The study uses social network analysis and combines the power of Google Flu Trends' "big data" with traditional flu monitoring data from the US Centers for Disease Control and Prevention (CDC).
"Our innovation is to construct a network of ties between different US health regions based on information from the CDC," said corresponding author Michael Davidson, a doctoral student in political science at UC San Diego.
"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," said Davidson.
Google Flu Trends (GFT) is very good, Davidson said, at showing where in the US 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 to better target prevention and treatment efforts, especially during epidemics," Davidson said.
The study appears in the journal Scientific Reports.