The CDC can’t actually see every COVID case. Instead it relies on a modeling concept called a nowcast that uses realtime data.
At the end of December, the CDC sharply revised its estimates of the prevalence of Omicron relative to other COVID variants across the US. Before Tuesday, it reported that Omicron made up 73 percent of new COVID cases from December 12 to 18. Now, it puts the prevalence for that week at 23 percent, and estimates that Omicron made up 59 percent of new cases between December 19 and 25.
Right now, there’s still considerable uncertainty over how quickly Omicron spreads relative to Delta, and how easily it’s able to infect people with prior immunity. “The mathematical models that describe COVID transmission dynamics are becoming more and more complex,” says Anass Bouchnita, an infectious disease modeler with the University of Texas at Austin’s COVID-19 Modeling Consortium.
But quirks in the underlying data also led to the change. Because Omicron spreads so quickly, small changes in our understanding of its initial prevalence can lead to hugely different model outcomes. To a certain extent, those data quirks are probably something pretty basic: The US was looking for Omicron cases, and so it found lots of them.
Queen’s team currently sequences every sample that comes in from LSU’s testing lab, which works with healthcare facilities and community testing sites, which gives them a clearer picture of Omicron’s prevalence. Still, that national sequencing bias likely led to an overestimate of the true prevalence of Omicron.
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