Earlier this week in a Washington Post editorial, Facebook founder and chief executive Mark Zuckerberg referenced an opt-in symptom survey being shown on Facebook that could help researchers at Carnegie Mellon forecast Covid-19 cases, based on location. If successful, the project would offer county-by-county insights and be imminently useful to public health officials and hospitals that need to prepare for potential surges in patients.

Now, following a few weeks of initial data-gathering, Carnegie Mellon has published five interactive maps of Covid-19 indicators across the US. The maps will be updated once a day and appear under separate tabs based on their data sources: self-reported symptoms from Facebook and Google surveys, Google searches for specific symptoms, medical tests, and doctor visits. Researchers at CMU, who have worked on epidemiological forecasting for several years but recently ramped up efforts around Covid-19, say the work being done with Facebook and Google is significant because of the sheer scale of those platforms. By distributing the symptom surveys throughout Facebook’s News Feed and Google’s survey tool, researchers are gaining access to millions of data points.

Because survey participants are self-reporting their symptoms, and many of the symptoms for COVID-19 are nonspecific, this kind of data gathering can lead to potential overestimation of the disease if correction methods aren’t used, says Maimuna Majumder, a computational epidemiologist who works at Harvard Medical School and the computational health informatics program at Boston Children’s Hospital. Search trends, one of the data sources for these maps, aren’t always reliable indicators in health studies. And privacy advocates are wary of Big Tech’s involvement in gathering health data from consumers and using it to build location-specific products, even though in this case both Google and Facebook insist they are giving all of the data directly to CMU and retaining very little data themselves.

But the CMU researchers believe the Covid-19 forecasting maps, called COVIDCast, could be much more detailed and effective than any of their prior projects for tracking influenza and dengue fever, due in large part to the surveys. “I think if we weren’t in a pandemic, I don’t think the biggest players in tech would have considered returning my emails, and I don’t think the public would have been keen on taking these surveys,” says Ryan Tibshirani, a statistician and one of two lead researchers for CMU’s Covid-19 response team.

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The Carnegie Mellon team working on the COVIDCast maps call themselves the Delphi group, which since 2012 has been tracking seasonal influenza in the US and dengue in Puerto Rico and Peru. Usually Delphi projects include six or seven team members; for the Covid-19 project, which began four weeks ago, 27 researchers got involved.

A significant part of Delphi’s work in the past has been epidemic forecasting: Using various data sources to make a prediction on where a flu outbreak might occur in two to four weeks. Now, according to Delphi coleader and machine learning professor Roni Rosenfeld, the team is trying to both “nowcast”—use some of the same indicators to determine where an epidemic is at any one time, in any one location—and forecast. “When the pandemic came around, we pivoted our entire group to try to use some of the techniques we’ve developed over the past seven years to Covid-19,” Rosenfeld says. “Some of the tools carry over, and some of them you have to reinvent.”

To build the maps, the Delphi group is pulling in data from at least five sources: Google search trends (which Delphi has used in earlier projects); flu tests administered by test-maker Quidel; instances of doctor visits and telehealth appointments during which Covid-like symptoms were identified; and symptoms surveys being promoted or hosted by Facebook and Google. Some of the data streams are near-continuous, and the research team is sometimes changing methods on the fly. For example, the CMU researchers were initially looking at flu tests that were negative, believing that elimination mechanism was a strong signal that an ill person’s symptoms were related to Covid; now the team is factoring in all Quidel flu tests. They declined to share which national health care service is providing the data on visits to doctors offices and telehealth appointments.