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Algorithm estimates US COVID-19 infections are three times higher than reported

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An algorithm developed at the UT Southwestern medical center has estimated that there are almost three times as many COVID-19 infections in the US than the number of confirmed cases.  

The model makes daily predictions of both total and current infections across the US and in the 50 countries worst affected by the virus.

It calculated that more than 71 million people in the US had contracted COVID-19 by February 4, when there were only 26.7 million confirmed cases.

The model also estimated that 7 million people in the country currently have infections and are potentially contagious.

In other countries, the algorithm also calculated far higher numbers of infections than those reported. In the UK, it calculated that there were nearly 25 million — rather than around 4 million confirmed cases — while in Mexico, it predicted that there were almost 27.6 million instead of 1.9 million.

“The estimates of actual infections reveal for the first time the true severity of COVID-19 across the U.S. and in countries worldwide,” said study author Jungsik Noh in a statement.

 

Credit: Jungsik Noh via GitHub