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.
This weekend, I spoke on a panel at the Association of Health Care Journalists (AHCJ) annual conference, held in person in Austin, Texas. (Yes, the irony of my first air travel post-end of the mask mandate being a trip to a health journalism conference is not lost on me.)
My session focused on COVID-19 data, particularly the data gaps left by the federal government—which, longtime readers know, is a topic I’ve reported on extensively. We provided attendees with a tipsheet, sharing some source suggestions and tips for reporting on this topic.
Derek Kravitz, my editor and project lead of the Documenting COVID-19 project, moderated the session; Nsikan Akman, health and science editor at Gothamist/WNYC, was another panelist. Dr. Katelyn Jetelina, author of the incredible Your Local Epidemiologist newsletter, was scheduled to be a panelist as well, but unfortunately couldn’t make it to the conference last minute. (Her contributions are still reflected in our slides and tipsheet, though.)
During the session, we discussed a range of COVID-19 data topics, including:
The lack of standardization for COVID-19 data collection and reporting in the U.S., exacerbated by our decentralized healthcare and public health systems;
Discrepancies between New York State and New York City data, as an example of this lack of standardization (a specialty for Nsikan);
Challenges around (and some recommendations for) communicating COVID-19 data to audiences;
One of the most challenging aspects of reporting on the pandemic has been accessing reliable, accurate data about COVID-19 and its impact on Americans. The need for trustworthy, real-time data has caused a few journalism and nonprofit groups to create repositories to pull together data from varying sources.
A Thursday session at Health Journalism 2022 in Austin, “The quest for COVID-19 data: Where “official sources” fell short and journalism stepped in,” focused on these efforts and provided journalists with a wealth of resources for up-to-date data related to the pandemic.
Most high-income countries have national health care systems, so data collection and collation is far more straightforward than in the federalized U.S. health care system, where a mix of private and public payers are governed by national and differing state laws. Without a national registry or centralized healthcare system, it’s been harder to track statistics on COVID cases, hospitalizations, deaths, vaccinations, and other relevant numbers.
The tipsheet from this session includes links and blurbs to a few of the panel’s favorite COVID-19 data sources, including:
CDC mortality data, cleaned and sorted by Documenting COVID-19
HHS Community Profile Reports
MCH Strategic Data’s COVID-19 in schools dashboard
COVIDPoops19 dashboard
Historical wastewater data from CDC NWSS
Long COVID Source List
Health Equity Tracker
Open Government Data
And the tipsheet also includes a few tips for communicating COVID-19 data, supplemented with stories that provide useful examples. Here are the tips:
Explain where data come from and how they’re collected – using as little jargon as possible.
Be clear about uncertainties and caveats; what can’t the data tell you?
Share how data points, trends or study results can be translated into actionable information for readers.
Use questions from readers to guide reporting.
Look for differences in how state and municipalities track their COVID-19 data.
Look for ways that data collection methods (or the lack of clear methods) may hide health inequities.
Use visuals/multimedia to communicate the pandemic’s immense tolls.
Provide larger context about the reasons why COVID-19 data in the U.S. are often fractured and difficult to interpret.
The CDC’s new Center for Forecasting and Outbreak Analytics (CFA) intends to modernize the country’s ability to predict disease outbreaks. Image via the CDC.
This week, the CDC introduced a new team focused on modeling infectious diseases, called the Center for Forecasting and Outbreak Analytics (or CFA). The agency aims to hire about 100 scientists and communicators for the center; they’ll currently focus on COVID-19, but will expand to other diseases in the future.
“We think of ourselves like the National Weather Service, but for infectious diseases,” Caitlin Rivers, the new center’s associate director for science, told the Washington Post.
This idea of forecasting infectious diseases like the weather was a major theme of an event that the White House hosted last Tuesday, timed with the introduction of the CDC’s new center. This event, a three-hour summit, featured speeches from the administration’s COVID-19 response leaders (Dr. Ashish Jha, Dr. Rochelle Walensky, etc.), as well as panels bringing together the scientists who have joined CFA so far, healthcare leaders, and public health workers from around the country.
I watched the event on a livestream, and kept a running Twitter thread of key points:
this morning, I'm watching the White House's summit on "strengthening the nation's early warning system for health threats" (aka pandemic preparedness). so far, a lot of talking about predicting infectious diseases like we predict the weather.
As discussed at the summit and on CFA’s new website, this center has three main functions:
Predict: A team of disease modelers, epidemiologists, and data scientists will establish methods for forecasting disease spread and severity, in collaboration with state and local leaders.
Inform: A team of science communicators will share information from the Predict team’s modeling efforts with public health officials and with the public, ensuring that this information is actionable.
Innovate: In addition to its in-house analysis and communication, CFA will fund research and development to drive better data collection and forecasting strategies.
According to the CDC, CFA has already awarded $26 million in funding to “academic institutions and federal partners” working on forecasting methods, as part of this “innovate” priority. Neither CFA’s website nor the summit provided any indication of what these institutions are or what they’re working on; I wrote to the CDC’s media team asking for more information, and have yet to hear back from them.
At last Tuesday’s summit, it was nice to hear health officials from the local to the federal levels describe COVID-19 data issues that I’ve been writing about for two years. These included: the need for more timely data on issues like new variants and vaccine effectiveness; the need for more demographic data that can inform health equity priorities; the need for more coordination (and standardization) between different state and local health agencies; and the need for actionable data that are communicated in a way people outside science and health settings can understand.
But for all this discussion of the problems with America’s current health data systems, the event included very little indication of potential solutions. For instance, as Bloomberg health editor Drew Armstrong pointed out, nobody mentioned that many of our problems would be solved with a national healthcare system, following the lead of the U.K.—whose data we’ve relied on throughout the pandemic.
Oddly enough, nobody has (as far as I've heard) said: "You know what would make a unified national health IT system a lot easier…" and then gestured across the Atlantic
Moreover, Tuesday’s event was very rushed: each panel was just half an hour long, with only a few minutes for each expert panelist to make their points and barely any time for questions. I would’ve loved to hear entire keynote speeches from people like Dr. Anne Zink, director of Alaska’s public health agency, and Dr. Loretta Christensen, chief medical officer for the Indian Health Service. But they were relegated to brief comments.
It almost felt like the Biden administration had taken a couple of hours in their schedule to appease the science and health experts who wanted to see some acknowledgment of the COVID-19 data issues—and then went right back to downplaying the pandemic. (Also not lost on me: this same day, administration officials were “weighing the political risks” of appealing the blocked travel mask mandate.)
… it remains to be seen how much any of the great panels and speeches today will translate to actual action, when so many US leaders apparently want to forget about COVID entirely.
I would love to be proven wrong, and to see this new CDC center usher in an era of standardized, actionable infectious disease data and modeling across the country. But right now, I’m not very optimistic.
A study published in the CDC’s own journal indicated that about half of people infected with Omicron are still contagious 5-10 days after their isolation period starts. Chart via CDC MMWR.
Maybe it’s because I’m a twenty-something living in the Northeast, but: quite a few of my friends have gotten COVID-19 in the last couple of weeks. The number of messages and social media posts I’m seeing about positive rapid tests isn’t at the level it was during the Omicron surge, but it’s notable enough to inspire today’s review of the CDC’s isolation guidance.
Remember how, in December, the CDC changed its recommendations for people who’d tested positive for COVID-19 to isolating for only five days instead of ten? And a bunch of experts were like, “Wait a second, I’m not sure if that’s sound science?” Well, studies since this guidance was changed have shown that, actually, a lot of people with COVID-19 are still contagious after five days. Yet the CDC has not revised its guidance at all.
(Also, to make sure we’re clear on the terms: isolation means avoiding all other human beings because you know that you have a contagious disease and don’t want to infect others. Quarantine means avoiding other humans because you might have the disease, due to close contact with someone who does or another reason for suspicion.)
The current CDC guidance still says that, if you test positive: “Stay home for 5 days and isolate from others in your home.” Yet, in recent weeks, I’ve had a couple of friends ask me: “Hey, so it’s been five days, but… I’m not sure I’m ready to rejoin society. Should I take a rapid test or something?”
Yes. The answer is yes. Let’s unpack this.
Studies indicating contagiousness after five days
As this NPR article on isolating with Omicron points out, the CDC guidance was “largely based on data from prior variants.” At the time of this five-day recommendation, in late December, scientists were still learning about how Omicron compared to Delta, Alpha, and so on, particularly examining the mechanisms for its faster spread and lower severity.
But now, almost four months later, we know more about Omicron. This version of the coronavirus, research suggests, is more capable of multiplying in the upper respiratory tract than other variants. People infected with Omicron are able to spread the virus within a shorter time compared to past strains, and they are able to spread it for a higher number of days—even if their symptoms are mild.
One study that demonstrates this pattern is a preprint describing Omicron infections among National Basketball Association (NBA) players, compared to cases earlier in 2021. Researchers at Harvard’s and Yale’s public health schools, along with other collaborators, compared 97 Omicron cases to 107 Delta cases. NBA players are a great study subject for this type of research, because their association mandates frequent testing (including multiple tests over the course of a player’s infection).
The big finding: five days after their Omicron infections started, about half of the basketball players were still testing positive with a PCR test—and showing significant viral load, indicating contagiousness. 25% were still contagious on day six, and 13% were still contagious on day seven. These patients also saw less of a consistent pattern in the time it took to reach their peak contagiousness than the players infected with Delta.
From the NPR article:
“For some people with omicron, it happens very, very fast. They turn positive and then they hit their peak very quickly. For others, it takes many days” – up to eight or even 10 days after turning positive, says the study’s senior author, Dr. Yonatan Grad, an associate professor of immunology and infectious diseases at the Harvard T.H. Chan School of Public Health.
While this NBA study is a preprint, other research has backed up its findings. One study from Japan, shared as a “preliminary report” in January, found that people infected with Omicron had the highest levels of viral RNA—indicating their highest levels of contagiousness—between three and six days after their symptoms started. The researchers saw a “marked decrease” in viral RNA only after ten days.
Another preprint, from researchers at the University of Chicago (and antigen test proponent Michael Mina), examined Omicron infections among healthcare workers at the university medical center. Out of 309 rapid antigen tests performed on 260 healthcare workers, 134 (or about 43%) were positive results received five to ten days after these workers started experiencing symptoms.
The highest test positivity rate for these workers, according to the study, was “among HCW returning for their first test on day 6 (58%).” In other words, more than half of the workers were still infectious six days after their infection began, even though the CDC guidance would’ve allowed them to return to work.
Later in February, a study in the CDC’s Morbidity and Mortality Weekly Report (MMWR)—or, the CDC’s own journal—shared similar results. The report, authored by CDC researchers and practitioners at a healthcare system in rural Alaska, looked at antigen test results from hundreds of infections reported to this health system during the Omicron wave.
The main finding: between five and nine days after patients were diagnosed with COVID-19, 54% (396 out of 729 patients) tested positive on rapid antigen tests. “Antigen tests might be a useful tool to guide recommendations for isolation after SARS-CoV-2 infection,” the authors wrote.
News@CDCgov publishing its own results showing how POOR their own guidance is surrounding leaving isolation at 5 days w/out a negative rapid test
When starting isolation bc of symptoms:
65%-80% Rapid Ag test Pos at days 5-7 40% Pos through day 9!
Like past research, this study found that over half of patients (with both Omicron and Delta) were still contagious five days into their infections. About one-fourth were still contagious at day eight.
Guidance for people testing positive
All of the above studies suggest similar conclusions: about half of people infected with Omicron will still be contagious five days after their positive test results or the start of their symptoms, despite what the CDC’s guidance says. If you get infected with BA.2 in the coming surge, the best way to figure out whether you’re contagious after day five is by taking a rapid antigen test.
In fact, for the highest accuracy (and peace of mind), I’d recommend taking two antigen tests, two days in a row. If both are negative, then you’re probably good to return to society—but maybe don’t travel to visit an elderly relative just yet.
Seeing a lot of “rapid tests don’t work against BA.2” takes and it’s hard to see that as anything but one more capitulation.
Rapid tests are not perfect. However, they still do work & are arguably more important at this stage of the pandemic than they’ve ever been.
— Dr. Angela Rasmussen (@angie_rasmussen) April 12, 2022
This two-rapid-test guidance comes from the U.K. Health Security Agency, which recommended in December that Brits could isolate for seven days instead of ten if they tested negative on days six and seven of their isolation. (The U.K.’s guidance has since become more lenient, but this is still a good rule for reference—more based in science than the CDC’s guidance.)
What else should you do if you test positive? Here are a few recommendations that I’ve been giving friends and family:
Be prepared to isolate for a week or two, even if you may be able to leave isolation after a shorter period (with rapid tests).
After leaving isolation, wear a good mask (i.e. an N95 or KN95) in all public spaces.
There’s also the HHS Test to Treat program, which allows people to get tested for COVID-19 and receive treatment in one pharmacy visit. This program has faced a pretty uneven rollout so far, though.
Many readers may know that, since last fall, I’ve been working part-time at the Documenting COVID-19 project: a public records, data, and investigative project at Columbia University’s Brown Institute for Media Innovation and the public records site MuckRock.
One major focus at Documenting COVID-19 is our Uncounted investigation, an effort to understand how COVID-19 deaths—and other deaths indirectly caused by the pandemic—have gone under-reported in the last two years. The CDC has reported nearly one million official COVID-19 deaths; but that figure doesn’t include over 300,000 deaths of natural causes that occurred over what researchers expected in 2020 and 2021.
These natural causes logged on Americans’ death certificates—such as diabetes, heart disease, and respiratory conditions—may have been linked to COVID-19. In fact, about 158,000 deaths during the pandemic were specifically linked to natural causes that the CDC considers potentially COVID-related. But the official records make it hard to say for sure.
In a story with USA TODAY published late last year, Documenting COVID-19 found massive gaps and inconsistencies in the U.S.’s death system, which likely contributed to these undercounts. These include: a lack of standardization for medical examiners and coroners’ offices, workers in these positions becoming overwhelmed during the pandemic, and failures in some cases to order COVID-19 tests for patients or push back when families insisted a death wasn’t COVID-related.
The data come from the CDC’s provisional mortality database; our team signed a data-use agreement with the agency so that we can use their API to gather data more quickly and efficiently than what’s possible with the CDC’s WONDER portal.
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All deaths by race and ethnicity, with age-adjusted rate, regardless of underlying cause of death;
Information to help contextualize the CDC data, including excess mortality numbers modeled by demographers at Boston University, vaccination rates, and a Department of Justice survey released in December of all medical examiner and coroner offices in the country.
And here are some other links related to Uncounted and the CDC’s mortality data:
If you’re a journalist who wants to use these data, the Documenting COVID-19 team is happy to help! If you have questions or want support, feel free to reach out to the team at covid@muckrock.com, or to me specifically at betsy@muckrock.com.
This week, I had a big retrospective story published at FiveThirtyEight: I looked back at the major metrics that the U.S. has used to track COVID-19 over the past two years—and how our country’s fractured public health system hindered our use of each one.
very excited to share this one: for @FiveThirtyEight, I looked back at the metrics that the U.S. has used to track COVID-19 over the last two years — and how our fractured public health system doomed each one. (1/5) https://t.co/wgzTYL8Dh9pic.twitter.com/sqxwIrufQT
The story is split into seven sections, which I will briefly summarize here:
Case counts, January to March 2020: Early on in the pandemic, the U.S. had a very limited picture of COVID-19 cases due to our very limited testing: after rejecting a test made by the WHO, the CDC made its own test—which turned out to have contamination issues, further slowing down U.S. testing. In early March 2020, for example, the majority of cases in NYC were identified in hospitals, suggesting that official counts greatly underestimated the actual numbers of people infected.
Tests administered, March to September 2020: Test availability improved after the first wave of cases, with organizations like the COVID Tracking Project keeping a close eye on the numbers. But there were a lot of challenges with the testing data (like different units across different states) and access issues for Americans with lower socioeconomic status.
Hospitalizations, October to December 2020: By late 2020, many researchers and journalists were considering hospitalizations to be a more reliable COVID-19 metric than cases. But it took a long time for hospitalization data to become reliable on a national scale, as the HHS launched a new tracking system in the summer and then took months to work out kinks in this system.
Vaccinations, January to June 2021: When the vaccination campaign started in late 2020, it was “tempting to forget about all other COVID-19 metrics,” I wrote in the story. But the U.S.’s fractured system for tracking vaccinations made it difficult to analyze how close different parts of the country were to prospective “herd immunity,” and distracted from other public health interventions that we still needed even as people got vaccinated.
Breakthrough cases, July to November 2021: The Delta surge caused widespread infections in people who had been vaccinated, but the CDC—along with many state public health agencies—was not properly equipped to track these breakthrough cases. This challenge contributed to a lack of good U.S. data on vaccine effectiveness, which in turn contributed to confusion around the need for booster shots.
Hospitalizations (again), December to January 2022: The Omicron surge introduced a need for more nuance in hospitalization data, as many experts asked whether COVID-19 patients admitted with Omicron were actually hospitalized for their COVID-19 symptoms or for other reasons. Nuanced data can be useful in analyzing a variant’s severity; but all COVID-related hospitalizations cause strain on the healthcare system regardless of their cause.
New kinds of data going forward: In our post-Omicron world, a lot of public health agencies are shifting their data strategies to treat COVID-19 more like the flu: less tracking of individual cases, and more reliance on hospitalization data, along with newer sources like wastewater. At this point in the pandemic, we should be fortifying data systems “for future preparedness,” I wrote, rather than letting the systems we built up during the pandemic fall to the wayside.
I did a lot of reporting for this piece, including interviews with some of the U.S.’s foremost COVID-19 data experts and communicators. As long as the piece is, there were a lot of metrics (and issues with these metrics) that came up in these interviews that I wasn’t able to include in the final story—so I wanted to share some bonus material from my reporting here.
Long COVID:
As I’ve discussed in previous issues, the U.S. has done a terrible job of collecting data on Long COVID. The NIH estimates that this condition follows a significant share of coronavirus infections (between 10% and 30%), but we have limited information on its true prevalence, risk factors, and strategies for recovery.
Here’s Dr. Eric Topol, the prolific COVID-19 commentator and director of the Scripps Research Translational Institute, discussing this data problem:
[Long COVID has] been given very low priority, very little awareness and recognition. And we have very little data to show for it, because it hasn’t been taken seriously. But it’s a very serious matter.
We should have, early on, gotten at least a registry of people —a large sample, hundreds of thousands of people prospectively assessed, like is being done elsewhere [in the U.K. and other countries]. So that we could learn from them: how long the symptoms lasted, what are the symptoms, what are the triggers, what can be done to avoid it, the role of vaccines, the role of boosters, all this stuff. But we have nothing like that.
The NIH’s RECOVER initiative may answer some of these questions, but it will take months—if not years—for the U.S. to actually collect the comprehensive data on Long COVID that we should have started gathering when the condition first began gaining attention in 2020.
Demographic data:
In the testing section of the story, I mention that the U.S. doesn’t provide much demographic data describing who’s getting tested for COVID-19. There is actually a little-known provision in the CARES Act that requires COVID-19 testing providers to collect certain demographic data from all people who seek tests. But the provision is not enforced, and any data that are collected on this subject aren’t making it to most state COVID-19 dashboards, much less to the CDC’s public data dashboard.
Here’s Dr. Ellie Murray, an epidemiologist at the Boston University School of Public Health, discussing why this is an issue:
We don’t collect reason for seeking a test. We don’t collect age, race, ethnicity, occupation of people who seek a test. Those kinds of things could provide us with some really valuable information about who is getting tested, when, and why—that could help us figure out, what are the essential occupations where people are having a lot of exposures and therefore needing to get a lot of tests? Or are there occupations where we’re seeing a lot of people end up in hospital, who have those occupations, but they’re not getting tests, because actually, the test sites are nowhere near where they need to work, or they don’t have the time to get there before they close.
And so we don’t really know who is getting tested, and that, I think, is a bigger problem, than whether the numbers that are being tested tell us anything about the trajectory of COVID. Because we have case data, and hospitalization data, and death data to tell us about the trajectory. And the testing could really tell us more about exposure, and concern, and access—if we collected some more of this data around who is getting tested and why.
Test positivity:
Speaking of testing: another metric that I didn’t get into much in the story was test positivity. Test positivity—or, the share of COVID-19 tests that return a positive result—has been used from the CDC to local school districts as a key metric to determine safety levels. (For more on this metric, check out my FAQ post from this past January.)
But even when it’s calculated correctly, test positivity faces the same challenges as case data: namely, bias in who’s getting tested. Here’s Lauren Ancel Meyers, director of the University of Texas at Austin’s COVID-19 Modeling Consortium, explaining this:
Test positivity is just as fraught [as cases]. It’s just as difficult, because you need to know the numerator and the denominator—what’s influencing the numerator and the denominator? Who is going to get tested, who has access to tests? … It used to be, at the very beginning [of the pandemic], nobody could get a test who wanted a test. And now, today, everybody has a test in their medicine cabinet, and they don’t get reported when they test. It’s different issues that have ebbed and flowed throughout this period.
Often, if you’re a good data analyst or a modeler, and you have all the information, you can handle those kinds of biases. But the problem is, we don’t know the biases from day to day. And so even though there are statistical tools to deal with incomplete bias, without knowing what those biases are, it’s very hard to do reliable inference, and really hard to understand what’s actually going on.
Genetic surveillance:
Also related to testing: genetic surveillance for coronavirus variants of concern. Genetic surveillance is important because it can help identify new variants that may be more transmissible or more likely to evade protection from vaccines. It can additionally help track the qualities of concerning variants once they are identified (if variant data is linked to hospitalization data, vaccination data, and other metrics—which is not really happening in the U.S. right now.)
Our current genetic surveillance systems have a lot of gaps. Here’s Leo Wolansky, from the Rockefeller Foundation’s Pandemic Prevention Institute (PPI), discussing how his organization seeks to address these challenges:
[We’re trying to understand] where our blind spots are, and the bias that we might experience with a lot of health system reporting. One of the things that PPI has been doing is identifying centers of excellence in different parts of the world that can improve the sequencing of new cases in underrepresented countries. And so for example, we’ve provided quite a bit of support to the folks in South Africa that ultimately rang the alarm on Omicron.
We’re also doing this by actually trying to systematically assess countries’ capacity for this type of genomic surveillance. So thinking about, how many tests have been recorded? What’s that test positivity rate? Do we have confidence in the basic surveillance system of the country? And then, do we also see enough sequences, as well as sequencing facility data, to demonstrate that this country can sequence and just isn’t doing enough—or cannot sequence because it needs foundational investment in things like laboratories and devices. We’ve been mapping this capacity just to make sure that we understand where we should be investing as a global community.
The Pandemic Prevention Institute is taking a global perspective in thinking about data gaps. But these gaps also exist within the U.S., as is clear when one looks at the differences in published coronavirus sequences from state to state. Some states, like Wyoming, Vermont, and Colorado, have sequenced more than 10% of their cumulative cases, according to the CDC. Others, like Oklahoma, Iowa, and South Dakota, have sequenced fewer than 3%. These states need additional investment in order to thoroughly monitor coronavirus transmission among their residents.
Cohort studies:
In a cohort study, researchers follow a group of patients over time in order to collect long-term data on specific health conditions and/or the outside factors that influence them. The U.S. has set up a few cohort studies for COVID-19, but they haven’t been designed or utilized in a way that has actually provided much useful data—unlike cohort studies in some other countries. (The U.K., for example, has several ongoing cohort studies collecting information on COVID-19 symptoms, infections in schools, seroprevalence, and more.)
Here’s Dr. Ellie Murray explaining the lost potential of these studies in the U.S.:
There are a number of existing cohort studies that have been asked or who asked to pivot to collecting COVID information and therefore collecting long-term COVID information on their cohorts. But there doesn’t seem to be any kind of system to [determine], what are the questions we need answered about COVID from these kinds of studies? And how do we link up people who can answer those questions with the data that we’re collecting here, and making sure we’re collecting the right data? And if this study is going to answer these questions, and this one is going to answer those questions—or, here’s how we standardize those two cohorts so that we can pull them together into one big COVID cohort.
And so, we end up in this situation where, we don’t know what percent of people get Long COVID, even though we’ve been doing this for over two years. We don’t even really know, what are all the different symptoms that you can get from COVID? … There are all these questions that we could be sort-of systematically working our way through, getting answers and using them to inform our planning and our response. [In addition to having] standardized questions, you also need a centralized question, instead of just whatever question occurs to someone who happens to have the funding to do it.
Excess deaths:
Excess deaths measure the deaths that occur in a certain region, over a certain period of time, above the number of deaths that researchers expect to see in that region and time period based on modeling from past years’ data. Excess deaths are the COVID-19 metric with the longest lag time: it takes weeks from initial infection for someone to die of the disease, and can take weeks further for a death certificate to be incorporated into the public health system.
Once that death information is available, however, it can be used to show the true toll of the pandemic—analyzing not just direct COVID-19 deaths, but also those related to isolation, financial burden, and other indirect issues—as well as who has been hit the hardest.
Here’s Cecile Viboud, a staff scientist at the NIH who studies infectious disease mortality, discussing this metric:
We’ve been using the excess death approach for a long time. It comes from flu research, basically starting in 1875 in the U.K. And it was used quite a lot during the 1918 pandemic. It can be especially good in examining historical records where you don’t have lab confirmation—there was no testing ability back in those days…
So, I think it’s kind of natural to use it for a pandemic like COVID-19. Very early on, you could see how useful this method was, because there was so little testing done. In March and April 2020, you see substantial excess, even when you don’t see lab-confirmed deaths. There’s a disconnect there between the official stats, and then the excess mortality… [We can also study] the direct effect of COVID-19 versus the indirect effect of the pandemic, like how much interventions affected suicide, opioids, death, accidents, etc. The excess approach is also a good method to look at that.
Viboud also noted that excess deaths can be useful to compare different parts of the U.S. based on their COVID-19 safety measures. For example, one can analyze excess deaths in counties with low vaccination rates compared to those with high vaccination rates. This approach can identify the pandemic’s impact even when official death counts are low—an issue that the Documenting COVID-19 project has covered in-depth.
As COVID-19 safety measures are lifted and agencies move to an endemic view of the virus, I’m thinking about my shifting role as a COVID-19 reporter. To me, this beat is becoming less about reporting on specific hotspots or control measures and more about preparedness: what the U.S. learned from the last two years, and what lessons we can take forward—not just for the future COVID-19 surges that are almost certainly coming, but also for future infectious disease outbreaks.
To that end, I was glad to see the Biden administration release a new COVID-19 plan focused on exactly this topic: preparedness for new surges, new variants, and new infectious diseases beyond this current pandemic.
From the plan’s executive summary:
Make no mistake, President Biden will not accept just “living with COVID” any more than we accept “living with” cancer, Alzheimer’s, or AIDS. We will continue our work to stop the spread of the virus, blunt its impact on those who get infected, and deploy new treatments to dramatically reduce the occurrence of severe COVID-19 disease and deaths.
The Biden plan was released last week, in time with the president’s State of the Union address. I read through it this morning, looking for goals and actions connected to data collection and reporting.
Here are a few items that stuck out to me, either things that the Biden administration is already doing or should be doing:
Improving surveillance to identify new variants: The U.S. significantly improved its variant sequencing capacity in 2021, multiplying the number of cases sequenced by more than tenfold from the beginning to the end of the year. But the new Biden plan promises to take these improvements further, by adding more capacity for sequencing at state and local levels—and, crucially, “strengthening data infrastructure and interoperability so that more jurisdictions can link case surveillance and hospital data to vaccine data.” In plain language, that means: making it easier to track breakthrough cases (which I have argued is a key data problem in the U.S.).
Expanding wastewater surveillance:As I’ve written before, in the current national wastewater surveillance network, some states are very well-represented with over 50 collection sites; while other states are not included in the data at all. The Biden administration is committed to bring more local health agencies and research institutions into the surveillance network, thus expanding our national capacity to get early warnings about surges.
Standardizing state and local data systems: I’ve written numerous times that the U.S. suffers from a lack of standardization among its 50 different states and hundreds of local health agencies. According to the new plan, the Biden administration plans to facilitate data sharing, aggregating, and analyzing data across state and local agencies—including wastewater monitoring and other potential methods of surveillance that would provide early warnings of new surges. This would be huge if it actually happens.
Modernize the public health data infrastructure: One thing that could help health agencies better coordinate and share data: modernizing their data systems. That means phasing out fax machines and mail-in reports (which, yes, some health departments still use) and investing in new electronic health record technologies, while hiring public health workers who can manage such systems.
Use a new variant playbook to evaluate new virus strains: Also in the realm of variant preparedness, the Biden administration has developed a new “COVID-19 Variant Playbook” that may be used to quickly determine how a new variant impacts disease severity, transmissibility, vaccine effectiveness, and other factors. The new playbook may be used to quickly update vaccines, tests, and treatments if needed, by working in partnership with health systems and research institutions.
Collecting demographic data on vaccinations and treatments: The Biden plan boasts that, “Hispanic, Black, and Asian adults are now vaccinated at the same rates as White adults.” However, CDC data shows that this trend does not hold true for booster shots: eligible white Americans are more likely to be boosted than those in other racial and ethnic groups. The administration will need to continue collecting demographic data to identify and address gaps among vaccinations and treatments; indeed, the Biden plan discusses continued efforts to improve health equity data.
Tracking health outcomes for people in high-risk settings: Along with its health equity focus, the Biden plan discusses a need to better track and report on health outcomes in nursing homes, other long-term care facilities, and other congregate settings like correctional facilities and homeless shelters. Congregate facilities continue to be major COVID-19 hotspots whenever there’s a new outbreak, so improving health standards in these settings should be a major priority.
Studying and combatting vaccine misinformation, vaccine safety: The new plan acknowledges the impact of misinformation on vaccine uptake in the U.S., and commits the Biden administration to addressing this trend. This includes a Request for Information that will be issued by the Surgeon General’s office, asking researchers to share their work on misinformation. Meanwhile, the administration will also continue monitoring vaccine safety and reporting these data to the public.
Test to Treat: One widely publicized aspect of the Biden plan is an initiative called “Test to Treat,” which would allow people to get tested for COVID-19 at pharmacies, health clinics, long-term care facilities, and other locations—then, if they test positive, immediately receive treatment in the form of antiviral pills. If this initiative is widely funded and adopted, the Biden administration should require all participating health providers to share testing and treatment data. This would allow researchers to evaluate whether this testing and treatment rollout has been equitable across different parts of the country and minority groups.
Website for community risk levels and public health guidance: The Biden plan includes the launch of a government website “that allows Americans to easily find public health guidance based on the COVID-19 risk in their local area and access tools to protect themselves.” The CDC COVID-19 dashboard was recently redesigned to highlight the agency’s new Community Level guidance, which is likely connected to this goal. Still, the CDC dashboard leaves much to be desired when it comes to comprehensive information and accessibility, compared to other trackers.
A new logistics and operational hub at HHS: In the last two years, the Department of Health and Human Services (HHS) built up an office for coordinating the development, production, and delivery of COVID-19 vaccines and treatments. The new Biden plan announced that this office will become a permanent part of the agency, and may be used for future disease outbreaks. At the same time, the Biden administration has added at-home tests, antiviral pills, and masks to America’s national stockpile for future surges; and it is supporting investments in laboratory capacity for PCR testing.
Tracking Long COVID: Biden’s plan also highlights Long COVID, promoting the need for government efforts to “detect, prevent, and treat” this prolonged condition. The plan mentions NIH’s RECOVER initiative to study Long COVID, discusses funding new care centers for patients, and proposes a new National Research Action Plan on Long COVID that will bring together the HHS, VA, Department of Defense, and other agencies. Still, the plan doesn’t discuss actual, financial support for patients who have been out of work for up to two years.
Supporting health and well-being among healthcare workers: The new Biden plan acknowledges major burnout among healthcare workers, and proposes a new grant program to fund mental health resources, support groups, and other systems of combatting this issue. Surveying healthcare workers and developing systematic solutions to the challenges they face could be a major aspect of preparing for future disease outbreaks. The Biden plan also mentions investing in recruitment and pipeline programs to support diversity, equity, and inclusion among health workers.
More international collaboration: The new Biden plan also focuses on international aid—delivering vaccine donations to low-income nations—and collaboration—improving communication with the WHO and other global organizations that conduct disease surveillance. This improved communication may be especially key for identifying and studying new variants in a global pandemic surveillance system.
This week, a group of experts—including some who have advised the Biden administration— followed up on the Biden plan with their own plan, called “A Roadmap for Living with COVID.” The Roadmap plan also emphasizes data collection and reporting, with a whole section on health data infrastructure; here, the authors emphasize establishing centralized public health data platforms, linking disparate data types, designing data infrastructure with a focus on health equity, and improving public access to data.
Both the Biden administration’s plan and the Roadmap plan give me hope that U.S. experts and leaders are thinking seriously about preparedness. However, simply releasing a plan is only the first step to making meaningful changes in the U.S. healthcare system. Many aspects of the Biden plan involve funding from Congress… and Congress is pretty unwilling to invest in COVID-19 preparedness right now. Just this week, a $15 billion funding plan collapsed in the legislature after the Biden administration already made major concessions.
Readers, I recommend calling your Congressional representatives and urging them to support COVID-19 preparedness funding. You can also look into similar measures in your state, city, or other locality. We need to improve our data in order to be prepared for future disease outbreaks, COVID-19 and beyond.
The CDC is not publicly releasing a lot of its COVID-19 data. Last weekend, New York Times reporter Apoorva Mandavilli broke the news that the CDC has withheld a lot of its COVID-19 data from the public, including information on breakthrough cases, demographic data, and wastewater data. This news was honestly not surprising to me because it follows a pattern: the CDC doesn’t like to share information unless it can control the interpretations. But I appreciated the conversation brought on by this article, with public health experts saying they’d rather have imperfect data than a complete data void. (I agree!)
BA.2 is definitely more transmissible than the original Omicron strain, but it does not appear to be significantly more severe or more capable of evading vaccines. Two recent posts, one in the New York Times COVID-19 updates page and one from Your Local Epidemiologist, share some updates on what scientists have learned about BA.2 in the past couple of weeks. In the U.S. and other countries with BA.2, this sublineage doesn’t seem to be causing a major rise in cases—at least so far.
New CDC study shows the utility of rapid testing out of isolation. More than half of patients infected with the coronavirus tested positive on rapid antigen tests between five and nine days after their initial diagnosis or symptom onset, a new CDC report found. The report includes over 700 patients at a rural healthcare network in Alaska. These findings suggest that rapid testing out of isolation is a good way to avoid transmitting the virus to others, if one has the tests available.
January saw record-high coronavirus infections in hospitals.POLITICO reporters analyzed hospitalization data from the Department of Health and Human Services (HHS), finding that: “More than 3,000 hospitalized patients each week in January had caught Covid sometime during their stay, more than any point of the pandemic.” This high number demonstrates Omicron’s high capacity to infect other people.
Hong Kong’s surge shows the value of vaccinations. Hong Kong has been a global leader in keeping COVID-19 cases low throughout the pandemic, yet Omicron has tested this territory’s strategy—causing record cases and overwhelming hospitals. One major issue for Hong Kong has been low vaccination rates, particularly among the elderly, as people did not see the need to get vaccinated when cases in the territory were practically nonexistent.
Under the new CDC guidance, about 70% of Americans live in counties where they can go unmasked in public. Chart via CDC.gov.
This past Friday, the CDC announced a major shift to its guidance for determining COVID-19 safety measures based on county-level community metrics. The new guidance is intended to replace COVID-19 thresholds that the agency developed last summer, during the Delta wave; here, the CDC is promoting a shift from using cases and test positivity for local decision-making to using metrics tied directly to the healthcare system.
This shift away from cases isn’t new: state health departments have been moving in this direction recently, as I wrote last week. Similarly, the CDC’s recommendation for when Americans should feel safe in taking off their masks aligns with recent guidance changes from state leaders.
The new guidance is essentially a lot more lenient when it comes to mask removal. Overnight, the U.S. goes from under 5% of counties in “low” or “moderate” transmission (under the old guidance) to over 60% of counties, representing 70% of the population, in a “low” or “medium” COVID-19 community level.
🚨🚨 the CDC has updated its guidance on using county-level COVID-19 metrics to drive recommended safety measures.
with the new guidance, we go from 82% of U.S. counties in "high" transmission (on the left) to only 37% of counties in high transmission (on the right). pic.twitter.com/Xz1Xj6FHDN
This shift will embolden more states, local health departments, and individual organizations to lift safety measures and change how they track COVID-19. In this post, I’ll unpack why I believe the CDC made certain choices with this new guidance, what critiques I’m seeing from public health experts, and some recommendations for thinking about your COVID-19 risk during this highly confusing pandemic era.
Rationale for the CDC’s new guidance
With this new framework, the CDC is essentially telling Americans to watch hospitalization numbers—not case numbers—as the most important metric to inform how hard COVID-19 is hitting their community. One piece of their logic is, I suspect, that case numbers are less reliable in this pandemic era than they have been since March 2020.
That lack of reliability largely stems from the rise of at-home rapid antigen tests, which gained popularity during the Omicron surge and are now largely unconstrained by supply issues. (For example: iHealth Labs, one major at-home test provider, now allows shoppers to buy up to 50 test kits per person, up from a limit of 10 during Omicron’s peak.)
Unlike PCR test results, which are systematically processed in labs and reported to public health agencies, at-home test results typically do not travel beyond a patient’s trash can. And while a few local jurisdictions (like D.C.) have given residents options to self-report their antigen tests, the majority have opted not to take on this challenge. As a result, current case numbers for almost everywhere in the U.S. are not very reflective of actual infections in the community.
In previous pandemic eras, researchers could use PCR test positivity as an indicator of how reliable case numbers might be for a particular jurisdiction: higher test positivity usually means that more cases are going unreported. But in the era of widespread rapid tests, test positivity is also less reliable, because rapid tests aren’t accounted for in the test positivity calculations either.
Case numbers do still have some utility, because people who have COVID-19 symptoms or need a test result to travel will continue seeking out PCR tests. The CDC guidance reflects this by keeping cases as one factor of its COVID-19 community level calculation. But cases are no longer the star of the show here.
Instead, the CDC is focusing on hospitalizations: specifically, new COVID-19 admissions per 100,000 people and the share of inpatient beds occupied by COVID-19 patients. New hospital admissions are a more reliable—and more timely—metric than the total number of patients hospitalized with COVID-19, because admissions reflect only the people coming in with symptoms that recently started, not the people who have been hospitalized for days or weeks.
The share of inpatient beds occupied by COVID-19 patients, meanwhile, reflects the strain that this disease is currently putting on a hospital system. The CDC is choosing to include all COVID-19 patients here, not only those who are hospitalized for COVID-specific symptoms (the correct choice, in my view). Agency director Dr. Rochelle Walensky gave a good explanation for this at a media briefing on Friday:
We are considering anybody in a hospital bed with COVID, regardless of the reason for admission, and the reason that we landed there is multifold. First, many jurisdictions can’t differentiate, so that was important for us to recognize and realize. Second, whether or not a patient is admitted with COVID or for COVID, they increase the hospital capacity and they’re resource intensive. They require an isolation bed. They require PPE. They probably require a higher staff ratio. And so they are more resource intensive and they do take a COVID bed potentially from someone else.
Interestingly, as well, as we have less and less COVID in certain communities, the amount of people who are coming into the hospital with COVID will necessarily decrease. We will not have as many people walking around asymptomatically because there will just be less disease out there. So increasingly, as we have less disease in the community, we anticipate that more of the people who are coming into the hospital are going to be coming in because of COVID.
And then finally, as we have even less disease in the community, we anticipate that not every hospital is going to screen every patient for COVID as they walk in the door, especially if we have less and less disease in the community. And when that happens, we won’t actually be able to differentiate. In fact, people who are coming in, who are tested will necessarily be coming in for COVID. So for all of those reasons, comprehensively, we decided to stay with anybody coming in with a COVID diagnosis.
Also, a note on wastewater: I’ve seen some commentators express surprise that the CDC didn’t include wastewater in its new guidance, as this sewage surveillance can be a useful leading indicator for COVID-19 that’s more reliable than cases. The problem here is, wastewater surveillance is not available in much of the country—just look at all the empty space on this map. To use wastewater for decisionmaking, a county or state needs to have enough wastewater collection sites actually collecting these data, and most states are not there yet.
Critiques of the new guidance
While hospitalizations are a more reliable COVID-19 metric than cases, especially in our rapid testing era, they come with a few major issues. First, hospitalizations are a lagging indicator, meaning that they start to rise a couple of weeks after a new surge has started. If we rely on hospitalizations as a signal to put mask requirements or other safety measures in place, those changes will come weeks delayed.
As Boston University epidemiologist Dr. Ellie Murray put it in a recent Twitter thread: “Using lagging indicators like hospitalizations could be okay for turning *off* precautions IF we are sure that no new surge has begun. But that means we need leading indicators, like infection surveillance to guide turning *on* precautions.”
The CDC seems to have forgotten the basics of infectious disease in making these new risk categories.
People infected with SARS-CoV-2 can transmit for days before they show symptoms, and for a week or more before they develop “severe disease”.
Another issue with relying heavily on this lagging indicator is, new COVID-19 safety measures may come too late to protect essential workers, children in schools, and others who face high risk of coronavirus exposure. “These high exposure populations get COVID first and most,” writes health policy expert Julia Raifman.
In other words, by the time case and hospitalization rates are high enough for a community to institute new safety measures under this new CDC guidance, those high-risk people are likely to be the ones already in the hospital. Raifman points to data from the U.S. Census’ Household Pulse Survey, showing that low-income workers were most likely to miss work for COVID-19 throughout last year.
Beyond this lagging indicator issue, another challenge with relying on hospitalizations is that, for many Americans, the hospitals that they might go to if they come down with severe COVID-19 are not located in their county. Plenty of counties, particularly in rural areas, don’t have hospitals! To deal with this, the CDC is actually using regional hospitalization figures, compiling statistics from multiple counties that rely on the same healthcare facilities.
University of South Florida epidemiologist Jason Salemi lays out this calculation in an excellent Twitter thread, linked below. While it makes sense that the CDC would need to use regional instead of local figures here, the agency is being pretty misleading by labeling this new guidance as county-level metrics when really, the metrics are not that localized.
The numerator is based on WHERE PEOPLE ARE ADMITTED.
The denominator is based on WHERE PEOPLE LIVE.
If you just look at FL, there are many counties that either do not report hospitalizations (NO HOSPITAL) or reported 0 in the past 7d.
There are more equity concerns embedded in the new CDC guidance as well. For counties with “low” or “medium” community COVID-19 levels, the CDC recommends that most Americans do not need to wear masks in public. But people who are immunocompromised or at high risk for severe disease should “talk to a healthcare provider” about the potential need to wear a mask indoors, stock up on rapid tests, or consider COVID-19 treatments.
For one thing, telling people, “talk to your doctor” is not a great public health strategy when one in four Americans do not have a primary care physician, and one in ten do not even have health insurance! For another thing, one-way masking among immunocompromised and otherwise high-risk people is also not a great strategy, because masks protect the people around a mask-wearer more than they protect the mask-wearer themselves. (I recommend this recent Slate piece on one-way masking for more on this topic.)
Many of my colleagues on here have eloquently explained their concerns with the new CDC guidelines, so I’ll just add that any guidelines that include “Talk with your provider.” when 25% of Americans don’t have a PCP and 10% are uninsured is inherently problematic.
It is also pretty unclear how the CDC landed on a case threshold for “low transmission” that is much higher in this new guidance than in the old guidance, as Dr. Katelyn Jetelina points out in a recent Your Local Epidemiologist post. If anything, honestly, I would expect that the CDC needs to lower its case threshold, given that current case numbers are not accounting for millions of rapid tests done across the country.
Finally, the new CDC guidance completely fails to account for Long COVID. Of course, it would be very difficult for the CDC to do this, since the U.S. basically isn’t tracking Long COVID in any comprehensive way. Still, overly focusing this new guidance on hospitalizations essentially ignores the fact that a “mild” COVID-19 case which does not lead to hospitalization can still cause major, long-term damage.
Which metrics you should follow right now
Here are my recommendations of COVID-19 metrics to watch in your area as you navigate risk in this confusing pandemic era.
Both the old and new CDC thresholds. While the CDC pushes its new guidance with a brand-new page on CDC.gov, community transmission metrics calculated under the old guidance are still available on the CDC’s COVID-19 dashboard. If you’re not feeling comfortable taking off your mask in public and want to wait until transmission is seriously low in your area, you can look at the old thresholds; though keep in mind that case data are seriously unreliable these days, for the reasons I explained above.
Remember that masks are useful beyond COVID-19. Not a metric, but an additional note about thinking through risk: masks reduce risk of infection for a lot of respiratory diseases! We had a record-low flu season last winter and many Americans have avoided colds for much of the pandemic, thanks in part to masking. Helen Branswell has a great article in STAT News that unpacks this further.
Wastewater data, if available to you. As I mentioned above, wastewater surveillance data are not available in much of the country. But if you live somewhere that this surveillance is happening, I highly recommend keeping an eye on those trends to watch for early warnings of future surges. You can look at the CDC dashboard or Biobot’s dashboard to see if your county is reporting wastewater data.
Vaccination levels. It makes sense that vaccination was not included in the CDC guidance, because vaccinated people can still spread highly contagious variants like Omicron. Still, more highly-vaccinated counties—particularly those with high vaccination rates for seniors—are likely to have less burden on their healthcare systems when a surge arises, so knowing the vaccination rate in your county can still be useful when thinking about your risk tolerance.
Rapid test availability. This is a bit more anecdotal rather than an actual data source, but: looking at rapid test availability in your local pharmacies may be another way to get a sense of community transmission in your area. Right now, these tests are easy to find in many places as case numbers drop; if finding these tests becomes more competitive again, it could be a signal that more people are getting sick or having exposures.
As always, if you have any questions or topics that you’d like me to tackle in this area, please reach out.
Omicron has caused more U.S. COVID-19 deaths than Delta. Despite numerous headlines proclaiming the Omicron variant to be “milder” than previous versions of the coronavirus, this variant infected such a high number of Americans that it still caused more deaths than previous waves, a new analysis by the New York Times shows. Between the end of November and this past week, the U.S. has reported over 30 million new COVID-19 cases and over 154,000 new deaths, the NYT found, compared to 11 million cases and 132,000 deaths from August 1 through October 31 (a period covering the worst of the Delta surge).
124 countries are not on target to meet COVID-19 vaccination targets. The World Health Organization (WHO) set a target for all countries worldwide to have 70% of their populations fully vaccinated by mid-2022. As we approach the deadline, analysts at Our World in Data estimated how many countries have already met or are on track to meet the goal. They found: 124 countries are not on track to fully vaccinate 70% of their populations, including the U.S., Russia, Bangladesh, Egypt, Ethiopia, and other large nations.
Anime NYC was not an omicron superspreader event, CDC says. In early December, the Minnesota health department sounded the alarm about a Minnesotan whose COVID-19 case had been identified as Omicron—and who had recently traveled to New York City for the Anime NYC convention. The CDC investigated possible Omicron spread at this event, both by contact tracing the Minnesota case and by searching public health databases for cases connected to the event. Researchers found that this convention was not a superspreader for Omicron, despite what many feared; safety measures at the event likely played a role in preventing transmission, as did the convention’s timing at the very beginning of NYC’s Omicron wave. I covered the new findings for Science News.
ATTN WEEBS! two new reports, published today by the CDC, find that #AnimeNYC was not actually a superspreader event, despite Omicron's presence at the convention — but the situation is still complicated. I covered this for @ScienceNews: (1/11) pic.twitter.com/zSyCWl2t7H
Americans with lower socioeconomic status have more COVID-19 risk, new paper shows. Researchers at Brookings used large public databases to investigate the relationship between socioeconomic status and the risk of COVID-19 infections or death from the disease. Their paper, published this month in The ANNALS of the American Academy of Political and Social Science, found that education and income are major drivers of COVID-19 risk, as are race and ethnicity. The researchers also found that: “ socioeconomic status is not related to preventative behavior like mask use but is related to occupation-related exposure, which puts lower-socioeconomic-status households at risk.”
The federal government has failed to disclose how much taxpayers are spending for “free” COVID-19 tests. One month into the Biden administration’s distribution of free at-home COVID-19 tests to Americans who request them, millions have received those tests. But the government has not shared how much it spent for the tests, making it difficult for journalists and researchers to determine how much taxpayer money was paid for each testing kit. “The reluctance to share pricing details flies against basic notions of cost control and accountability,” writes KHN reporter Christine Spolar in an article about this issue. The government has also failed to share details about who requested these free tests or when they were delivered, making it difficult to evaluate how equitable this distribution has been.
COVID-19 Data Dispatch 