Earlier this year, in a blog post on Faster Than 20 about George Floyd, I tried to point out that, as terrible and visceral as his murder was, the overall racial disparity in police killings should feel far more horrifying. But, I explained:
No one has ever looked at a number and taken to the streets. There are lots of mental hoops required to make sense of that number, to trust its implications, and then to get outraged by it.
Later, in an exchange with a colleague in the comments, I wrote:
There’s also a larger question worth asking about whether the 1,000 police killings a year is too high, regardless of what you think of the racial disparity, which gets you into questions about police militarization and policies for community safety in general.
I suppose now is as good a time as any to ask the larger question: Is 1,000 police killings a year too high?
All things being equal, my first guess as to what the “right” number of police killings should be is zero. Hard to argue with that, right?
Well, that depends. Consider a school shooting, for example. If somebody is spraying bullets at a school with the clear intent to kill as many people as possible, I definitely want the police to shoot and kill that person. It’s not hard to think of other situations where a police killing is not only justified, but where it might save many other lives.
So the “right” number of police killings is probably greater than zero. But how much greater?
I might try going down that rabbit hole another day, but I want to pivot to a different question: How many COVID-19 deaths are too high?
As of today, 240,000 people have officially died of COVID-19 in the U.S. (This doesn’t count indirect fatalities, which would put the number well over 300,000.) Over the past week, we’ve averaged 940 deaths a day from COVID-19. On the one hand, it’s less than half of our peak on April 24, when we averaged 2,240 deaths a day. On the other hand, the number is trending in the wrong direction.
Is a thousand deaths a day too much? What would an “acceptable” number of daily deaths be?
Let’s try to think of this question in a different way. How many car deaths per day are too many? How many car deaths per day are “acceptable”? Don’t do any research. Just try to come up with two numbers and some explanation as to how you came up with them. Don’t worry about being “right.” This is simply an experiment.
Got an answer? Okay, suppose that you’re surpassing your “too many” number. What would you do to get those numbers down?
Think about this for a second. Now compare your numbers from the 2016 U.S. numbers listed in this Wikipedia page.
I don’t have good answers to any of these questions. (I’d love to hear yours in the comments below.) I think that a thousand deaths a day is too many, but I really can’t justify the tradeoffs.
I do know two things. First, human intuition is pretty much useless when it comes to these questions. Joseph Stalinsupposedly said, “The death of one man is a tragedy. The death of millions is a statistic.” It turns out that this is a fact of human nature. It’s known as psychic numbing.
Second, economists estimate that the value of one human life in the U.S. is roughly $10 million. So 240,000 deaths is equivalent to the loss of $2.4 trillion, over 10 percent of our GDP last year. By these admittedly crass and undoubtedly wrong estimates, it seems like a 10 percent drop in GDP is worth the tradeoff of saving 240,000 lives.
I’m a lifelong Dodgers fan, and I watched elatedly as the Dodgers stormed the field a few weeks ago after winning their first World Series since 1988. I left the TV on to watch the celebration. I love seeing the joy and tears on the players’ faces, watching them hugging their loved ones, listening to the fans cheering. That’s right, fans. They were playing in a sort-of bubble in Texas, where the rules around large gatherings are looser, and there were some fans in the stadium, most of them rooting for the Dodgers, so it felt like a home game.
In the course of the celebration, the sportscasters reported that Justin Turner, the Dodgers steady third-baseman and long-time leader, had tested positive for COVID-19, which was why he had been abruptly pulled from the game and isolated in the eighth inning. Hearing this left a pall on the celebration. It was a stark reminder that this was not normal times, and it led to many questions. Who else on both teams had already been exposed? Would they be okay? What if the Dodgers hadn’t won, and there was another game scheduled the following day? Would they have played?
Then Justin Turner came back onto the field to join his teammates for their celebration. He hugged his teammates and family members, he took off his mask, and he participated in the team photo. My sobriety shifted to shock, then unhappiness. What the heck was he doing?! Why wasn’t anyone stopping him?!
It took a few weeks, but Turner and Major League Baseball’s commissioner, Rob Manfred, finally released statements explaining what happened. Turner had apparently asked to step onto the field with his wife (who was isolating with him) for a photo. In his statement, Turner wrote:
I assumed by that point that few people were left on the field. I was under the impression that team officials did not object to my returning to the field for a picture with my wife. However, what was intended to be a photo capturing the two of us turned into several greetings and photos where I briefly and unwisely removed my mask. In hindsight, I should have waited until the field was clear of others to take that photo with my wife. I sincerely apologize to everyone on the field for failing to appreciate the risks of returning to the field. I have spoken with almost every teammate, coach and staff member, and my intentions were never to make anyone uncomfortable or put anyone at further risk.
According to the ESPN article on the statements:
Manfred said teammates “actively encouraged” Turner to leave his isolation room and return to the field, adding that “many teammates felt they had already been exposed” and were willing to tolerate additional risk. Manfred’s statement said Turner believed he received permission from at least one Dodgers employee and that an unidentified person incorrectly told him that other teammates had tested positive, “creating the impression in Mr. Turner’s mind that he was being singled out for isolation.”
MLB previously chided Turner for breaking protocol, adding that Turner “emphatically refused to comply” when asked to leave the field. But Manfred acknowledged Friday that the league “could have handled the situation more effectively” by assigning a security person to closely monitor Turner and quickly transporting him to the team hotel.
“Mr. Turner has publicly recognized that his conduct was wrong and has expressed remorse for that conduct,” Manfred wrote. “I have spoken to him personally and I know that he is extraordinarily upset by the incident. By all accounts, Justin is a leader in the clubhouse, a contributor to his community and a responsible person who was instrumental in the Dodgers diligently following the health protocols all season long.”
I think this was a good outcome, and I applaud everyone involved. There was no single person at fault. It was a collective responsibility, and everyone owned up. The next step is to learn from this and to improve the system.
I live in San Francisco, where our local leaders have moved cautiously in accordance with public health officials and scientists, and where there’s been a culture of compliance and support. People wear masks for the most part, and folks are generally well-intentioned in supporting differing tolerances for risk.
Still, it hasn’t been easy. I’ve felt more cautious than many of my peers, and I’ve drawn some boundaries around distancing and being outdoors, which has meant not doing a lot of the things that my friends want me to do. Everyone has been supportive in principle, but I constantly feel that support tested in practice. People ask to go on a distanced walk, and then they walk right next to me, or they wear their masks below their noses. People gather outdoors, and then it gets cold, and they say, “Why don’t we go inside?” Even though I’m generally good at protecting my boundaries and I’m not conflict-averse by any means, I’ve given in more than once. I feel like I’m constantly fighting a number of forces and tendencies — many of them based on my own longing for normalcy — and afterward, I always feel crappy and scared. But I don’t blame anyone. I know it’s hard for everyone, and I can’t imagine living in other places right now where there’s violent disagreement around what the norms should be.
Last month, my sister shared this comic by Ali Solomon that exactly encapsulates how I feel about all of this. Check it out. It’s brilliant.
Last May, Atul Gawande wrote a wonderful article about how we might safely transition out of lockdown based on what he had learned from his hospital’s practices. He wrote:
These lessons point toward an approach that we might think of as a combination therapy—like a drug cocktail. Its elements are all familiar: hygiene measures, screening, distancing, and masks. Each has flaws. Skip one, and the treatment won’t work. But, when taken together, and taken seriously, they shut down the virus. We need to understand these elements properly—what their strengths and limitations are—if we’re going to make them work outside health care.
And later in the article:
As I think about how my workplace’s regimen could be transferred to life outside the hospital, however, I have come to realize that there is a fifth element to success: culture. It’s one thing to know what we should be doing; it’s another to do it, rigorously and thoroughly.
In my professional life, which is fundamentally about systems change, we get so caught up with finding high-leverage strategies, it’s easy to forget that nothing works in isolation. And among the different combinations that are necessary for success, culture is almost always one of the required strategies. As we’re experiencing right now in a large-scale, visceral way, culture change is really, really hard, even when everybody is aligned and has the best of intentions, which is rarely the case.
In August 2011, Kristin Cobble, Rebecca Petzel, and I had a planning meeting for Groupaya, the consulting firm we would start several months later. As part of that, Rebecca led us through some initial scenario thinking, which consisted of brainstorming certainties (trends we thought were almost certainly going to happen by 2016) and uncertainties (trends we thought were possibilities).
Here were the initial lists we brainstormed:
Certainties
Uncertainties
Economy really crappy in 2015
Mobile dominance
Africa will be online
Design firms flooding into the business (good design the price of entry)
Communication and Advertising Firms coming into the business
There’s a backlash against “collaboration”?
There’s a backlash against “social”?
Earthquake in San Francisco
Skilled, cheaper consultants coming here from developing countries
Knowledge work in the US in the decline
Knowledge work undervalued in the US
Net Neutrality
Trust in Internet services? Things like Wikipedia, AirBnB, eBay rely on trust
Institutional clamp down or continued democratization
Middle East political situation
U.S. “Arab Spring” coming?
Backlash against rationalism; rise of fundamentalism
Large factory consulting firms hijacking our business
Our “Certainties” list wasn’t very good. The economy was not “crappy” by conventional metrics in 2015, although we were continuing to feel the impacts of widening inequality. And we didn’t really see communications firms come into the business.
Our “Uncertainties” list was far more interesting. We no longer have net neutrality, at least at the federal level. Trust in several social media (Facebook and Twitter in particular) is down, and deservedly so. And reading the bullet point, “Backlash against rationalism; rise of fundamentalism,” now makes me want to cry.
I review these notes every few years out of curiosity and sentimentality, and I pulled them up again last month as COVID-19 was wreaking havoc on our lives. A few things come up for me when I look at these:
It’s possible to have an interesting scenarios conversation without a lot of prep. We were clearly already connected to a lot of interesting people and perspectives, which was how stuff like “backlash against rationalism” made it onto our list. (Kristin contributed that one based on conversations she had had with her friend and former colleague at Global Business Network, Eamonn Kelly.)
Prep would have helped broaden our perspectives and address some blind spots.
Pandemic wasn’t on the list of uncertainties.
The biggest thing that comes up for me is that we never truly benefited from the power of scenario thinking, because we treated it as a one-off. Imagine if we had returned to this list once a year, even without any additional prep, and talked through the possibilities. What might have come up? How might this have changed our thinking? What might we have done differently as a result?
This is a regret I often have about my own past work, and it’s something I find with consulting work in general: We barely benefit from the work (which is often time- and resource-intensive), because we never revisit it. There are lots of reasons we never revisit it, but the most common one is that we’re going too fast. I’ve been able to correct this with my own work (although it took several years and lots of focus and failure), and I continue to try to help others do the same. It’s been really, really hard, which is sad, because it’s so beneficial.
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.
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.)
