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Youyang Gu
The 27-Year-Old Who Became a Covid-19 Data Superstar
In the contest over who could make the most accurate coronavirus forecast, it was global institutions vs. a guy living with his parents in Santa Clara.
"In the contest over who could make the most accurate coronavirus forecast, it was global institutions vs. a guy living with his parents in Santa Clara.
Spring 2020 brought with it the arrival of the celebrity statistical model. As the public tried to gauge how big a deal the coronavirus might be in March and April, it was pointed again and again to two forecasting systems: one built by Imperial College London, the other by the Institute for Health Metrics and Evaluation, or IHME, based in Seattle.
But the models yielded wildly divergent predictions. Imperial warned that the U.S. might see as many as 2 million Covid-19 deaths by the summer, while the IHME forecast was far more conservative, predicting about 60,000 deaths by August. Neither, it turned out, was very close. The U.S. ultimately reached about 160,000 deaths by the start of August.
The huge discrepancy in the forecasting figures that spring caught the attention of a then 26-year-old data scientist named Youyang Gu. The young man had a master’s degree in electrical engineering and computer science from the Massachusetts Institute of Technology and another degree in mathematics, but no formal training in a pandemic-related area such as medicine or epidemiology. Still, he thought his background dealing with data models could prove useful during the pandemic.
In mid-April, while he was living with his parents in Santa Clara, Calif., Gu spent a week building his own Covid death predictor and a website to display the morbid information. Before long, his model started producing more accurate results than those cooked up by institutions with hundreds of millions of dollars in funding and decades of experience.
“His model was the only one that seemed sane,” says Jeremy Howard, a renowned data expert and research scientist at the University of San Francisco. “The other models were shown to be nonsense time and again, and yet there was no introspection from the people publishing the forecasts or the journalists reporting on them. Peoples’ lives were depending on these things, and Youyang was the one person actually looking at the data and doing it properly.”
The forecasting model that Gu built was, in some ways, simple. . .
Christopher Murray, the director of IHME, says that once the organization got a better handle on the virus after April, its forecasts radically improved.
But that spring, week by week, more people started to pay attention to Gu’s work. He flagged his model to reporters on Twitter and e-mailed epidemiologists, asking them to check his numbers. Toward the end of April, the prominent University of Washington biologist Carl Bergstrom tweeted about Gu’s model, and not long after that the U.S. Centers for Disease Control and Prevention included Gu’s numbers on its Covid forecasting website. As the pandemic progressed, Gu, a Chinese immigrant who grew up in Illinois and California, found himself taking part in regular meetings with the CDC and teams of professional modelers and epidemiologists, as everyone tried to improve their forecasts.
Traffic to Gu’s website exploded, with millions of people checking in daily to see what was happening in their states and the U.S. overall. More often than not, his predicted figures ended up hugging the line of actual death figures when they arrived a few weeks later.
With such intense interest around these forecasts, more models began to appear through the spring and summer of 2020. . .
In November, Gu decided to wind down his death forecast operation. Reich had been blending the various forecasts and found that the most accurate predictions came from the this “ensemble model,” or combined data.
“Youyang stepped back with a remarkable sense of humility,” Reich says. “He saw the other models were doing well and his work here was done.” . . .
After taking a bit of a break, Gu, now 27 and living in a New York apartment, did get back into the modeling game. This time, he’s creating figures related to how many people in the U.S. have been infected by Covid-19, how quickly vaccines are being rolled out, and when, if ever, the country might reach herd immunity. His forecasts suggest that about 61% of the population should have some form of immunity—either from the vaccine or past infection—by June.
Before the pandemic, Gu hoped to start a new venture, possibly in sports analytics. Now he’s considering sticking to public health. He wants to find a job where he can have a large impact while avoiding politics, bias, and the baggage that sometimes comes with large institutions. “There are a lot of shortcomings in the field that could be improved by people with my background,” he says. “But I still don’t know quite how I would fit in.”
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