Carnegie Mellon University has been working with Facebook and Google to survey the U.S. population for symptoms of COVID-19 (coronavirus).

Researchers are aggregating that data, plus more information coming from public health institutions, to come up with real-time indicators showing the spread of COVID-19 by state, county, and metropolitan area.

This data will inform public officials so they can make educated decisions about when it's safe to reopen local economies.

While a majority of the U.S. is staying home and practicing social distancing, some restless Americans are participating in public COVID-19 (coronavirus) protests and calling for the country to reopen. While the science doesn't support such protests, the protesters do raise the pivotal question: When can we resume our lives, get back to work, go see a movie, or even finally attend our loved ones' funerals?

Carnegie Mellon University has developed a set of real-time models that may hold the answers. Armed with public health data from research hospitals and the U.S. Centers for Disease Control and Prevention (CDC), plus a trove of new self-reported symptom data from Facebook and Google surveys, CMU researchers have put together a comprehensive, aggregate picture of what the wave of COVID-19 cases looks like in the U.S. in real time.

COVIDCast offers an interactive heat map that lets you drill down into COVID-19 indicator data by state, county, and metropolitan area. The data will update daily to give decision-makers like governors a comprehensive tool to track the expansion and contraction of the disease in their geographical region over time.

Lead researchers Ryan Tibshirani and Roni Rosenfeld, part of CMU's Delphi Research Group, have used machine learning to forecast influenza in the U.S. since 2013. As a result, the scientists speak with the CDC almost daily. Their Delphi COVID-19 Response Team has grown to over 30 faculty members, students, and volunteers.

While the CDC has at least 20 years' worth of data on influenza, "with pandemics, the picture changes—there's very little data," Tibshirani tells Popular Mechanics. Without history to draw on, researchers must learn from data as it's accumulated.

Understanding the current status of COVID-19 by location will help the CMU team eventually forecast the virus's spread. The ultimate goal, Rosenfeld tells Popular Mechanics, is to create short-term forecasts that will give hospitals four weeks' notice before a spike in cases actually hits their region.

Not only could this save lives by ensuring new ventilators are sent to the hospitals that need them most, but it could also shed insight into the efficacy of social distancing.

The Art of Tracking Symptoms

Maybe you've already noticed a new widget at the top of your Facebook news feed, asking you to fill out an optional survey that CMU put together to track COVID-19 symptoms. It's the result of a collaboration between the university and the social media giant to collect that previously nonexistent data. Elsewhere on the web, you can find the surveys in Google Opinion Rewards and AdMob.

Facebook/CMU

These surveys ask you a few basic health questions, like if anyone you know is currently experiencing a fever, cough, or other symptoms related to COVID-19. The global reach has been a boon to CMU's symptom tracking, as some 600,000 Google users take the survey each day. Meanwhile, Facebook has generated an average of about one million responses per week.

Here's a symptom tracking map that Facebook has put together with the aggregate data:

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Just as bluetooth contact tracing apps require scale to work, CMU's models will only become more accurate as more people take these surveys.

If you're concerned about your privacy, the two tech companies are mostly just serving as the distribution point for these surveys—they don't handle the data. For each participant in the survey, Facebook shares a random ID number with CMU. Once that person completes the survey, CMU sends the ID number back, but with none of the responses.

Keeping tabs on self-reported symptoms is a great arrow to have in your statistical quiver, but that data alone doesn't do enough heavy lifting. That's because selection bias can creep in; only a certain type of person will actually respond to the survey, Tibshirani says.

"Our first line of defense is to think of these indicators as measuring something that is not a ground truth ... but useful nonetheless in forecasting," he says. Facebook provides the second line of defense with a statistic known as a "weight value" that will help correct sample bias. After all, the demographic population on Facebook—and the subgroup filling out the surveys—doesn't perfectly represent a slice of the U.S.

To round out their data sources for their eventual forecasting model, the researchers use three other indicators in addition to the two self-reported surveys from Facebook and Google:

Google search trends: Through the Google Health Trends API, scientists can gain an understanding of where and when searches relating to COVID-19 symptoms are spiking.

Through the Google Health Trends API, scientists can gain an understanding of where and when searches relating to COVID-19 symptoms are spiking. Outpatient visits: A national health system has provided CMU with statistics on patient visits to see doctors, as well as telemedicine visits. This will help the researchers estimate the percentage of visits for COVID-19-related symptoms by location on any given day.

A national health system has provided CMU with statistics on patient visits to see doctors, as well as telemedicine visits. This will help the researchers estimate the percentage of visits for COVID-19-related symptoms by location on any given day. Influenza test statistics: Quidel Corp., a maker of medical tests, provides the CMU researchers with stats on flu tests. In lieu of COVID-19 tests that are still largely unavailable, medical workers routinely administer flu tests to people complaining of COVID-19 symptoms as a way to rule it out as a diagnosis. What remains are possible cases of COVID-19.

The five indicators alone are only really enough to speculate. But together, they can show trends in the data, especially if multiple indicators are painting the same picture.

The Ebb and Flow of Social Distancing

Governors may have to institute stay-at-home orders in waves over the next 12 to 18 months or longer, because we have no idea how COVID-19 will progress across the seasons. The second coming of the 1918 Spanish Flu was notoriously virulent, and studies show that RNA viruses "quickly acquire genetic variants through random mutations," meaning the viruses adapt and come back with a vengeance.



Still, if an area's number of new COVID-19 cases is declining, that just "shows social distancing and closing methods are working," Rosenfeld says. "It doesn't mean [people] should celebrate and be done. You're doing the right thing—keep doing it until you're told otherwise."

Social distancing measures should go into effect as ICU admissions rise, and relax in the interim. Imperial College London

Imperial College London has already modeled how public health measures could impact subsequent bouts of COVID-19. Neil Ferguson, director of the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), says:

We use the latest estimates of severity to show that policy strategies which aim to mitigate the epidemic might halve deaths and reduce peak healthcare demand by two-thirds, but that this will not be enough to prevent health systems being overwhelmed. More intensive, and socially disruptive interventions will therefore be required to suppress transmission to low levels. It is likely such measures—most notably, large scale social distancing—will need to be in place for many months, perhaps until a vaccine becomes available.

Eventually, the CMU indicators could help governors decide when to implement strict stay-at-home orders, and when to ease back on them.

"We believe that decisions about social distancing ... will be driven to a great extent by concerns about exceeding capacity [in hospitals]," Rosenfeld says. "[The models] can help them think about reopening the economy in measured terms."

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