Big Data and Collaboration Seek to Fight COVID-19

Researchers try unprecedented data sharing and cooperation to understand COVID-19—and develop a model for diseases beyond the coronavirus pandemic.

emma yasinski
| 5 min read
n3c nih covid-19 database coronavirus pandemic National COVID Cohort Collaborative ncats big data artificial intelligence machine learning The Covid Symptom Tracker

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Although cough and fever have been considered the most tell-tale signs of COVID-19, in May, researchers published a study suggesting that loss of smell and taste were better able to predict who would test positive for the disease. The insight came from data shared by millions of individuals who logged on to a phone app to report what, if any, symptoms they were experiencing on a given day.

The Covid Symptom Tracker app now has nearly 4 million users. Researchers are extracting the massive amounts of data they gather to anticipate COVID-19 outbreaks in particular communities and to explore different risk factors for the disease.

“We were one of the earliest bodies to actually identify the importance of a loss of taste or smell as a predictor,” says Andrew Chan, a physician and epidemiologist at Massachusetts General Hospital and the lead researcher on the project. “We ...

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Meet the Author

  • emma yasinski

    Emma Yasinski

    Emma is a Florida-based freelance journalist and regular contributor for The Scientist.
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