COVID-19 Data Dispatch

Tag: CDC

  • Unpacking U.S. data gaps and lack of public health action with Jason Salemi

    Unpacking U.S. data gaps and lack of public health action with Jason Salemi

    The CDC’s Community Level guidance contributes to current inaction on COVID-19 in the U.S. Image by Jason Salemi, from his June 10 Twitter thread.

    In April, the CDC launched a new center called the Center for Forecasting and Outbreak Analytics (or CFA). The new center aims to develop models of COVID-19 and other infectious diseases, while also helping public health agencies and individual Americans act on the information. One of CFA’s lead scientists compared it to the National Weather Service.

    But the problem is—as I discussed in a new story for FiveThirtyEight—the CFA currently does not have the data it needs to accomplish its goals. Among the challenges this new center is facing:

    • COVID-19 case data are becoming increasingly unreliable as PCR testing is less accessible and more people use at-home tests;
    • Hospitalization data are more reliable, but lag behind actual infections and may soon be unavailable in their current, comprehensive format;
    • Wastewater surveillance and other promising sources are not yet ready to replace clinical datasets;
    • A slow hiring process, as the center aims to bring on 100 scientists and communicators;
    • The CDC’s limited authority over state and local health agencies, and over the public.

    At the COVID-19 Data Dispatch today, I’m sharing one of the interviews I did for the FiveThirtyEight story. I talked to Jason Salemi, an epidemiologist at the University of South Florida College of Public Health, whom you may know from his excellent dashboard and Twitter threads providing detailed COVID-19 updates.

    While Salemi isn’t focused specifically on forecasting, he has a lot of insight about interpreting COVID-19 data and using the data for public health decisions. And I think he shares my frustration about the lack of safety measures that are being implemented across the U.S. at this dangerous point in the pandemic.

    For context, this interview took place about one month ago, while BA.2/BA.2.12.1 were driving a surge in the Northeast but hadn’t quite hit other parts of the country yet. This interview has been lightly edited and condensed for clarity.

    Betsy Ladyzhets: I wanted to start by asking, what do you see as the current state of trying to keep track of COVID in the United States? Like, what are some of the metrics that you’re looking at right now? What are some challenges that you’re facing as we deal with case numbers becoming less reliable?

    Jason Salemi: Definitely the case numbers issue. Throughout the entire pandemic, we all know that the case numbers that we learn about when somebody actually tests positive and that information gets recorded somewhere, reported to a State Department of Health and ultimately to the CDC, that’s always underestimated the true number of infections that’s been circulating in the population. Obviously, very early in the pandemic, that was really, really bad—we were mostly picking up people as they were getting sick and landing in the hospital. But as testing expanded, obviously, we did a much better job of being able to gauge what was happening with true infections by relying on the reported case numbers.

    However, during Omicron, and especially with the increased use of at-home testing—a lot of those at-home tests, if the person tested positive, were not making their way into a system that would actually get translated into the officially reported numbers. And negative at-home test results—those are definitely not making their way to public health agencies. I think in some jurisdictions, you were finding that 30%, 40% of all testing was actually antigen testing, and a significant portion of those were the at-home antigen test. 

    More recently, I think the official numbers that we hear about on a daily basis in terms of official COVID-19 cases, is becoming an increased undercount of the true number of infections that are circulating. Which is pretty striking, considering how much we’ve seen the numbers go up in the past few weeks. So, relying on officially reported cases does mean a lot less. But I still do believe that if you’re looking at—not necessarily where the numbers are exactly, but the trends in the numbers, how those numbers are changing over time—you can at least get a good feel for whether or not things are getting better or worse, even by using the COVID-19 case numbers. 

    Now, when you supplement that with things like wastewater numbers, data that are not biased by people taking advantage of testing or how they test, the wastewater numbers are maybe a better gauge for truer trends in the amount of viral spread. But again, even with wastewater numbers, two big things about those: number one is, it’s certainly not available, at least not that I can tell, for a lot of jurisdictions throughout the United States… It’s not available consistently across the country. 

    And number two, there’s nothing in those wastewater numbers where you actually can gauge: okay, this is the actual level of infection. What it helps us to do is, it’s a leading-edge indicator, where early on, we can say, “Oh, wow, we see an increase, a pretty pronounced increase in a particular area over time.” And hopefully, if we were doing things proactively, we could use those data to then implement some sort of concerted mitigation. So, this issue has become more of a challenge. But in many communities, we still can rely on how case numbers are changing over time to loosely gauge transmission rates. 

    Then, of course, a lot of people say, “It’s all about the hospitalization data, let’s utilize that.” Although I’d always love more metrics included in that [hospitalization] data set, it is something that, for some time now, we actually have consistently measured, at the national level, for every single state. You can get down to the hospital level, in some cases, and even by age group. We can have a decent understanding of how many people are being hospitalized with COVID 19. The nice thing about that is the consistency, and the fact that this [hospitalization dataset] is available everywhere, and we therefore have a decent resource that is capable of picking up indicators of more severe illness.

    But there are a lot of problems with the hospitalization data: namely, it’s a lagging indicator. Ultimately, if we were to rely exclusively on COVID-19 hospitalization rates and hospital capacity issues—those indicators lag new infections, often by five to seven days, at least. So, by the time we see those particular metrics rise, we will have lost valuable time to prevent morbidity and mortality. That’s the big [problem].

    The other thing is, there’s a lot of legitimacy to when people say, “Well, if a person went to the hospital for a non-COVID-19 related issue, and they just happened to test positive, they may not have been hospitalized because of COVID-19.” I think most are using the term “incidental.” Again, the numbers are not perfect. And when community transmission is as pronounced as it has been during many phases of Omicron, I think we do have a lot of situations where a lot of people are not being hospitalized because of COVID-19. But they are testing positive.

    For example, Jackson Health System in Florida was Tweeting out every day during the Omicron phase. And they would say—giving hypothetical numbers here—“We’ve got 250 people who are hospitalized, and that are positive for COVID-19. Of those 250 people, 51% were hospitalized for non-COVID-related reasons.” Some areas would give you more specifics, they would also break down by vaccinated versus unvaccinated. You get a lot of more rich, detailed data from some areas, but obviously, that’s not consistent across the country. In fact, I think it’s pretty rare.

    BL: Yeah, that point about hospitalizations being a lagging indicator is definitely something I want to highlight the story. And it seems very complicated, because I have heard from a couple of the modeling experts I’ve talked to that if you look at something like hospital admissions, specifically, that is less lagging. But still, overall, if you think about, like you were saying, trying to prevent more people from getting sick—even by the time you just see more hospital admissions, that’s still bad. You’ve still lost your chance to put in new mask measures, or whatever the case may be.

    JS: Oh, absolutely. And, you know, if we really were in a state right now, where getting infected really did no damage to people, it never caused any severe illness, we would obviously care less about transmission levels. Although you could always use the argument that the more we let COVID-19 circulate, the more likely it is that new variants will emerge with potentially more dangerous characteristics. So, even if it wasn’t causing a lot of severe illness, you’ve always got that aspect of it.

    But we are certainly not yet at a stage in which we can say [getting infected does no damage]—even though for the average individual Omicron is less severe when we compare it to something like Delta. But we paid a steep price in many areas in the United States to get the infection-acquired immunity and vaccine-acquired immunity that seems to be blunting the effects of Omicron. Right now, that’s why we’re not seeing the rise in hospitalization rates as steep as the rise in case rates. 

    But we are still seeing people getting hospitalized, an increasing number of people over the past couple of months. We’re not yet in a position where COVID-19 is not causing any damage. And we’re largely ignoring things like Long COVID. Just because somebody doesn’t get hospitalized, that doesn’t mean that [the virus is] still not causing a decrease in the quality of life for many people, and a decreased quality of life that can linger for some time.

    BL: Yeah, definitely. And then, another issue with hospitalization data that I wanted to ask you about, because I know you’ve looked at this, is the fact that if you’re using these county risk levels, or community levels, whatever the CDC is calling them—not every county has a hospital. So really, this is data at a somewhat larger regional level. I’m wondering if you could explain why this is an important distinction.

    JS: Yeah. And you know, this is not at all a criticism, this is kind-of the nature of the beast, so to speak. There are a lot of communities where—I’ll use Florida, because I’m most familiar with Florida, as an example. We have got a major health care system in Alachua County, which is really not a big county in Florida, not even in the top 20 largest counties. But it is a major area where a lot of people from surrounding smaller counties, like a nine- or ten-county catchment area, if they were to get really sick, that’s where they’re most likely going for treatment. And so, if you have a metric that is based on hospitalization rates, and you don’t have a hospital, obviously, you can no longer really provide a county-level indicator. It has to be more regional. And so you see a lot of variation in how the CDC has to now go from the county level to what they call health services areas.

    These [health service areas] are established groupings. In these regions, the overwhelming majority of people in these locations are going to a hospital in the broader health service area. And so it’s confusing, I think, to people: with this newer CDC metric, they wonder, “How is it that there’s no hospital in my county or the county next to me, yet you’re giving me a county-level risk measure that is supposed to be based primarily on hospitalization data?”

    And again, I think, some of the nuances of the metric get lost on people… Hospitalization data comes from a broader region [than cases], and there’s a lot of variation. There are some counties that are standalone, like Manatee County in Florida, so there is no health service area, it’s just one county for all measures. But there are some others where more than 15 counties that feed into that health service area. So again, for some people in some communities, I can understand where it’s just confusing and frustrating as to, “What does this risk level really mean, for me and the people that live near me, since the catchment region is so much larger?” This is not a right or a wrong, I understand why CDC does it the way that they do it if they’re trying to get a hospitalization-based measure. But it’s just challenging for people to digest.

    BL: Yeah, it’s challenging on that communications front. With the previous transmission levels, you could just kind-of look at the case rate and the positivity rate and be like, “Okay, I get where this is coming from.” But yeah, now it’s a little trickier. Another thing on this topic: I saw a report from POLITICO this morning that is suggesting, basically, if the National Public Health Emergency gets ended this summer, then the CDC might lose its ability to require states or hospitals to actually report the hospitalization data that is basically our best source right now. So, what would the implications be if that happens in a few months?

    (Editor’s note: After this interview, the Biden administration extended the public health emergency beyond July 15. But it’s unclear how many more times the emergency will be extended.)

    JS: I’d say pretty significant implications. Look, I’ve tried to give credit where credit is due, like the gains made with improving the federal hospitalization data. I’ve also been a critic when I feel as though we are missing key data sources or data elements. An example is the hospitalization data not having race and ethnicity information, I feel like that’s a big component that would be meaningful.

    But even with its limitations, the hospitalization data have been a very, very, very important tool for us to be able to report what’s happening in communities. And obviously, nobody wants to fly blind as it pertains to the pandemic. So if we don’t have uniform reporting from all of these states and jurisdictions, then we have to rely on the willingness of leaders at each state or community level to make similar information available, and to report that information in a timely and consistent manner. 

    Right now, we are fortunate that we continue to get the hospitalization data updated on a daily basis. And so yeah, that would obviously be a big loss if it were—it’s one thing to not have it required. But if states chose not to report that information, which certainly some states would choose not to… it would be a big loss, depending on what states choose to do to keep the population informed.

    Because, to be honest, when we get this national data, it’s a gut reaction that we want to compare states on everything—on death rates, on case rates, on hospitalization rates. To me, this can be a huge mistake. One of the obvious reasons that everybody talks about is age differences, right? Some states have a much higher percentage of older people. But it’s not just age that makes state comparisons difficult. It’s weather, and racial and ethnic distribution, and the job industries in which people can work, population density. So, I don’t really care too much about national-level data being used primarily to make state comparisons and inferences that can be misguided.

    But to have consistently reported information across the country, again, is important for us to be able to make more responsible decisions even at the local level. I would hope if that happens [losing the national dataset], we would still have states and cities and counties and communities and all these different geospatial areas continuing to report, collect, and make available to the public meaningful information in a timely manner so we can make responsible decisions.

    BL: Yeah, that makes sense. And I know that question of authority and like, what can and can’t you require the states to do, is a large issue for the CDC. I was able to talk to Mark Lipsitch yesterday, he’s one of the scientists who’s working on this new forecasting center. And one challenge he mentioned to me is that the CDC really doesn’t have the authority that it would like to in terms of requiring data reporting. They can’t require every state to start doing wastewater surveillance, they can’t require every state to report vaccine effectiveness data or breakthrough cases. And to me, that just seems like a massive hurdle that they face in trying to do this kind of long-term improvement of infectious disease forecasting.

    JS: Yeah—and it’s not just the ability, it’s also having the will. I’ve collaborated with some truly amazing scientists from the CDC for a very long time on a myriad of different initiatives, and I have little doubt that they will compile a team of experts that can analyze meaningful metrics to generate what I imagine will be a wealth of data on where we’re going in the pandemic. But it’s not just about analytic proficiency. I did read on their [CFA’s] site that their stated goal is to enable timely, effective decision-making to improve outbreak response. But how are we going to utilize those data to make recommendations? What outcomes are they going to emphasize? What communities are we thinking about when we make those recommendations?

    A lot of people talk about the measures we use, and which ones are best, and how we collect the data, and the validity, and the sophistication of the approaches that we use to either nowcast or forecast into the future. But to me, it’s also the way in which we operationalize those measures for public health recommendations. That’s where a lot of the talk is now about the measures being utilized by CDC. So whether it’s their four-level community transmission measure, or that newer three-level measure that’s based mainly on hospitalization data—how we’re using that to make recommendations, it says something about what the agencies who establish those boundaries are willing to accept.

    For example, I was just looking at some data again, when I did that thread this morning. The highest level on the community transmission metric, that used to indicate 100 cases or more per 100,000 people over the most recent seven-day window. Right now, based on the data that I just ran, we’ve got 105 counties in the United States with a population of at least 250,000—not just small counties, but large ones—that have a low community level [the CDC’s more recent metrics], the lowest possible, but they have a transmission level that is higher than that 100 per 100,000 threshold.

    And more importantly, we’ve got 28 counties—again, with a population of 250,000 or more—that are classified as medium level. That is a level with no recommendations for mask-wearing in public indoor settings. And those 28 counties have a case rate that is more than triple the threshold for high transmission, that’s 300 per 100,000, over the past seven days. You expect that medium level to change to high in the not-to-distant future for many of these areas.

    So again, it’s one thing to collect the metrics and have skilled analysis. But what we do with those measures and that analysis, is just as meaningful. And what does it mean, if we have an area that has really pronounced transmission—and we know in the past, that pronounced transmission means that the virus is going to be exceedingly good at finding vulnerable populations—and we’re not having any meaningful population-based recommendations… 

    When I looked, some of these counties were at like 400 per 100,000 [cases in a week], four times the threshold for the high transmission level [under the old CDC guidance], and they’re still not at a level where we’re supporting or recommending mask-wearing in public indoor settings. That’s pretty shocking. And I think that’s why anecdotally, now, even in my area, I’m just hearing about more and more people daily, that are not able to come to work. A lot of people are getting infected. And you’re seeing that in the rising numbers.

    BL: Absolutely. I mean, isn’t the threshold for moving from low to medium under the new community levels 200 new cases per 100,000 [per week, regardless of hospitalization numbers]?

    JS: Yeah, right. So even if you had no rise in hospitalizations, you can have a progression to the medium level. But that is now twice what the highest transmission threshold used to be. And again, I’m looking at counties that are in that medium level that now have almost twice even that newer threshold.

    We’re not yet in a situation where COVID is not causing any severe illness whatsoever. We’re ignoring a lot of the ramifications of Long COVID, we’re ignoring the fact that, when community spread has gotten so pronounced, you tend to have the virus easily, efficiently finding the most vulnerable people in those communities and still inflicting damage.

    I just feel like we’re missing an opportunity. We’re not talking about shutdowns, we’re talking about simple measures that we can put in place and recommend to people to try and balance having, normal living with putting reasonable but important precautions in place. Because that ultimately will prevent a lot of morbidity and mortality. And I feel like that’s maybe the big missed opportunity right now.

    So, I’d be excited to see a new forecasting center come out of the CDC. They are very adept scientists. But it’s ultimately, what do we do? What do we do with the data that emerges out of that center? And what recommendations, simple recommendations, do we end up giving to the public based on those analyses?

    BL: I totally agree. One of the new center’s focuses is that they want to hire a bunch of science communicators to think about these things. But still, I guess I’m a little skeptical about how much they’re gonna really be able to have an impact here, when we’re already at such a polarized position in the pandemic.

    JS: Yeah, it’s not that any of this is easy. No matter what you do, you’re going to upset a whole lot of people nowadays. I speak strictly from a scientist’s perspective. And I really do get all sides of this equation, like the businesses and the very real toll that the pandemic has taken on people. And so it is, no matter what you do, there is a balance that you have to achieve.

    But when I start to see—again, I’m going more from what has transpired specifically in Florida. And a lot of the talk this time last year, after we had the availability of vaccines, things were looking great for Florida. Numbers were really low. And that was pretty much throughout the United States, we had the vaccines, though we still heard a lot about protecting the most vulnerable, the oldest in our communities. And even as the cases started to rise, during Delta, it was like, well, just protect the vulnerable.

    But again, when community transmission gets that pronounced, the virus will continue to find the most vulnerable. And it ended up inflicting by far the largest death toll in Florida than we’ve had at any point in the pandemic, after vaccines were available for a long period of time. And that included a significant percentage of people who were not seniors. So, it’s tough, but still, people’s livelihood and lives are on the line when we’re talking about COVID.

    More federal data

  • COVID source callout: CDC Community Levels

    COVID source callout: CDC Community Levels

    (Useless) Community Levels on the left; (useful) Community Transmission Levels on the right. Charts via the CDC.

    Anyone who’s been regularly reading the COVID-19 Data Dispatch for the last few weeks can probably tell that I think the CDC’s Community Levels are pretty useless. I was critical of these new metrics when the agency changed its guidance from the old Community Transmission Levels back in February. And during the BA.2 surge, I’ve pointed out how the CDC’s Community Levels map makes it look like the U.S. is doing fine at managing COVID-19 when, in fact, we are doing anything but.

    If you need a refresher, here are a few of the problems with the Community Levels:

    • The guidance overly uses hospitalization metrics; while these metrics (especially hospital admissions) are very reliable in showing COVID-19’s impact on the healthcare system, they lag behind actual infections and completely ignore Long COVID.
    • Hospitalizations are actually a regional metric, not a county-level metric (since plenty of U.S. counties do not have hospitals). As a result, the CDC’s Community Levels calculations are confusing and difficult to replicate in some places.
    • Thresholds in the Community Levels system, already using lagging indicators, are set very high—to the point that, by the time a county reaches the high level, its healthcare system is already in big trouble.
    • The CDC does not recommend universal masking until a county reaches the high level; it only recommends one-way masking for vulnerable people, which we know doesn’t really work, at lower levels.

    Essentially, these Community Levels are so lenient that many state and local leaders have taken the guidance as an excuse to avoid instituting new COVID-19 safety measures during the BA.2 surge. In Philadelphia, business owners even cited the CDC’s lenient guidance when suing the city for instituting a new indoor mask mandate.

    Moreover, as revealed by a recent article in the Tampa Bay Times, it appears that the CDC is not even consistent with its calculations of these Community Levels. The agency labeled three Florida counties as at medium COVID-19 risk, even though they met all the criteria for high risk, due to a data reporting issue from the Florida state health department.

    To quote from the article: “A public health tool isn’t useful if it can be undone by a single data issue, said University of South Florida virologist Michael Teng.”

    Reminder, you can still see the CDC’s old Community Transmission Level guidance (which is somewhat more useful for determining one’s actual COVID-19 risk) on the agency’s COVID-19 data portal. Just click the dropdown menu on the county view tab and select Community Transmission Levels.

    more source spotlights
  • New Long COVID studies demonstrate danger of breakthrough cases

    New Long COVID studies demonstrate danger of breakthrough cases

    About one in five adults who have COVID-19 will face a health condition potentially related to long-term symptoms, a new CDC study found.

    Two new studies on Long COVID, published this week, provide an important reminder of the continued dangers this condition poses to people infected with the coronavirus—even after vaccination. Neither study provides wholly new information, but both are more comprehensive than many other U.S. papers on this condition as they’re based on large databases of electronic health records.

    First: a team at the Veterans Affairs (VA) Health Care System in St. Louis, Missouri used the VA’s extensive health records database to study breakthrough COVID-19 cases. The VA database includes more than 1,400 healthcare facilities serving veterans across the country; this St. Louis team has previously used it to characterize Long COVID symptoms more broadly, to study long-term heart disease risks of COVID-19, and for other research.

    In the new paper, published this week in Nature Medicine, the researchers put together a cohort of about 34,000 people who had breakthrough COVID-19 infections. They compared this group to larger control groups of people who hadn’t been infected and people who had been infected prior to vaccination, along with comparisons to the seasonal flu.

    Vaccination does reduce the risk of Long COVID, the researchers found: people with breakthrough cases were 15% less likely to report Long COVID symptoms than those who were infected prior to vaccination. Breakthrough Long COVID patients were notably less likely to have blood clots and respiratory symptoms than non-breakthrough patients.

    But a risk reduction of 15% is pretty minimal, compared to the protection that vaccination offers against COVID-related hospitalization and death. Moreover, for most Long COVID symptoms, patients who had breakthrough infections showed relatively little difference to those who had non-breakthroughs, the researchers found.

    “Overall, the burden of death and disease experienced by people with breakthrough SARS-CoV-2 infection is not trivial,” lead researcher Dr. Ziyad Al-Aly wrote in a Twitter thread summarizing the study. That’s scientist speak for, “A breakthrough COVID-19 case can really fuck you up in the long term!” Later in his thread, Dr. Al-Aly advocated for additional public health measures—beyond simply vaccines—to reduce Long COVID risks.

    And second: a paper from the CDC’s COVID-19 Emergency Response Team, published in the CDC’s Morbidity and Mortality Weekly Report (MMWR) last week, used electronic health records to examine overall Long COVID risk after an infection. These health records came from Cerner Real-World Data, a dataset including about 63.4 million records from over 100 health providers.

    The CDC researchers identified about 353,000 adults who had received either a COVID-19 diagnosis or a positive test result between March 2020 and November 2021. They matched this group of COVID-19 patients with a larger cohort of people who hadn’t tested positive, then looked at the COVID-19 patients’ risks of developing further symptoms more than a month after they were diagnosed.

    The findings are striking: About one in five COVID-19 survivors between the ages of 18 and 64 developed at least one “incident condition” (or, prolonged symptoms) that could be connected to their coronavirus infection. For COVID-19 survivors over age 65, that risk is one in four.

    Among the patients who potentially developed Long COVID, common symptoms were blockages in the lungs and other respiratory issues. Seniors were also likely to develop neurological and mental health symptoms, and the CDC researchers warned that Long COVID in this older age group could be linked to an increased risk of strokes and neurocognitive conditions, such as Alzheimers.

    In their paper, the CDC authors noted that patients represented in this health records database may not represent the U.S. overall, and that the methods used to identify possible Long COVID symptoms might be “biased toward a population that is seeking care.” Similar caveats apply to the VA study.

    Still, both studies clearly show the risk of just “letting COVID-19 rip” through the U.S. population, even after widespread vaccination. Studies like these should be headlines in every news publication, warning people that COVID-19 is not as mild as many of our leaders would like us to believe.

    Also, for journalists covering the pandemic: I highly recommend listening to this interview with Long COVID journalist and advocate Fiona Lowenstein, which aired on the WNYC show On the Media this weekend. (And I’m not just saying that because they plugged my recent story on the RECOVER study!) The Long COVID source list that Fiona and I collaborated on also continues to be a great resource for reporters covering this topic.

    More Long COVID reporting

  • Wastewater data gap follow-up: an update from Biobot

    Wastewater data gap follow-up: an update from Biobot

    Quite a few sites on the CDC NWSS dashboard are still not reporting recent data, but Biobot is working to bring them online.

    Last week, I pointed out a data gap on the CDC’s National Wastewater Surveillance System (NWSS) dashboard: hundreds of sewershed sites on the dashboard have not been updated with recent data in weeks.

    In this post, I hypothesized that the gap was likely a result of a shift for NWSS, as the CDC has switched from a contract with testing company LuminUltra to a contract with Biobot. In both cases, the outside company had been hired to conduct wastewater sampling and analysis for sites that don’t have capacity to do this themselves; as NWSS transitioned between testing providers, some sites were left without recent data.

    This week, I’m excited to share an update on the situation, courtesy of Becca Malizia, Biobot’s science communication manager, who reached out after seeing last week’s post. Below, you’ll find more details on the recent data gaps and Biobot’s new relationship with the CDC.

    Malizia confirmed that the transition between LuminUltra and Biobot has led to delays in wastewater data availability for some sites in the NWSS network. She pointed me to a footnote on the CDC dashboard, labeled “May 2022 Coverage Limitations”:

    Beginning April 15, 2022, approximately 150 wastewater sites in 29 states began transitioning to a new wastewater testing provider. During this transition, these sites will not have recent data displayed and will be colored gray on the map. It will take several weeks for enough data to be collected to calculate the metrics displayed on this page. Results for these sites are expected to be available again between mid-May and June 2022.

    Now, if the CDC was going for full transparency and ease of dashboard interpretation here, the agency should have placed this important note somewhere more obvious to the average user—not buried at the bottom of the page. But I’m glad to see this public information, including the estimate of when results for the transitioning sites will be available.

    Biobot also acknowledged the data gap in a Tweet on May 19, explaining that the company has experienced issues in distributing testing kits to participating sites:

    Further contributing to data delays, the sewershed sites for which Biobot is now in charge of sampling require a “minimum number of samples” before key metrics on the CDC NWSS dashboard can be calculated, Malizia said. The calculations for these metrics change from one lab to another, so Biobot needs to use data from its protocols rather than data from the prior LuminUltra contract.

    “Sites in the process of switching over from the previous contract may have some lag until there is enough data to do the calculations for the CDC metrics,” Malizia wrote. She also pointed to several other reasons why a sewershed site might collect wastewater data, but not have its data appear on the CDC dashboard, such as: a sewershed serving under 3,000 people, a sewershed serving a specific institution (like a college campus), and a sewershed where local leaders have elected not to send data to the CDC.

    Biobot has already onboarded more than 200 wastewater treatment plants, Malizia said; this includes sites that were previously included in the NWSS contract with LuminUltra, though a full list of those sites is not publicly available. By mid-July, Biobot aims to have 500 sites participating in its CDC program. The company works with state and local health departments to select wastewater sampling sites and coordinate with treatment plants.

    Now, it’s important to note that, outside of its CDC NWSS contract, Biobot coordinates wastewater testing and analysis for hundreds of sewershed sites through a program called the Biobot Network. This program is a public service offering from Biobot: the company does testing and analysis at no cost to sewersheds. But Biobot also does not send individual, sewershed-level data back to the participants. “Rather, the data is aggregated at the county level on our public dashboard (biobot.io/data) for the benefit of policymakers and the general public,” Malizia explained.

    (The free Biobot Network includes two sampling sites in Hillsborough, Florida which used to be paid Biobot customers in 2021, Malizia said, in comments responding to the Tampa Bay Times article I quoted last week. The final reports those sites received when they were paying customers were in August and October of last year. “Individual wastewater treatment plants can choose whether or not to share these reports with local government agencies,” Malizia said.) 

    Before it became a CDC contractor, Biobot was not able to submit wastewater data to the agency. Only state government agencies have access to the CDC NWSS platform used for data reporting, Malizia said; the CDC has made an exception for Biobot under the new contract. To me, this helps explain why there’s not a lot of overlap between Biobot Network sites and CDC NWSS sites, as well as why some other wastewater sampling (done by universities, research institutions, etc.) does not appear on the CDC dashboard.

    But, now that Biobot is a CDC contractor, will the company provide Biobot Network data to the agency? I asked Malizia this question, to which she responded:

    The Biobot Network will remain separate from the NWSS, however sites enrolled for the CDC NWSS Program will also be given the option to opt into the Biobot dashboard.

    In summary: Biobot is working hard to restore data from sites already in the CDC NWSS network and expand that network to more sites that don’t have capacity for wastewater sampling on their own. However, thanks to a combination of CDC bureaucracy and complex public and private data systems, it seems unlikely that we will get a singular dashboard including all wastewater testing sites in the country anytime soon.

    Also, Biobot’s current contract is only for nine months. Are we going to see another round of data gaps next winter, if the CDC decides to switch wastewater testing companies again?

  • Interpreting limited data in our undercounted surge

    Interpreting limited data in our undercounted surge

    Comparing the CDC’s new Community Levels (left) and old Community Transmission Levels (right), as of May 22. Red indicates higher transmission.

    There’s no sugarcoating it: we are in an extremely confusing and frustrating phase of the pandemic. We see the rising (yet undercounted) case numbers, we hear from friends and family members who have recently tested positive. And yet the CDC’s official COVID-19 guidance is still based on a mostly-green map, while local leaders refuse to reinstate mask mandates or other safety measures.

    I wrote about this tension for the New York City news site Gothamist last week, in a story about COVID-19 outbreaks in city public schools. As official case counts approach the levels of the winter Omicron surge and the city officially goes into “high COVID-19 alert level,” no action has been taken to slow the spread beyond distributing more rapid at-home tests to students.

    Moreover, students, parents, and teachers have limited (and often conflicting) information about COVID-19 cases in their schools. The issues include:

    • While the NYC Department of Education does allow parents to report positive results from at-home tests, reporting is not required and can take several days, potentially leading to undercounts and delays.
    • Data sources from the city and the state often do not match due to reporting differences, and both may lag behind anecdotal reports from students and teachers.
    • Other NYC data sources are also unreliable, since the city health department does not count at-home tests at all and novel sources such as wastewater surveillance aren’t readily available.

    In this phase of the pandemic, much of the official guidance from the federal government and aligned experts centers around individual responsibility. There may not be a mask mandate, but you can wear a mask if you feel it’s necessary. Large events may be taking place, but you can take a rapid test before and afterwards if you want. And so on.

    Of course, I’m not saying that you shouldn’t wear a mask or use testing. But the problem with this narrative is that, as our data sources become less reliable, it becomes harder and harder to figure out when or how one should take these individual-level actions.

    As Sarah Allen, a NYC teacher and parent whom I interviewed for my Gothamist story, put it: “You can’t say, ‘It’s up to you as an individual,’ when the level of risk is being withheld from you.”

    Still, even as our official data sources become harder to use, there are still ways to keep track of the COVID-19 risk in your community—you just may need to look at more sources and acknowledge more uncertainty in the numbers. While I was working on the Gothamist story, I received a question from a reader along similar lines; she asked what datasets I would recommend looking at right now, particularly when seeking to protect seniors and other vulnerable populations.

    Here’s what I responded (edited lightly for publication):

    • Case rates are still useful, if we acknowledge that they are undercounts. Jeffrey Shaman, an infectious disease expert at Columbia University whom I talked to for the Gothamist story, put it this way: our current datasets “will tell you the trends of what’s happening, but it won’t necessarily tell you the magnitude.” In other words, if case counts are going up and you’re also hearing about a lot of friends testing positive on rapid tests, that is still a good indication that more transmission is happening in your area. But you need to consider that the actual transmission is several times higher than the official case counts, due to more rapid testing and less PCR testing.
    • Hospitalization rates are useful, particularly new hospital admissions. As you may have noticed, COVID-19 Data Dispatch National Numbers posts in the last few months have used new hospital admissions at the same level as cases to discuss national COVID-19 trends. Some modelers I talk to really like this metric, because it’s more reliable than cases and has less of a lag than other kinds of  hospitalization metrics (such as total patients in the hospital or bed capacity), since it is driven by new people with COVID-19 coming into the hospital for treatment. The big caveat with hospitalization numbers is that they mainly tell you about healthcare system capacity, eg. if you get severely ill, will there be a bed in the hospital for you?  It’s harder to extrapolate from hospitalization numbers to other impacts of COVID-19, like Long COVID.
    • The CDC’s old transmission level guidance is still actually pretty helpful for guiding health policies, especially for vulnerable populations. In this guidance, the “high” level means that a county is reporting over 100 new COVID-19 cases for every 100,000 people, over the course of a week. This high level may also be associated with high test positivity rates, a sign of high transmission and/or undertesting. So, if your county is reporting high transmission under this old guidance, it’s a pretty decent signal that there is a lot of COVID-19 circulating there — and reaching this level is actually even more concerning now than it would’ve been a few months ago, since so many rapid tests are going unreported. (The CDC itself actually recommends that healthcare facilities use this guidance, in a note at the top of its COVID-19 dashboard.)
    • Wastewater surveillance, if it’s available in your area. That “if” is a pretty big caveat; and even in some places where wastewater surveillance has been available, data have been scarce recently (see: later in this issue). But if you do have access to COVID-19 prevalence data from sewersheds in your community, this information of how coronavirus spread is changing in your area: is transmission increasing; or if you’re in a wave, has it started to decrease again yet? Beyond the CDC NWSS and Biobot dashboards, you can use the COVIDPoops19 dashboard to look for wastewater surveillance near you.
    • The COVID Cast dashboard, from Carnegie Mellon University’s Delphi Group, is another helpful source recommended to me recently by a modeling expert. The Delphi group does modeling work and provides data based on surveys of the U.S. population, answering questions like, “How many people are wearing masks?” Their dashboard also incorporates other unique data points you won’t find elsewhere, including antigen test positivity from one major test provider (Quidel), trends in COVID-related doctors visits, and analysis of Google search trends for COVID-like symptoms.

    As always, if you have further questions, please reach out.

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  • Sources and updates, May 15

    • COVID-19 deaths that could’ve been prevented with vaccines: A new analysis from the Brown University School of Public Health suggests that almost 319,000 U.S. COVID-19 deaths could have been avoided if all adults had gotten vaccinated against the disease. This number differs significantly by state; there were 29,000 preventable COVID-19 deaths in Florida, compared to under 300 in Vermont. For more context on the analysis, see this article in NPR.
    • CDC dashboard in Spanish: The CDC has translated its COVID-19 Data Tracker into Español. At a glance, the Spanish version appears to include all the major aspects of the tracker: cases, deaths, vaccinations, community transmission, variant prevalence, wastewater, etc. Of course, it would have been great if the agency could’ve devoted resources to this translation effort well below spring 2022, when the number of people looking to the agency for COVID-19 guidance is pretty low.
    • CDC may lose access to COVID-19 data: According to reporting from POLITICO, the CDC and other national health agencies may no longer have the authority to require COVID-19 data reporting from states and individual health institutions if the Biden administration allows the country’s federal pandemic health emergency to end this summer. Such a change in authority could lead to the CDC (and numerous other researchers across the country) losing standardized datasets for COVID-19 hospitalizations, transmission in nursing homes, PCR testing, and other key metrics. Considering that hospitalizations are considered the most reliable metric right now, this could be a major blow.
    • COVID-19 testing declines globally: Speaking of losing reliable data: this report from the Associated Press caught my eye. The story, by Laura Ungar, explains that the U.S. is not the only country to see a major decrease in reported COVID-19 tests (a.k.a. Lab-based PCR, not at-home rapid tests) in recent months. “Experts say testing has dropped by 70 to 90% worldwide from the first to the second quarter of this year,” Ungar writes, “the opposite of what they say should be happening with new omicron variants on the rise in places such as the United States and South Africa.”
    • More promising data on Moderna kids’ vaccine: While Pfizer’s vaccine for children under five remains in development, Moderna continues to release data suggesting that this company is further ahead in providing protection for the youngest age group. This week, Moderna announced a half-dose of its vaccine provides a “strong immune response” in children ages six to 11; the announcement was backed up by a scientific study published in the New England Journal of Medicine (so, more rigorous than your typical press release). The FDA is currently evaluating a version of Moderna’s vaccine for children between ages six months and six years.

    All featured sources
  • More transparency needed on CDC wastewater data

    More transparency needed on CDC wastewater data

    Why are so many wastewater surveillance sites in the CDC’s network currently labeled as “new sites” (white) or “no recent data” (gray)? Here’s what I believe is going on.

    Update, May 22, 2022: After this post was published, Becca Malizia, Biobot’s science communication manager, reached out to provide some clarification on the current state of wastewater data in the U.S., as well as on Biobot’s relationship with the CDC. See this follow-up post for more details.

    As I mentioned last week: something strange is going on with the CDC’s National Wastewater Surveillance System (NWSS) data.

    Hundreds of sites on the CDC NWSS dashboard have been labeled as showing “no recent data” for a couple of weeks. As a New Yorker, I pay special attention to the New York City sites; twelve sites in the city were actually removed from the dashboard, then re-added as “new sites,” even though researchers in the city have been testing wastewater for the coronavirus since 2020.

    I have yet to do dedicated reporting on this topic, but I wanted to share a bit of information on why I think this is happening. (Please take this with a grain of salt!)

    Last winter, as part of CDC NWSS’ efforts to enable more states and counties across the country to test their wastewater for the coronavirus, the agency contracted with LuminUltra, a biological testing company that has become one of the leading wastewater testers during the pandemic. LuminUltra, in conjunction with NWSS and the Water Environment Federation, was responsible for testing and analyzing wastewater for state and local health departments that wanted to set up this monitoring, but didn’t have internal capacity to do so themselves.

    LuminUltra’s contract expired last month. In its place, CDC NWSS has set up a new contract with Biobot, another leading wastewater contractor, the company announced this week. Biobot will be responsible for collecting and analyzing wastewater data at 500 sites across the country; it’s unclear from the press release how many of these sites were also part of the old LuminUltra contract, but I’m guessing there’s a lot of overlap.

    Here’s the problem, though: LuminUltra stopped testing wastewater at hundreds of NWSS sites last month, but Biobot hasn’t yet resumed testing, data analysis, reporting to the CDC, etc. at all of these locations. As a result, the CDC NWSS dashboard currently includes a number of sites labeled as “new” or “no recent data,” making it difficult to actually use this federal source for evaluating wastewater trends.

    Considering that we’re facing a surge and U.S. case data are less reliable than ever, this is not exactly a great time to have a gap in our wastewater data! (Also, I think that something else may be going on with the New York City sites, but that’s a topic for another post.)

    As I said above, I haven’t done much reporting on this myself yet, but I wanted to share a relevant section from a recent Tampa Bay Times investigation about wastewater surveillance in Florida:

    One of the largest players in the private testing market is Biobot Analytics, an MIT start-up that wants to market the technology.

    Last year the company raised $20 million. It already has contracts with more than 700 municipalities in all 50 states, according to the Boston Globe, including Hillsborough County. But Biobot does not report that data to the CDC. Instead the company shares the data on its website, using its methodology.

    Biobot, which collects data from six Florida counties, said it’s up to local governments to share COVID-19 data with the CDC.

    The company started collecting Hillsborough’s data in June 2021. A county spokesperson said they haven’t received any reports from Biobot.

    The amount of coronavirus detected in Hillsborough’s wastewater has doubled in the past month, according to Biobot’s website. It’s an estimate based on the county’s Northwest Regional Water Reclamation Facility and the city of Tampa’s Howard F. Curren facility.

    It’s unclear if Biobot’s data-sharing policy will affect the nation’s wastewater surveillance program. The company took over the federal program on April 15, when it was awarded a $10.2 million contract to oversee the next year of testing.

    That contract covers 500 utility providers across the country, according to the CDC. But Biobot and the CDC say the company won’t share COVID-19 data from the 700 utilities not covered by the contract.

    I’m elevating this because, first of all, everyone should read the Tampa Bay Times piece—it is excellent local reporting on this issue. And second of all: does this mean that, even as Biobot takes over sampling and analysis for sites in the CDC NWSS network, the Biobot and CDC data systems will not be fully integrated?

    This seems like a major challenge for a data network that is already quite fragmented, and I would love to see more transparency from the CDC on the whole situation. If anyone else is reporting on this or has additional information, please reach out!

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  • COVID source callout: “No recent data” at hundreds of sewershed sites

    COVID source callout: “No recent data” at hundreds of sewershed sites

    Screenshot of the CDC’s wastewater dashboard. Note all the gray dots indicating “no recent data.”

    Anyone who’s pulled up the CDC’s National Wastewater Surveillance System (NWSS) dashboard in the last week or two has likely noticed this trend: hundreds of sewershed sites are currently marked as “no recent data.” I keep a particularly close eye on the sites in New York City, which have been reporting “no recent data” for at least two weeks.

    According to the CDC, a site marked as “no recent data” has reported findings from fewer than two wastewater samples in the last 15 days. In practice, it could either indicate that a site actually isn’t sampling its wastewater frequently enough for the CDC’s standards, or it could indicate a lack of capacity to process those samples. Probably, both things are happening at a lot of these sites.

    From corresponding with press officers at NWSS, I know that the team is working with state and local health agencies, as well as with individual sewershed sites, to ensure accurate data and standardize reporting. This is a massive task, considering that wastewater surveillance started as a grassroots effort in labs with many different sampling and analytical methods, and I appreciate the NWSS team’s efforts.

    But I think they could increase transparency about the sites where recent data isn’t available—either on the dashboard itself or in other public communications. I shouldn’t have to hunt through Twitter replies to find the most basic info about wastewater data updates! Especially when this source is becoming increasingly important in the wake of less reliable case data.

    https://twitter.com/amethystarlight/status/1521146474373029888

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  • The “one million deaths” milestone fails to capture the pandemic’s true toll

    The “one million deaths” milestone fails to capture the pandemic’s true toll

    This week, many headlines declared that the U.S. has reached one million COVID-19 deaths. While a major milestone, this number is actually far below the full impact of the pandemic; looking at excess deaths and demographic breakdowns allows us to get closer.

    NBC News was the first outlet to make this declaration, announcing that its internal COVID-19 tracker had hit the one million mark. Other trackers, including the CDC itself, have yet to formally reach this number, but major publications still jumped on the news cycle in anticipation of this milestone. (Various trackers tend to have close-but-differing COVID-19 counts due to differences in their methodologies; Sara Simon wrote about this on the COVID Tracking Project blog back when the official death toll was 200,000.)

    But the recent articles about “one million deaths” fail to mention that the U.S. actually reached this milestone a long time ago. This is because the official count only includes the deaths formally logged as COVID-19, in which the disease was listed on a death certificate or diagnosed before a patient passed. Such a count fails to include deaths that were tied to COVID-19, but never proven with a positive test result, or deaths that were indirectly linked to the pandemic for a myriad of reasons.

    To get closer to the pandemic’s true toll, demographers use a metric called excess deaths: the number of deaths that occurred in a given region and time period above what would be expected for that region and time period. Experts calculate that “expected death” number with statistical models based on patterns from previous years.

    In total, the U.S. has reported 1,118,540 excess deaths between early 2020 and last month. 221,026 of those deaths have not been formally tied to COVID-19. According to a new World Health Organization report, the U.S. was already close to one million COVID-related deaths by December 2021.

    To give a more specific example: in the U.S., in the week ending January 22, 2022, CDC analysts estimated that 61,303 deaths would have occurred if there were no COVID-19 pandemic. But actually, a total of 85,179 deaths occurred in the country that week. The difference between the observed and expected values, 23,876, is the excess deaths for this week.

    I selected the week ending January 22 as an example here because it has one of the highest excess death tolls of any week in the last two years. This week marked the peak of the Omicron surge, a variant that many U.S. leaders called “mild” and dismissed without instituting further safety measures.

    During this week, the CDC reports 21,130 official COVID-19 deaths. That suggests most of the excess deaths in this week, the deaths which occurred over pre-pandemic expectations, were directly caused by the virus.

    But what about the 2,746 deaths that weren’t? How many of these deaths were also caused by COVID-19, but in patients who were never able to access a PCR test? How many occurred in counties like Cape Girardeu, Missouri, where coroner Wavis Jordan claimed his office “doesn’t do COVID deaths” and refuses to put the disease on a death certificate without specific proof?

    And how many deaths resulted from people being unable to access the healthcare they needed because hospitals were full of COVID-19 patients, or people dying in car accidents during an era of less road safety, or people dying of opioid overdoses brought on by increased stress and financial instability?

    Answering these questions takes a lot of in-depth reporting, which I know well because the Documenting COVID-19 team has been doing our best to answer them through our (award-winning!) Uncounted investigation.

    As we’ve found, every state—and in some cases, every county—has a unique system for investigating and reporting deaths, especially those linked to the pandemic. In some places, coroners or medical examiners are elected officials who face political pressure to report COVID-19 deaths in a particular way. In others, they face chronic underfunding and a lack of training, leaving them to work long hours in an attempt to produce accurate numbers.

    You can see the resource difference when comparing officially-reported COVID-19 deaths to excess deaths by state or county. Some states, like those in New England, have COVID-19 death numbers that closely match or even exceed their excess death numbers; medical examiners in these states have centralized death reporting systems and a lot of resources for this process, reporting by my colleague Dillon Bergin showed.

    Other states, like Alaska, Oregon, and West Virginia, have officially logged fewer than three in four excess deaths as COVID-19 deaths. Such a number may signal that a state is failing to properly identify all of its COVID-19 fatalities.

    For more granular data on this topic, I recommend reading the work of Andrew Stokes and his team at Boston University. Andrew is the Documenting COVID-19 project’s main academic collaborator on Uncounted; his team just shared their latest county-level excess death estimates in a preprint. (County-level data are also available in the Uncounted project’s GitHub repository.)

    Excess deaths can also show how the pandemic continues to hit disadvantaged Americans harder. In 2020, COVID-19 death rates (i.e. deaths per 100,000 people) for Black, Indigenous, and Hispanic Americans were higher than the rates for White Americans; in 2021, some of these disparities actually got worse despite the broad availability of vaccines and other mitigation measures. Non-white groups also saw all-cause mortality (not just COVID-19 deaths) increase more from 2019 in both 2020 and 2021, compared to white Americans.

    Please note, the chart below shows crude death rates, which don’t account for differences in age breakdowns between race and ethnicity groups. For example, crude death rates for white Americans tend to be higher because white people generally live longer than people of color in the U.S., and more seniors have died of COVID-19. You can see the difference that ade-adjustment makes in the CDC charts here.

    Why is it important to acknowledge and investigate these excess deaths, going beyond the reported COVID-19 numbers? At an individual level, family members who lost loved ones to COVID-19 find that diagnosis important; they can access FEMA aid for funerals, and can receive acknowledgment of how this one death fits into the broader pandemic.

    And at the county, state, and national levels, looking at excess deaths allows us to see a full picture of how COVID-19 has affected us. Experts say that inaccurate COVID-19 death numbers can create a negative feedback loop: if your community has a too-low toll, you may not realize the disease’s impact, and so you may be less likely to wear a mask or practice other safety precautions—contributing to more deaths going forward.

    As a data journalist, sharing these statistics and charts is my way of acknowledging the one million deaths milestone, and all of the uncounted deaths that are not included in it. But this pales in comparison to actual stories shared by family members and friends of those who have died in the last two years.

    To read these stories, I often turn to memorial projects like Missing Them (from THE CITY), which captures names and stories of over 2,000 New Yorkers who died from COVID-19. Social media accounts like FacesOfCOVID also share these stories. And if any COVID-19 Data dispatch readers would like to share a story of someone they lost to this disease, please email me at betsy@coviddatadispatch.com; I would be honored to share your words in next week’s issue.

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  • Seroprevalence, incomplete data in the wake of the Omicron wave

    Seroprevalence, incomplete data in the wake of the Omicron wave

    Almost 60% of Americans had antibodies from a prior COVID-19 case in February 2022, a CDC study found. This rate was even higher among young children and teenagers.

    More than half of Americans have some antibodies from a recent coronavirus infection, according to a new CDC report. The study was published Tuesday in the CDC’s Morbidity and Mortality Weekly Report (MMWR), accompanied by a press conference and other fanfare. To me, this report (and its publicity) reflects the CDC’s current lack of urgency around addressing the pandemic and its continued impacts.

    The CDC regularly surveys COVID-19 antibody levels among the U.S. population, a metric that scientists call seroprevalence. For these surveys, the agency works with commercial laboratories to measure antibody rates from a nationally representative sample of Americans, with updates provided about once a month. The survey specifically looks at a type of antibody that develops in response to infection, not vaccination.

    This most recent iteration of the survey, providing data from February 2022, is particularly notable: the CDC estimates that 58% of Americans had this immune system indicator of a recent COVID-19 infection, immediately after the nation’s massive Omicron wave. Not all of these people got COVID-19 during the Omicron wave, though, since some of these antibodies stem from earlier infections.

    Other notable findings include:

    • National seroprevalence increased from 34% in December 2021 to 58% in February 2022—the largest jump recorded in this survey—reflecting the Omicron wave’s impact.
    • Children and teenagers had the highest antibody levels. For the 12 to 17 age group, seroprevalence went from 46% in December 2021 to 74% in February 2022.

    There are some major caveats to this study, though, including:

    • The imprecise nature of this antibody measurement. The type of antibody measured in this seroprevalence survey “stays positive for at least two years after infection,” CDC scientist Dr. Kristie Clarke said on the agency’s press call.
    • Antibodies wane at different rates and levels for different people, so it’s unclear to what extent this 58% finding actually reflects the share of Americans who have gotten COVID-19 since spring 2020.
    • Plus, some people infected by the coronavirus never seroconvert, meaning that they don’t develop antibodies at all (and thus wouldn’t show up in this study).
    • While we know that the COVID-19 antibodies identified in this study confer some protection against new infections, it’s unclear how long that protection lasts or how it might hold up against new variants.

    To me, this study (and the CDC’s choice to promote it with one of the agency’s infrequent press calls) exemplifies the Biden administration’s COVID-19 response right now.

    As I listened to the press call, the CDC’s interpretation of this study was clear: more than half of Americans have some protection against COVID-19 from a prior infection, and many of those people also have protection against vaccination. Much of that protection applies specifically to Omicron and will likely help us avoid a crisis from BA.2, so it gives the U.S. additional reason to relax safety measures, the CDC suggested.

    (Worth noting: the CDC still recommends vaccination and booster shots for anyone who had a previous coronavirus infection, including children. But that message is not getting across right now, as evidenced by our low booster shot uptake.)

    When you ask for more specifics on that “protection” from prior infections, though, the CDC isn’t able to provide much information. Again, we don’t know how long the protection lasts or how it holds up against other variants. And we have no idea how many people had mild or asymptomatic COVID-19 cases, then did not seroconvert.

    The CDC’s press call also failed to mention Long COVID, which is a risk from any COVID-19 case—no matter how mild. Some Long COVID researchers have also suggested that lack of seroconversion, or even a prior infection in general, may increase a patient’s future risk for prolonged symptoms the next time they get infected.

    And, of course, the CDC report also exemplifies our current lack of surveillance. How many of those Omicron infections between December and February were actually caught by PCR testing and reported to the CDC? A small fraction. At the press call. Dr. Clarke mentioned an upcoming CDC study that estimates how many infections go uncounted for every one reported case:

    In the Omicron period, we found that over that time period, the infection to case ratio was the highest that it’s been, at over three estimated infections per reported case. And that varied by region, so depending on which US census region the estimates were, you know, the ratios were higher or lower.

    Surely that ratio is getting even higher now. To me, this forthcoming study, combined with the seroprevalence report, is a reminder that the cases we see in our datasets and dashboards are a very incomplete picture of actual coronavirus transmission in the U.S. And yet the CDC is using this incomplete picture to suggest we all relax, take our masks off, and forget about the pandemic.

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