RAPID: Collaborative: Transfer Learning Techniques for Better Response to COVID-19 in the US

RAPID：协作：迁移学习技术以更好地应对美国的 COVID-19

• 批准号: 2028004
• 负责人: Madhav Marathe
• 金额: $ 2.5万
• 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: 2020
• 资助国家: 美国
• 起止时间: 2020-06-15 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028004&HistoricalAwards=false
• 关键词: RAPID; Collaborative; Transfer; Learning; Techniques

This project will use available data sets for COVID-19 in other countries, and in NYC, Virginia, and Maryland to build compartmental and metapopulation models to quantify the events that transpired there, and what interventions at various stages may have achieved. This will permit gaining control of future situations earlier. The epidemic models developed during this project will lead to innovations in computational epidemiology and enable approaches that mitigate the negative effects of COVID-19 on public health, society, and the economy.Based on publicly available data sets for COVID-19 in other countries, and in NYC, Virginia, and Maryland, the researchers propose to build compartmental and metapopulation models to quantify the events that transpired there, understand the impacts of interventions at various stages, and develop optimal strategies for containing the pandemic. The basic model will subdivide the population into classes according to age, gender, and infectious status; examine the impact of the quarantine that was imposed; and then consider additional strategies that could have been imposed, in particular to reduce contact rates. The project will apply and extend the approach of "transfer learning" to this problem. The research team is well positioned to conduct this research; they have a long history of experience tracking and modeling infectious disease spread (e.g., Ebola, SARS) and are already participating in the CDC forecasting challenge for COVID-19.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.

本项目将利用其他国家以及纽约市、弗吉尼亚州和马里兰州的现有COVID-19数据集，建立分区和人口模型，量化那里发生的事件，以及不同阶段的干预措施可能取得的成果。这将使你能够更早地控制未来的情况。本项目开发的流行病模型将引领计算流行病学的创新，并使减轻COVID-19对公共卫生、社会和经济的负面影响的方法成为可能。根据其他国家以及纽约市、弗吉尼亚州和马里兰州的COVID-19公开可用数据集，研究人员建议建立分区和元人口模型，以量化那里发生的事件，了解不同阶段干预措施的影响，并制定遏制大流行的最佳策略。基本模型将根据年龄、性别和感染状况将人口细分为不同的阶层；审查实施隔离的影响；然后考虑本可以采取的其他策略，特别是降低接触率的策略。该项目将应用并扩展“迁移学习”的方法来解决这个问题。研究团队有能力进行这项研究；他们在追踪和模拟传染病传播（例如埃博拉、SARS）方面有着悠久的经验，并且已经参与了疾病预防控制中心对COVID-19的预测挑战。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识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

High resolution proximity statistics as early warning for US universities reopening during COVID-19

高分辨率邻近统计数据作为美国大学在 COVID-19 期间重新开放的预警

• 发表时间: 2020
• 期刊: medRxiv
• 作者: Mehrab, Z;Ranga, AG;Sarkar, D;Venkatramanan, S;Baek, Y;Swarup, S;Marathe, M
• 通讯作者: Marathe, M

Cohorting to isolate asymptomatic spreaders: An agent-based simulation study on the Mumbai Suburban Railway

• DOI: 10.5555/3463952.3464199
• 发表时间: 2020-12
• 期刊: ArXiv
• 作者: A. Talekar;S. Shriram;N. Vaidhiyan;G. Aggarwal;Jiangzhuo Chen;S. Venkatramanan;Lijing Wang;A. Adiga;A. Sadilek;A. Tendulkar;M. Marathe;R. Sundaresan;M. Tambe

Using Active Queries to Infer Symmetric Node Functions of Graph Dynamical Systems

• 发表时间: 2022
• 期刊: J. Mach. Learn. Res.
• 作者: Abhijin Adiga;C. Kuhlman;M. Marathe;Sujith Ravi;D. Rosenkrantz;R. Stearns

Asymptomatic individuals can increase the final epidemic size under adaptive human behavior.

• DOI: 10.1038/s41598-021-98999-2
• 发表时间: 2021-10-05
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Espinoza B;Marathe M;Swarup S;Thakur M
• 通讯作者: Thakur M

Effective Social Network-Based Allocation of COVID-19 Vaccines

基于社交网络的有效 COVID-19 疫苗分配

• DOI: 10.1145/3534678.3542673
• 发表时间: 2022
• 期刊: Proceedings of the KDD Health Day (2022
• 作者: Chen, Jiangzhuo;Hoops, Stefan;Marathe, Achla;Mortveit, Henning;Lewis, Bryan;Venkatramanan, Srinivasan;Haddadan, Arash;Bhattacharya, Parantapa;Adiga, Abhijin;Vullikanti, Anil
• 通讯作者: Vullikanti, Anil