COVID-19 Data Dispatch

Tag: Reader feedback

  • Answering reader questions: Incubation period, vaccines coming this fall, nasal sprays

    I received a couple of reader questions in recent weeks that I’d like to answer here, in the hopes that my responses will be more broadly helpful. As a reminder, if you ever have a COVID-19 question that you’d like to ask, you can email me at betsy@coviddatadispatch.com, or send it anonymously through this Google form.

    COVID-19’s incubation period

    One reader asked:

    I’d love to learn more about COVID’s incubation period. I have read that it’s 2 to 14 days … but the median time seems to be on the low end (and could be as low as 24 hours?) How likely is it that it’s more like 14 days? I’d love to better understand this so that I know how to better handle exposures… Should I avoid someone who has had an exposure for two full weeks?

    This is a tricky question for two reasons. First, the incubation period—or the time between exposure to COVID-19 and starting to show symptoms of infection—does indeed vary a lot. One review of studies on this topic, posted as a preprint in May, found a range from two to seven days, though it can be even longer. The CDC recommends precautions for up to ten days after exposure.

    Second, the incubation period has changed as the coronavirus has mutated. The virus is constantly evolving to keep infecting us even as people build up immunity; shortening the incubation period is one of its strategies. Omicron has a notably shorter period than past variants; Katherine Wu at The Atlantic wrote an article about this in December 2021 that I think is still informative.

    The preprint I cited above found that Omicron had an average incubation period of 3.6 days, shorter than other variants. I think it’s reasonable to assume that this period has continued to get shorter as Omicron has evolved into the many lineages we’re dealing with now. But the pace of research on this topic has slowed somewhat (with less contact-tracing data available for scientists to work with), so it’s hard to say for certain.

    So, with these complexities in mind, how should one handle exposures? My personal strategy for this (noting that I’m not a doctor or qualified to give medical advice, just sharing my own experience) is to rely on a combination of timing, testing, and symptom monitoring. For the first couple of days after exposure, you wouldn’t be likely to have a positive test result even if you are infected, as it takes time for enough virus to build up in the body for tests to catch it. So, for those days, I’d just avoid people as much as possible.

    After three to four days, PCR tests would start to be effective, and after five to six days, rapid tests would be. So at that point, I’d start testing: using a mix of PCR and rapid tests over the course of several days, up to two weeks after exposure. Studies have shown that the more tests you do, the more likely you are to catch an infection (and this applies to both PCRs and rapids). Daily is the best strategy, but less frequent regimens can still be useful if your access to tests is limited. At the same time, I’d keep track of any new symptoms, as that can be a sign of infection even if all tests are negative.

    I’d personally be comfortable hanging out with someone who has had an exposure but consistent negative test results and no symptoms. But others who are less risk-tolerant than I am might avoid any contact for two weeks. The type of contact matters, too: a short, outdoor meeting or one with masks on is safer than a prolonged indoor, no-mask meeting.

    Vaccine effectiveness

    Another reader asked:

    Is there any information on the effectiveness of the latest vaccines, including vaccines that combine Covid and RSV, and are there similarities between these viruses (related?)

    As we head into respiratory virus season in the U.S., there will be, for the first time, vaccines available for all three major diseases: COVID-19, the flu, and RSV. I’ll talk about effectiveness for each one separately, because they are all separate vaccines for separate viruses. There’s no combined COVID-RSV vaccine on the market.

    COVID-19: We know the fall boosters will target XBB.1.5, a variant that has dominated COVID-19 spread in the U.S. recently. There isn’t much data available on these vaccines yet, because the companies developing them (Pfizer, Moderna, Novavax) have yet to present about their boosters to the FDA and CDC, as is the typical process. The CDC’s vaccine advisory committee is meeting this coming Tuesday to talk fall vaccines, though, so it’s likely we will see some data from that meeting.

    Also worth noting: some early laboratory studies suggest that vaccines based on XBB.1.5 will provide good protection against BA.2.86, despite concerns about differences between these variants. (More on this later in today’s issue.)

    Flu: Every year, scientists and health officials work together to update flu vaccines based on the influenza strains that are circulating around the world. Effectiveness can vary from year to year, depending on how well the shots match circulating strains.

    This week, we got a promising update about the 2023 flu vaccines: CDC scientists and colleagues studied how well these shots worked in the Southern Hemisphere, which has its flu season before the Northern Hemisphere. The vaccine reduced patients’ risk of flu-related hospitalization by 52%, based on data from several South American countries that participate in flu surveillance. This is pretty good by flu vaccine standards; see more context about the study in this article from TIME.

    RSV: There are two new RSV vaccines that will be available this fall, both authorized by the FDA and CDC in recent months. These vaccines—one produced by Pfizer, one by GSK—both did well in clinical trials, reducing participants’ risks of severe RSV symptoms by about 90% (for the first year after infection, with effectiveness declining over time).

    Both vaccines were authorized specifically for older adults, and Pfizer’s was also authorized for pregnant people as a protective measure for their newborns. We’ll get more data about these vaccines as the respiratory virus season progresses, but for now, experts are recommending that eligible adults do get the shots. This article from Yale Medicine goes into more details.

    Nasal sprays as COVID-19 protection

    Another reader asked:

    I’m thinking of researching what foods and supplement are anti-viral anti-COVID. I’m wondering if anyone has done any research on that?

    I haven’t seen too much research on about foods and supplements, since dietary options are usually not considered medical products for study. Generally, having a healthy diet can be considered helpful for reducing risk from many health conditions, but it’s not something to rely on as a precaution in the same way as you might rely on masking or cleaning air.

    Another thing you might try, though, would be nasal sprays to boost the immune system. I have yet to try these myself, but have seen them recommended on COVID-19 Safety Twitter and by cautious friends. The basic idea of these nasal sprays is to kill viruses in one’s upper respiratory tract, essentially blocking any coronavirus that might be present from spreading further. People take these sprays as a preventative measure before potential exposures.

    A couple of references on nasal sprays:

    https://twitter.com/Clean_Air_Club_/status/1685790242681331713

  • Resources from last week’s community event

    Thank you to everyone who logged onto Slack for last Sunday’s community event! I really appreciated the opportunity to hear your COVID-19 questions and concerns, and I hope the discussion was helpful for those who attended.

    One thing I loved about the event was that it didn’t just consist of me answering questions. The readers who attended also helped answer each other’s questions and shared resources, such as information about air filters and local COVID-safe meet-up groups.

    To bring those resources outside those who attended the event, I’ve compiled the list here:

    For both readers who attended the event and those who didn’t, I would love to hear your feedback. Should I host more of these? If yes, what would you like to discuss at events—general COVID-19 questions, or more focus on specific topics? Is Slack a good platform to use? (I.e., would it be worthwhile to pay for pro options on the server?)

    Let me know what you’re thinking: email me, comment on this blog post, etc. And thank you again to those who attended last Sunday, I learned a lot from all of you.

    SIGN UP FOR WEEKLY UPDATES

  • Ask your COVID-19 questions at the CDD community event next Sunday

    It’s a confusing, stressful time for those of us still following COVID-19 news and trying to avoid infection. Services like testing have become more limited, thanks to the end of the federal public health emergency, while changes in data availability make it harder to even recognize the ongoing risk.

    I’d like to give you—readers of the COVID-19 Data Dispatch—an opportunity to share your concerns about this latest stage of the pandemic and connect with others who feel similarly. So, I’m hosting a community event: a live Q&A in a private Slack server.

    Here’s how this will work. Next Sunday, June 11, at 5 PM Eastern time, I will log onto the private COVID-19 Data Dispatch Slack server. I’ll start a live audio chat in a channel labeled “community_events”, using Slack’s huddle feature.

    Attendees will be able to ask questions through audio or through text, in the Slack channel, and I’ll try to answer them in both formats. I also hope that attendees will respond to each other’s questions and connect about shared challenges. Remaining COVID-19 cautious these days can be an isolating experience, and I hope this event will help folks find a bit of community.

    I’ve tried using Slack for the COVID-19 Data Dispatch before; I actually created my server in early 2021, when I launched the publication’s independent website and financial support options. At the time, readers weren’t particularly interested in community discussions. But I suspect that may be different now, with the current phase of the pandemic—so I’m testing this out again. If the event next weekend goes well, I might make it a regular occurrence.

    In order to keep the community event to a manageable size, I’m going to limit it to readers who have financially supported the COVID-19 Data Dispatch. If you’ve donated at any point in the last three years, please expect a Slack invitation in your email later this afternoon.

    If you haven’t donated before but would like to attend the event, please do so before next Sunday. It can be any amount, and can be a one-time donation through my Ko-fi page or a reoccurring contribution through the website. I’ll also reserve a few spots for folks who would like to attend but are unable to donate right now—just email me to ask about that.

    You can also email me with any logistical questions! I’m looking forward to the event and hope to hear from many of you there.

  • How COVID-19 Data Dispatch readers use CO2 monitors

    Last week, I wrote about my experience with a CO2 monitor that I recently bought, and have been using to informally study the air quality in my New York City apartment. I asked readers to share their experiences with these monitors, and several of you did!

    Here are some snippets from those responses; I hope it will be helpful for some readers to see how other folks are using air quality monitors.

    Joyce R.:

    I bought a much less expensive one (this one for $60), and I’ve been using it in my downtown office that I go to twice a week (it’s a WeWork facility and I’m in a small 2-person office there; my co-worker and I take turns using it so only one of us is in it at any given time). I of course am always masked in the building—unlike about 98% of the other tenants, sadly—except when I eat (alone in my office, or outdoors on the roof if the weather is good enough). I also have one of those personal HEPA air purifiers going all day. The monitor is showing that usually the range is 800-900, so I’m pretty happy with that.

    Mark:

    I have an Aranet4 and I’m amazed at the accuracy of it. I put it in my kid’s backpack and could track when he changed classes, went outside or got on the bus. It was REALLY interesting.

    In a follow-up email, Mark added:

    When I got his class schedule, the changes matched up to the exact 5 minute windows of changes. When I saw high numbers and asked my kid (who couldn’t see the numbers) what the class was like, he said ‘biggest class, 30 kids.’ Yep, the CO2 monitor picked it up. Really really impressive.

    Kate:

    Not only do I use a CO2 monitor to make risk decisions in my personal life—which shops and concert venues to avoid, are the university shuttles where I work safe (no), what setup of windows and fan make my car safer, what is the natural ventilation air exchange rate… But also, I used CO2 readings to persuade my boss to get facilities to fix a broken vent in my workplace… And EVEN BETTER, the non-profit I co-founded, ‘Community Access to Ventilation Information,’ has been helping libraries in Canada and the US lend patrons CO2 monitors and understand how to act on the readings.

    I will be talking to Kate and her co-founder at Community Access to Ventilation Information this coming week, and plan to share a Q&A based on that conversation in next Sunday’s newsletter. If there are any specific questions you’d like me to ask them, please reach out!

  • Answering reader questions about data interpretation, good masking

    Answering reader questions about data interpretation, good masking

    As this chart from Biobot shows, trends in wastewater and case data often look a bit different. But how do you compare wastewater numbers to true infection numbers?

    This week, I’m sharing answers to three questions from readers that came in recently, through emails and the COVID-19 Data Dispatch Google form. The questions discuss interpreting wastewater and case data, and an interesting masking conundrum.

    Q1: Comparing wastewater trends to case trends

    I would love to know if there is any data on what levels of COVID in wastewater equals what risk level—are there any guidelines that could be used to turn masking policies on or off, for example? We know going up is bad and that the data is noisy but, if there’s any information on what concentrations in sewage corresponds to what level of cases I would love to know.

    I would love to be able to point you to specific guidelines about matching wastewater levels to cases, but unfortunately this isn’t really available right now. And if it were available, you would likely need to tailor the analysis pretty closely to where you live.

    An ongoing challenge with using wastewater surveillance data, as I wrote about for FiveThirtyEight and MuckRock in the spring, is that this type of environmental information is categorically pretty different from traditional case data. When a public health agency provides case numbers, they are adding up results from tests done in hospitals, doctors’ offices, and other healthcare settings. Each test result generally represents one person and can be interpreted with that framework.

    But with wastewater data, figuring out exactly what your test results represent can be more complicated. The data generally include people sick with COVID-19 who shed the coronavirus in their waste, but different people might shed different amounts of virus depending on what stage of illness they’re at, the severity of their symptoms, and possibly other factors that scientists are still working to figure out. Environmental factors like a big rainstorm or runoff from nearby agriculture could also interfere with the data. Population shifts, like college students returning to their campus after a break, can cause noise, too.

    As a result, public health experts who interpret wastewater data generally need a lot of data—like, a year or more of testing’s worth of data—from a specific location in order to analyze how wastewater trends correlate with case trends. And the data has to be consistent; if your wastewater collection team switches their sample processing methods halfway through the year, that might interrupt the analysis.

    A few institutions have figured out the wastewater-to-cases correlation for their communities. For examples, see the section on San Diego in this story and this paper by researchers in Gainesville, Florida. But for most research groups and health departments, it’s still a work in progress.

    All of that said, I don’t think this complexity should stop individuals or organizations from using wastewater data to recommend turning mask policies (or other policies) on or off. This surveillance might be less precise, but a sustained increase in coronavirus concentrations in the sewer is still certainly cause for concern and can be used to inform public health guidance.

    Q2: Estimating case underreporting

    How do you estimate how undercounted COVID testing is? Asking because I work for Whentotest.org—our COVID Risk Quiz assumes that COVID testing is undercounted by 7x, but I believe I’ve seen you estimate that it could be undercounted by as much as 20x. Wondering how you get to that number—we want to keep our Quiz as up to date as possible, and that number is a moving target.

    It is definitely a moving target, since COVID-19 testing (especially the lab-based PCR testing that generally contributes to official case numbers) can go up or down depending on people’s access to tests, perceptions of how much transmission is going on, and so many other factors.

    That said, I would personally put undercounting in the 10 times to 20 times range for this fall, likely with different levels of undercounting for different locations. I have two sources for the 20 times number: the first is an estimate from the Institute for Health Metrics and Evaluation made in September, suggesting that 4% to 5% of infections in the U.S. were reported at that time. (If 5% of infections are reported, case counts are 20 times higher than reported cases.)

    My second source is a paper from epidemiologist Denis Nash and his team at the City University of New York, released as a preprint earlier this fall. The researchers surveyed a representative sample of 3,000 U.S. adults, finding that about 17% of the respondents had Omicron during a two-week period in the summer BA.5 surge. Extrapolating from the survey findings, the researchers estimated that about 44 million people across the country had COVID-19 in this timeframe—compared to 1.8 million reported cases. This estimate suggests reported cases were undercounted by a factor of 24.

    Unfortunately, I have to use months-old estimates here because the U.S. does not have a regular data source comparing cases to true infections. The Census and CDC’s Household Pulse Survey comes close to this, as it includes questions about whether survey respondents have recently received a COVID-19 diagnosis; but it doesn’t ask about rapid tests, recent exposure, or other factors needed to determine the true infection rate, so the numbers here are also underestimates.

    Personally, I keep a close eye out for survey studies like those done by Nash and his team at CUNY and use those results to inform how I interpret national case data. I’ll make sure to flag any future studies like this for readers.

    Q3: Nose-only masking

    I follow some masking subs on Reddit and folks periodically suggest to others or refer to hacking masks that only cover their nose (KN95, N95s, etc.) for dental appointments or unavoidable indoor eating scenarios. Assuming they’re successful in creating a proper seal for these “half masks,” would there actually be any scientific backing this is helpful in minimizing risk?

    I wasn’t sure how to answer this question, so I shared it on Twitter, tagging a couple of masking and ventilation experts I know.

    Overall, the consensus that emerged from my replies is that it could be helpful to wear a mask over one’s nose for short periods of time, but it’s hard to say for sure due to a lack of rigorous research in this area. Behavior also plays a big role in how effective such a mask might be in alleviating risk.

    One expert, Devabhaktuni Srikrishna, pointed out that having a sealed filter over one’s nose could reduce the amount of virus that gets inhaled, if the coronavirus is present in the space. (This “inhalation dose” might correlate with one’s chances of infection and/or severity of symptoms if infected, though research is still ongoing on these questions.)

    Achieving a sealed filter over the nose is easier said than done, though. You can’t just use a standard mask, since that’s designed for the nose and mouth. One commenter shared a system that he uses, an elastomeric nose mask held in place with a headband. Another suggested using nasal filters designed to block allergens. As far as I know, there hasn’t been any research showing what might be most successful—unlike the extensive research that has gone into showing the value of high-quality face-masks and respirators.

    In addition to the discussion of designing a nose-only mask, this reader’s question led to some discussion about the careful behavior needed to use it successfully. One commenter pointed out that, if you’re eating alone, it’s easier to stay focused on breathing patterns than if you’re eating in a group and engaged in conversation. I also appreciated this reply from a Louisiana-based behavioral scientist:

    So, to summarize, I’d say that a nose filter could be helpful for situations like a dentist appointment and could be helpful (but trickier) for indoor dining—but it’s hard to say for sure. A much easier conclusion: avoid indoor dining as much as possible during COVID-19 surges like the one we’re in right now.

    More reader responses

  • Donate to the COVID-19 Data Dispatch!

    This week, you’ve probably been inundated with shopping deals and donation requests from every business that has you on its email list. ‘Tis the season to ask for money, after all.

    But how many of those asks are from a one-reporter operation delivering crucial COVID-19 news and resources?

    I often hear from readers that the COVID-19 Data Dispatch is their favorite source for pandemic news, or even their only source for pandemic news. This is something that flatters and terrifies me—like, I cannot stress enough that I am not a health professional, and I should not be taking the place of an actual public health institution. I am simply a science and health journalist who cares very much and works very hard.

    That said, our actual health institutions (like our media, our political leaders, and so many other institutions) continue to pretend COVID-19 is no longer a problem—leaving people like me to pick up their slack.

    So, if you have found this newsletter, this blog, or any of my other COVID-related writing and analysis valuable: please consider donating to support my work. You can either make a monthly donation with Pico or a one-time donation with Ko-fi.

    This support helps to cover my website and other tech costs (detailed here), as well as the occasional article from a guest contributor. It also helps support my time, as I work on this project largely during my evenings and weekends.

    Even small donations go a long way. And if you’re not able to support the COVID-19 Data Dispatch financially right now, your regular readership still means a lot. Thank you!

    Support the COVID Data Dispatch
  • Reflections and format shifts following the CDD’s first hiatus

    Reflections and format shifts following the CDD’s first hiatus

    Beach selfie from my last week of vacation!

    This is my first COVID-19 Data Dispatch issue after my August hiatus! Here are a few updates on how that went and changes I’m thinking about for the publication going forward.

    First off, I owe a big thank you to everyone who reached out with well wishes on this break. After two years without missing a single week of publication, I was (perhaps irrationally) nervous that some readers would be upset that I was taking off for a bit. But that didn’t happen! I appreciate everyone’s understanding and patience.

    Besides a few hours at my part-time job (MuckRock’s Documenting COVID-19 project) and one day of poll working for New York City’s August primary, I didn’t work at all between August 4 and August 29. Here are a few fun things I did instead:

    • Spent a lot of time outside (mostly at various beaches and NYC parks);
    • Biked in every NYC borough (longest trip: to Van Cortlandt Park and Woodlawn Cemetery in the Bronx);
    • Read three books (highly recommend “An Immense World” by Ed Yong!);
    • Drafted most of a long fanfiction project;
    • Watched a lot of Star Trek: The Original Series.

    If you’re curious about the logistics of taking a longer vacation as a freelancer, you can check out this Twitter thread I wrote last week:

    The break was really all that I had hoped it would be: a refreshing step away from constant COVID-19 coverage that allowed me to reflect on why I do this job. With the COVID-19 Data Dispatch and my other work, I aim to give readers the information they need to make individual health decisions, while also thinking about how they fit into broader communities. While my work has evolved a lot over the last two years, that basic tenet hasn’t changed.

    At the same time, though, my hiatus made me think more about how I can balance the newsletter and blog with other activities that are important for maintaining my mental health: getting off my computer, spending time outside, non-journalism writing, investing in new skills, and so on. I’m probably not unlike other independent creators when I say there are weeks when writing this newsletter/blog feels more like a chore than a useful service; I want to keep it feeling like the latter.

    With that in mind, here are a few shifts I’m thinking about for the coming months:

    • The “National numbers” and “Sources and updates” sections will stay consistent. In considering this project’s format, I knew that these two sections are particularly valuable for readers (and they tend to be fairly straightforward for me to write). So, don’t expect any big changes here.
    • Shorter posts. As any editor who’s worked with me could tell you, I am a writer who constantly goes over my assigned word count. And when I have no set word count, as is the case for these issues… They often get quite long, even though I know that shorter summaries tend to be more helpful for readers. In the future, I’m aiming to keep posts shorter, and only write a longer explainer or commentary when it seems actually necessary.
    • More reader engagement. We continue to be in a confusing phase of the pandemic, with less reliable data or reliable guidance. I want to prioritize answering your questions. To that end, I’ve made an anonymous Google form for submitting COVID-19 queries. It’ll be linked in every issue going forward and on the website’s homepage. While you can still reply to emails too, I hope this will be an easier way to send me ideas for topics I should cover.
    • Potential events and multimedia: As older readers may remember, in spring 2021, the COVID-19 Data Dispatch ran a series of virtual data workshops— which I really enjoyed putting together! I’m thinking about more possible virtual events for the future, as well as ideas for new content formats (maybe a podcast miniseries? should I do explainers on TikTok?). I’d love to hear from you, readers, if there’s anything in particular you want to see here.
    • Expanding beyond COVID-19: Between the continued monkeypox outbreak, the resurgence of polio in New York, and plenty of climate disasters this summer, it’s clear that COVID-19 is far from our only public health concern right now. While my projects in the immediate future are still mostly COVID-focused, you might see some other public health and data information creep into newsletters/blogs going forward. (For example, this week’s “Sources and updates” section includes a couple of non-COVID items.)
    • Occasional breaks. While I have no intention of making like the CDC and pausing my newsletter for any federal holiday, you can expect that the COVID-19 Data Dispatch will not run every single weekend going forward. Like, I’ll take Christmas off. Reasonable stuff.

    None of these are particularly major changes, but in the spirit of transparency, I wanted to share what I’m thinking about with all of you. As always, comments or questions are very welcome: just shoot me an email or fill out the new anonymous question form.

  • Reflecting on 100 issues of COVID-19 data reporting

    Reflecting on 100 issues of COVID-19 data reporting

    The author, working on a February 2021 issue after moving apartments. Weekend newsletter writing often looks like this.

    100 weeks ago, I wrote the first issue of this newsletter on Substack.

    I wrote about a change in hospitalization data, which had just shifted from the purview of the CDC to a different team at the Department of Health and Human Services (HHS). This felt like a niche topic at the time, but I wanted to provide a clear explanation of the change after seeing some misleading articles and social media posts suggesting that the CDC was losing control of all COVID-19 data.

    At the time, my goals were simple: explain where COVID-19 data come from and how to interpret the numbers; provide tips and resources for other reporters on this complicated beat; and help people in my broader social network understand pandemic trends. The COVID-19 Data Dispatch’s aims haven’t changed too much, even as I’ve expanded it to its own website, worked with guest writers, coordinated events, and more.

    As I look back on 100 issues, I wanted to share a few lessons for other reporters still on the COVID-19 beat (and, more broadly, anyone working on public health communications). I’m also sharing a couple of notes from readers about how the publication has helped them.

    Lessons I’ve learned:

    • Lay readers can handle complicated topics! You don’t need to overly simplify things, just use clear language and examples that are easy to follow. This is honestly my entire ethos as a science writer so I found it hard to pick an example post, but one may be my piece on why U.S. Long COVID research is so difficult, which built on reporting for a Grid feature.
    • FAQs are good formats for breaking down complex topics or new information. I like to use FAQ formats and lots of subheaders whenever I’m writing about a new variant (or subvariant) of concern, like this post on BA.4 and BA.5, or when walking through the implications of a federal guidance change, like this post discussing testing and isolation with the Omicron variant.
    • Consistency is key. One thing I frequently hear from readers is that they appreciate the regularity of COVID-19 Data Dispatch issues; if they tune out of other pandemic news, they can still expect me to deliver some important updates once a week. This is definitely a built-in advantage of the newsletter format, but I try to take the consistency further by having regular sections (such as “National numbers”) with statistics reported in a similar way each week.
    • Emphasizing the same issues over and over can feel repetitive to the writer, but it’s helpful for readers. Whenever I remind readers about holiday data reporting lags, for example, I have to remind myself that most people are not constantly thinking about COVID-19 trends the way that I am—and might not be consistently reading my newsletters, either. It’s another aspect of being consistent.
    • Provide trends and context, not just isolated numbers. This is another key aspect of my “National numbers” updates: I always explain how a given week’s case or hospitalization numbers compare to previous weeks. Another important piece of context, I think, is where numbers come from: for example, reminding readers that case numbers mainly include PCR test results, not at-home antigen test results.
    • Acknowledge uncertainty! This is crucial in any kind of data reporting, especially when reporting from data systems that are as flawed and incomplete as the U.S.’s COVID-19 data systems. For example, last month’s post about interpreting limited data during our undercounted surge explains the limitations of several common sources, as well as what the sources can still tell us.
    • Provide readers with tools to see local data. This is a central reason why so many publications built COVID-19 dashboards in 2020, and why some outlets continue to maintain them now. People love to look up their states or counties! I often don’t have the bandwidth for hyperlocal visualizations myself, but point to these resources in “Featured sources” updates whenever possible.
    • Use readers’ questions to drive reporting. Some of my favorite COVID-19 Data Dispatch posts have been inspired by reader questions, from the “Your Thanksgiving could be a superspreader” post in fall 2020 to my explanation of why the CDC’s isolation guidance is not based on scientific evidence earlier this spring. If you write to me with a question, you’re probably not the only person with that question—at least, if my metrics on these posts are anything to go by.

    Testimonials from readers:

    Josh Zarrabi (software engineer at the Health Equity Tracker): “You’re, like, the only COVID news I get anymore. Every Sunday morning with my coffee.”

    Chris Persaud (data reporter, Palm Beach Post): “Thanks to your newsletter, I’ve found useful data for my news reports.”

    Jeremy Caplan (director of Teaching & Learning at Newmark J School): “COVID-19 Data Dispatch is consistently informative. I limit my COVID news diet, so it’s helpful to have this singularly focused resource for keeping up with the data.”

    My Grandma: “In our Berkeley family (C, P and me) we have relied on you and your newsletter for helping us through these difficult times.  The research, guidance and advice in your Data Dispatch, is invaluable.”

    Thank you to all of my readers for your support over the last 100 weeks. I hope the COVID-19 Data Dispatch can continue to provide you with the news and resources you need to navigate the (continuing!) pandemic.

    And of course, if you’d like to support this work, consider setting up a reoccurring donation or buying me a coffee!

    Support the COVID Data Dispatch

  • CDD is almost at 100 issues: Share your favorite posts

    Next Sunday, the COVID-19 Data Dispatch reaches a major milestone: issue #100.

    When I started this project on Substack in July 2020, I did not expect to reach 100 issues. Honestly, I wasn’t thinking beyond the next federal data dump or COVID Tracking Project data entry shift. (If you’d like to read more about the CDD’s origins, see this Medium post.)

    But here we are, 100 weeks later. I’ve written hundreds of posts on data quirks, data gaps, interpretations, visualizations, and reader questions. The topics I’ve covered have ranged from superspreader events to wastewater surveillance to explainers about (so many) new variants. I’ve hosted guest articles, hired an intern, and run a series of data workshops.

    In honor of this milestone, I’d like to hear from you, dear readers. What have been your favorite issues or topics? Are there any posts that you frequently reference, or that you forwarded to friends and family members? How has this newsletter and blog helped you make sense of the pandemic? You’re also welcome to share any questions you’d like me to answer at this highly confusing time.

    If you’d like to share, please comment below! You can also email me at betsy@coviddatadispatch.com or reach out on Twitter. I’ll share a few responses (with permission) in next week’s issue.

    Also, if you’d prefer not to comment, consider sharing the COVID-19 Data Dispatch with a few friends instead! We are getting very close to my longtime goal of 1,000 free newsletter subscribers, and I would love to reach that by next week.

  • Interpreting limited data in our undercounted surge

    Interpreting limited data in our undercounted surge

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

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

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

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

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

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

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

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

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

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

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

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

    More federal data