PIPP Phase I: Predicting Emergence in Multidisciplinary Pandemic Tipping-points (PREEMPT)

PIPP 第一阶段：预测多学科流行病临界点的出现 (PREEMPT)

• 批准号: 2200140
• 负责人: Nina Fefferman
• 金额: $ 99.98万
• 依托单位: University of Tennessee Knoxville
• 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=2200140&HistoricalAwards=false
• 关键词: PIPP; Phase; Predicting; Emergence; Multidisciplinary

Pandemics arise from the confluence of many contributing factors. These factors may be individually inconsequential but become critical when acting together, and a complex set of seemingly unrelated factors can result in a perfect storm for pandemic emergence. Yet prevailing approaches to predicting pandemic emergence remain focused on disciplinary investigations of individual or subsets of factors. Preparing for and preventing the next pandemic will require multidisciplinary approaches that leverage knowledge of complex interdependencies across scales from molecular to social and from individual diagnosis to global surveillance. This project assembles a multi-disciplinary team of scientists, representing expertise spanning the gamut from basic biology, to social, behavioral, and economic sciences, to engineering, computer, and information sciences, to focus on understanding how to identify, recognize, and predict when emerging disease threats create a perfect storm of factors that cause an otherwise localized outbreak to “tip over” into a pandemic. The project team will work together to leverage their collective diversity of expertise, experience, and perspective to innovate a collaborative framework for knitting together disciplinary pursuits into a complete, multifaceted, and predictive understanding of pandemic tipping points. Going beyond the confines of this project, the resulting framework will serve as a blueprint for all institutions dedicated to the discovery and analysis of complex linkages and thus will improve capacity to predict and prevent coming pandemics and other emergent threats to the modern world. The fact that pandemic tipping points are multifactorial makes their study fundamentally more challenging than system- or discipline-specific tipping points. The project will develop a blueprint for an institution dedicated to advancing understanding and analysis of systems with dynamics that require interrogation by multiple disciplines. The driving hypothesis for the institute is that the greatest barriers to multidisciplinary insights exist when disciplinary researchers fail to converge on shared intuition for the value other fields could provide in addressing complex research questions. The framework directly addresses this challenge by employing a Give-Take methodology: to investigate multidisciplinary research hypotheses, project teams of researchers will assemble via a bidirectional process. Researchers will identify (a – “giving”) hypotheses from other fields their own discipline could meaningfully impact and (b – “taking”) disciplines from which they anticipate useful input for their own hypotheses, and teams will include both directions of identification. This innovative framework improves the capacity for researchers in disparate fields to better recognize their interdependence and eliminates the need for researchers to understand other fields before benefiting from or contributing to investigations. The research will apply these methodologies to the complex challenge of multidisciplinary tipping points in the form of a set of case studies that will directly support our ability to address many of the complex and interconnected challenges in pandemic preparedness and response.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.

大流行源于许多促成因素的融合。这些因素可能是单独无关紧要的，但在一起起作用时会变得至关重要，而且一组复杂的看似无关的因素可能会导致大流行紧急情况的完美风暴。然而，预测大流行紧急情况的主要方法仍集中在对因素或子集的纪律研究上。为下一个大流行做准备和预防将需要多学科的方法，以利用从分子到社会以及从个人诊断到全球监视的范围的复杂相互依赖的知识。该项目组成了一个多学科的科学家团队，代表了从基本生物学到社会，行为和经济科学的专业知识，再到工程，计算机和信息科学，专注于理解如何识别，识别和预测何时出现的疾病威胁何时会导致何时造成何时造成本地局部化的因素，从而导致“小费过时”，这是“ pervip off to to to to to a perfection to a Perdepic”。该项目团队将共同努力，利用他们的集体专业知识，经验和观点多样性，以创新的协作框架，将纪律追求共同编织成对大流行点的完整，多方面和预测性的理解。超越了该项目的范围，由此产生的框架将成为所有致力于发现和分析复杂联系的机构的蓝图，因此将提高预测和防止对现代世界的Pandemics和其他新兴威胁的能力。大流行点是多因素的事实使他们的研究在根本上比系统或纪律特定的临界点更具挑战性。该项目将为一个致力于通过动态进行理解和分析的机构开发蓝图，这些动态需要多个学科的审讯。该研究所的假设是，当纪律研究人员未能融合其他领域的共同直觉时，其他领域的共同直觉可能会为解决复杂的研究问题提供时，存在最大的多学科见解障碍。该框架通过采用给予方法来直接解决这一挑战：为了调查多学科研究假设，研究人员的项目团队将通过双向过程组装。研究人员将确定（a - “给予”）来自其他领域的假设，他们自己的学科可能意味着完全影响，并且（b - “接受”）学科，他们期望从中为自己的假设提供有用的投入，并且团队将包括两个识别方向。这种创新的框架提高了不同领域的研究人员更好地认识其相互依存关系的能力，并消除了研究人员在受益或贡献研究之前了解其他领域的需求。这项研究将以一组案例研究的形式将这些方法应用于多学科介绍点的复杂挑战，这些案例研究将直接支持我们解决大流行准备和回应中许多复杂和相互联系的挑战的能力。这项奖项由跨导向预测智能的Pandemic Precection Itsective for Pandemit Pecortive I（PIPP）计划（PIPP）计划（PIPP），该计划是由I（PIPP）的生物学基础，该计划是由共同资助的（BIO）供验证（Bio）的（BIO）的（BIO）的（BIO）的（BIO）的（BIO）。计算机，信息科学与工程（CISE）；工程（ENG）以及社会，行为和经济科学（SBE）。该奖项反映了NSF的法定使命，并使用基金会的知识分子优点和更广泛的影响评估标准，被视为通过评估来获得珍贵的支持。