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As the United States set a record of 441,278 new COVID-19 cases on Monday, the Centers for Disease Control and Prevention came out with a major revision.
The federal health agency revised its estimate of how extensive the omicron variant of COVID-19 is in the nation, deflating the previous alarming report that 73.2% of new cases in the week ending Dec. 18 were due to the omicron variant.
As it turns out, the new variant was actually only responsible for 22.5% of the new COVID cases for that particular week, a very significant 51% difference.
“There was a wide predictive interval posted in last week’s chart, in part because of the speed at which Omicron was increasing,” CDC spokesperson Jasmine Reed told Politico. “We had more data come in from that timeframe and there was a reduced proportion of Omicron.”
CDC’s estimate for the prevalence of Omicron last week dropped significantly from 73.2% w/ a 95% prediction interval of 34-94.9% down to 22.5% w/ a 95% prediction interval of 15.4-31.5%.
12/25 estimate is 58.6% with a 95% prediction interval of 41.5-74%. https://t.co/eaMCVk3TEc pic.twitter.com/ZjEvREb7Gs
— David Lim (@davidalim) December 28, 2021
Omicron accounted for 58.6% of all new cases for the week ending Dec. 25, the CDC noted.
“CDC’s models have a range, and… we’re still seeing steady increase in the proportion of Omicron,” Reed told Fox News Digital. “In some regions in the country, Omicron accounts for ~ 90% or more of cases.”
Former Trump administration surgeon general Dr. Jerome Adams noted that a testing oddity known as the “S gene dropout” may be behind the discrepancy and then revision in numbers.
“A lot of people were seeing this S dropout on the tests even before they got the follow-up genetic testing, and so those samples were disproportionately more likely to be sent in for sequencing,” he told Fox News Digital.
Dr. Li Tang noted the “large uncertainty” when speaking to the news outlet.
“Earlier, they probably relied on a small number of available sequences. It should be also noted, although the confidence interval now is narrower, the range is still big, covering from 41.5% to 74%, suggesting large uncertainty,” said Tang, an associate faculty member at St. Jude Children’s Research Hospital.
The CDC explained the change in response to a question from Politico health reporter David Lim:
CDC just responded to my initial inquiry:
“There was a wide predictive interval posted in last week’s chart, in part because of the speed at which Omicron was increasing. We had more data come in from that timeframe and there was a reduced proportion of Omicron.” pic.twitter.com/kokHyYVCtb
— David Lim (@davidalim) December 28, 2021
Founder and editor-in-chief of FiveThirtyEight, Nate Silver, tweeted that “we have to assume the CDC’s method is crap and should be ignored going forward.”
“Setting aside the question of how the initial estimate was so inaccurate, if CDC’s new estimate of #Omicron prevalence is precise then it suggests that a good portion of the current hospitalizations we’re seeing from Covid may still be driven by Delta infections,” former Food and Drug Administration Commissioner Scott Gottlieb tweeted.
Others on Twitter reacted to the major CDC revision and the reason for the large number difference.
IMO newsrooms that published the 73% number on their front page should be doing a retrospective about how a number with a 95% confidence interval of 34-94% got such prominent play https://t.co/Ni05CGkK7C
— Simon Fondrie-Teitler (@varlogsimon) December 28, 2021
Why would people be expected to trust or have faith in the CDC when they get something so so so so wrong
— Dino1975 (@Dino11975) December 29, 2021
CDC “estimates”…. Seriously 😐 how can anyone still listen to these folks who have played an instrumental role leading this Society ruining project? When’s the accountability phase start? Asking for a planet?
— Ryan Kineshanko (@rhkshanko) December 28, 2021
That “omicron is 73% of U.S. infections” number, as only a few people pointed out at the time, was based on an incredibly imprecise CDC model. https://t.co/8uk7qu6vvy (I’m not knocking CDC for having such a model, worth a try, but we need to acknowledge when we don’t know things)
— Patrick Brennan (@ptbrennan11) December 28, 2021
Everyone realizes HHS stopped allocating monoclonal antibodies because of the initial incorrect data on omicron prevalence, right? Unbelievable. https://t.co/AkAfE7XnKi
— Anish Koka (@anish_koka) December 29, 2021
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