Data Collection for Public Health Surveillance

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.

Register at

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)