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

2019冠状病毒病大流行凸显了人类对传染病暴发的脆弱性，并加强了改进数据驱动的应对和准备工作的必要性。BEHIVE（行为相互作用和病毒进化）研究团队的大流行预防预测智能（PIPP）旨在通过使用计算数据驱动的镜头整合人类行为研究，解决疾病爆发预防方面的基本挑战。人类行为和社会互动的影响在情景制定、预测和流行病缓解方面仍未得到充分利用。拟议的教育和推广活动将培训PIPP的早期职业研究人员和实践者。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个面向公众的领域。一些加速发展的趋势，如扩大数据收集、新的人工智能（AI）和机器学习（ML）技术、高保真计算建模以及最近因COVID-19而激增的社会/行为知识，为利用协同团队科学方法应对将人类行为纳入流行病应对的挑战创造了机会。该项目将开发结合行为反馈的方法，以架起机械模型和人工智能模型的桥梁，通过基因组监测和稳健预测提供所需的特异性和背景，并通过建立战略组合来增强协调决策的能力。这些努力将导致新的人工智能/机器学习框架、新的建模/决策范例、促进预警系统并为缓解工作提供信息，以首先预防大流行和降低爆发风险。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，以及医学和公共卫生专家（来自佐治亚理工学院、麻省理工学院、密歇根大学、佐治亚大学和梅奥诊所），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2019冠状病毒病大流行期间的基础研究和以干预为重点的工作。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。该奖项由流行病预防阶段的跨部门预测情报（PIPP）计划提供支持，该计划由生物科学理事会（BIO）联合资助；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）。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 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.