COVID-19 Data would matter...

... if we had decent data and knew what to do with it.

I'm not an epidemiologist so I'm not going to write a lot about COVID-19 on this blog but there are some interesting data lessons.

I'm also going to post only one link (edit: see the end of this post). There are many articles about COVID-19 that I could reference, but I'm not going to. Instead, I'm going to give you a single data source (https://coronavirus-realtime.com/) and comment on "things we're hearing about" instead of citing specific sources.


Lesson 1: Simple calculations don't work if you're using the wrong data.

As of this morning, the site above shows 247,400 total cases and 10,067 total deaths worldwide. For the moment, let's assume that both of those numbers are accurate. What's the fatality rate?

The simple calculation that many people are doing is 10,067/247,400 = 4.1%

The arithmetic is correct, but the number doesn't answer the question at all. Most of the 247,400 cases are still in-process. We don't know how they will end.

To answer the fatality rate, we need to look at cases that are resolved. That restricts us to cases that are recovered (86,037) or deceased (10,067). That's 96,104 cases and, tragically, 10.5% of them had ended in death.

But that's still not the fatality rate...

Lesson 2: Bad data can be worse the no data

Let's look at all three of those numbers:

• 247,400 total cases
• 86,037 recovered
• 10,067 deaths

None of them are correct. NONE. Let's take them in order.

First, Total Cases: There have been multiple reports of people being denied tests because they weren't ill enough or they didn't show the right symptoms. However, we've also been told that many who get COVID-19 will show mild or no symptoms. I've read claims that more than half of those who get COVID-19 will be completely asymptomatic. 

Therefore, the total number of cases could be double the reported number. Tests are becoming more widely available, but they're still being reserved for people who actually have symptoms. We would need to test large samples of asymptomatic people in order to properly estimate total cases. This might eventually happen for research purposes, but it's not going to happen in the midst of the crisis.

The total cases number also has interpretive problems. As of yesterday, my county had four confirmed cases. One was a woman in her 50's with no known travel or contact with infected people (what they're calling "community spread"). The other three cases were all in one family that traveled together. To eventually compute fatality rates, those are four separate cases. In terms of contagion, I would say that they are just two cases. I think that's an important distinction but I doubt that we'll ever have solid data that allows us that distinction. 

Second, Recovered: The inaccuracy of total cases makes this number wrong too. If we never knew that you had it, then we'll never count you as recovered. 

But it's worse than that. One news story said that patients weren't cleared until they tested negative on two tests administered 24 hours apart. Remember the shortage of tests? How many people are currently recovered but not officially recovered? 

The source above shows zero recovered cases in either California or Washington. I've been watching this site for more than two weeks and not everyone who was active two weeks ago has died. By now they should be recovered so I don't know why there are no reported recoveries. It could be the issue in the prior paragraph or something else.

Third, Deaths: As with recoveries, the total cases number makes this number wrong too. If we never knew that you had COVID-19, then we won't attribute your death to it. Even if we know that you had COVID-19, death can be complicated. 

My 90-year old mother passed away last year 10 days after a bad fall. Did she die from "falling" or from "complications of a fall" or ...? Several months earlier, she nearly died from sepsis. But the sepsis was the result of an untreated urinary tract infection. The recurring urinary tract infections were a result of other medical complications. If she had died during the sepsis incident, what would the real cause be?

Consider someone who is completely healthy. Then they test positive for COVID-19 and they die fairly soon. It would be pretty clear that COVID-19 killed them. On the other hand, if someone has a myriad of health problems and COVID-19 becomes the tipping point, then maybe COVID-19 "sort-of" killed them.

When I say that "bad data can be worse than no data", it's not because the data shouldn't be collected. It's because people don't understand it, they make simple calculations with it, and then they push for public policy and private decisions based on incorrect numbers. However, we do want the data...

Lesson 3: Keep collecting the data and keep a level of skepticism

There are other issues that could be raised with all of these numbers. I touched on age and health but didn't dive deeply into those. I've also ignored differing data methods in different countries.

Still, we'll eventually have better data. It will never be perfect (no useful data is) but it will get better. I'm a big fan of statistical literacy and public access to raw data but "armchair" statisticians need to be careful about their own number crunching. This is a situation where you should listen to the experts but focus on

a) admitted uncertainties in their calculations - like margins of error - and 

b) disagreements among them.

Admitted uncertainties and disagreements from the experts will give you a good idea of how uncertain their conclusions are.

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NOTE: The day after I wrote this, I ran across a great article on the data aspect of COVID-19 so I'm posting it here and not re-writing my post.
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Puppies, Mandopop, and Causality

Here's the best puppy video that I've ever seen on YouTube. It's only 9 seconds. Trust me, it's worth watching.

It's even better looped. Try it with YouTube's loop feature. It's just gets better and better.

After I watched it a few times, I wondered what that catchy song was. It didn't take long for Shazam to identify it, but I struggled a bit getting the Chinese characters into a search field. I still don't know the name of the song, but it's recorded by the Mandopop* group By2.

Here are some comments for the song's video.

There's a clear causal claim here: the popularity of the puppy video caused the popularity of the song video. 

That sounds reasonable, but...    ... is it true?

Think about why the puppy video is so great. Sure, the rear dog's face plant into the snow is funny and the can-do spirit he shows by quickly jumping back up is inspirational. But I think it's the way the song perfectly syncs to the front dog's bounces that makes the video great.

At first, you think that the dog syncs to the song but that's not true. The song was added later. The dog does not sync to the song. The song syncs to the dog. That's an important distinction.

Compare the first version of the puppies to this one. It's longer, about a minute, but you need to watch only the first 10 to 20 seconds.

It's the same puppies doing the same thing but it's not nearly as cute. 

That should make you think about the causal claim in the music video's comments. Maybe the puppy video is not the cause of the song's popularity. Maybe they have it backwards. Maybe, just maybe, the song is the cause of the puppy video's popularity. 

Then again, maybe the comments are right. Either way, you can't confirm a causal relationship from a simple observational relationship**.

Unjustified causal conclusions are a common statistical error. When two variables show a relationship and "it makes sense" that A causes B***, we often jump to a causal conclusion without question. The jump to a causal conclusion is so common that I think we'd benefit from training ourselves to think the other way. Our first reaction to a statistical relationship should be to question any causal relationship. 

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* Thanks to my colleague Chao Zheng for clarifying that the song is Mandarin instead of Cantonese.

** To be clear. I don't think the presence of two different sound tracks on the same puppy video is sufficient to claim there's an experiment. This is still observational. However, it's a good thought exercise to think about how you would make it an experiment.

*** Or, worse yet, we already believed the causal relationship and we were just looking for data to prove ourselves right.

Qualitative Data is Data Too (Part 3)

I laid out the value of qualitative data in Part 1 and and gave an example in Part 2. In this part, I've got my own analysis. Here's a series of qualitative data points.

Once upon a time, Council Bluffs Iowa was a major stopping point for westward travelers due to the presence of a steam powered boat to ferry wagons and livestock over the Missouri River. Then someone decided that a transcontinental railroad would be a good idea. Wagon trains were fine, but railroads would be faster and more efficient for people, products, and livestock.

The big question: where would it go?

Several broad routes were considered and a "central" route was selected. Within the central route, an eastern starting point had to be selected. As POTUS, Abraham Lincoln would make the decision.

Just imagine the economic loss for Council Bluffs if Kansas City Missouri were selected as the starting point. Council Bluffs would have turned into a ghost town.

As luck would have it, one of the investors had previously employed Lincoln as an attorney and this investor wanted Council Bluffs selected. There's no way to be certain, but it would be reasonable to conclude that his connection to Lincoln influenced the decision. Council Bluffs was selected and by the 1930's its status as a wagon train stopping point was replaced by its status as the 5th largest rail center in the US.

The invention of the automobile again changed the nature of transportation and Route 66 was established in 1926. When completed it ran from Chicago to Santa Monica and became one of the most famous highways in US history. By 1930, trucks rivaled rail for dominance in shipping.

Route 66 was a financial boon for towns and businesses along the route but, like many highways, its path wasn't static.

In Atlanta Illinois, the original route ran right through town and businesses thrived. Twenty years later, a bypass was built and businesses died.

In New Mexico, an angry Governor used his lame-duck power to move Route 66 and bypass the state capital: "In 1924, Democrat Arthur Thomas Hannett was unexpectedly elected for a single term (1925–1927) as governor, only to be defeated with various dirty tricks in the next election. Blaming the Republican establishment in Santa Fe for his defeat, Hannett used the lame duck remainder of his term to force through a sixty-nine mile cutoff from Santa Rosa directly to Albuquerque, bypassing Santa Fe entirely."

These are just a few of many examples of Route 66 changes over the years. Most were not as blatantly political as in New Mexico, but each change still had politics in the background. Whether they were elected or appointed, someone or some group made the decision. 

Just as rail usurped wagons and highways usurped rail, interstates began usurping highways in the 1950s. Portions of these interstates (I55, I44, I40) follow Route 66 very closely but most of Route 66 was bypassed. Much of the current Route 66 nostalgia focuses on the economic impact of the interstates and how they created a new generation of ghost towns. 

This might not look like "data". Maybe you think it's just a story, a narrative of changing modes of transportation (admittedly a very abbreviated narrative). However, I would argue that there's a discernible pattern here and that we can derive insights from that pattern.

1) Things change. Downtown dime stores (Kresge's, Ben Franklin, Grants) had a little of everything and threatened their neighboring, specialized stores. Then Kresge's morphed in big-box discount K-Mart and threatened all of downtown. Walmart came along and knocked K-mart off the top of the hill. The internet came along and Amazon knocked Walmart off the top. 

2) When things change, politics matters. This is actually the main point, but I used #1 to emphasize the inevitability of change. Changes in transportation required political decisions about routes and right-of-ways. Changes in retail required political decisions about zoning, building codes, and taxation. If you plan to be in business, then you need to understand politics. Study politics. Study political economy. Read Travels of a T-shirt and pay attention to the policy and regulatory decisions made by a myriad of political bodies. 

3) Nostalgia is selective. This is a minor point, but I still find it interesting. Route 66 is "hot" at the moment. Perhaps it's because of Disney's Cars or maybe it's relative to age of the Baby Boomers. Either way, there are books, blogs, articles, etc. about the sad loss of prosperity on Route 66. I don't see this level of concern over the ghost towns created when wagon trains disappeared. More recently, Lena Wisconsin's downtown was bypassed by reconstruction of Highway 141. Maybe the locals talk about the impact on Lena's downtown, but there's no national discussion that I've ever heard about.

Sources:

• https://en.wikipedia.org/wiki/U.S._Route_66
• https://www.national66.org/history-of-route-66/
• http://library.nau.edu/speccoll/exhibits/route66/paths.html
• https://en.wikipedia.org/wiki/U.S._Route_66_in_New_Mexico
• https://www.route66news.com/2007/03/19/road-to-albuquerque-was-a-joke/
• Personal experience gathered on a recent Route 66 vacation

Millenials Again - They Still Aren't All That Different

Last year, I wrote a post about millennials, demographics, and the risk of making overly broad generalizations.

Here's another report telling us that millennials aren't all that different than previous generations. They might be hitting some "life moments" later, but they still want the same things that their parents wanted.