PIPP Phase I: Computational Foundations for Bio-social Modeling of Unseen Pandemics

PIPP 第一阶段：看不见的流行病生物社会建模的计算基础

• 批准号: 2200161
• 负责人: Pavan Turaga
• 金额: $ 89.75万
• 依托单位: Arizona State University
• 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: 2022-09-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2200161&HistoricalAwards=false
• 关键词: PIPP; Phase; Computational; Foundations; Bio

Pandemics unfold in a social, behavioral, and decision-making context that alters the geospatial patterns of spread, depending on an existing underlying landscape of risk and adaptive behavior. Layering socioeconomic factors into traditional predictive modeling frameworks is not sufficient to understand this complexity, nor does it account for the dynamics and vicissitudes of human behavior and free-will. Unexpected human behaviors play a major role, as well as broader factors such as vaccine availability, seasonal effects from human contact patterns, viral environmental persistence, and federal and state-level policy changes around masking and business closures. Enumerating a finite list of factors that should form the basis of a predictive model itself seems like a grand challenge. This project will advance modeling as a continuous, iterative, and dynamic component of pandemic response, where incremental predictions are far more robust, and approaches that innately allow for complexity, adaptation, and surprise can be expected to be operationally useful.Pandemic prevention for unseen pandemics requires several interconnected efforts across immunology, mechanistic modeling, data-driven modeling, and understanding sociopolitical contexts of decision making. Technical aspects of the project include machine learning based tools for predicting immune response from pathogen mutations, switching dynamical systems based models of time-series for fast adaptation, adaptive population sampling techniques, and model predictive control methods for designing behavioral interventions. The project will develop integrative protocols and frameworks that a) leverage techniques for using binding patterns of pathogens for never-before-seen viruses and advances in wastewater-based epidemiology, b) understand the variation in performance of predictive models over geospatial scales using regularizing models, c) design effective interventions under resource constraints, and d) understand their impact on policy making. 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.

大流行是在社会、行为和决策背景下展开的，这取决于现有的潜在风险和适应性行为，改变了传播的地理空间模式。将社会经济因素分层到传统的预测建模框架中不足以理解这种复杂性，也不能解释人类行为和自由意志的动态和变迁。意外的人类行为以及更广泛的因素，如疫苗可得性、人类接触模式的季节性影响、病毒环境持久性以及联邦和州一级围绕掩蔽和企业关闭的政策变化，都起着重要作用。列举一个有限的因素列表，这些因素应该构成预测模型本身的基础，这似乎是一个巨大的挑战。该项目将推进建模，使其成为大流行应对的一个连续、迭代和动态组成部分，其中增量预测要稳健得多，而且可以预期，天生允许复杂性、适应性和突然性的方法在操作上是有用的。预防看不见的大流行需要在免疫学、机制建模、数据驱动建模和理解决策的社会政治背景方面做出若干相互关联的努力。该项目的技术方面包括基于机器学习的工具，用于预测病原体突变的免疫反应，基于切换动态系统的时间序列模型，用于快速适应，适应性群体抽样技术，以及用于设计行为干预的模型预测控制方法。该项目将制定综合方案和框架，a)利用利用病原体结合模式的技术来应对从未见过的病毒，并在基于废水的流行病学方面取得进展；b)利用正则化模型了解预测模型在地理空间尺度上的表现差异；c)在资源限制下设计有效的干预措施，以及d)了解其对政策制定的影响。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）、社会、行为和经济科学（SBE）和工程（ENG）委员会联合资助。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。

项目成果

Differential Analysis for Networks Obeying Conservation Laws

• DOI: 10.1109/icassp49357.2023.10094876
• 发表时间: 2023-01
• 期刊: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Anirudh Rayas;Rajasekhar Anguluri;Jiajun Cheng;Gautam Dasarathy

Constrained Adaptive Distillation Based on Topological Persistence for Wearable Sensor Data

• DOI: 10.1109/tim.2023.3329818
• 发表时间: 2023
• 期刊: IEEE Transactions on Instrumentation and Measurement
• 影响因子: 5.6
• 作者: Eunyeong Jeon;Hongjun Choi;Ankita Shukla;Yuan Wang;M. Buman;Pavan Turaga

Class GP: Gaussian Process Modeling for Heterogeneous Functions

GP 类：异质函数的高斯过程建模

• 发表时间: 2023
• 期刊: LION 17
• 作者: Malu, M.;Pedrielli, G.;Dasarathy, G.;Spanias, A.
• 通讯作者: Spanias, A.

Nonparanormal graph quilting with applications to calcium imaging

• DOI: 10.1002/sta4.623
• 发表时间: 2023-01-01
• 期刊: STAT
• 影响因子: 1.7
• 作者: Chang，Andersen;Zheng，Lili;Allen，Genevera I.
• 通讯作者: Allen，Genevera I.