫苗优先分配，分配和分配有关的一系列问题。由于免疫力下降和/或第一波的有限传播，第二次浪涌显着强调了2021年初的宽度敏感性。由于不受控制的差异以及疫苗开发中新兴的可能性引起的新型变体的可能性，加快，有效和公平的疫苗运动仍然是控制Covid-19的最重要途径，在印度和其他地方控制Covid-19。该项目将导致新方法将多尺度模拟与AI和机器学习中的最新技术相结合，从而为上述问题获得可实施的解决方案。这些方法也将是可推广的 - 目标是开发所需的技术能力来应对未来的大流行。美国和印度的学术机构之间的这种创新伙伴关系将构成未来对全球重要性主题领域的联​​合合作的基础。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛影响的评估审查标准来通过评估而被视为珍贵的支持。

项目成果

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

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

Enhancing COVID-19 ensemble forecasting model performance using auxiliary data sources

• DOI: 10.1109/bigdata55660.2022.10020579
• 发表时间: 2022-01-01
• 期刊: 2022 IEEE International Conference on Big Data (Big Data)
• 作者: Adiga, Aniruddha;Kaur, Gursharn;Marathe, Madhav
• 通讯作者: Marathe, Madhav

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.