PIPP Phase I: BEHIVE - BEHavioral Interaction and Viral Evolution for Pandemic Prevention and Prediction

PIPP 第一阶段：BEHIVE - 用于流行病预防和预测的行为相互作用和病毒进化

• 批准号: 2200269
• 负责人: B Aditya Prakash
• 金额: $ 100万
• 依托单位: Georgia Tech Research Corporation
• 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=2200269&HistoricalAwards=false
• 关键词: PIPP; Phase; BEHIVE; BEHavioral; Interaction

The COVID-19 pandemic has highlighted human vulnerability to infectious disease outbreaks and reinforced the need for improved data-driven response and preparedness. The BEHIVE (BEHavioral Interaction and Viral Evolution) research team for Predictive Intelligence for Pandemic Prevention (PIPP) aims to tackle a fundamental challenge in disease outbreak prevention by integrating the study of human behavior using a computational data-driven lens. The impact of human behavior and social interactions remain underutilized in efforts spanning scenario development, forecasting, and epidemic mitigation. Proposed educational and outreach activities will train early career researchers and practitioners in PIPP. The trans-disciplinary nature of the project including connections with the humanities will encourage diverse cohorts of researchers to engage in this public-facing field.Several accelerating trends, such as widening data collection, new Artificial Intelligence (AI) and Machine Learning (ML) techniques, high fidelity computational modeling and recent surge in social/behavioral knowledge due to COVID-19, create an opportunity to tackle the challenge of integrating human behavior into epidemic response using a synergistic team-science approach. Project will develop methods incorporating behavioral feedback to bridge mechanistic and AI models, to provide specificity and context needed via genomic surveillance and robust predictions, and to empower coordinated decision making by building strategic portfolios. These efforts will lead to novel AI/ML frameworks, new modeling/decision-making paradigms, facilitate early warning systems and inform mitigation efforts to prevent pandemics and reduce risk of outbreaks in the first place. The diverse interdisciplinary team consists of computer scientists, biologists, engineers, and behavioral scientists, along with experts in medicine and public health (from Georgia Tech, MIT, Michigan, UGA and Mayo Clinic) with broad and significant expertise in epidemic research spanning foundational research and intervention-focused work during the COVID-19 pandemic. The project team will also partner with multiple academic, industrial, government public health and non-profit setups, which will help enlarge the impact of the proposed research. 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冠状病毒病大流行凸显了人类对传染病爆发的脆弱性，并加强了改善数据驱动的应对和准备的必要性。BEHIVE（行为互动和病毒进化）大流行预防预测智能（PIPP）研究团队旨在通过使用计算数据驱动的透镜整合人类行为研究来应对疾病爆发预防的根本挑战。人类行为和社会互动的影响在情景开发、预测和流行病缓解方面仍然没有得到充分利用。拟议的教育和外联活动将培训PIPP的早期职业研究人员和从业人员。该项目的跨学科性质，包括与人文学科的联系，将鼓励不同的研究人员群体参与这一面向公众的领域。几个加速发展的趋势，如扩大数据收集，新的人工智能（AI）和机器学习（ML）技术，高保真计算建模和最近因COVID-19而激增的社会/行为知识，创造机会，利用协同的团队科学方法，应对将人类行为纳入流行病应对工作的挑战。该项目将开发结合行为反馈的方法，以连接机械模型和人工智能模型，通过基因组监测和强大的预测提供所需的特异性和背景，并通过构建战略组合来增强协调决策的能力。这些努力将导致新的AI/ML框架，新的建模/决策范式，促进早期预警系统，并为缓解工作提供信息，以预防流行病并首先降低爆发风险。多元化的跨学科团队由计算机科学家，生物学家，工程师和行为科学家组成，沿着医学和公共卫生专家（来自格鲁吉亚理工学院，麻省理工学院，密歇根大学，佐治亚大学和马约诊所），他们在流行病研究方面拥有广泛而重要的专业知识，涵盖了COVID-19大流行期间的基础研究和以干预为重点的工作。该项目团队还将与多个学术，工业，政府公共卫生和非营利机构合作，这将有助于扩大拟议研究的影响。该奖项由跨部门的大流行预防阶段预测情报（PIPP）计划支持，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

Autoregressive Diffusion Model for Graph Generation

• DOI: 10.48550/arxiv.2307.08849
• 发表时间: 2023-07
• 作者: Lingkai Kong;Jiaming Cui;Haotian Sun;Yuchen Zhuang;B. Prakash;Chao Zhang

Differentiable Agent-based Epidemiology

基于可微分代理的流行病学

• 发表时间: 2023
• 期刊: AAMAS Conference proceedings
• 作者: A. Chopra, A. Rodriguez

Modelling Healthcare Associated Infections with Hypergraphs.

使用超图对医疗保健相关感染进行建模。

• 发表时间: 2023
• 期刊: ACM SIGKDD Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK
• 作者: Vivek Anand;B. Aditya Prakash
• 通讯作者: B. Aditya Prakash

Detecting Sources of Healthcare Associated Infections

• DOI: 10.1609/aaai.v37i4.25554
• 发表时间: 2023-06
• 作者: Hankyu Jang;Andrew Fu;Jiaming Cui;M. Kamruzzaman;B. Prakash;A. Vullikanti;B. Adhikari;Sriram V. Pemmaraju

EINNs: Epidemiologically-Informed Neural Networks

• DOI: 10.1609/aaai.v37i12.26690
• 发表时间: 2022-02
• 作者: Alexander Rodr'iguez;Jiaming Cui;Naren Ramakrishnan;B. Adhikari;B. Prakash