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

• 批准号: 2200274
• 负责人: Wei Wang
• 金额: $ 99.6万
• 依托单位: University of California-Los Angeles
• 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
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2200274&HistoricalAwards=false
• 关键词: PIPP; Phase; End; Pandemic; Early

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.

正在发生的全球新冠肺炎 (COVID-19) 疫情凸显了为流行病做好准备的必要性，并尽可能进行预测，而在地球下一次流行病爆发之前获得尽可能多的预警至关重要。下一次全球疫情爆发的可能性绝对不会与当前的 COVID-19 危机完全相同。 开源情报 (OSINT) 对于提供大流行早期预警至关重要。疾病，尤其是传染病，是一种社会生物学现象。离开社会和微生物流行病预防预测情报（PIPP）第一阶段：发展赠款项目将探索开发开源情报系统的可行性，该系统根据来自不同来源的生物、环境、社会经济、行为和媒体数据提供信息，以监测人类社会是否有异常活动的迹象，这些迹象反映了具有大流行潜力的新型病原体的出现。这项多学科研究将在生物学、社会科学、流行病学和计算机科学之间架起桥梁，以应对这一重大挑战，并开始构建一个半自治系统，以提供解决方案。一个PIPP第一阶段项目将开发一个由人工智能（AI）、机器学习、数据科学和开源技术驱动的端到端大流行预警系统原型。可以同时寻找新出现的传染病或已知疾病的迹象，预测其传播，并检测和监测空间和时间上的风险因素。该系统将把人类智能作为一个集成组件，并且可转移和扩展以支持额外的数据。来源和机器学习模型，使其适合检测和预测新疾病的爆发，无论其性质和规模如何。第一阶段活动包括四个协同试点项目，强调应对重大挑战的创新方法，以及发展社区和提高整个中心能力的详细计划。该奖项由跨部门的流行病预防第一阶段预测智能 (PIPP) 项目支持，该项目由生物科学 (BIO)、计算机信息科学与工程 (CISE)、社会、行为和经济科学理事会联合资助（SBE）和工程（ENG）。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。

项目成果

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.
• 作者: Zuozhi Wang;Shengquan Ni;Avinash Kumar;Chen Li-