The project is supported by a $1 million grant from the National Science Foundation as part of the Phase 1 Development of Predictive Intelligence for Epidemic Preparedness grant.
The right mix of social hybrid data
Recent visitors to the School of Public Health may have noticed the small white boxes installed on the walls and ceiling. Project researcher Guixing Wei explains that the team installed about 2,000 of these beacons and, with the help of Bluetooth and Wi-Fi on participants’ phones, the system created “spatial-temporal” movement data. Wei said the team will study factors such as how often people interact with each other, how much time they spend together, and when and where they meet, which are basic parameters for epidemiological modeling.
Until now, scientists have had little access to indoor movement data because GPS, which uses satellite technology, does not perform well indoors, which is part of the reason that has hampered contact tracing during the pandemic. But SPH’s Bluetooth beacons will allow researchers to triangulate indoor locations.
“This is the largest indoor movement and mobility study of its kind,” Wei said.
The team is constantly refining the data collection process to not only be as accurate as possible, but also to protect privacy. The custom app, created for MAPPS in partnership with the Brown Center for Computing and Visualization, collects only minimal demographic information, scientists said.
The long-term goal of MAPPS is to gain insights about people’s movements or social connections without actually tracking individuals, said Julia Netter, an adjunct assistant professor of the practice of computer science who coordinates the Socially Responsible Computing Project in Brown’s computer science department.
“How to streamline information to get only what you need for a specific project or research question: This is an important area of current computer science research,” Nate said. “Integrating these key insights into a project like MAPPS will be really useful for public health researchers looking for specific types of data to conduct social mix analyses, without requiring additional and potentially sensitive information about individuals.”
Aware of the privacy issues that may arise, the MAPPS team invites the SPH community to learn with them as the project progresses. In January 2023, a four-day workshop led by Lurie, Netter, and co-investigators from Brown’s Institute for Mathematical Computation and Experimentation explored the ethical issues of big data to create a framework for data collection and storage. Nate emphasized that the team also held information sessions in the weeks leading up to the project’s launch—transparency was key.
Lurie introduced MAPPS at Associate Professor Abigail Harrison’s fall session on the Global Burden of Disease, explaining the motivation, process and goals. As expected, students raised questions: Does the app only collect data while participants are inside the building? (Yes.) How can the findings be generalized to urban populations? (This feasibility study is for the School of Public Health only.) Can additional questions be added to the application? (Hopefully in the next phase.)
Lurie then asked students to raise their hands and asked if they would sign up for the app. Three-quarters of the class raised their hands without hesitation.Lurie asked if any students did this no Wanted to be tracked; no one raised their hand. However, three or four students admitted that they were on the fence.
Class members acknowledged that Brown public health students are often eager to participate in research and therefore tend to be interested in projects like this. One student commented that the barrier to entry was lower compared to another study she was involved in and “this seems very cold.” Another student agreed: My data is already being collected by a lot of applications, he said , so he might as well take advantage of it.
get out of class has ended. Students applauded Lurie’s speech before packing up.