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Forecasting the flu better

Date:
January 29, 2015
Source:
University of California, San Diego
Summary:
Researchers say they can predict the spread of flu a week into the future with as much accuracy as Google Flu Trends can display levels of infection right now. The study uses social network analysis and combines the power of Google Flu Trends' "big data" with traditional flu monitoring data from the U.S. Centers for Disease Control and Prevention (CDC).
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Three UC San Diego researchers say they can predict the spread of flu a week into the future with as much accuracy as Google Flu Trends can display levels of infection right now.

The study -- appearing in Scientific Reports, an online journal from the publishers of Nature -- uses social network analysis and combines the power of Google Flu Trends' "big data" with traditional flu monitoring data from the U.S. Centers for Disease Control and Prevention (CDC).

"Our innovation," said corresponding author Michael Davidson, a doctoral student in political science at UC San Diego, "is to construct a network of ties between different U.S. health regions based on information from the CDC. We asked: Which places in years past got the flu at about the same time? That told us which regions of the country have the strongest ties, or connections, and gave us the analytic power to improve Google's predictions."

Google Flu Trends (GFT) is very good, Davidson said, at showing where in the U.S. people are searching for information on flu and flu-like symptoms. And these data are valuable because they come in real time, he said, about two weeks ahead of when the CDC can issue its reports. But GFT has also made some infamous errors -- errors that probably reflect widespread public concerns about flu more than actual confirmed illness.

By weighting GFT predictions with a social network derived from CDC reports on laboratory-tested cases of flu, the researchers were able to refine and improve GFT's predictions.

The researchers are optimistic their work will soon be put to public use. "We hope our method will be implemented by epidemiologists and data scientists," Davidson said, "to better target prevention and treatment efforts, especially during epidemics."


Story Source:

Materials provided by University of California, San Diego. Note: Content may be edited for style and length.


Journal Reference:

  1. Michael W. Davidson, Dotan A. Haim, Jennifer M. Radin. Using Networks to Combine “Big Data” and Traditional Surveillance to Improve Influenza Predictions. Scientific Reports, 2015; 5: 8154 DOI: 10.1038/srep08154

Cite This Page:

University of California, San Diego. "Forecasting the flu better." ScienceDaily. ScienceDaily, 29 January 2015. <www.sciencedaily.com/releases/2015/01/150129104023.htm>.
University of California, San Diego. (2015, January 29). Forecasting the flu better. ScienceDaily. Retrieved May 26, 2017 from www.sciencedaily.com/releases/2015/01/150129104023.htm
University of California, San Diego. "Forecasting the flu better." ScienceDaily. www.sciencedaily.com/releases/2015/01/150129104023.htm (accessed May 26, 2017).

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