SCC-IRG JST: Multimodal Data Analytics and Integration for Effective COVID-19, Pandemics and Compound Disaster Response and Management

SCC-IRG JST：多模式数据分析和集成，实现有效的 COVID-19、流行病和复合灾害响应和管理

• 批准号: 2301552
• 负责人: Shu-Ching Chen
• 金额: $ 75万
• 依托单位: University of Missouri-Kansas City
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2301552&HistoricalAwards=false
• 关键词: SCC; IRG; JST; Multimodal; Data

The COVID-19 pandemic has resulted in huge amounts of confirmed cases and deaths both in the United States and globally. The nation experienced grave repercussions to citizens’ lives, health, and the economy. Due to its high contagiousness, policies such as quarantine and lockdowns were put in place to slow the virus’ rapid spread. Some major challenges are identifying vulnerable communities to provide immediate help and determining policies that are effective in slowing down the spread with minimal adverse effects on people’s livelihood, mental health, and the economy. This project aims to develop tools that can locate communities in crisis, identify their problems and demands, and predict pandemic transmission trends and impacts in diverse communities based on mobility and social media data. The developed tools and technologies are critical for effective disaster management and pandemic recovery. Furthermore, pandemic and other natural disasters’ co-occurrence is even more challenging given that mass evacuation and sheltering processes may cause a spike in cases of transmissible pandemic diseases. This project will develop new technologies that can aid emergency managers under a pandemic scenario based upon our previously developed tools for natural disaster management.The proposed research provides potential solutions to solve crucial disaster information management challenges for COVID-19, future pandemics, and compound disasters while leveraging the team's previous work. Furthermore, the proposed techniques will help better understand the disaster situation to assist the preparation and recovery for a broad range of communities, including minority and low-income populations. This project will also have the potential to have societal and economic impacts by providing the most accurate information on pandemics and compound disasters to prevent unexpected losses. The developed solutions could be later expanded for other disaster and information management. This project fosters collaboration between the Florida International University (FIU) and the University of Tokyo, as well as institutions across the public and private sectors (including the cities of Miami-Dade, Florida, and Tokyo, Japan), to develop advanced techniques for effective emergency response and management for COVID-19, future pandemic, and compound disasters. This work’s broader impact is aligned with the national goal of building smart and connected communities by developing innovative disaster information exchange and analysis tools with real-life data. In addition, FIU is one of the nation’s leading minority-serving research universities and ranks first in awarding undergraduate and graduate degrees to Hispanic students. The research findings of this project will be disseminated through workshops, publications, and presentations.This project is a joint collaboration between the National Science Foundation and the Japan Science and Technology Agency.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.

COVID-19 大流行在美国和全球造成大量确诊病例和死亡，由于其高传染性，隔离和隔离等政策对公民的生活、健康和经济造成了严重影响。采取封锁措施是为了减缓病毒的快速传播，一些主要挑战是确定弱势社区以提供立即帮助，并确定有效减缓传播的政策，同时将对人民生计、心理健康和经济的不利影响降至最低。该项目旨在开发开发的工具和技术对于有效的灾难管理和大流行病恢复至关重要。鉴于大规模疏散和避难过程可能会导致传染性大流行病病例激增，因此，自然灾害和其他自然灾害的同时发生更具挑战性。该项目将根据我们之前开发的技术，开发新技术，在大流行情况下为应急管理人员提供帮助。自然灾害管理工具。拟议的研究提供潜在的解决方案来解决 COVID-19、未来流行病和复合灾害的关键灾害信息管理挑战，同时利用团队之前的工作。此外，所提出的技术将有助于更好地了解灾害情况，以协助广泛的准备和恢复。该项目的损失还可能产生社会和经济影响，因为它提供了有关流行病和复合灾害的最准确信息，以防止意外事件的发生。该项目促进了之间的协作。佛罗里达国际大学 (FIU) 和东京大学，以及公共和私营部门的机构（包括佛罗里达州迈阿密戴德市和日本东京），开发有效应急响应和管理的先进技术这项工作的广泛影响与通过利用现实数据开发创新的灾害信息交换和分析工具来建设智能和互联社区的国家目标是一致的。全国领先的少数族裔服务研究型大学和在向西班牙裔学生授予本科和研究生学位方面排名第一。该项目的研究成果将通过研讨会、出版物和演示文稿进行传播。该项目是美国国家科学基金会和日本科学技术振兴机构之间的联合合作。该奖项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，NSF 的法定使命被认为值得支持。

项目成果

MepoGNN: Metapopulation Epidemic Forecasting with Graph Neural Networks

• DOI: 10.1007/978-3-031-26422-1_28
• 发表时间: 2023-06
• 作者: Qi Cao;Renhe Jiang;Chuang Yang;Z. Fan;Xuan Song;R. Shibasaki

Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster

• DOI: 10.1145/3543507.3583991
• 发表时间: 2023-04
• 期刊: Proceedings of the ACM Web Conference 2023
• 作者: Renhe Jiang;Zhaonan Wang;Yudong Tao;Chuang Yang;Xuan Song;R. Shibasaki;Shu‐Ching Chen;Mei-Ling Shyu-Mei-L

Event-Aware Multimodal Mobility Nowcasting

• DOI: 10.1609/aaai.v36i4.20342
• 发表时间: 2021-12
• 期刊: ArXiv
• 作者: Zhaonan Wang;Renhe Jiang;Hao Xue;Flora D. Salim;Xuan Song;R. Shibasaki

Multi-Source Weak Supervision Fusion for Disaster Scene Recognition in Videos

• DOI: 10.1109/mipr54900.2022.00058
• 发表时间: 2022-08
• 期刊: 2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR)
• 作者: Maria Presa-Reyes;Yudong Tao;Rui Ma;Shu‐Ching Chen;Mei-Ling Shyu

Deep Learning With Weak Supervision for Disaster Scene Description in Low-Altitude Imagery

• DOI: 10.1109/tgrs.2021.3129443
• 发表时间: 2022-01-01
• 期刊: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
• 影响因子: 8.2
• 作者: Presa-Reyes, Maria;Tao, Yudong;Shyu, Mei-Ling
• 通讯作者: Shyu, Mei-Ling