PIPP Phase I: An End-to-End Pandemic Early Warning System by Harnessing Open-source Intelligence
PIPP Phase I: An End-to-End Pandemic Early Warning System by Harnessing Open-source Intelligence
批准号:
2200274
负责人:
Wei Wang
金额:
$99.6万
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
项目状态:
未结题
起止时间:
2022-09-01 至 2025-02-28
中文摘要
点击翻译按钮获取中文摘要
英文摘要
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.
期刊论文(2)
专著(0)
科研奖励(0)
会议论文
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:
Proc. VLDB Endow.
影响因子:
--
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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依托单位:
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