Data Science & Surveillance
Led by Drs. Kayoko Shioda and Laura White
The CEID data science and surveillance core focuses on bringing together researchers working to develop new data sources, methods, and tools to identify, monitor, and characterize emerging infectious disease threats. This involves hosting events to bring together researchers to synergize their efforts and encourage new collaborations, particularly between data owners, methodologists, and practitioners to generate novel approaches to surveillance. The core strives to be a group that can generate ambitious ideas and plans for the role of data science in preparing for and combatting emerging infectious disease threats.
EPISTORM: The Center for Advanced Epidemic Analytics and Predictive Modeling Technology (CDC)
The COVID-19 pandemic has underscored the critical need for enhanced epidemic modeling and analytic tools. Despite advances in epidemic modeling and analytics, issues persist around data and model quality, accessibility, and accurate representation of diverse population groups, potentially exacerbating health inequities. Addressing these challenges necessitates the transformation of high-quality data into meaningful insights for equitable decision-making and policy formulation.
The “Epistorm: Center for Advanced Epidemic Analytics and Predictive Modeling Technology” project emerged in response to these needs. Led by PI Alessandro Vespignani, Ph.D. of Northeastern University, Epistorm is a multi-organization, collaborative project bringing together researchers from Northeastern University, Boston University, the University of California San Diego, Los Alamos National Laboratory, the Fred Hutchinson Cancer Research Center, Indiana University, the University of Florida, and Ginkgo Bioworks, as well as several hospital networks. Among this group include Boston University faculty members Laura White, Ph.D. and Kayoko Shioda PhD, DVM, MPH. Both are faculty at the university’s School of Public Health (SPH) and Center for Emerging Infectious Diseases Policy & Research (CEID).
Predicting and Preventing Epidemic to Pandemic Transitions (NSF)
The Predicting and Preventing Epidemic to Pandemic Transitions study, funded by the National Science Foundation (NSF), seeks to develop a rich model that predicts both disease emergence and potential for significant spread, and formulate pandemic prevention strategies. This project aims to evaluate factors that impact disease emergence and spread by incorporating input from multidisciplinary fields that span biology, ecology, epidemiology, medicine, computer & information science & engineering, and social sciences. The detailed knowledge base gathered from this work aims to generate novel predictive models, provide insight to strengthen current models, and formulate rapid pandemic mitigation strategies.
CEID Project Faculty & Collaborators
- Dr. Nahid Bhadelia, MD, MALD, CEID Founding Director (co-PI)
- John Connor, PhD, Associate Professor Microbiology, BU School of Medicine
- Michael Dietze, PhD, Professor, Ecological Forecasting Lab at BU
- Traci Hong, PhD, Associate Professor, Media Science, BU College of Communication
- Nina Mazar, PhD, Professor of Marketing, BU Questrom School of Business
- Andrew Stokes, PhD, Assistant Professor, Department of Global Health, BU School of Public Health
- Gianluca Stringhini, PhD, Assistant Professor, Electrical and Computer Engineering, BU College of Engineering
- Laura White, PhD, Associate Professor of Biostatistics,
BU School of Public Health
- Kevin Gallagher, PhD, Professor & Director, Global Development Policy Center
Additional Co-PIs include Drs. Yannis Paschalidis, Eric Kolaczyk, and Diane Joseph-McCarthy of Boston University, as well as Dr. Jon Epstein of EcoHealth Alliance.