On any other day, I would have barely paid attention. But these are not ordinary times. So I stopped, and I watched, and I wondered, as the sandpipers played by the crashing waves, unaware of the strange things occupying my mind in these strange times.
As I suggested might happen, I’ve stopped updating my spreadsheet, and I’ve started relying on two of the great dashboards that have emerged in recent weeks — Wade Fagen-Ulmschneider’s dashboard (which I mentioned last week) for international and state-wide comparisons and this dashboard (hat tip to Yangsze Choo).
The regular attempts at sensemaking, however, continue. Here’s what I’m learning this week. The usual disclaimer applies: I’m just an average citizen with above average (but very, very rusty) math skills trying to make sense of what’s going on. Don’t trust anything I say! I welcome corrections and pushback!
From the beginning, the main thing I’ve been tracking has been daily new cases country-by-country. Here’s Fagen-Ulmschneider’s latest log graph:
This week’s trend is essentially a continuation of last week’s, which is good news for Italy (whose growth rate is slowing), and bad news for the U.S. (whose growth rate seems more or less consistent.
Early on, I started using a log graph, because it showed the growth rate more clearly, especially in the early days of growth, when curves can look deceivingly flat and linear. Now that some time has passed, one of the challenges of the log graph is becoming apparent: It dulls your sensitivity to how bad things are as you get higher in the graph (and the scale increases by orders of magnitude). You could conceivably look at the above graph and say to yourself, “Well, our curve isn’t flattening, but we’re not that much worse than Italy is,” but that would be a mistake, because you have to pay attention to your scale markers. You don’t have this problem with a linear graph:
Yeah, that looks (and is) a lot worse. The other challenge with these graphs is that the daily points create a spikiness that’s not helpful at best and deceiving at worst. If you’re checking this daily (which I’m doing), you can see a drop one day and think to yourself, “Yay! We’re flattening!”, only to see the the curve rise rapidly the next two. That is, in fact, what happened over the last three days with the national numbers, and it’s an even worse problem as you look at regional data. It would probably be better to show averages over the previous week or even weekly aggregates instead of daily (which might make more sense after a few more weeks).
In addition to the nice interface, one of the main reasons I started using Fagen-Ulmschneider’s dashboard is that he’s tracking state-by-state data as well. He’s even normalizing the data by population. My original impetus for doing my own tracking was that I couldn’t find anyone else normalizing by population. What I quickly realized was that normalizing by population at a national level doesn’t tell you much for two reasons. First, I was mainly interested in the slope of the curve, and normalizing by population doesn’t impact that. Second, outbreaks are regional in nature, and so normalizing by a country’s population (which encompasses many regions) can be misleading. However, I think it starts to become useful if you’re normalizing by a region’s population. I think doing this by state, while not as granular as I would like, is better than nothing. Here’s the state-by-state log graph tracking daily new cases normalized by population:
California (my state) was one of the first in the U.S. to confirm a COVID-19 case. It was also the first to institute a state-wide shelter-in-place directive. And, you can see that the curve seems to have flattened over the past five days. If you play with the dashboard itself, you’ll notice that if you hover over any datapoint, you can see growth data. In the past week, California’s growth rate has gone down from 15% daily (the growth rate over the previous 24 days) to 7% daily. Yesterday, there were 30 new confirmed cases of novel coronavirus per million people. (There are 40 million people in California.)
An aside on growth rates. One of the things that’s hard about all these different graphs is that they use different measures for growth rates. Fagen-Ulmschneider chooses to use daily growth percentage, and he shows a 35% growth curve as his baseline, because that was the initial growth curve for most European countries. (Yikes!) Other folks, including the regional dashboard I started following this past week, show doubling rate — the number of days it takes to double.
Finance folks use a relatively straightforward way of estimating the conversion between doubling rate and growth rate. I have a computer, so there’s no reason to estimate. The formula is
ln 2 / ln r, where
r is the growth rate. (The base of the log doesn’t matter, but I use a natural log, because that’s how the Rule of 72 is derived.) However, what I really wanted was a more intuitive sense of how those two rates are related, so I graphed the function:
You can see that the 35% growth rate baseline is equivalent to a doubling of cases every 2.2ish days. (Yikes!) Over the past 24 days, California’s growth rate was 15%, which means there was a doubling of cases every five days. Over the past week, the growth rate was 7%, which is the equivalent of doubling approximately every 10 days. (Good job, California!)
Which brings me to the regional dashboard I’ve been using. I love that this dashboard has county data. I also like the overall interface. It’s very fast to find data, browse nearby data, and configure the graph in relatively clean ways. I don’t like how it normalizes the Y-axis based on each region’s curve, which makes it very hard to get a sense of how different counties compare. You really need to pay attention to the growth rate, which it shows as doubling rate. Unlike the above dashboard, it doesn’t show you how the growth rate over the previous seven days compares to the overall growth curve, so it’s hard to detect flattening. My biggest pet peeve is that it doesn’t say who made the dashboard, which makes it harder to assess whether or not to trust it (although it does attribute its data sources), and it doesn’t let me share feedback or suggestions. (Maybe the latter is by design.)
Here’s the California data for comparison:
Another nice thing about this dashboard is that it shows confirmed cases (orange), daily new cases (green), and daily deaths (black). I keep hearing from folks saying that the reported cases data is useless because of underreporting due to lack of tests. These graphs should help dispel this, because — as you browse through counties — the slopes (which indicate growth rates) consistently match. Also, the overall growth rate shown here (doubling every 5.1 days) is consistent with the data in the other dashboard, so that’s nice validation.
Here’s what the Bay Area looks like:
You can see what I meant above about being hard to compare. This graph looks mostly the same as the California graph, but if you look at the scale of the Y-axis and the doubling rate, it’s very different. The Bay Area (which declared shelter-in-place even before the state did) is doing even better, curve-wise. (Good job, Bay Area!)
My next project is to try and get a better sense of what all the death numbers mean. More on that in a future blog post, perhaps. In the meantime, here are some other COVID-19 things I’m paying attention to.
First and foremost, I’m interested in how quickly we create an alternative to shelter-in-place, most likely some variation on test-and-trace. Until we have this in place, lifting shelter-in-place doesn’t make sense, even if we get our curve under control, because the growth rate will just shoot up again. This is nicely explained in Tomas Pueyo’s essay, “Coronavirus: The Hammer and the Dance.” My favorite systems explainer, Nicky Case, has partnered with an epidemiologist to create a dashboard that lets regular folks play with different scenarios. They haven’t released it yet, but this video nicely gives us the gist:
Unfortunately, the media isn’t really talking about what’s happening in this regard (other than the complete clusterfuck that our national response has been), so I have no idea what’s happening. Hang tight, I suppose.
On the other hand, there are some things we can learn from past pandemics. This National Geographic article shares these lessons (and visualizations) from the 1918 flu pandemic, a good warning about lifting shelter-in-place prematurely. (Hat tip to Kevin Cheng.) Similarly, Dave Pollard shares some lessons learned from SARS, several of which are very sobering.
In the meantime, the most pressing concern is hospital capacity. Last week, I mentioned the Institute for Health Metrics and Evaluation’s dashboard, which got some national play too and apparently had a role in waking up our national leadership. Carl Bergstrom, an epidemiologist who also happens to study how disinformation spreads, tweeted some useful commentary on how to (and how not to) interpret this data.
Speaking of disinformation, these are interesting times, not just because of the horrific role that disinformation campaigns are playing in our inability to response, but also because it’s surfacing in a more nuanced way the complicated nature of expertise. FiveThirtyEight published an excellent piece explaining why it’s so hard to build a COVID-19 model. Zeynep Tufekci’s article, “Don’t Believe the COVID-19 Models,” complements the FiveThirtyEight piece nicely. Ed Yong demonstrates how this complexity plays out in his excellent piece on masks. And Philippe Lemoine nicely explains where common sense fits into all of this. (Hat tip to Carmen Medina.)
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:
- Italy’s growth rate seems to be flattening, which is a positive sign
- U.S.’s growth curve continues to rise at a steady rate; more on this below
- 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.
Yesterday, I hiked the Dipsea Trail with my sister, Jessica. It was a beautiful, warm, Bay Area day, perfect for a long, ambling hike toward the coast. The Dipsea Trail, best known for hosting the second oldest foot race in the U.S., is a 7.5 mile trail that goes from Mill Valley to Stinson Beach. There are two steep hills along the trail, totaling about 4,000 feet of elevation gain, which is one of the reasons why the annual race is known as the “Race from Hell.” Hiking the trail, though, is not so bad if you take it slow.
I had a good reason to take it slow. Groupaya recently acquired a DSLR, the Canon Rebel T2i, and I wanted to take it for a spin.
Choosing a DSLR
I’ve had a Canon S95 point-and-shoot camera for a few years now, and I absolutely love it. It has a large sensor for a point-and-shoot, which means it takes pictures in rich colors, even in relatively low light. Its compact size has made it perfect for travel, for casual use, and for work.
However, despite its relatively large sensor, we were starting to run into problems when using the camera to record meetings, where lighting conditions are often less than optimal. It was particularly bad at capturing large artifacts, including the beautiful graphic recordings our designer, Amy Wu Wong, was creating in some of our meetings. Furthermore, it’s nice to have a camera with a big zoom lens and large depth-of-field for photographing individuals in conversation, which is something you just can’t do with a point-and-shoot.
We decided that it was worth investing in a DSLR for the company. Not only would this address our meeting capture requirements, it would also give us a high-quality video recorder as well. All we had to do was choose a camera.
To do this, I went to my go-to place for crowdsourcing recommendations — Twitter — and made sure some of my go-to photographer friends — Eugene Chan, Justin Lin, and Andy Wang — saw my post. Everybody came through with some really good advice, which allowed me to triangulate quickly and make a good decision.
Interestingly, Andy was the only person who took my original question literally, and we ended up going with his recommendation, the Canon Rebel T2i. The key word, in this case, was “starter,” and if I had had room to spare in my tweet, I might have clarified that this would be a company camera, not a personal one, and that others in the company would need to be comfortable using it.
The reason this would have been a useful distinction emerged from Eugene and Justin’s answers. Both of them suggested purchasing a great lens and not worrying as much about the body. Justin suggested getting a used Canon 20D, 30D, or 40D body, older (in the case of the 20D, almost 10 years) professional camera bodies. If I were getting a DSLR for myself, I probably would have went with this advice. But, I wanted to be sure that the camera we purchased would have great auto modes and good usability, so that anyone at Groupaya could easily take solid pictures with it without having to take a photography course. I essentially wanted a DSLR-equivalent of a point-and-shoot.
Choosing Obsessions Carefully
That said, the discussion — and Eugene and Justin’s assumptions in particular — made me wonder about my own skills and commitment as a photographer. I like taking pictures. I take a lot of them, as my large Flickr stream suggests. I also have a soft spot for tools and for craftsmanship. I’ve framed my career around treating collaboration (and the tools we use to collaborate) as craft, and I frame a lot of my personal interests (such as cooking and even sports) the same way.
However, I’m not obsessive about my obsessions, or I’m disciplined about them at least. I choose my obsessions carefully, simply because I know that I cannot possibly go deeply down all of the paths that interest me.
For example, several years ago, Justin and his wife, Cindy, turned me onto Santa Maria-style BBQ, which is tri-tip grilled slowly over red oak, a wood that’s native to the Santa Maria Valley. Of course, upon learning about it, we had to try recreating it, which meant that we needed red oak logs. At the time, I convinced some friends who were driving down to Santa Barbara to take a side trip to Santa Maria to find some red oak. That led to a bit of a wild goose chase, but we got our wood. Then, of course, we had to do a side-by-side comparison with a different kind of wood (in this case, mesquite) to see if the red oak version was better or even detectably different.
Some might call this behavior obsessive, but to me, this was only mildly so. If we were truly hardcore, we would have driven down to Santa Maria ourselves to get the wood, rather than depending on serendipity. Heck, if we were truly hardcore, we probably would have harvested the wood ourselves. We also would have done a better job of controlling our variables when cooking and comparing the different versions of tri-tip.
Which brings me back to photography. I love to take pictures, and I’d like to get a lot better at it. However, I’m not sure I want to go down the path of obsession with it, and so I’ve been careful to pace myself. I’ve felt ready to take another leap for a while now, but I never had the push until this professional need came up.
And so the question I found myself asking was, if I had decided to purchase a DSLR for myself, would I have taken Andy’s advice, or would I have taken Justin and Eugene’s?
Tools vs Craft
In a way, my adventures yesterday with the Rebel T2i would be a way for me to explore this question. Would I take better pictures with the new camera? Would I even know how to leverage the capabilities of the new camera? What would a better lens or a better body enable me to do?
At minimum, I knew that I should be able to take better low light pictures, but I didn’t expect to see that taking pictures outdoors during the day. My Canon S95 has a plethora of manual controls, but they would be easier to manipulate on the bigger body of the Rebel T2i. Similarly, the quicker trigger on the DSLR meant I would be less likely to miss a shot. The main difference I expected would be from the lens. It was a stock 18-55mm lens, nothing special from a DSLR point of view, but certainly better than the lens on my S95.
I’m happy about the pictures I took, but I’m not sure they were significantly better than what I would have taken with my S95. As expected, the main difference was from zoom and depth-of-field:
Last week, I went to Pop-Up Magazine, where I saw Aaron Huey preview his upcoming photo essay of Pine Ridge Indian Reservation for National Geographic. It was absolutely stunning, an amazing example of how a technical master can use his craft to tell a moving story.
I clearly have much more to learn about the craft of photography (especially lighting and composition), and so I’m not sure that investing in an expensive lens or a better DSLR body (used or otherwise) would have been worth it for me. I also don’t do any post-processing right now, which doesn’t require any equipment I don’t already have, so I know I’m missing out on a lot of possibilities there.
That said, I’m curious about what I could do with a better lens, and I might try renting one to play around. I loved Sohail Mamdani’s recent essay on this topic, “Gear Doesn’t Matter — Except When It Does.” I’m looking forward to more learning and playing… in a non-obsessive way, of course!
Today was a very Bay Area day, from work meetings to dinner with an old friend. I was surrounded by positive people, even when the subject matter wasn’t particularly positive. Even the street and sidewalk art was positive. And you know something. It makes a difference. A big difference. (N1U)
I haven’t been blogging much recently, although I’ve tried to sneak in an occasional tidbit on Twitter and Identi.ca. I’ve been doing a lot of reflection and synthesis using IBIS and Compendium, and I’ve had a bunch of great conversations. There’s been a lot of work, but it’s been very gratifying. Looking forward to sharing more here soon. (N1V)