My New Favorite COVID-19 Dashboard

TL;DR I’m now using this dashboard as a way to make sense of what’s happening with COVID-19. It’s still too soon to draw any conclusions about how well the U.S.’s interventions overall are working.

I started trying to make sense of the COVID-19 growth rate data myself on March 13, 16 days ago. I learned a lot along the way, and my daily ritual of looking up numbers and updating my spreadsheet has been strangely calming. Here’s my latest graph:

Three observations when comparing this to last week’s graph:

  1. Italy’s growth rate seems to be flattening, which is a positive sign
  2. U.S.’s growth curve continues to rise at a steady rate; more on this below
  3. Even though China and Korea’s growth rates have been steady for a while now, it’s not zero. They have this under control (for now), but it’s far from over, and it won’t be until we have a vaccine, which folks keep saying is at least 12-18 months away.

My friend, Scott Foehner, chided me last week for saying that the results are lagging by about a week. He’s right. Based on Tomas Pueyo’s analysis (which I cited in my original blog post), the lag is more like 12 days. This is why the Bay Area shelter-in-place ordinance was for three weeks — that’s how much time you need to see if you’re containing your growth rate.

Shelter-in-place in the Bay Area started on March 17, exactly 12 days ago and four days after I started tracking. California’s order started on March 20. Other states followed after that, but not all.

It’s hard to make sense of all this when aggregated as a country. I’ve been wanting regional data for a while now, but have felt too overwhelmed to parse it out myself. Fortunately, other people have been doing this.

One of the positive outcomes of me doing this for myself for the past few weeks is that it’s given me a better sense of how to interpret other people’s graphs, and it’s helped me separate the wheat from the chaff. It’s also made me realize how poor data literacy seems to be for many media outlets, including major ones. They’re contributing to the problem by overwhelming people with graphs that are either not relevant or are not contextualized.

One media outlet that’s been doing a great job, in my opinion, has been The Financial Times, thanks to John Burn-Murdoch. Inspired by John’s work, Wade Fagen-Ulmschneider has produced this excellent dashboard, which has provided me exactly what I’ve wanted. (Hat tip to Rashmi Sinha.) I may stop updating my spreadsheet as a result, although I might miss the ritual too much.

Wade’s dashboard is pretty configurable overall, although you have limited control over which region’s data you’re showing. Here’s the closest equivalent to what I’ve been tracking:

And here’s what I’ve really wanted to see: the state-by-state data:

What does this tell us about the interventions so far? Again, not much. It’s too soon. Check back in another week.

I’ve seen some articles floating around with graphs comparing California to New York, crowing that sheltering-in-place is already working here. That may be the case, but it’s still too early for us to know that, and it’s irresponsible to point to a chart and suggest that this is the case. There are lots of reasons why New York might be doing so poorly compared to California that have nothing to do with interventions, density being the obvious one. Regardless, history has proven that even a few days can make a huge difference when it comes to containing epidemics, and I feel incredibly grateful that our local leaders acted as quickly as they did.

I think there are two questions that are on people’s minds. One is about hospital capacity. I’ve seen various attempts to model this, including the Covid Act Now site I mentioned last week. The one I find easiest to browse is this dashboard from the Institute for Health Metrics and Evaluation. They publish their model, which I haven’t even attempted to parse yet. (I doubt that I have the expertise to evaluate it anyway.) It suggests that, even if our current measures have flattened the curve in California, we’ll still exceed our capacity of ICU beds needed in about two weeks, although we should be okay in terms of general hospital capacity.

The second question is how much longer we’ll need to shelter-in-place (or worse). Even if we flatten the curve, lifting shelter-in-place will just get that curve going again unless we have an alternative way of managing it (e.g. test-and-trace). I haven’t seen any indications of when that will happen, so we’ll just have to continue to be patient. I feel like every day is a grind, and I’m one of the lucky ones. I can’t imagine how folks on the frontlines and those far less fortunate than me are dealing right now.

How Are We Doing with COVID-19 in the U.S. Right Now? (Take 3)

Update: More recent iterations are available:

Many thanks to all of the feedback about my latest attempt to make sense of the Coronavirus pandemic. I listened, and I played, and I learned, and I now have a new graph that I think better represents how the U.S. is doing:

This is a semi-logarithmic graph of daily new cases over time. I’m comparing the U.S. (blue and bold), China (red), South Korea (yellow), and Italy (green). I’ll explain the changes I made from last time below, but first, three quick takeaways:

First, don’t trust my analysis. I’m an amateur at this, my math is incredibly rusty, and it turns out that my statistics (which were always suspect) are even more suspect than I thought. Critiques, corrections, and constructive discussion encouraged!

Second, don’t passively consume what you read. This started off as a quick exercise to try to make sense of the craziness. It’s led to lots of encouragement, but also lots of (welcome) critiques, which has helped me sharpen my analysis, correct some assumptions, and feel like I have a better grasp of what’s going on and how to assess other things I read. I feel a lot better than I did a few days ago

Third, the U.S. isn’t doing great right now. Our line is more or less tracking China, Korea, and Italy’s initial growth rate, but both China and Korea had started slowing their growth rate about a week before we did. Our curve is looking a lot more like Italy’s, which does not speak well of the weeks ahead here.

Here are the changes I made from last time:

First, I switched over to a semi-logarithmic graph. Hat tip to Ken Chase for encouraging me to do this and to Matt Bruce for this pointer as to why this is important.

When I originally started mapping this out, I didn’t think that the semi-logarithmic graph would tell me much more than the linear graph did. Italy was off the chart, which was all I felt like I needed to know, and I felt like I could make sense of the rest of the curves on the chart. Still, after receiving this feedback, I decided to try the semi-logarithmic version to see what I would learn. As you can see above, my conclusion changed quite dramatically. We in the U.S. are not doing well right now. You can see that the slope of our graph tracks quite closely with Italy.

The other (more math-y) benefit is that I can measure the slope of the line (0.15), which gives me the rough power law for how viral Coronavirus has been in both the U.S. and Italy (y = 100.15x). China and Korea’s containment slope (-0.1) provides the rough power law for the potential impact of containment (y = 10-0.1x). You can use these equations to model out different scenarios (which my friend, Charlie Graham, has been doing).

Second, I’m no longer normalizing by population. Two people questioned whether or not this was useful. (Thank you, Majken Longlade and Corey O’Hara.) Their argument was that normalizing by population doesn’t tell you much in the case of epidemics, because transmission and virality are more a function of closeness and density. The U.S. is a huge country geographically compared to Korea or Italy. The better approach would be to try to normalize based on population of regional outbreaks.

I agree, and I’d like to try to do this. The data is a lot harder to come by, but I think it would be possible to manually pull together with a little bit of elbow grease, especially if we’re constraining the countries where we’re trying to do this. (Leave a comment below if you’d be interested in trying this.)

(Side note: I am extremely grateful for the wide availability of data and for all the people doing an incredible job of analyzing and sharing. This is no accident. A lot of folks have invested an incredible amount of time and energy over the past two decades advocating for the open web and open data, all of which is required to make this work. This becomes even more clear when realizing what’s missing. Martin Cleaver asked me to include Canada in my graph. Easy enough, I thought. Turns out it’s not. Canada doesn’t make this data available. Majken shared this article that explains Canada’s situation.)

Nevertheless, I thought it was still useful to look at data normalized by population. My reasoning was that, at worst, it wouldn’t make the data worse, and, at best, it might make it better. I decided I would try to test this assumption by comparing the graphs of the normalized data with the non-normalized data above:

Again, I think switching to a semi-logarithmic graph made a difference, because if you compare these to graphs, the slopes (which I’m most concerned about) are largely the same. Normalizing the data doesn’t impact the slopes. On the one hand, my assumption was correct — normalizing didn’t seem to hurt the data. On the other hand, it also didn’t tell me anything new, either. So, I decided to stop normalizing and stick with the data as is. (I’d still like to try normalizing by outbreak region, though.)

One point that came up often was that these graphs don’t take into account the underreporting in the U.S. due to lack of testing. I tried to take this into account in my very first sketch, as I mentioned in my original blog post. However, I decided to move away from this for a few reasons. First, every country is underreporting. It didn’t feel useful to add in hand-wavy multipliers. Second — and this is where the semi-logarithmic graph again comes to the rescue — adding a multiplier won’t change the slope, which is what I’m really interested in. It just moves the curve up or down.

Remember, these are all lagging indicators anyway. In all likelihood, any changes we make today won’t be reflected for at least a week. What’s done is done. The best thing we can do right now is to be as proactive as possible, given the circumstances. If we’re going to implement policies like Italy has, it’s better that it happens today than a week from now. Public policy aside, there is one thing we all can do that will absolutely make a difference: STAY HOME!

One more aside: My friend, Greg Gentschev, has often said that the best thing we can do to become better systems thinkers and doers is to learn how compounding works. (Turns out that the physicist, Albert Allen Bartlett, said this too. Great minds!) Maybe one of the positive outcomes of all this is that this will start to happen. I’ve seen two great resources for this so far. One is the Washington Post’s Coronavirus Simulator, which they published yesterday. The other is this video on exponential growth and epidemics. (Hat tip to Nicky Case and James Cham.)

Many thanks to Martin Cleaver and Matt Bruce for sharing my previous blog post, which led to a lot of the discussion that shaped this latest iteration. And many thanks to all who have engaged with this so far. Stay home, wash your hands, and take care!

A Taste of How Korean Culture Has Become International… in Southern California

It’s no secret that Korean culture is huge internationally and has been for a long time, whether it’s K-pop, Korean dramas, or kimchi. I love it, but I still find it weird, especially when I’m in Southern California, where I remember (from many, many, many years ago) Korean culture being the exclusive province of Korean people, and everyone else being completely ignorant or suspicious of it.

Yesterday, I had lunch with my mom at Yigah in Garden Grove, which specializes in Korean beef soups. As we left, I held the door open for a UPS delivery man carrying a large box. As this older white man walked through the door, he said, “감사합니다” (“thank you”) without missing a beat, which left me chuckling.

Afterward, my mom and I went to Arirang Market to pick up some groceries. At the Korean barbecue stand, I noticed to my surprise that each menu item had the Vietnamese equivalent written underneath (pictured above). I pointed this out to my mom, who shrugged her shoulders, and said, “Vietnamese people love 불고기 (bulgogi).” (Garden Grove is also known as Little Saigon because of its large Vietnamese population.)

As folks become more exposed to and enamored with Korean culture, I delight in the subtle nuances that most people don’t know. At Yigang, I had 육개장 (yukgaejang), a delicious, spicy, beef brisket soup made with mountain vegetables. I imagine many people enjoy it. What they may not realize is that “개” translates to “dog,” which is what this dish was originally made with. 육개장 was a peasant dish, but when the Korean nobility (양반) discovered they liked it, they started making it with beef instead.

A Funny Thing Happened the Other Day on the Internet…

This past week, I spent two days in Tiburon supporting my former colleague and bootcamper, Dana Reynolds, who was facilitating the Code for America staff retreat. Any time spent with the good folks at Code for America is going to be inspiring time, and I couldn’t help expressing this sentiment on Twitter after the retreat was over:

Total time spent tweeting this: Maybe 30 seconds.

Then a funny thing happened. Someone named Jang from Korea responded to my tweet with a question:

I didn’t know Jang, so I glanced at his Twitter profile, and I saw that my friends, June Kim and SeungBum Kim, followed him. That was a good sign, so I responded, resulting in the following exchange, each message less than 140 characters:

I was planning to send an email to some folks at Code for America to follow up, but it wasn’t necessary. Conversations on Twitter happen out in the open, and Cyd Harrell, Code for America’s UX evangelist, saw the thread and responded. This is what happened:

I don’t know what’s going to emerge from this whole interaction, but something good will. At worst:

  • I learned something new about an issue I care about in a country I care about
  • I made some new connections
  • I facilitated some new connections
  • I strengthened some old connections

All from simply tweeting how I was feeling one evening.

This is what can happen when you have ways to communicate with lots of people transparently and with very little friction. But it’s also critical to recognize what underlies the technology that makes this sort of thing possible: people, trust, relationships, and literacy.

Bottom line: This sort of thing makes me very, very happy.

A Day In A Networked Life

I live a networked life, but there was something about yesterday that made me fully appreciate how lucky I am and how amazing this world is. Here’s yesterday’s rundown:

6am — Up early. Long day of work ahead of me.

7:30amAsaf Bartov (currently in Israel, soon to be in San Francisco) and Moushira Elamrawy, newly hired global community reps at the Wikimedia Foundation, are holding IRC office hours. Decide to listen in. Happy to see several old friends from around the world there. It’s just text scrolling on the screen, but it almost feels like we’re in the same room together. Moushira lives in Egypt, which was serendipitous, because while we chat, something cool happens there. Again, networks.

8:30am — A colleague of mine in North Carolina passes along an unusual request from a colleague of hers in Belgium. She wants to use a YouTube video of a Korean rap song for a workshop, and she wants to make sure the lyrics aren’t offensive. I’m amused, but my Korean isn’t good enough to help her. I ping a friend from Korea on Facebook, whom I met at a conference here in San Francisco last summer.

9am — Take a peek on Twitter, and see my friend, Nancy White (based in Seattle), asking for stories about social media in public health education. I don’t know any off the top of my head, and I could easily have retweeted Nancy’s request and left it at that. But I immediately think of two friends on Twitter who could help — Steve Downs (based in NYC) and Susannah Fox (based in D.C.) — and I decide to introduce the three of them instead, in public and over Twitter. Total time spent on this: About a minute.

I had met Steve in person almost a year ago. I discovered Susannah accidentally through an article that evoked a blog post here. I still haven’t met her in person, but I’ve enjoyed all of our interactions since. Steve and Susannah immediately get to work, retweeting the request to specific people and supplying a stream of great stories to Nancy. I check in a few hours later, and I’m blown away by the response.

9:30am — Plotting a surprise for a dear friend. Can’t share the details here in case said friend reads this blog post. I’m in the early stages of scheming, and after talking to a few people, I decide to set up a Facebook group. A few hours later, 30 people are on the group and work is happening. Many of those folks are friends I haven’t seen or talked to in a long time.

Throughout the day — Lots of work, and I need to focus. I have calls for four different projects. On three of them, we use Google Docs for collective, real-time synthesis. How were we ever productive before real-time, collaborative editing?!

I end up working until 7pm, then settle in for the evening. I disconnect, cook dinner, chat with a friend, do some reading, then go to bed early.

This morning — I wake up before 6am, refreshed. My friend from Korea has responded. Not only does she verify that the lyrics are indeed not offensive, but she sends me a transcription of the entire song! I thank her, and forward the news to my friend in North Carolina.

Later in the morning, I ponder all that happened in the past 24 hours, and I sit to write this blog post. As I write, Travis Kriplean IMs me from Seattle. He pings me about some great news, and we end up having a great, thought-provoking conversation about tools for engagement. My mind is racing again, and now I have to go read one of Travis’s papers.

Israel, Egypt, Belgium, Korea, and all throughout the U.S.: Over a 24-hour period, I interacted with friends and colleagues from all over the world, including one in Egypt while incredible things are happening there.

I spent about 20 of those minutes on my computer in my office here in San Francisco connecting people to others, creating online spaces, and walking away. Amazing stuff magically happened.

While all this was happening, I focused and worked productively, again from the comfort of my home office, using tools that have only recently become widely available.

What an amazing, wonderful world we live in, where possibility is reality.