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Stanford's computer model: best prediction of COVID-19 spread so far

The multidisciplinary team also involved researchers from the Northwestern University and was led by the Slovenian born Stanford computer scientist Jure Leskovec.

A team of researchers from the Stanford University has created a computer model that accurately predicted the spread of COVID-19 in 10 major cities this spring, by analysing three factors that drive infection risk: where people go in the course of a day, how long they linger and how many other people are visiting the same place at the same time. The multidisciplinary team also involved researchers from the Northwestern University and was led by the Slovenian born Stanford computer scientist Jure Leskovec.

The study, published in November in the journal Nature, merges demographic data, epidemiological estimates and anonymous cellphone location information, and appears to confirm that most COVID-19 transmissions occur at “superspreader” sites like full-service restaurants, fitness centers and cafes, where people remain in close quarters for extended periods. Researchers say their model’s specificity could serve as a tool for officials to help minimize the spread of COVID-19 as they reopen businesses by revealing the tradeoffs between new infections and lost sales if establishments open, say, at 20 percent or 50 percent capacity. The team led by Leskovec will use the model to develop a user-friendly tool for policymakers and public health officials.

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