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What the numbers tell us, or don't tell us, about the omicron surge


The U.S. is now averaging more than 400,000 new COVID cases a day. This following a week where new case counts shattered the previous day's records again and again and then again. And even those staggering numbers are probably an undercount. The head of the CDC, Rochelle Walensky, told my colleague Ari Shapiro last week that with so many people testing at home, it is hard to capture the true number of cases. So how meaningful are these case counts at this phase of the pandemic? We'll put that question to Natalie Dean. She's an assistant professor of biostatistics at Emory University, and she specializes in infectious disease epidemiology. Dr. Dean, welcome back to ALL THINGS CONSIDERED.

NATALIE DEAN: Hi. Thank you so much for having me.

KELLY: Let's dive in right there. Does the focus on case numbers remain useful at this stage of the pandemic?

DEAN: I think it's always useful. I mean, it's one element of multiple numbers that we're tracking. And, you know, case counts are always going to reflect how much infection is out there. And even though there are limitations, we still are able to see these trends, these staggering increases in cases over time that really do reflect large epidemics.

KELLY: I can see how it would be useful for tracking trends. Is it going up? Is it going down? But the raw numbers which keep just making these huge headlines - are those numbers actually a huge undercount with so many people - I'll say myself included - using rapid tests, then not reporting the results?

DEAN: Yes, it's definitely an undercount. I mean, we've had undercounts throughout the pandemic. That's always been an issue in that there's always some fraction of cases that are not reported for whatever reason. And so the truth is just something even larger. And what we're really seeing is these really stark trends.

KELLY: Are there other ways to gauge level of infection in the U.S.?

DEAN: Yeah, there are a few other things we can pay attention to. One is wastewater surveillance. So basically you can track sewage, test it for viral particles. And that's nice because you remove that dependency on who's deciding to get tested, how many tests are available. It's just looking at a very community level, how much virus is there out there? Another thing in the U.K., they do random sampling. And that's a really reliable way to get a measure of how many people are infected at any one time. And, you know, that doesn't depend upon, again, who's getting tested and whether tests are available. So that's kind of a scientific way to get a sense of community prevalence.

KELLY: That sounds like it makes total sense. Why aren't we doing more of this?

DEAN: I don't know. That's a great question. Sometimes you'll see these types of efforts at smaller scales, but it's just not at a national scale.

KELLY: I'm also curious, and I know that we're still waiting on data for this, but the anecdotal evidence is that omicron seems to be giving people milder symptoms than other variants. Does that change how accurate and how useful a measure case numbers are of the seriousness of the pandemic - meaning somebody who tests positive with omicron may not be nearly as sick as they were with another variant.

DEAN: Yeah, I mean, the public health impact is made up a lot of different things, and we're most acutely interested in severe disease and death. But, of course, infections have impacts and we think about the disruption - you know, all the people who are going to need to miss work, including health care professionals and, you know, frontline workers. So the numbers have meaning, but it is a different public health impact when someone is mildly ill or doesn't even have symptoms than when someone is severely ill.

KELLY: So may I just ask, what goes through your mind when you open the morning paper and you see these graphs we're all looking at that show the vertical axis? And it's almost - it's straight up and down in terms of case numbers exploding in parts of the U.S.

DEAN: Yeah, it's really beyond comprehension. I mean, we think about - as statisticians and mathematical modelers, we're used to a certain range of numbers. And when we saw delta, I mean, that was really - just seeing the transmissibility of delta was remarkable already, but this is just even beyond that. So I think that's something that's hard to wrap our heads around. Just how many people are likely to be infected over the next month is just going to be a lot of disruption, unfortunately.

KELLY: Natalie Dean, an assistant professor of biostatistics at Emory University, thank you.


Megan Lim