New machine-learning algorithm estimates number of COVID-19 cases in the U.S.World health experts have long suspected that the incidence of COVID-19 has been higher than reported. Now, a machine-learning algorithm developed at UT Southwestern estimates that the number of COVID-19 cases in the U.S. since the pandemic began is nearly three times that of confirmed cases.
The algorithm, described in a study published today in PLOS ONE, provides daily updated estimates of total infections to date as well as how many people are currently infected across the U.S. and in 50 countries hardest hit by the pandemic.
As of Feb. 4, according to the model's calculations, more than 71 million people in the U.S. – 21.5 percent of Americans – had contracted COVID-19. That compares with the substantially smaller 26.7 million publicly reported number of confirmed cases, says Jungsik Noh, Ph.D., a UT Southwestern assistant professor in the Lyda Hill Department of Bioinformatics and first author of the study.
Of those 71 million Americans estimated to have had COVID-19, 7 million (2.1 percent of the U.S. population) had current infections and were potentially contagious on Feb. 4, according to the algorithm.
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