Strong data matters for surveilling and anticipating blooms of illness. When flu viruses peak at unpredicted moments—or in unexpected places—medical centers can be caught off guard and left short on supplies. That happened this season when flu flushed across the country at more or less the same time. A number of cities were running low on critical antiviral medications by mid-January. “That wasn’t typical—typically flu is staggered from region to region,” Shaman says. “That can really strap resources in a big country.”

A better grasp on fevers to come can also help the public. If people can see the wave of sickness about to hit their town, they can take extra precautions—being vigilant about handwashing, keeping the kids home from coughing classmates, or making more of an effort to get a flu shot. One can imagine highly accurate flu forecasts becoming a feature of local weather reports.

Using an algorithm similar to those used in weather prediction, Shaman and his lab publish weekly flu forecasts for 50 states, 10 CDC regions, and 108 cities nationwide, predicting how and when cases will rise and fall. They pull together multiple data sets: CDC reports, lab reports of patients who’ve tested positive for influenza (drawn from a pool of WHO data), and Google Search activity. The algorithm simulates flu-transmission dynamics using data from past flu seasons and produces the real-time forecasts of future flu outcomes based on the new information. The results have been published weekly since 2013.

Right now, according to Shaman’s system, California is clearly past the height of its season. Texas has stayed at near-peak levels for five weeks straight. The Northeast has probably just crescendoed, though some cities like Boston and Providence are still about a week away.

The model has proven accurate up to 10 weeks out, but it’s not perfect for every city and every week. This season, Shaman’s predictions of peak timing in New York were spot-on going back to the second week of December. But for Houston, which experienced unprecedented levels of flu activity throughout this season, the predictions were shakier, since historical data was not as informative.

Until flu season is over, Shaman won’t be able to say how well his algorithms did overall. And he’s still refining the approach. “The hope is that it gets better over time with more observations and stronger models,” he says.

He is not alone in trying to develop better models of how flu spreads, waxes, and wanes. Google famously attempted to use search trends to approximate flu prevalence. Scientists at Osnabrück University in Germany partnered with IBM to link Twitter data with CDC reports. Harvard University and Boston Children’s Hospital developed an online map called Flu Near You, which analyzes and charts self-reported user data to show where signs of the virus are clustered. Researchers at New York University are conducting an ongoing study that asks citizen scientists to self-report symptoms online and mail in nasal swabs and saliva samples.