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

University Of Tennessee Knoxville, Knoxville TN

Investigators

Abstract

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.