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

大流行是多种致病因素共同作用的结果。这些因素可能单独是无关紧要的，但在共同行动时变得至关重要，而一组看似无关的复杂因素可能会导致一场大流行出现的完美风暴。然而，预测大流行出现的主流方法仍然侧重于对个别因素或因素子集进行纪律调查。准备和预防下一次大流行将需要多学科方法，从分子到社会，从个人诊断到全球监测，利用关于复杂相互依存关系的知识。这个项目汇集了一个多学科的科学家团队，他们代表着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各种专业知识，专注于了解如何识别、识别和预测何时出现的疾病威胁造成了一系列因素的完美风暴，这些因素本来会导致局部暴发“倾覆”成一场大流行。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，将学科追求结合在一起，形成对大流行临界点的全面、多方面和预测性的理解。超越这一项目的范围，由此产生的框架将成为致力于发现和分析复杂联系的所有机构的蓝图，从而将提高预测和预防即将到来的大流行病和现代世界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特定于系统或学科的临界点更具挑战性。该项目将为一个致力于促进对具有需要由多个学科审问的动态系统的理解和分析的机构制定蓝图。该研究所的推动力假设是，当学科研究人员未能就其他领域在解决复杂研究问题方面提供的价值的共同直觉达成一致时，多学科洞察力的最大障碍就存在。该框架通过采用互惠互利的方法直接应对这一挑战：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。研究人员将从他们自己的学科可能有意义地影响的其他领域识别(a-“给予”)假说，以及(b--“接受”)他们预期对自己的假说有用的输入的学科，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的研究人员更好地认识到它们之间的相互依存关系的能力，使研究人员不再需要在从调查中受益或对调查作出贡献之前了解其他领域。这项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应对大流行准备和应对中许多复杂和相互关联的挑战的能力。该奖项由跨部门大流行预防第一阶段预测情报(PIPP)计划支持，该计划由生物科学(BIO)、计算机、信息科学和工程(CEISE)联合资助；工程学(ENG)和社会、行为和经济学(SBE)。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.