PIPP Phase I: Dynamics of Pandemic Spread and Prevention in Indigenous Communities

It hardly needs stating in today's world how deadly and destructive to society pandemics can be, and how important it is to understand and predict the evolution of pandemics over time and space. Notably, the severity of pandemics impacts communities differently, often causing much greater mortality in underserved and isolated communities. For example, over the decades and across different types of epidemics, including the current COVID-19 pandemic, mortality rates among Indigenous populations have been more than three times that of the general public. This research team aims to contribute to global pandemic prediction and prevention by advancing knowledge about pandemic dynamics within isolated and underserved populations. Specifically, the investigators examine how pandemics of infectious disease affect and are affected by their spread in Indigenous communities. The focus will be on how specific features of these communities influence various aspects of epidemics, such as the initial spillover to humans, the human-to-human spread of the pathogen, the biological behavior of the pathogen, and countermeasures that can mitigate the impact of the disease. The assembled team is highly interdisciplinary, covering the broad scientific expertise, including members of the Cheyenne River Sioux and Prairie Band Potawatomi tribes, and connecting with external partners. Team meetings serve as observational data for a study of convergent team science, and student training and mentoring activities provide opportunities for bridging to the next generation of researchers. The investigators have identified three overarching questions that motivate four specific research thrusts: (1) What factors, environmental to molecular, contribute to the mortality rates in Indigenous communities? (2) Can the findings of this study translate to broader understanding of pandemic dynamics? (3) What engineering, education, biological, and policy solutions can be devised to address the root causes of the problems? The project initially focuses on Influenza, although it is expected that the findings will apply to other viral diseases such as COVID-19, EBOLA, and Zika. In order to address these overarching questions, the project will innovate collaboratively in the fields of engineering, biology, data science, and cognitive psychology along four interlinked thrusts: (I) Community Understanding of Cause and Prevention, which will establish the beliefs in Indigenous communities regarding disease and its amelioration. The investigators will employ a Community Based Participatory Research (CBPR) approach, which involves communities in all aspects of the research, so as to build trust between communities and researchers. (II) Predicting Virus Spillover and Spread, which will bring computational, statistical, and data science expertise in Catastrophe and Network Modeling to predicting dynamics in isolated communities. (III) Engineering Devices for Point-of-Care Sensing, which will develop sensitive and simplified tools to enhance testing capability in underserved Indigenous populations and improve pandemic tracking. (IV) Biological Differentiators: Predicting Infection, which will investigate virus-host cell interactions, and phenotypic and genotypic differentiators. 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.