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.
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.
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.
Stevens University Professor & twenty-third Penn Integrates Knowledge Professor
Affiliate Research Scientist
Jorge Barreras Cortes
Undergraduate Student Researcher
In: Nature Human Behavior, 2022, ISSN: 2397-3374.
In: arXiv Preprint, pp. 6, 2021.
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.
Whether developing a safe vaccine or figuring out how to encourage its adoption, the same scientific method — systematically experimenting to see what works and what doesn’t — is key.
On July 29th, the CSSLab held its inaugural Student Research Conference to share the summer progress made in the Lab’s various projects.