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

2019冠状病毒病大流行表明，流行病学模型可以在危机和不确定时期为决策提供信息。它还突出了必须解决的重大差距，以便建立持续的跨学科合作，为预防大流行病提供更有效的预测情报。强大的流行病监测和建模（RESUME）团队具有在COVID-19大流行期间支持公共卫生利益攸关方的经验，并利用这一经验确定了三个广泛领域的关键差距：1)研究人员和公共卫生利益攸关方之间的沟通与协作，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。调查人员将与建模人员和公共卫生利益攸关方接触，扩大对协作建模的参与，开展试点项目，并为协作大流行情报的可推广方法开发培训模块。该项目将编制大流行病预防教育和劳动力发展课程。培训活动和新课程将通过将从高中到研究生院的多样化工作人员纳入研究管道，并加强公共卫生工作人员的专业知识，帮助建立一种融合和包容的公私合作方案能力。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。活动将在以下三个重点领域推进科学和实践：1)共同设计政策、实施和风险分析：尽早和持续地与公共卫生利益攸关方接触，共同设计大流行预防要求；深度不确定性、信息价值和适应性干预下的决策方法开发与应用；开发新的计算方法，利用人工智能、数据管理和高性能计算方法的进步，创建集成的多保真度、多方法和多时空尺度建模分析。2)稳健的建模数据：开发新型实时传感器和来自基于传感器的空气、废水和人体监测（包括新型病原体）的近实时数据流；通过基于模型的数据同化方法整合传感器、公共卫生监测和临床数据，将数据流与流行病学模型预测相结合；为流行病学建模者创建大规模开放科学数据存储和索引能力。3)病原预测：病原结构与演化的实验与理论基础研究；新出现病原体的流行病学和决策支持建模的情景开发。为了支持这些重点，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，即稳健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，用于利用即将到来的百亿亿级和日益普及的HPC和数据资源的投资。该奖项由流行病预防的跨部门预测情报第一阶段（PIPP）计划提供支持，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、行为和经济科学（SBE）委员会共同资助。该奖项反映了美国国家科学基金会的法定使命，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。

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.