Data Collection for Public Health Surveillance

Jun 10, 2021 1:00 PM — 5:30 PM
Data Collection and Integration to Enhance Public Health – Making Sense of a Patchwork of Data

As policymakers and community leaders have worked to respond to the COVID-19 pandemic, it has become increasingly clear that statistics and data science can play a critical role in protecting public health and determining the best path forward. Moving from theory to practice presents challenges for working with a patchwork of data from many different sources across public and private sectors.

Join the National Academies for a symposium on June 10, 2021 from 1:00-5:30pm ET to explore the latest statistics and data science methods and how they can be applied to real-world situations. Speakers will discuss how their work in modeling, inference, predictive analysis, and machine learning has been applied to track the spread of COVID-19, drug use, air pollution, and human trafficking. Panelists will explore the strengths and weaknesses of available surveillance data and how to integrate and draw insight from multiple imperfect data sources.

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Session 1: Data Collection, Surveillance, and Modeling

Moderator: Amy Herring (Duke University)

Veronica Berrocal (University of California Irvine)

Stephanie Eckman (RTI International)

Nick Reich (University of Massachusetts Amherst)

Ryan Tibshirani (Carnegie Mellon University)

Session 2: Data Integration

Moderator: Elizabeth Stuart (Johns Hopkins University)

Joe Hogan (Brown University)

Bernard Silverman (University of Nottingham)

Minge Xie (Rutgers University)

Bhramar Mukherjee (University of Michigan)