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.