Could Cell Phone Data Predict the Spread of Infectious Disease?

Data tracking may someday become a key component in limiting disease spread.

Cell phone tracking is oftentimes associated with privacy issues. But what if it had the potential to prevent you from getting sick?

A new study published recently in the Proceedings of the National Academy of Sciences revealed that tracking cell phone data could be the key to understanding how infectious diseases are spread seasonally.

Researchers at Princeton University and Harvard University tracked the data of 15 million cell phone users to track the spread of rubella in Kenya. They were able to show for the first time that cell phone data is positively linked to seasonal disease prediction patterns.

Harnessing cell phone data in this way could help policymakers guide and evaluate health interventions like the timing of vaccinations and school closings, the researchers said. The methodology could also apply to various seasonally transmitted diseases such as the flu and measles.

“One of the unique opportunities of mobile phone data is the ability to understand how travel patterns change over time,” said lead author C. Jessica Metcalf, assistant professor of ecology and evolutionary biology and public affairs at Princeton’s Woodrow Wilson School of Public and International Affairs. “And rubella is a well-known seasonal disease that has been hypothesized to be driven by human population dynamics, making it a good system for us to test.”

In the past, collecting these data proved to be difficult, especially for low-income families and undeveloped countries due to a lack of technology usage. However, in recent years, cell phone ownership has rapidly increased, allowing scientists to study a whole demographic of people previously ignored.

Because of the mobility of cell phones, it is possible that phone records could predict certain health-related patterns, causing the researchers to take a closer look at this hypothesis.

“The potential of mobile phone data for quantifying mobility patterns has only been appreciated in the last few years, with methods pioneered by authors on this paper,” said lead author Amy Wesolowski, a postdoctoral fellow at Harvard’s School of Public Health. “It is a natural extension to look at seasonal travel using these data.”

Using the location of the routing tower and the timing of each call and text message, the researchers were able to determine a daily location for each user as well as the number of trips these users took between the provinces each day. More than 12 billion phone communications were tracked and recorded anonymously and linked to a province using this method.

After collecting the data, researchers compared it with a highly detailed dataset on rubella incidence in Kenya. The 2 sets were a match, as the cell phone movement patterns lined up with the rubella incidence figures.

Overall, the research proved the scientists’ hypothesis right that rubella is more likely to spread when children interact with one another at the start of school and after holiday breaks.

“Our analysis shows that mobile phone data may be used to capture seasonal human movement patterns that are relevant for understanding childhood infectious diseases,” Metcalf said. “In particular, phone data can describe within-country movement patterns on a large scale, which could be especially helpful for localized treatment.”

Cell phone data could be an invaluable tool for predicting the spread of infectious diseases. The researchers hope to apply their methodology to measles and other infections shaped by human movement.