New flu simulation map using health and social media data

Researchers at the University of Chicago have created computer simulations to predict spread of flu across the United States utilizing datasets of demographics, healthcare visits and geographic movements of 150 million people over nine years. The study is published in the journal eLife.

Simulation map of flu spread. Credit: Andrey Rzhetsky, UChicago

Researchers utilized deidentified patient data from more than 40 million families in the US using Truven MarketScan to analyze insurance claims for treatment of flu-like conditions from 2003 to 2009. To get people movement data they used 1.7 billion geolocated twitter posts. Researchers also incorporated data on “social connectivity,” which is information about how often they visit friends and neighbors, air travel, weather, vaccination rates and changes in the flu virus itself.

The study results show that seasonal flu outbreaks originate in warm, humid areas of the south and the southeastern U.S. and move northward. The team utilized newer models based on all above variables to understand what factors drive the northward spread of the flu each year. In the paper, they liken the typical outbreak to a forest fire.

The researchers were able to use these models to recreate three years of historical flu data fairly accurately.

Citation: Chattopadhyay, Ishanu, Emre Kiciman, Joshua W. Elliott, Jeffrey L. Shaman, and Andrey Rzhetsky. “Conjunction of factors triggering waves of seasonal influenza.” ELife 7 (2018). doi:10.7554/elife.30756.

Research funding: National Institutes of Health, Defense Advanced Research Projects Agency, Liz and Kent Dauten.

Adapted from press release by the University of Chicago.