Kimani C Toussaint
Anna A Lysyanskaya
Computer and Information Science and Engineering (CISE)
Infectious diseases naturally follow the movement of people. Once a transmissible disease emerges, human mobility and patterns of social mixing are largely responsible for its spread. Therefore, understanding patterns of human mobility and social interaction is essential for identifying the potential for pandemic spread. This information is also critically important for developing science-based targeted interventions aimed at stopping or slowing the spread of a new pathogen. In addition, understanding how humans move across space and time and interact with each other has broad implications beyond pandemic prevention. This project will provide a framework to collect, capture, curate and analyze complex, multi-scale human movement and social interaction data that can be adapted to understand a wide variety of pressing problems including climate change, tracking of wildlife, disaster relief, management and urban planning. A fully functioning, accurate computational system for pandemic prediction could save millions of lives and billions of dollars in health care costs. With the establishment of the Center for Mobility Analysis for Pandemic Prevention Strategies (MAPPS) at Brown University, this project aims to: categorize, organize and synthesize existing data on mobility and social mixing; develop new and innovative tools for measuring mobility and social mixing; and use that information to develop and populate mathematical models aimed at predicting and preventing future pandemics. Existing data on these issues will be systematically collected, catalogued and organized in a federated, publicly available database, while new data will be generated through targeted pilot projects. In addition, the project aims to develop a new device, possibly wearable, that is capable of measuring human mobility, social mixing and biometrics. The research team will develop a series of complex, flexible mathematical models that use these data to predict the emergence of new pandemics, describe epidemic dynamics, evaluate the effectiveness and impact of different intervention strategies, and inform health policy decisions for prevention and control. The work will be infused with a strong focus on ethics and data privacy. Focused, thematic workshops will bring together national and international experts with the aim of identifying innovative solutions to the technical and ethical issues raised by this project. Finally, an integrative activity across the Brown University campus will serve as a template for similar data collection and analysis: the goals are to map the entire social network at the University, to use this information to better understand patterns of social mixing and to use that information to explore a variety of interventions that could potentially mitigate or eliminate pathogenic spread. 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.