Our team is building a collection of interactive data dashboards that visually summarize human mobility patterns over time and space for a number of cities, starting with Philadelphia, along with highlighting potentially relevant demographic correlates. We are estimating a series of statistical models to identify correlations between demographic and human mobility data (e.g. does age, race, gender, income level predict social distancing metrics?) and are using mobility and demographic data to train epidemiological models designed to predict the impact of policies around reopening and vaccination.

Data Overview

We use a proprietary combination of cell phone GPS data, demographic data derived from the American Community Survey, and COVID-19 caseload data from the New York Times.

COVID Philly Dashboard
ABOVE: COVID-19 Places of Interest and Census Tracts

A sample from our in-progress COVID mapping dashboard. The map above visualizes Philadelphia’s COVID data and high-traffic destinations by Census Tract, providing an overview of how people move throughout the city.

See more about this project here.


Duncan Watts

Stevens University Professor & twenty-third Penn Integrates Knowledge Professor

Homa Hosseinmardi

Homa Hosseinmard

Research Scientist

Mark Whiting

Mark Whiting

Research Scientist

Amir Ghasemian

Affiliate Research Scientist

Jorge Barreras Cortes

Ph.D. Researcher

Henry Ge

Henry Ge Headshot

Undergraduate Student Researcher


Milkman, Katherine L.; Gandhi, Linnea; Ellis, Sean F.; Graci, Heather N.; Gromet, Dena M.; Mobarak, Rayyan S.; Buttenheim, Alison M.; Duckworth, Angela L.; Pope, Devin; Stanford, Ala; Thaler, Richard; Volpp, Kevin G.

A citywide experiment testing the impact of geographically targeted, high-pay-off vaccine lotteries Journal Article

In: Nature Human Behavior, 2022, ISSN: 2397-3374.

Abstract | Links | BibTeX

Barreras, Francisco; Hayhoe, Mikhail; Hassani, Hamed; Preciado, Victor M.

AutoEKF: Scalable System Identification for COVID-19 Forecasting from Large-Scale GPS Data Journal Article

In: arXiv Preprint, pp. 6, 2021.

Abstract | Links | BibTeX


Researcher Spotlight: Jorge Barreras Cortes

Having just earned his Ph.D. in applied math in December, Jorge “Paco” Barreras Cortes kicks off 2023 as a fully fledged post-doctoral researcher at the CSSLab. He has driven the Lab’s work on epidemic modeling since 2020, grappling with the types of data, machine learning, and network science quandaries that underpin the toughest challenges in the field. Read on to learn more about his research journey in this month’s Researcher Spotlight.