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Study finds mobile phone data can help predict seasonal infectious diseases

Study finds mobile phone data can help predict seasonal infectious diseases

shutterstock_49753717Mobile phone data could help predict how seasonal infectious diseases are spread and help policymakers make decisions about interventions, researchers from Princeton University, Harvard University and other institutions said in a recently published study.

The researchers used anonymous phone records from more than 15 million people to track the spread of rubella in Kenya, quantitatively demonstrating that such data can predict seasonal disease patterns, according to an Aug. 20 press release from Princeton.

Changes in people’s movements and where they gather are suspected of driving such disease outbreaks, but historically, there’s been a lack of data on travel behavior and population flux, making it difficult to test the concept, according to the study (pdf) published in the journal Proceedings of the National Academy of Sciences.

However, as mobile phone ownership proliferates, including in low-income and undeveloped countries, it’s generating large, complex datasets on millions of people.

“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, in the release. “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.”

Researchers analyzed mobile phone usage of the 15 million people in Kenya between June 2008 and June 2009. Specifically, they mapped the daily location of each user and the number of daily trips they made between provinces by using the location of the routing tower and timing of each call and text message.

The release said that more than 12 billion mobile phone communications were recorded anonymously and linked to a province. The researchers then compared the cellphone data with data on a rubella outbreak in Kenya, matching cellphone movement patterns with rubella incidents. In both analyses, they found that incidents mainly peaked in September and February, and then in a few places during May.

Apart from a few anomalies, they found that rubella is more likely to spread at the start of the school year when children interact and then after holiday breaks. The risk of rubella decreased across most of the country during the rest of the school year.

“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.”

Researchers plan to apply their methodology to measles and other diseases like malaria and cholera that’s shaped by human movement.

Source: Fierce Mobile Government

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