PIPP Phase I: An End-to-End Pandemic Early Warning System by Harnessing Open-source Intelligence

PIPP 第一阶段:利用开源情报的端到端流行病预警系统

基本信息

  • 批准号:
    2200274
  • 负责人:
  • 金额:
    $ 99.6万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-09-01 至 2025-02-28
  • 项目状态:
    未结题

项目摘要

The ongoing global COVID-19 outbreak highlights the need to prepare for pandemics. Early detection, and, to the extent possible, prediction, are the key. While it is crucial to get as much early warning as possible in advance of the planet’s next pandemic, the odds are overwhelmingly against the next global outbreak being an exact repeat of the current COVID-19 crisis. Open source intelligence (OSINT) is vital to the provision of pandemic early warning. Diseases, especially infectious diseases, are socio-biological phenomena and leave both social and microbiological footprints. This Predictive Intelligence for Pandemic Prevention (PIPP) Phase I: Development Grants project will explore the feasibility of developing an open-source intelligence system, informed by biological, environmental, socio-economical, behavioral, and media data from diverse sources, to monitor human society for signs of unusual activities that reflect the emergence of novel pathogens with pandemic potential. This multidisciplinary research will bridge biology, social sciences, epidemiology, and computer science to address this grand challenge and start construction of a semi-autonomous system to give an early warning of the next pandemic.The PIPP Phase I project will develop a prototype of an end-to-end pandemic-early-warning system powered by artificial intelligence (AI), machine learning, data science, and open-source technologies, which can simultaneously look for signs of an emerging infectious disease or a known disease, predict its spread, and detect and monitor risk factors over space and time. The system will embrace human intelligence as an integrated component and will be transferable and extensible to support additional data sources and machine learning models, making it suitable for detecting and predicting outbreaks of a new disease regardless of its nature and scale. The Phase I activities include four synergistic pilot projects highlighting innovative approaches to addressing the grand challenges, as well as a detailed plan to develop communities and capacity of a full center.This award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Social, Behavioral and Economic Sciences (SBE) and Engineering (ENG).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.
持续的全球Covid-19爆发凸显了为大流浪者做准备的必要性。早期检测,并且在可能的情况下预测是关键。尽管在地球下一次大流行之前,要尽可能多的早期警告至关重要,但与下一次全球爆发的赔率相对于当前的Covid-19-19危机的重复是绝大多数。开源智能(OSINT)对于提供大流行预警至关重要。疾病,尤其是传染病,是社会生物学现象,并留下社会和微生物足迹。这种大流行预防(PIPP)的预测智力I阶段:发展赠款项目将探索开发开源智能系统的可行性,该智力系统由生物学,环境,社会经济,社会经济,行为,行为和媒体数据所告知,以监控人类社会对人类社会的不寻常活动的迹象,这些活动反映了与pandecementipection的新型病原体出现的迹象。这项多学科研究将桥接生物学,社会科学,流行病学和计算机科学,以应对这一巨大的挑战,并开始构建半自治系统,以对下一个大流行病发出预警。PIPPI期项目将开发一个端到端到的端到端到的流行式锻造系统的原型。在紧急感染或已知疾病中,预测其扩散,并在时空上检测和监测风险因素。该系统将采用人类智能作为综合组成部分,并将被转移并广泛以支持其他数据源和机器学习模型,使其适合于发现和预测新疾病的爆发,无论其性质和规模如何。 The Phase I activities include four synergistic pilot projects highlighting innovative approaches to addressing the grand challenges, as well as a detailed plan to develop communities and capacity of a full center.This award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Social, Behavioral and Economic科学(SBE)和工程(ENG)。该奖项反映了NSF的法定任务,并通过使用基金会的知识分子优点和更广泛的影响评估标准来评估诚实地支持了支持。

项目成果

期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Fries: Fast and Consistent Runtime Reconfiguration in Dataflow Systems with Transactional Guarantees
  • DOI:
    10.14778/3565816.3565827
  • 发表时间:
    2022-10
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Zuozhi Wang;Shengquan Ni;Avinash Kumar;Chen Li-
  • 通讯作者:
    Zuozhi Wang;Shengquan Ni;Avinash Kumar;Chen Li-
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Wei Wang其他文献

Synergistic antitumor efficacy of combined DNA vaccines targeting tumor cells and angiogenesis.
针对肿瘤细胞和血管生成的联合 DNA 疫苗的协同抗肿瘤功效。
Design and test of a 10 kV HV brushing for triaxial HTS cable termination
三轴高温超导电缆终端 10 kV 高压电刷的设计与测试
ARIMA Forecasting Chinese Macroeconomic Variables Based on Factor and Principal Component Backdating
基于因子和主成分回溯的 ARIMA 预测中国宏观经济变量
  • DOI:
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Wei Wang;Yan Liu
  • 通讯作者:
    Yan Liu
[Visual search in Alzheimer disease--an functional magnetic resonance imaging study].
[阿尔茨海默病的视觉搜索——一项功能性磁共振成像研究]。
  • DOI:
  • 发表时间:
    2005
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Jing Hao;Kun Li;Wei Wang;Yan;Ke Li;Bin Yan;D. Zhan
  • 通讯作者:
    D. Zhan
Design and Autonomous Co ntrol of 12-Rotor Type Flying Robot
12旋翼式飞行机器人设计与自主控制
  • DOI:
  • 发表时间:
    2013
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Yuze Song;Daisuke Iwakura;Wei Wang;Kenzo Nonami
  • 通讯作者:
    Kenzo Nonami

Wei Wang的其他文献

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{{ truncateString('Wei Wang', 18)}}的其他基金

CAREER: Harnessing the Interplay of Morphology, Viscoelasticity, and Surface-Active Agents to Modulate Soft Wetting
职业:利用形态、粘弹性和表面活性剂的相互作用来调节软润湿
  • 批准号:
    2336504
  • 财政年份:
    2024
  • 资助金额:
    $ 99.6万
  • 项目类别:
    Continuing Grant
An Educational Tool for Teaching and Learning Concurrent Computer Programming Techniques
用于教授和学习并行计算机编程技术的教育工具
  • 批准号:
    2215359
  • 财政年份:
    2022
  • 资助金额:
    $ 99.6万
  • 项目类别:
    Standard Grant
Collaborative Research: SHF: Small: Exploiting Performance Correlations for Accurate and Low-cost Performance Testing for Serverless Computing
协作研究:SHF:小型:利用性能相关性对无服务器计算进行准确且低成本的性能测试
  • 批准号:
    2155096
  • 财政年份:
    2022
  • 资助金额:
    $ 99.6万
  • 项目类别:
    Standard Grant
Collaborative Research: EAGER: Enhancing Security and Privacy of Augmented Reality Mobile Applications through Software Behavior Analysis
合作研究:EAGER:通过软件行为分析增强增强现实移动应用程序的安全性和隐私性
  • 批准号:
    2221843
  • 财政年份:
    2022
  • 资助金额:
    $ 99.6万
  • 项目类别:
    Standard Grant
Enhancing Programming and Machine Learning Education for Students with Visual Impairments through the Use of Compilers, AI and Cloud Technologies
通过使用编译器、人工智能和云技术加强对视力障碍学生的编程和机器学习教育
  • 批准号:
    2202632
  • 财政年份:
    2022
  • 资助金额:
    $ 99.6万
  • 项目类别:
    Standard Grant
Collaborative Research: A Bioinspired Approach towards Sustainable Membranes for Resilient Brine Treatment
合作研究:用于弹性盐水处理的可持续膜的仿生方法
  • 批准号:
    2226501
  • 财政年份:
    2022
  • 资助金额:
    $ 99.6万
  • 项目类别:
    Standard Grant
III: Medium: Collaborative Research: Collaborative Machine-Learning-Centric Data Analytics at Scale
III:媒介:协作研究:以机器学习为中心的大规模协作数据分析
  • 批准号:
    2106859
  • 财政年份:
    2021
  • 资助金额:
    $ 99.6万
  • 项目类别:
    Continuing Grant
RAPID: Dynamic Graph Neural Networks for Modeling and Monitoring COVID-19 Pandemic
RAPID:用于建模和监测 COVID-19 大流行的动态图神经网络
  • 批准号:
    2031187
  • 财政年份:
    2020
  • 资助金额:
    $ 99.6万
  • 项目类别:
    Standard Grant
Collaborative Research; RUI: Non-Orthogonal Multiple Access Pricing for Wireless Multimedia Communications
合作研究;
  • 批准号:
    2010284
  • 财政年份:
    2020
  • 资助金额:
    $ 99.6万
  • 项目类别:
    Standard Grant
SusChEM: Direct functionalization of aldehydes enabled by aminocatalysis
SusChEM:通过氨基催化实现醛的直接官能化
  • 批准号:
    1903983
  • 财政年份:
    2019
  • 资助金额:
    $ 99.6万
  • 项目类别:
    Continuing Grant

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