PIPP Phase I: Robust Epidemic Surveillance and Modeling (RESUME)

PIPP 第一阶段：强大的流行病监测和建模（RESUME）

• 批准号: 2200234
• 负责人: Jonathan Ozik
• 金额: $ 100万
• 依托单位: University of Chicago
• 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: 2022-08-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2200234&HistoricalAwards=false
• 关键词: PIPP; Phase; Robust; Epidemic; Surveillance

The COVID-19 pandemic has shown how epidemiologic modeling can inform decision making in times of crisis and uncertainty. It has also highlighted significant gaps that must be addressed to create ongoing, interdisciplinary collaborations that can provide more effective predictive intelligence for pandemic prevention (PIPP). The Robust Epidemic Surveillance and Modeling (RESUME) team has experience supporting public health stakeholders during the COVID-19 pandemic and drawing on that has identified critical gaps in three broad areas: 1) communication and collaboration among researchers and public health stakeholders, 2) integration of diverse data streams including surveillance data, and 3) foundational work to predict future pathogens and their evolution. To address these three gaps, the project brings together an interdisciplinary team with expertise in epidemiologic modeling, public health, policy and risk analysis, social sciences, decision modeling, artificial intelligence (AI), high-performance computing (HPC), molecular engineering, structural biology, and large-scale data management and assimilation. The investigators will engage modelers and public health stakeholders to broaden participation in collaborative modeling, carry out pilot projects, and develop training modules for generalizable approaches to collaborative pandemic intelligence. The project will develop curricula for pandemic prevention education and workforce development. The training activities and new curricula will help build a convergent and inclusive PIPP capacity by bringing a diverse workforce into the research pipeline from high school through graduate school and enhancing the expertise of the public health workforce.The project will convene workshops with stakeholders and researchers and carry out pilot studies to refine the vision for an interdisciplinary PIPP center. Activities will advance the science and practice in the following three foci:1) Co-design of policy, implementation, and risk analyses: Early and sustained engagement with public health stakeholders for the co-design of pandemic prevention requirements; Development and application of methods for decision making under deep uncertainty, value of information, and adaptive interventions; Development of novel computational approaches exploiting advances in AI, data management, and HPC methods for creating integrated multi-fidelity, multi-method, and multi-spatiotemporal scale modeling analyses.2) Robust data for modeling: Development of novel real-time sensors and near real-time data streams from sensor-based air, wastewater, and human monitoring (including for novel pathogens); Integration of sensor, public health surveillance and clinical data through model-based, data assimilation approaches for combining data streams and epidemiological model forecasts; Creation of large-scale open-science data storage and indexing capabilities for epidemiologic modelers.3) Prediction of future pathogens: Fundamental research in experimental and theoretical pathogen structure and evolution; Scenario development for epidemiological and decision support modeling of emerging pathogens.To support these foci, the project will demonstrate a sustainable simulation, data, decision support, and learning collaborative platform, the Open Science Platform for Robust Epidemic analYsis (OSPREY). The OSPREY platform will serve as crucial PIPP cyberinfrastructure built to leverage investments in forthcoming exascale and increasingly ubiquitous HPC and data resources. 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), Engineering (ENG) and Social, Behavioral and Economic Sciences (SBE).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.

2019冠状病毒病大流行表明，流行病学建模如何在危机和不确定的情况下为决策提供信息。它还强调了必须解决的重大差距，以建立持续的跨学科合作，为流行病预防提供更有效的预测情报（PIPP）。强大的流行病监测和建模（RESUME）团队在COVID-19大流行期间拥有支持公共卫生利益相关者的经验，并利用这一经验确定了三大领域的关键差距：1）研究人员和公共卫生利益相关者之间的沟通和协作，2）整合包括监测数据在内的各种数据流，以及3）预测未来病原体及其演变的基础工作。为了解决这三个差距，该项目汇集了一个跨学科团队，他们在流行病学建模，公共卫生，政策和风险分析，社会科学，决策建模，人工智能（AI），高性能计算（HPC），分子工程，结构生物学以及大规模数据管理和同化方面具有专业知识。研究人员将让建模者和公共卫生利益相关者参与进来，以扩大协作建模的参与，开展试点项目，并为协作流行病情报的可推广方法开发培训模块。该项目将为大流行病预防教育和劳动力发展编制课程。培训活动和新的课程将有助于建立一个融合和包容的PIPP能力，将多样化的劳动力纳入从高中到研究生院的研究管道，并提高公共卫生劳动力的专业知识。该项目将与利益相关者和研究人员召开研讨会，并开展试点研究，以完善跨学科PIPP中心的愿景。活动将在以下三个方面推进科学和实践：1）政策、实施和风险分析的共同设计：早期和持续地与公共卫生利益相关者合作，共同设计流行病预防要求;开发和应用在深度不确定性、信息价值和适应性干预措施下的决策方法;开发新的计算方法，利用人工智能、数据管理和HPC方法的进步，创建集成的多保真度、多方法和多时空尺度建模分析。2）用于建模的强大数据：开发新型实时传感器和基于传感器的空气、废水和人体监测的近实时数据流通过基于模型的数据同化方法，将传感器、公共卫生监测和临床数据结合起来，将数据流和流行病学模型预测结合起来;为流行病学模型创建大规模开放科学数据存储和索引能力。3）预测未来病原体：实验和理论病原体结构和进化的基础研究;为支持这些重点，该项目将展示一个可持续的模拟，数据，决策支持和学习协作平台，即强大的流行病分析开放科学平台（OSPREY）。OSPREY平台将作为关键的PIPP网络基础设施，旨在利用对即将到来的亿级和日益普遍的HPC和数据资源的投资。 该奖项得到了跨部门的大流行预防阶段预测情报（PIPP）计划的支持，该计划由生物科学（BIO），计算机信息科学与工程（CISE），工程（ENG）和社会部门共同资助。行为与经济科学（SBE）该奖项反映了NSF的法定使命，并通过使用基金会的知识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

Trajectory-Oriented Optimization of Stochastic Epidemiological Models

随机流行病学模型的轨迹导向优化

• DOI: 10.1109/wsc60868.2023.10408258
• 发表时间: 2023
• 期刊: IEEE
• 作者: Fadikar, Arindam;Collier, Nicholson;Stevens, Abby;Ozik, Jonathan;Binois, Mickaël;Toh, Kok Ben
• 通讯作者: Toh, Kok Ben

PSI/J: A Portable Interface for Submitting, Monitoring, and Managing Jobs

PSI/J：用于提交、监控和管理作业的便携式界面

• DOI: 10.1109/e-science58273.2023.10254912
• 发表时间: 2023
• 期刊: IEEE
• 作者: Hategan-Marandiuc, Mihael;Merzky, Andre;Collier, Nicholson;Maheshwari, Ketan;Ozik, Jonathan;Turilli, Matteo;Wilke, Andreas;Wozniak, Justin M.;Chard, Kyle;Foster, Ian
• 通讯作者: Foster, Ian

Developing Distributed High-performance Computing Capabilities of an Open Science Platform for Robust Epidemic Analysis

开发开放科学平台的分布式高性能计算能力以进行稳健的流行病分析

• DOI: 10.1109/ipdpsw59300.2023.00143
• 发表时间: 2023
• 期刊: 2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW
• 作者: Collier, Nicholson;Wozniak, Justin M.;Stevens, Abby;Babuji, Yadu;Binois, Mickaël;Fadikar, Arindam;Würth, Alexandra;Chard, Kyle;Ozik, Jonathan
• 通讯作者: Ozik, Jonathan