Earlier this week, I asked readers to share what the COVID-19 Data Dispatch has meant to them over this past year. Thank you to everyone who responded—it was wonderful to hear how my work has helped you make sense of the pandemic.
Here are a couple of responses that I wanted to share out with everyone:
I made a career change to data analytics in November last year. Reading your newsletter has been very inspiring, i’m very interested in data journalism but I’m still very new to the field so everything is difficult still 😅 however I love reading your newsletter and seeing what’s possible! I also find it very comforting to read compared to the hyped nature of the general media. I think it’s the only corona news I read without feeling like someone is trying to wind me up 😅
Harriet
A whole year??!?!?!?! Damn. Is it weird that the steady pace of your updates has felt very much like having a friend who was out there keeping me updated on shit when I’ve been entirely out of cope? I feel informed, which sometimes is reassuring, and sometimes terrifying. I’ve definitely shared things I’ve gotten from you when I’ve been in discussions elsewhere, and I’m sure it’s incredibly stressful work for you but I’m so glad to be a recipient of it.
Elaine
Many of the resources you cited/brought to my attention were really helpful in assessing risk, especially over the summer and fall. Reading the CDD also made me more aware of how people (especially in the media) were talking about the COVID-19 numbers, and made me more likely to interrogate their sources/interpretation of data. And as a bonus, I sounded REALLY smart talking to other people about COVID-19 data.
Abby
The CDD has meant a lot to me: I’ve seen someone I love find meaning in their work; I’ve been more informed, more alert, and less fearful about the pandemic; I haven’t spiraled emotionally over heated Twitter debates about the pandemic.
For the past five months, I’ve produced this publication for free. It’s been an act of service to my fellow COVID-19 reporters, public health communicators, and readers who simply want to understand the pandemic a bit better.
The newsletter will continue to be free, as will many of the COVID-19 data resources I publish. But in tandem with this new site, I’m launching a membership program.
This program will enable COVID-19 communicators to connect more directly with each other, as well as to provide feedback that will shape what I cover. It’ll also help me cover my own costs, which have grown significantly as I moved platforms.
I already talked about my technical reasons for moving from Substack to a full-fledged website. I have another big reason for setting up a site, though: I’m planning to keep the CDD going beyond this pandemic. Its name might change later in 2021 or 2022, but my mission will stay the same—building accessibility and accountability for public health data in the United States.
This publication won’t end when COVID-19 does. But even that idea, COVID-19 “ending,” feels tenuous to me. Maybe you feel that way, too. Maybe you’ve been reading articles like Ed Yong’s “Where Year Two of the Pandemic Will Take Us” or Maryn McKenna’s “2021 Will Be a Lot Like 2020,” that unpack how far we still need to go before life returns to some semblance of normalcy. Maybe you realize that America’s recovery from the pandemic won’t be so simple as 70% of the population getting vaccinated. Maybe you feel haunted by the structural inequities that COVID-19 revealed in our healthcare system and beyond, and you know you could never write enough stories or donate to enough mutual aid funds to make up the gap.
Covering COVID-19, I’ve realized, is not just about this virus. It’s about making sure we’re ready for the next public health crisis. And we do that not just by growing our scientific capability but by prioritizing the public in public health. To change the systems in which we live, we need to understand them—and we need to bring our communities along with us.
If you feel this way, too, join me! Help me build a network that will be ready to cover this pandemic and the next one.
And now, the technical details. Here are the benefits of membership:
Community: Join a Slack server where COVID-19 reporters and communicators share resources and advice.
Resources: Exclusive cleaned datasets, visualizations, and other tools to assist you in your work.
Shape the Dispatch: Your priorities and needs will shape what the CDD covers and which new resources are produced.
Accessibility: Keep the CDD free for all its readers! Support accountability for public health data!
The recommended membership fee is $10/month.
But I understand that the pandemic is a difficult time for financial commitments. As such, I’m also offering pay-what-you-will pricing, starting at $2/month. There’s no difference in benefits between the two price tiers.
In the interest of transparency, I’ve published my major costs here. To break even, I would need 120 members to join at the recommended $10/month tier.
I also want to call attention to the second line on that costs page: Intern’s research and writing time. That’s right—this is going from a one-person publication to a two-person publication! My friend (and current Barnard junior) Sarah Braner has agreed to join me as an intern for their spring semester. You’ll learn more about them next week.
As I am extremely against unpaid internships, my top financial priority right now is paying Braner’s salary. That shakes out to 18 members joining at $10/month.
It feels like every journalist started a Substack in 2020. I proudly joined that number when I launched the COVID-19 Data Dispatch in late July.
But after five months of screenshotting Tableau charts, struggling to keep organized, and hitting Gmail’s email size limit again and again—I realized the platform wasn’t serving my needs. I wanted to give my readers clear archives and easy-to-navigate resources, and Substack just wasn’t providing.
From now on, I’ll be publishing each issue as a series of posts on the site and sending out a newsletter with the highlights. This will help keep issues concise while still allowing me to do deep dives into important data issues.
More on the new site below. But first, some housekeeping.
Housekeeping
Here’s how to make sure you don’t miss my emails on the new platform.
If you have any questions or find that you’re missing my emails on Sundays, hit me up at betsy@coviddatadispatch.com.
Why I moved
The choice to switch platforms wasn’t an easy one. Substack allowed me to focus on content without worrying about any technical setup, and it provided an easy experience for new readers who wanted to sign up. But after deliberating the move, talking to mentors, and spending a few weeks setting up my new system, I’m feeling good about this decision.
Here are a few of the reasons why I made this move.
Linking out to posts: Probably the most common criticism of the CDD (Substack edition) was that it was simply too long. Emails got cut off in inboxes, and readers would need to scroll past thousands of words of analysis to get to new featured sources or my weekly snarky comment about a data dashboard. I wanted to make the email reading experience easier without compromising my desire to really dive into data sources. This new format—short blurbs in the newsletter, linked out to longer posts on the site—helps me do just that.
Organized archives: Publishing each newsletter as a series of posts rather than as one long article also helps me keep the site organized—and makes it easier for you to find the information you need. I’ve set up several major categories, such as “Federal data,” “K-12 schools,” and “Hospitalization,” which group similar newsletter segments together. The archives are also organized with tags (which get a little more specific than the categories) and by date.
Hosting data resources: In addition to posts from my newsletter issues, the new website includes dedicated resource pages. These pages pull together data source recommendations, annotations, and tips in a format that’s much more accessible than a Google spreadsheet. (Shout-out to the WordPress plugin TablePress, which is my new best friend.) The first couple of pages are up; more will be posted in the coming weeks.
Hosting visualizations: One big reason for moving off Substack: on this website, I can actually embed Tableau dashboards. And Datawrapper charts, and Flourish charts, and basically any other type of visualization. This will make it much easier for you to interact with the charts I feature, whether those are charts I produced specifically for the newsletter or figures I’m hosting from other sources.
Setting up for search: The new website is searchable both internally and externally. Internally: a “Search” widget on the site’s sidebar and at the bottom of every page allows you to search for topics like “Texas” or “Dr. Fauci.” Externally: I’m using a couple of WordPress tools to make the website more easily recognizable by Google and other search engines. This should help more readers find the publication.
Last week, I asked readers to share what information or context gaps they see in COVID-19 coverage from other publications. Thank you to everyone who responded—these answers will be driving what I report on going forward.
Here are a couple of responses I’d like to highlight:
Two readers discussed a need for more local data. State-level reporting can obscure COVID-19 patterns at the county level, while even county-level data can obscure differences between urban, suburban, and rural areas in the same county. Some states do report data at the ZIP code or Census tract level, but this is—as you can probably guess—very unstandardized and difficult to compare broadly. President-Elect Joe Biden promises a Nationwide Pandemic Dashboard with ZIP code-level data, though; hopefully we may see this granular information come January.
One reader discussed a need for data on how COVID-19 is impacting K-12 schools, suggesting a section in each week’s newsletter. I wrote about schools this week, but they are definitely a topic that demands more coverage, especially as K-12 districts and higher ed institutions alike begin planning how they will tackle the spring semester. Expect to see more school data in the coming weeks!
Another reader said, “I do not have a good idea of how many people are really affected by COVID-19.” How many people were hospitalized or had long-term health issues as a result of the disease, and how much did the disease cost these patients? COVID-19 long-haulers—those who have the disease for many months—are an increasing topic of data collection, and many long-haulers are even collecting data on themselves. I can certainly feature them in a future newsletter. But I believe many long-term impacts, ranging from lost income to excess deaths, will not be fully understood until years after the pandemic.
A reader who works as a local journalist discussed how they see other reporters “failing to fundamentally understand data and how it’s used.” They went on to add, “Make every journalist take a data class.” I couldn’t agree more with this sentiment. Journo readers, keep an eye out for more resources (and possibly even events) that could help you out along these lines.
Last week, I asked readers to share their go-to sources for COVID-19 data about their community. Thank you to everyone who responded! I am always on the lookout for great sources myself, so I appreciated seeing what folks are using.
Here are a couple of responses that I wanted to highlight:
The New York Times cases map: Two readers noted that they liked the NYT dashboard, which makes it easy to compare COVID-19 metrics in different parts of the country. The NYT offers data at the county level and provides annotations and context with much more detail than most government sources.
City and county sites: Seven readers said that they regularly check their county or city dashboards for local information. One reader complimented the City of Chicago dashboard as “consistently updated with official data, easy to use.”
Social media: Readers referred to Twitter links to articles shared by both national and local journalists. One reader praised daily COVID-19 update posts shared on a local Boston subreddit: “The posts take publicly available Massachusetts health data and synthesize them in a way I’ve gotten very used to. This is the source I depend on when I tell people that COVID hasn’t been getting better in Massachusetts since June.”
The Glastonbury Town Manager weekly email: My mom’s favorite source is the email newsletter sent by the local administration in my hometown, Glastonbury, Connecticut. This email—which I’ve highlighted in the newsletter before—includes data for the town, updates for the state, and public service announcements.
Editor’s note, Jan. 3, 2021: On Nov. 1, 2020, I ran a Q&A thread on Substack in order to answer readers’ questions in the lead-up to the U.S. election.
Thank you to everyone who asked questions in the thread today. I appreciated the chance to hear about your current COVID-19 concerns, and I got a few ideas for future issue topics. I hope that my answers were useful.
Here’s one question which I wanted to broadcast to everyone:
Ross asked: Hi Betsy—long time reader, first time asker. Have we seen significant spikes in COVID in connection with national holidays, or are spikes largely attributable to other factors? Should we be expecting a Thanksgiving spike? What about an election protest spike?
My response: Thanks Ross, that’s a good question! First of all, I need to clarify that it’s really hard to find a causal association between case spikes and specific events in the U.S., because our contact tracing apparatus simply isn’t up to it in most places. We can’t conclusively find out how many people were infected at a given event or location unless we can test all of them and get those test results to a central location and adjust for confounding factors, like other events that people attended/traveling they did. There have been a few scientific studies that look for these associations (Stanford University researchers recently published a paper about Trump rallies, for example) but largely it is difficult to make these conclusions as events are ongoing.
That being said, the COVID Tracking Project has noted case spikes in the South after Memorial Day, which occurred when many states were loosening lockdown orders. It’s important to note here that these kinds of case spikes are usually delayed; it takes a couple of weeks for people to notice symptoms and get tested (causing cases to spike), and then another week or two for hospitalizations to spike, and then another week or two after that for deaths to spike. (Caroline Chen has explained this lag for ProPublica.) But to answer your question of whether experts are expecting a Thanksgiving spike: yes, they definitely are. Here’s Fauci talking about it, from a couple of weeks ago.
And as for protests—this is also difficult to say for sure, as it is difficult to even estimate how many people attend a protest, let alone to test and contact trace them all. But, to my knowledge, no protest has been a superspreader event so far. Health experts cite the fact that protests are usually outside and have high mask compliance as a possible reason why they have not proven to be as risky as, say, Trump rallies.
And one more:
Martha asked: Hi Betsy, In this time of pandemic fatigue, I am interested in rankings of reasonable activities to keep some economic sectors going without becoming part of the problem (i.e. infected). What are your favorite (or a favorite) source that ranks activities? Do you know of any detailed studies that gets at nuances (with my pod vs. with people not in my pod)?
My response: Maryn McKenna has actually written a great story about COVID-19 risk charts, including the strengths and weaknesses of a couple of widely-cited resources. It has been a couple of months since this story, though, and since then, more interactive resources have popped up. One that I like is the microCOVID project, which estimates your risk based on your location, the number of people you’ll be seeing, mask types, and more. Another resource, which I’ve cited in the newsletter before, is Georgia Tech’s COVID-19 Event Risk Assessment Planning Tool. This tool is simpler, but it gets very precise about the risk levels in your state and county.
I haven’t seen specific studies that get at the nuances of risk levels inside/outside of a pod, largely because I think this is a hard thing for epidemiologists to track. (America! Bad at contact tracing!) But I will say that it is important for you to be clear and realistic about who is in your pod. For example, I live with three roommates in Brooklyn. I sometimes visit my sister, who lives in Manhattan. Two of my roommates are commuting to their respective offices on reduced schedules. So, if one of my sister’s roommates tests positive for COVID-19, that means that, depending on the timing, I, and all of my roommates, and all of my roommates’ coworkers should consider that we may have been exposed. The bigger your pod, the more regular testing can help assuage these types of concerns.
My comment sections are always open for questions about the week’s issue. Or, if you would like to use a less public platform, you can hit me up at betsy@coviddatadispatch.com.
Candid of me reading Hank Green’s new book (very good), beneath some fall foliage. It sure is great to go outside!
I like to answer questions. I’m pretty good at explaining complicated topics, and when I don’t know the answer to something, I can help someone find it. These days, that tendency manifests in everyday conversations, whether it’s with my friend from high school or a Brooklyn dad whose campsite shares a firepit with my Airbnb. I make sure the person I’m talking to knows that I’m a science journalist, and I invite them to ask me their COVID-19 questions. I do my best to be clear about where I have expertise and where I don’t, and I try to point them to sources that will fill in my gaps.
I want this newsletter to feel like one of those conversations. I started it when hospitalization data switched from the auspices of the Centers for Disease Control and Prevention (CDC) to the Department of Health and Human Services (HHS), and I realized how intensely political agendas were twisting public understanding of data in this pandemic. I wanted to answer my friends’ and family members’ questions, and I wanted to do it in a way that could also become a resource for other journalists.
This is the newsletter’s tenth week. As I took a couple of days off to unplug, it seemed a fitting time to reflect on the project’s goals and on how I’d like to move forward.
What should data reporting look like in a pandemic?
This is a question I got over the weekend. How, exactly, have the CDC and the HHS failed in their data reporting since the novel coronavirus hit America back in January?
The most important quality for a data source is transparency. Any figure will only be a one-dimensional reflection of reality; it’s impossible for figures to be fully accurate. But it is possible for sources to make public all of the decisions leading to those figures. Where did you get the data? Whom did you survey? Whom didn’t you survey? What program did you use to compile the data, to clean it, to analyze it? How did you decide which numbers to make public? What equations did you use to arrive at your averages, your trendlines, your predictions? And so on and so forth. Reliable data sources make information public, they make representatives of the analysis team available for questions, and they make announcements when a mistake has been identified.
Transparency is especially important for COVID-19 data, as infection numbers drive everything from which states’ residents are required to quarantine for two weeks when they travel, to how many ICU beds at a local hospital must be ready for patients. Journalists like me need to know what data the government is using to make decisions and where those numbers are coming from so that we can hold the government accountable; but beyond that, readers like you need to know exactly what is happening in your communities and how you can mitigate your own personal risk levels.
In my ideal data reporting scenario, representatives from the CDC or another HHS agency would be extremely public about all the COVID-19 data they’re collecting. It would publish these data in a public portal, yes, but this would be the bare minimum. This agency would publish a detailed methodology explaining how data are collected from labs, hospitals, and other clinical sites, and it would publish a detailed data dictionary written in easily accessible language.
And, most importantly, the agency would hold regular public briefings. I’m envisioning something like Governor Cuomo’s PowerPoints, but led by the actual public health experts, and with substantial time for Q&A. Agency staff should also be available to answer questions from the public and direct them to resources, such as the CDC’s pages on childcare during COVID-19 or their local registry of test sites. Finally, it should go without saying that, in my ideal scenario, every state and local government would follow the same definitions and methodology for reporting data.
Why am I doing this newsletter?
The CDC now publishes a national dataset of COVID-19 cases and deaths, and the HHS publishes a national dataset of PCR tests. Did you know about them? Have you seen any public briefings led by health experts about these data? Even as I wrote up this description, I realized how deeply our federal government has failed at even the basics of data transparency.
Research efforts and volunteer projects, such as Johns Hopkins’ COVID-19 Tracker and the COVID Tracking Project, have stepped in to fill the gap left by federal public health agencies. The COVID Tracking Project, for example, puts out daily tweets and weekly blog posts reporting on the state of COVID-19 in the U.S. I’m proud to be a small part of this vital communication effort, but I have to acknowledge that the Project does a tiny fraction of the work that an agency like the CDC would be able to mount.
Personally, I feel a responsibility to learn everything I can about COVID-19 data, and share it with an audience that can help hold me accountable to my work. So, there it is: this newsletter exists to fill a communication gap. I want to tell you what state and federal agencies are doing—or aren’t doing—to provide data on how COVID-19 is impacting Americans. And I want to help you attain some data literacy along the way. I don’t have fancy PowerPoints like Cuomo or fancy graphics like the COVID Tracking Project (though my Tableau skills are improving!). But I can ask questions, and I can answer them. I hope you’re reading this because you find that useful, and I hope this project can become more useful as it grows.
What’s next?
America is moving into what may be a long winter, with schools open and the seasonal flu incoming. (If you haven’t yet, this is your reminder: get your flu shot!) I’m in no position to hypothesize about second waves or vaccine deployment, but I do believe this pandemic will not go away any time soon.
With that in mind, I’d like to settle in this newsletter for the long haul. And I can’t do it alone. In the coming months, I want this project to become more reader-focused. Here are a couple of ideas I have about how to make that happen; please reach out if you have others!
Reader-driven topics: Thus far, the subjects of this newsletter have been driven by whatever I am excited and/or angry about in a given week. I would like to broaden this to also include news items, data sources, and other topics that come from you.
Answering your questions: Is there a COVID-19 metric that you’ve seen in news articles, but aren’t sure you understand? Is there a data collection process that you’d like to know more about? Is there a seemingly-simple thing about the virus that you’ve been afraid to ask anywhere else? Send me your COVID-19 questions, data or otherwise, and I will do my best to answer.
Collecting data sources: In the first nine weeks of this project, I’ve featured a lot of data sources, and the number will only grow as I continue. It might be helpful if I put all those sources together into one public spreadsheet to make a master resource, huh? (I am a little embarrassed that I didn’t think of this one sooner.) I’ll work on this spreadsheet, and share it with you all next week.
Events?? One of my goals with this project is data literacy, and I’d like to make that work a little more hands-on. I’m thinking about potential online workshops and collaborations with other organizations. I’m also looking into potential funding options for such events; there will hopefully be more news to come on this front in the coming weeks.
This section was inspired by a question my friend Abby messaged me yesterday. She asked:
How come there don’t seem to be any stats on active cases? Obviously it’s important to track new cases, but what I mostly want to know is, what is the likelihood that, if I run into someone on the street, they have COVID-19, and it doesn’t seem like new cases tells me that.
In response, I explained that active cases are pretty difficult to track in a country that hasn’t even managed to set up robust contact tracing at national or state levels. To keep tabs of active cases, a public health department would essentially need to call all infected people in its jurisdiction at regular intervals. Those people would need to answer questions about how they’re doing, what symptoms they have, and if they had gotten tested recently. This type of tracking might be doable for some smaller counties, but it’s challenging in larger counties, areas with swiftly rising COVID-19 case counts, areas without sufficient testing capacity, areas with health disparities where some residents aren’t likely to answer a call from a contact tracer… you get the idea.
But it’s still possible to model how many people sick with COVID-19 are likely present in a community at a given time. Epidemiologists and statisticians can use a region’s new case rate—the number of people recently diagnosed with COVID-19—and other COVID-19 metrics, along with population density and demographic information, to estimate how many people in that region are currently infected. A recent analysis in the New York Times used this type of method to estimate how many infected students might come to schools across the country.
If you’d like to see the likely infection rate in your area, check out the COVID-19 Event Risk Assessment Planning Tool developed by researchers at the Georgia Institute of Technology and Applied Bioinformatics Laboratory. Select a state and an event size, and the tool will tell you how likely it is that someone sick with COVID-19 is at this event. For example, at a 50-person event in New York: 2.2% risk. At a 50-person event in Florida: 21.3% risk.
COVID-19 Data Dispatch 