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

大流行病是许多促成因素共同作用的结果。这些因素可能是无关紧要的，但当它们共同作用时，就会变得至关重要，而一系列看似无关的复杂因素可能会导致大流行出现的完美风暴。然而，预测大流行出现的流行方法仍然集中在对个别或部分因素的学科调查上。准备和预防下一次大流行将需要多学科的方法，利用从分子到社会，从个体诊断到全球监测的复杂相互依存关系的知识。该项目汇集了一个多学科的科学家团队，代表了从基础生物学到社会，行为和经济科学，再到工程，计算机和信息科学的专业知识，专注于了解如何识别，识别和预测新兴疾病威胁何时产生一场完美的因素风暴，导致局部爆发“翻转”成大流行。项目团队将共同努力，利用他们的专业知识、经验和观点的集体多样性，创新一个协作框架，将学科追求编织成一个完整的、多方面的和对流行病临界点的预测性理解。超出本项目的范围，由此产生的框架将作为所有致力于发现和分析复杂联系的机构的蓝图，从而提高预测和预防即将发生的流行病和对现代世界的其他紧急威胁的能力。 大流行临界点是多因素的，这一事实使他们的研究从根本上比系统或学科特定的临界点更具挑战性。该项目将制定一个机构的蓝图，致力于推进对需要多学科审问的动态系统的理解和分析。该研究所的驱动假设是，当学科研究人员未能就其他领域在解决复杂研究问题时可能提供的价值达成共识时，存在着多学科见解的最大障碍。该框架通过采用给予-接受方法直接解决了这一挑战：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进行组装。研究人员将确定（a -“给予”）他们自己的学科可能有意义地影响的其他领域的假设，以及（B -“接受”）他们期望为自己的假设提供有用输入的学科，团队将包括两个方向的识别。这一创新框架提高了不同领域研究人员的能力，使他们能够更好地认识到相互依存关系，并消除了研究人员在受益于调查或为调查做出贡献之前了解其他领域的必要性。该研究将以一系列案例研究的形式将这些方法应用于多学科临界点的复杂挑战，这些案例研究将直接支持我们解决大流行准备和应对中许多复杂和相互关联的挑战的能力。该奖项由跨部门的大流行预防预测情报第一阶段（PIPP）计划支持，该项目由生物科学局（BIO）、计算机、信息科学与工程局（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。

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.