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.
According to the researchers, existing risk score tools do not provide enough detailed information about infection rates at specific places, or they make unrealistic assumptions about how populations mix.
"The risk of infection varies a lot based on the location, and having a single policy, for instance, at a county level, ignores how some areas are riskier than others," said Zeighami.
So, using real-world mobility data and existing knowledge about the spread of COVID-19, the team created a simulator to generate realistic infection patterns. In the simulation, some “agents” are initially infected and spread the disease as they move around.
Then, the researchers created a Hawkes process-based model, which assigns risk scores based on location density and mobility patterns at a given time and place. Using the simulator, the researchers tested the model to determine if it could accurately predict the number of infections at different locations. It turned out, the risk scores were indeed a reliable metric for tracking infections in cities across the US, including San Francisco, New York, Chicago and Los Angeles.
The researchers found, predictably, that popular destinations in a city are riskier. But they also found that incorporating the infection mobility - how people move - as opposed to just relying on the popularity of an area helped to improve infection prediction. This, said the researchers, underscores the importance of bringing together mobility patterns and infection spread prediction models to generate risk scores.
There are two key ways the system could be used in the real world, said the researchers. The more straightforward case is to make neighborhood-level policy decisions: for instance, bars in Santa Monica, CA, should close today due to high risk in that neighborhood.
For more targeted locations, such as a specific concert stadium event, the system would crunch the mobility data from similar concerts in the past to learn how the infection risk changes in the area following this type of event. Then, using the researchers’ model and current mobility data across LA, the system could make predictions and assign risk scores.
Going forward, the team plans to develop user-specific, yet still privacy-preserving, risk scores, and to include long-term forecasting capabilities for several weeks into the future.
"The very high resolution of this mobility data, as well as our scalable approach, will enable us to estimate risk scores at a very fine-grain spatial and temporal resolution, for example, a specific restaurant at dinner time, or a shopping mall at lunchtime," said Shahabi.
"As an individual, you may want to avoid areas deemed high-risk, and policymakers could warn the public to avoid an area known to be a potential hotspot of infection. The scores can also be used for closure or reduced capacity decisions. Instead of making these decisions at the county level, public health experts can make those decisions at city, neighborhood or zip code levels."
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