Tracking mobile phone data is often associated with privacy issues, but these vast datasets could be the key to understanding how infectious diseases are spread seasonally, according to a study published in the Proceedings of the National Academy of Sciences.
Princeton University and Harvard University researchers used anonymous mobile phone records for more than 15 million people to track the spread of rubella in Kenya and were able to quantitatively show for the first time that mobile phone data can predict seasonal disease patterns. “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.”
Overall, the results were in line with the researchers’ predictions; rubella is more likely to spread when children interact with one another at the start of school and after holiday breaks. Across most of the country, this risk then decreases throughout the rest of the school-term months. (The only anomaly was in Western Kenya where the risk during school breaks was relatively higher than when school was in session; the data were insufficient to clearly indicate why.)