SCC-IRG JST: Multimodal Data Analytics and Integration for Effective COVID-19, Pandemics and Compound Disaster Response and Management
SCC-IRG JST: Multimodal Data Analytics and Integration for Effective COVID-19, Pandemics and Compound Disaster Response and Management
批准号:
2125165
负责人:
Shu-Ching Chen
金额:
$75.0万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-10-01 至 2023-03-31
中文摘要
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英文摘要
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.
期刊论文(6)
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DOI:
10.1007/978-3-031-26422-1_28
发表时间:
2023-06
期刊:
影响因子:
--
作者:
[Qi Cao;Renhe Jiang;Chuang Yang;Z. Fan;Xuan Song;R. Shibasaki]
通讯作者:
Qi Cao;Renhe Jiang;Chuang Yang;Z. Fan;Xuan Song;R. Shibasaki
DOI:
10.1145/3543507.3583991
发表时间:
2023-04
期刊:
Proceedings of the ACM Web Conference 2023
影响因子:
--
作者:
[Renhe Jiang;Zhaonan Wang;Yudong Tao;Chuang Yang;Xuan Song;R. Shibasaki;Shu‐Ching Chen;Mei-Ling Shyu-Mei-L]
通讯作者:
Renhe Jiang;Zhaonan Wang;Yudong Tao;Chuang Yang;Xuan Song;R. Shibasaki;Shu‐Ching Chen;Mei-Ling Shyu-Mei-L
DOI:
10.1609/aaai.v36i4.20342
发表时间:
2021-12
期刊:
ArXiv
影响因子:
--
作者:
[Zhaonan Wang;Renhe Jiang;Hao Xue;Flora D. Salim;Xuan Song;R. Shibasaki]
通讯作者:
Zhaonan Wang;Renhe Jiang;Hao Xue;Flora D. Salim;Xuan Song;R. Shibasaki
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)
影响因子:
--
作者:
[Maria Presa-Reyes;Yudong Tao;Rui Ma;Shu‐Ching Chen;Mei-Ling Shyu]
通讯作者:
Maria Presa-Reyes;Yudong Tao;Rui Ma;Shu‐Ching Chen;Mei-Ling Shyu
DOI:
10.1145/3542818
发表时间:
2023-07-01
期刊:
ACM COMPUTING SURVEYS
影响因子:
16.6
作者:
[Tao,Yudong, Yang,Chuang, Luis,Steven]
通讯作者:
Luis,Steven
共 6 条
SCC-IRG JST: Multimodal Data Analytics and Integration for Effective COVID-19, Pandemics and Compound Disaster Response and Management
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批准号:2301552
-
项目类别:Continuing Grant
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资助金额:$75.0万
-
财政年份:2022
-
负责人:Shu-Ching Chen
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依托单位:
Island Population Responses to Environmental Stresses
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批准号:2131647
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项目类别:Standard Grant
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资助金额:$3.5万
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财政年份:2021
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负责人:Shu-Ching Chen
-
依托单位:
SCC-PG: JST: Multimodal Data Analytics and Integration for Emergency Response and Disaster Management
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批准号:1952089
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项目类别:Standard Grant
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资助金额:$7.5万
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财政年份:2020
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负责人:Shu-Ching Chen
-
依托单位:
Conference: Puerto Rico Honey Bee and Evolution of Invasive Organisms on Islands; August 13-15, 2019; San Juan, Puerto Rico
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批准号:1940621
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项目类别:Standard Grant
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资助金额:$5.0万
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财政年份:2019
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负责人:Shu-Ching Chen
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依托单位:
BDD: Data-Driven Critical Information Exchange in Disaster Affected Public-Private Networks
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批准号:1461926
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项目类别:Standard Grant
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资助金额:$30.0万
-
财政年份:2015
-
负责人:Shu-Ching Chen
-
依托单位:
国内基金
海外基金
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