Daily Productive Sharing 140 - How Does A Young Chinese Predict Pandemic in America Accurately?

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(The English version follows)


前几天看到有人推荐 Youyang Gu 的关于🇺🇸疫情的 tweets,正好 Bloomberg 写了一篇专访,所以找来读了读:

  1. 他的模型持续地做出了很准确的预测,而受限于数据集(只有死亡数据),他使用了 SEIR (susceptible-exposed-infectious-recovered) 模型来做预测;
  2. 毕业于 MIT,他之前从事量化交易工作,自从他的工作产生了很大的影响力之后,他决定放弃之前想做体育分析的决定,继续为公共卫生事业做出贡献。




The 27-Year-Old Who Became a Covid-19 Data Superstar

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We saw Youyang Gu's tweets on the 🇺🇸 epidemic recommended the other day, and Bloomberg wrote an interview about him recently:

  1. his model consistently makes very accurate predictions, and limited by the dataset (only death data), he uses the [SEIR (susceptible-exposed-infectious-recovered) model](https://covid19-projections.com/ model-details/) to make predictions.
  2. a graduate of MIT, he previously worked in quantitative trading, and since his work has made a big impact, he decided to abandon his previous decision of being a sports analyst and continue to contribute to public health.

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The 27-Year-Old Who Became a Covid-19 Data Superstar

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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.

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.

The forecasting model that Gu built was, in some ways, simple. He had first considered examining the relationship among Covid tests, hospitalizations, and other factors but found that such data was being reported inconsistently by states and the federal government.

The most reliable figures appeared to be the daily death counts. “Other models used more data sources, but I decided to rely on past deaths to predict future deaths,”

After MIT, Gu spent a couple years working in the financial industry writing algorithms for high-frequency trading systems in which his forecasts had to be accurate if he wanted to keep his job.

Gu kept comparing his predictions to the eventual reported death totals and constantly tuned his machine learning software so that it would lead to ever more precise prognostications.

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.

Before the pandemic, Gu hoped to start a new venture, possibly in sports analytics. Now he’s considering sticking to public health.