For most of my life, whenever I went on a walk, I would feel a pang of regret about not being able to identify trees or plants. Today, I passed 400 species observed on iNaturalist, 402 to be exact. I find this miraculous given how nature-blind I was up until four years ago. The silver lining of the pandemic was that I ended up learning a lot about birds and native plants, and I am deeply grateful for that.
I knew that they were swallows from their flight pattern, but I had never seen a flock of swallows just chilling out in a tree before. I’m used to Tree and Cliff Swallows, both of which tend to flutter about constantly and frenetically. I used Merlin to identify the exact species, which iNaturalist later confirmed. Then I just stood there with my Dad, watching them in wonder, before finally walking into the marsh to continue congregating with some other feathered friends.
Many thanks to Travis Kriplean, who helped catalyze my deep dive into the world around me by sharing his own journey so generously and comprehensively. I started my iNaturalist account in the Fall of 2000 with Travis’s encouragement and also with great skepticism, as I didn’t quite understand how iNaturalist worked, and the interface felt… challenging. I was dipping my toes into a mushrooming curriculum that Travis had developed, and I thought I would use iNaturalist to document my findings. I didn’t realize the giant nature-related U-turn I was about to take thanks to a run-in with a big, beautiful, brown bird.
I also have to give a lot of credit to Dario Taraborelli, who unwittingly primed me for all of this. I met Dario 15 years ago through Wikimedia, but I had no idea how much of a birder he was until I started following him on the Site Formerly Known As Twitter. (He, like me, is now mostly on Instagram.) He often posted glorious photos of birds, a stark and welcome contrast to the rest of my feed back in the day. He also sang the praises of iNaturalist, so much so that I knew about them well before I attempted to use the app.
Strangely enough, I don’t think this deep dive into nature would have been possible without iNaturalist and social media in general (and Instagram in particular). It still boggles my mind that iNaturalist’s interface manages to facilitate any kind of community, but it’s how I met Marisol Villareal, whose encouragement and engagement on Instagram helped me feel like I was a card-carrying member of a state-wide fan club, even though I’m still largely clueless. It’s how my friends, Jon and Linzy, met Rudy Wallen, an unassuming and generous nature savant, who also happens to live on our side of town. For all of the terrible that social media has wrought onto all of us, this is a great example of what social media can do when it works.
Last month, I decided to blog every day. As I explained earlier:
For whatever reason, I’ve found writing hard to do the past few years, and this year has been the hardest. I’ve also been disinclined to think out loud, even though I’ve had a lot I’ve wanted to say and share, both personally and professionally.
Mid-way through the experiment, I reported:
What it’s been doing is helping unlock whatever has been inside of me. I’ve been precious about sharing what I’ve been thinking, not wanting to say them unless I can say them well and feeling paralyzed as a result. I’ve also found it overwhelming at times to try to blog. I guess things are crazy in the world right now, and it’s not only affecting my mental health, it’s hard for me to make sense of it all.
Blogging as a practice has reminded me not to be too precious. The less I try to say, the less overwhelming I feel. The more frequently I share, the less I have to worry about saying it all in one piece, which makes it much easier to write. Plus, even though I don’t think I’ve shown it yet, I’m starting to remember what it feels like to write well. I’m rounding into shape again, which always feels good.
…
The biggest surprise has been that sharing regularly has helped me re-engage with my broader community. I didn’t think anyone really followed this blog anymore, and because I’m rarely on social media anymore, the algorithms seem to have decided I’m not worthy of most people’s feeds. Still, some people are paying attention to what I’m saying, and getting to hear from them has been a treat and is also motivating me to write more.
After having finished the experiment, I’m not sure I have anything different to report, other than to say that I don’t think I had any breakthroughs after 30 days, and I want to keep exercising this muscle. I thought seriously about extending my project through the end of the year, but I opted against it for a few reasons. Even though it wasn’t particularly stressful, it wasn’t stress-free either, and I don’t need the added pressure this month. It also tires out muscles that I’m using for work right now. I can focus on developing these muscles more when work settles down.
In the meantime, I think the exercise still is helping me share more than I was before. This is my third blog post in December. I think a good pace for me is to be blogging about once a week, especially when those posts are more or less organic.
Maybe the most interesting thing for me was seeing what I chose to blog about. This wasn’t just a writing exercise, it was a sharing exercise. I aggregated all of the tags from those 30 days of blog posts and ran them through WordClouds.com to see if I could detect any patterns.
Not surprisingly, I wrote a lot about COVID-19 and the elections. It was nice to see that I wrote quite a bit about collaboration. This wasn’t my goal, but I admit I was curious to see how often I felt compelled to write about “work stuff” — the original purpose of this blog — especially when I had so many other things on my mind. I loved that I wrote about a lot about making — food and art and photography and stories in general.
Finally, I was curious about the people and places I wrote about. Here were people I knew whom I mentioned in various posts (not including my partner and sister, whom I mentioned often and didn’t bother tagging):
I loved seeing this list. My interactions with others play such a huge role in what I think about and how I feel, and I love being able to share this space with the people in my life.
As of this morning, California finally has a COVID-19 contact tracing app! I installed it this morning, and I’d encourage fellow California residents to do the same. If you, like me, use Android, install the CA Notify app from the Google Play store. (You may have even gotten a notification this morning encouraging you to do it.) If you have an iPhone, you don’t even need to install anything. Just go into Settings, click on “Exposure Notification,” and go from there.
If you want to understand how a contact tracing app helps us keep the virus under control, how this implementation (which uses the Google and Apple APIs) works, and why it’s taken so long to get built and adopted in the U.S., this New York Times article has a pretty good summary. If you’re worried about privacy, The Markup was on this early, and they also published an informative followup article in August.
Two years ago, there was a ballot initiative in California, Proposition 10, which would enable city governments to enact rent control on any buildings. I had no idea whether or not this was a good idea, and I was going to go to my default in situations like this, which is to vote no. But I decided to check with my friend, Steph, who works in affordable housing. She was thoughtful, knowledgeable, and even, and after my conversation with her, I decided to vote Yes.
This year, there was a similar ballot initiative, Proposition 21, which would enable city governments to enact rent control on buildings that was first occupied over 15 years ago. Once again, I had no idea whether this was a good idea or not. Once again, I turned to Steph. Once again, I voted Yes.
Here’s what troubled me about finding myself in almost the exact same situation two years later: I had learned absolutely nothing. I didn’t even remember whether or not Proposition 10 had passed. (It didn’t. Neither did Proposition 21.) Even if it had, I would have had no idea what the impact of that measure was.
I believe strongly in collaboration, democracy, and the wisdom of crowds. It’s why I do what I do. And I understand why folks don’t have faith in a population’s ability to govern itself. Our track record, especially recently, is terrible.
Here’s the thing: In order to act in intelligent ways, people need to be responsible for the impact of their decisions. If we don’t know the impact of our decisions, we are not going to make good decisions.
In his book, The Signal and the Noise, Nate Silver explains that pundits are terrible at predicting financial markets, but meteorologists are exceptional at predicting the weather. Why? For starters, we are more likely to remember a meteorologist’s track record, because the feedback loop is tighter. Last night, the weatherperson said it would be sunny. Today, it rained. You’re going to remember that.
If we could figure out better ways to tie decisions with impact, I think we would find society generally doing the right things. This is obviously incredibly hard, but I don’t think it’s impossible.
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.)
