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.

该项目将在其他国家使用可用数据集，以及在纽约，弗吉尼亚州和马里兰州使用隔间和群体群体模型来量化那里发生的事件，以及在各个阶段进行哪些干预措施。这将允许更早地控制未来情况。 该项目期间开发的流行模型将导致计算流行病学的创新，并启用方法来减轻Covid-19对公共卫生，社会和经济的负面影响。基于其他国家的Covid-19的公共可用数据集，以及在纽约，弗吉尼亚州，弗吉尼亚州，纽约，尼西亚州和马里兰州的跨越型号的事件，以使其构建的模型能够实现构建模型，以了解该模型的构建模型。在各个阶段进行干预措施，并制定最佳策略以包含大流行。基本模型将根据年龄，性别和感染状态将人口分为阶级；检查所施加的隔离的影响；然后考虑可能采取的其他策略，特别是以降低接触率。 该项目将应用并将“转移学习”方法扩展到此问题。 研究团队在进行这项研究方面有好处。他们有悠久的经验跟踪和建模传染病传播的历史（例如埃博拉病毒，SARS），并且已经参与了COVID-COVID-12的CDC预测挑战。

项目成果

Prioritizing allocation of COVID-19 vaccines based on social contacts increases vaccination effectiveness

• DOI: 10.1101/2021.02.04.21251012
• 发表时间: 2021-02-16
• 期刊: medRxiv
• 作者: Chen, J.;Hoops, S.;Marathe, M.
• 通讯作者: Marathe, M.

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

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

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

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