Researchers have combined location density with real-world mobility data to predict the risk of infection from COVID-19 at specific locations with unprecedented accuracy.

At USC, researchers are advocating for a new approach to predict the chance of infection from Covid-19: combining anonymized cellphone location data with mobility patterns — broad patterns of how people move from place to place.

To produce «risk scores» for specific locations and times, the team used a large dataset of anonymous, real-world location signals from cell phones across the US in 2019 and 2020. The system shows a 50% improvement in accuracy compared to current systems, said the researchers.

«Our results show that it is possible to predict and target specific areas that are high-risk, as opposed to putting all businesses under one umbrella. Such risk-targeted policies can be significantly more effective, both for controlling Covid-19 and economically,» said lead author Sepanta Zeighami, a computer science Ph.D. student advised by Professor Cyrus Shahabi.

«It’s also unlikely that Covid-19 will be the last pandemic in human history, so if we want to avoid the chaos of 2020 and the tragic losses while keeping daily life as unaffected as possible when the next pandemic happens, we need such data-driven approaches.»

To address privacy concerns, the mobility data comes in an aggregated format, allowing the researchers to see patterns without identifying individual users. The data is not being used for contact tracing, identifying infected individuals, or where they are going, said the researchers.

Story Source: Materials provided by University of Southern California. Original written by Caitlin Dawson. Note: Content may be edited for style and length.