On the left, the CDC’s current Community Levels indicate that 90% of the U.S. is in a “low risk” region. On the right, the CDC’s old Transmission Levels indicate that only 15% of the country is in a “low risk” region.
In the past week (April 23 through 29), the U.S. reported about 370,000 new COVID-19 cases, according to the CDC. This amounts to:
An average of 53,000 new cases each day
113 total new cases for every 100,000 Americans
25% more new cases than last week (April 16-22)
In the past week, the U.S. also reported about 13,000 new COVID-19 patients admitted to hospitals. This amounts to:
An average of 1,900 new admissions each day
4.0 total admissions for every 100,000 Americans
19% more new admissions than last week
Additionally, the U.S. reported:
2,300 new COVID-19 deaths (0.7 for every 100,000 people)
97% of new cases are Omicron BA.2-caused; 29% BA.2.12.1-caused (as of April 23)
An average of 80,000 vaccinations per day (per Bloomberg)
Nationwide, new COVID-19 cases have increased in the U.S. for the fourth week in a row. The daily case average has doubled since early April, and that’s just the cases that we’re reporting; with less PCR testing availability and more people using at-home tests, we have very limited visibility into this current uptick.
New hospital admissions, a more reliable metric than cases, have also continued to rise this week. U.S. hospitals reported about 13,200 COVID-19 patients admitted this week, up from under 10,000 in the first week of April.
Wastewater data similarly continue to indicate increased coronavirus spread. The Northeast is still in a high plateau, according to Biobot’s dashboard, while other parts of the country are reporting upticks.
This mirrors the reported case data: Northeast states Vermont, Rhode Island, New York, and Massachusetts reported the highest case rates in the week ending April 27, according to the latest Community Profile Report. But states in the South and West, such as North Carolina and Nebraska, reported the sharpest increases.
Almost one-third of new cases were caused by the BA.2.12.1 subvariant in the week ending April 23, according to the CDC’s latest estimates. This version of Omicron, first identified by the New York state health department, is even more transmissible than BA.2. It’s causing more than half of cases in the New York and New Jersey region, the CDC estimates.
Despite all of these concerning signals, the vast majority of the country seems unaware that we are in a new COVID-19 spike. Indeed, I’m writing this from the plane back to New York City, on which I’m one of few mask-wearers.
And I can’t really blame my fellow travelers for their lack of awareness: if you just look at the CDC’s Community Level map—as the agency recommends people do—you’d think that the whole country is in a green, low-risk zone. The agency’s old guidance, with more stringent, case-based thresholds, paints a very different picture.
Here are the 2 CDC COVID maps. The one on the left is the community level map which suggests 90% of the US has low risk. The map on the right of actual virus transmission levels shows a different story where only 15% of the country is low risk. pic.twitter.com/gf2NyF9OKf
The U.S. has “reached the choose-your-own-adventure stage of the pandemic,” as Maryn McKenna put it in a recent WIRED article. We’re taking “individual responsibility” to a whole new level.
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.
The CDC’s updated wastewater dashboard includes more metrics and more context for people seeking to understand wastewater trends in their area.
After the CDC released its National Wastewater Surveillance System (NWSS) dashboard in February, the agency faced some criticism from scientists, data reporters, and others who found this dashboard didn’t actually do a very good job of presenting wastewater data. The initial dashboard only included one metric, percent change in wastewater levels over the last 15 days; without more context, it was difficult to understand where a high percent change was actually cause for concern.
But the CDC has responded to its criticism! This past Friday, the agency updated its wastewater dashboard to include a lot more context. Among the changes:
You can now toggle between three metrics: the old percent change value, along with current virus levels in a site’s wastewater and the share of wastewater samples at a given site that tested positive for the coronavirus in the last 15 days.
Clicking on a specific site provides users with a mini-chart of wastewater levels at that collection spot over the last few weeks.
The dashboard lets you toggle on and off sites with no recent data and sites that started sampling during the Omicron wave.
Above and below the dashboard, the CDC has added more definitions and explanations to help users understand what they’re looking at.
On Friday evening, I posted on Twitter, asking wastewater and data viz experts to weigh in on the updated dashboard:
wastewater people and/or data viz people, what are your thoughts on the updated CDC NWSS dashboard? is it clearer to read and interpret now? cc @COVIDPoops19pic.twitter.com/xOoCWEVFdE
Responses were mostly positive, with researchers saying that they were glad to see the CDC respond to criticism and add more information to the dashboard. Still, there’s more the agency can do, such as adding the population sizes covered by each site and, maybe, moving away from percentage changes as such a central metric.
To quote Claire Duvallet, data scientist at Biobot: “I think collapsing all three metrics to have the same units, and to choose percentage as that unit, is very confusing. Percentage changes are already so 🤯 to think about, & percentage changes of categories of percentages is just too much for my brain.”
I *really* appreciate the rapid response to public feedback from CDC NWSS & am happy to see this update, and actually think that the metric of "how high are recent samples relative to all other measurements from this site?" is a useful way to interpret WW data. https://t.co/tOznP5e6Nr
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, the CDC added a new feature to the vaccination section of its COVID-19 dashboard: you can now look at demographic vaccination trends at the state level, not just nationally and regionally.
But there’s a catch: the state-by-state demographic trends only include age and sex data. Vaccination trends by race and ethnicity are still only available at the national level; in fact, when you click on “Race/Ethnicity” on the booster shots section of this dashboard, the CDC directs you to “please visit the relevant health department website” for more local data.
For state-level race and ethnicity data, the CDC directs users to state public health agencies. Screenshot taken on March 20.
It is now over a year into the U.S.’s vaccine rollout, and the CDC is still failing to publicly share data on vaccinations by state and race/ethnicity. I actually wrote a callout post about this in March 2021, and nothing has changed since then!
This is a major issue because such data are needed to examine equity in the vaccine rollout. While it’s possible to compile data from the states that report vaccinations by race and ethnicity themselves, major inconsistencies in state reporting practices make these data hard to standardize. Why isn’t the CDC doing this? Or, if the CDC is doing this, why aren’t the data public?
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.
This week, the CDC added wastewater tracking to its COVID-19 data dashboard. Wastewater has been an important COVID-tracking tool throughout the pandemic, but it gained more public interest in recent months as Omicron’s rapid spread showed the utility of this early warning system. While the CDC’s new wastewater tracker offers a decent picture of national COVID-19 trends, it’s basically useless for local data in the majority of states.
Wastewater, as you might guess from the name, is water that returns to the public utility system after it’s been used for some everyday purpose: flushing a toilet, bathing, washing dishes, and so forth. In wastewater surveillance, scientists identify a collection point in the sewer system—either beneath a specific building or at a water treatment plant that handles sewage from a number of buildings. The scientists regularly collect wastewater samples from that designated point and test these samples for COVID-19 levels.
When someone is infected with the coronavirus, they are likely to shed its genetic material in their waste. This genetic signal shows up in wastewater regardless of people’s symptoms, so a wastewater sample may return a positive result for the coronavirus earlier than other screening tools like rapid antigen tests. And, because wastewater samples are typically collected from public sewer networks, this type of surveillance provides information for an entire community—there’s no bias based on who’s able to get a PCR or rapid test.
Scientists and organizations who utilize wastewater testing consider it an early warning system: trends in wastewater often precede trends in reported COVID-19 cases. For example, the coronavirus RNA levels identified in Boston’s wastewater shot up rapidly before Boston’s actual Omicron case numbers did, then also went down before case numbers did. Similarly, Missouri’s wastewater surveillance system—which includes genetic sequencing for variants—identified Delta cases last summer weeks before PCR testing did.
Wastewater surveillance is also a popular strategy for colleges and universities, which can set up collecting sites directly underneath student dormitories. Barnard College, where I went to undergrad, is one school that’s employed this strategy. At one point in the fall 2021 semester, the college instructed students living in the Plimpton residence hall (where I lived as a sophomore!) to get individual PCR COVID-19 tests because the wastewater surveillance program had found signals of the virus under their dorm.
The CDC has been coordinating wastewater surveillance efforts since September 2020, Dr. Amy Kirby, team lead for the National Wastewater Surveillance System, said during a CDC media briefing on Friday. “What started as a grassroots effort by academic researchers and wastewater utilities has quickly become a nationwide surveillance system with more than 34,000 samples collected representing approximately 53 million Americans,” Kirby said.
It’s a little unclear why it took the CDC so long to set up a dashboard with this wastewater data when surveillance efforts have been underway for a year and a half. Still, many researchers and reporters are glad to see the agency finally publishing this useful information. The dashboard represents wastewater collection sites as colored dots: blue dots indicate that coronavirus RNA levels have dropped at this site in the last two weeks; yellow, orange, and red dots indicate RNA levels have risen; and gray dots indicate no recent data. You can download data from a dropdown beneath the dashboard and on the CDC’s data portal site.
“More than 400 testing sites around the country have already begun their wastewater surveillance efforts,” Kirby said at the media briefing. But she failed to mention that, out of these sites—the actual total is 471, according to the CDC dashboard—more than 200 are located in just three states: Missouri, Ohio, and Wisconsin. Missouri, with 80 sites, has a long-established system to monitor wastewater, through a collaboration between state agencies and the University of Missouri. Ohio has 71 sites of its own, while Wisconsin has 61.
After these Midwest wastewater powerhouses, other states with a relatively high number of collection sites include North Carolina with 38, Texas with 35, New York with 32, Utah with 31, Virginia with 29, Colorado with 21, and California with 17. No other state has more than 10 wastewater collection sites, and 18 states do not have any wastewater collection sites at all.
So, the CDC dashboard is pretty useful if you live in one of these ten states with a high number of collection sites. Otherwise, you just have to… wait for more sites in your area to get added to the dashboard, I guess? (Kirby did say during the media briefing that several hundred more collection sites are in development.) Even within the states that are doing a lot of wastewater surveillance, though, reporting is uneven at more local levels; for instance, many New York sites are concentrated in New York City and surrounding suburbs.
In this way, biased wastewater surveillance coverage in the U.S. echoes biased genetic sequencing coverage, an issue I’ve written about many times before. (See the genetic surveillance section of this post, for example.) Some states, like California, New York, and others with high-tech laboratories set up for sequencing, have identified variants for a much higher share of their COVID-19 cases than states with fewer resources.
The CDC gives wastewater treatment plants, local health departments, and research laboratories the ability to join its national surveillance network. But again, this is much easier for institutions in some places than others. Consider the resources available for wastewater sampling in New York City compared to in rural parts of the Midwest and South.
In addition, for places that do have robust wastewater surveillance systems, there are some caveats to the data, the CDC expert told reporters. Data may be hard to interpret “in communities with minimal or no sewer infrastructure and in communities with transient populations, such as areas with high tourism,” she said. “Additionally, wastewater surveillance cannot be used to determine whether a community is free from infections.”
If you’re looking for more wastewater data beyond the CDC tracker, here are two sources to check out:
Biobot’s Nationwide Wastewater Monitoring Network,which I included in last week’s Featured Sources: This wastewater epidemiology company collects samples from water treatment facilities across the country; their dashboard includes both estimates of coronavirus levels in the U.S. overall and estimates for specific counties in which data are collected. Biobot’s data are available for download on Github. (Interestingly, it seems that some of the counties included in Biobot’s dashboard are not currently included in the CDC’s dashboard; I’ll be curious to see if that changes in the coming weeks.)
COVIDPoops19 dashboard: This dashboard, run by researchers at the University of California Merced, provides a global summary of wastewater surveillance efforts. It includes over 3,300 wastewater collection sites tied to universities, public health agencies, and other institutions; click on individual sites to see links to dashboards, align with related news articles and scientific papers.
I am a big fan of the CDC’s COVID Data Tracker Weekly Review newsletter, in which the agency sends key COVID-19 statistics, interpretations of the data, and other updates to my inbox every Friday afternoon (unless the data team is taking a holiday break). I use the emails regularly for my own National Numbers updates, and I find them helpful for flagging new CDC studies I may have missed.
However, I couldn’t help but notice that the writers of these Weekly Review newsletters are getting a bit… uncreative with their subject lines:
Last week’s email was titled, “Are You Up to Date?”, reminding readers to get their booster shots if they’re eligible. This week’s email got the remix of that title: “Stay Up to Date.”
CDC newsletter writers, if you’re reading this: I am available to spruce up your weekly review subject lines and, based on my track record with the COVID-19 Data Dispatch, I bet I could improve your open rates. Hit me up.
Here are six other COVID-19 news items from the past week that didn’t quite warrant full posts:
The CDC made its COVID-19 isolation guidance even more confusing, somehow. On Tuesday, the CDC updated its isolation guidance again—and the new guidance is, kind-of a “dumpster fire,” as the headline on this article by The Atlantic’s Katherine J. Wu aptly puts it. The agency still isn’t requiring rapid tests to get out of isolation early, but it says you can test if you have one available. Also, wear a mask if you leave isolation after five days and avoid travel, restaurants, and other high-exposure activities. Wu’s article provides a good summary of the guidance (and criticism of that guidance), as does this Your Local Epidemiologist post from Dr. Katelyn Jetelina.
New reporting recipe explains how to explore “uncounted” COVID-19 deaths with CDC data.Last week, I shared a new investigative story from my team at the Documenting COVID-19 project that dives into unreported COVID-19 deaths in the U.S. Up to 200,000 deaths may have gone unrecorded thanks to a lack of training, standardization, tests, and other issues with death reporting. This week, the team published a reporting recipe aimed to help other journalists do similar stories in their states, cities, and regions. If you have questions about the project or recipe, you can reach out to us at info@documentingcovid19.io.
B.1.640.2, or the “IHU variant” from France, is not currently cause for concern. In the past few days, you might have seen headlines about a new variant called B.1.640.2 that was identified in France last November. The variant has a number of mutations, including some mutations that have also been identified in other highly-contagious variants, according to a recent preprint from French researchers. But it’s not currently a concern, say experts at the World Health Organization and elsewhere. This variant actually predates Omicron, and only 20 cases had been reported between early November and early January (compared to well over 100,000 Omicron cases in the same timespan). Omicron is the main variant we should be worrying about right now.
“Flurona” means getting the flu and COVID-19 at the same time; it’s not a new mutant disease. Another buzzword you might’ve seen in headlines this week: “flurona,” a portmanteau of coronavirus and flu. Los Angeles and other places have recently reported cases in which a patient tests positive for both the flu and COVID-19 at the same time. While having two respiratory diseases at once is certainly unpleasant—and might lead to increased risk of severe symptoms—it’s not necessarily worth freaking out over. Roxanne Khamsi covered these potential coinfections in The Atlantic back in November 2021, writing: “Recent screening studies have found that 14 to 70 percent of those hospitalized with flu-like illness test positive for more than one viral pathogen.”
Senators call for HHS to answer key questions about COVID-19 testing. This week, Senators Roy Blunt (Missouri) and Richard Burr (North Carolina) wrote to Health and Human Services (HHS) Secretary Xavier Becerra requesting information on COVID-19 test spending. The Senators note that over $82.6 billion has been “specifically appropriated for testing,” yet the U.S. continues to experience dire shortages and delays for both PCR and rapid tests. The letter includes questions about Biden’s initiative to distribute 500 million rapid tests for free; little information has been shared about the initiative so far.
New meta-analysis estimates one in three COVID-19 patients have persistent symptoms for 12 weeks or more. In a meta-analysis, scientists compile results from a number of studies on the same topic in order to provide overall estimates for an important metric, like the risk of developing a particular condition. A new analysis from researchers at a Toronto hospital network and other co-authors examined the risk of Long COVID symptoms following a COVID-19 diagnosis, combining results from 81 studies. Their main findings: about 32% of patients had fatigue 12 weeks after their diagnosis, while 22% had cognitive impairment at 12 weeks; and the majority of those patients still had these symptoms at six months. (H/t Hannah Davis.)
COVID-19 Data Dispatch 