According to a study, tracking mobile phone data may aid in understanding how infectious diseases are spread seasonally. Researchers from Princeton University and Harvard University used anonymous mobile phone records for more than 15 million people to track the spread of rubella in Kenya. The team was then able to quantitatively show for the first time that mobile phone data can predict seasonal disease patterns, as indicated on a Princeton University news release.
By 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. The data was then compared to the cellphone analysis with a detailed dataset on rubella incidence in Kenya. The cellphone movement patterns matched and lined up with the rubella incidence figures.
In the analyses, rubella spiked three times a year; primarily in September and February as well as in a few locations in May. The Princeton University news release notes that this showed the researchers that cellphone movement can be a predictor of infectious-disease spread.
The Princeton University news release notes that harnessing cellphone data in this manner could help policymakers guide and evaluate health interventions, such as the timing of vaccinations and school closings. The methodology may also apply to a number of seasonally transmitted diseases, including the flu and the measles.
Lead author C. Jessica Metcalf says, “One of the unique opportunities of mobile phone data is the ability to understand how travel patterns change over time. 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.”
Metcalf adds, “Our analysis shows that mobile phone data may be used to capture seasonal human movement patterns that are relevant for understanding childhood infectious diseases. In particular, phone data can describe within-country movement patterns on a large scale, which could be especially helpful for localized treatment.”
Source: Princeton University