Pasteur's Blend

I'm not convinced that the current efforts at combating the spread of COVID-19 are a wise course of action. I realize this statement is currently outside the Overton window for polite society. In the past I've advised people who are skeptical to at least try to make the experiment work. At this point I'm recommending that ...

...well, that you not look to random bloggers for advice on this kind of thing. However, if you're looking for a bit more understanding that's something I can supply. I know this is a contentious issue, so I'd like to start on what I think we can all consider to be common ground, given the state of the evidence to date.

Firstly, the defining nature of this crisis - and the beginning of any pandemic for that matter - is uncertainty. Back in early March, I noted this fact, and that some things we would likely discover over time and other things we might never know. Some of the things I noted back then turned out to be incorrect, as is expect…

Note: This post is based on incomplete data sets, and will likely not age well.
How's that working out for you? A few nations took a different approach to the global pandemic, but then changed course to fall in line with what most of the rest of the developed world is doing with social distancing and shutdowns. Great Britain tried it for awhile before they capitulated, then Japan did the same. From a soulless data analysis perspective, this is unfortunate, because both have large population centers and make good comparators to other nations in Europe and Asia.

There are a few holdouts remaining, including one I'd like to take a look at today: Sweden. A few caveats before we dive into the charts (which you can find at this source). First, I'll be making comparisons among Nordic countries, but that doesn't mean those nations are interchangeable. There could be reasons for the differences we're seeing beyond just their chosen approaches to curtailing the spread of SAR…

Once, back in grad school, I was talking to my thesis adviser about a particularly difficult experiment I was trying to figure out how to do. I was coming off an experiment run that took about two weeks from the start until I got the results back for analysis:

"It didn't work." I told her.
"What do you mean, 'It didn't work'?" She asked.
"Something about the way the experiment was run was wrong. I got no signal, not even for the standard curve and the controls. It didn't work."
"The only failed experiment is one you don't learn anything from." She chided me. "If you can't use this experience to figure out why it didn't work - or even to learn how you might figure that out with the next experiment - then you're just wasting time and money, hoping to learn something by random chance."

This is a powerful lesson, and one I want to expand on more generally. It's a lesson that should influence how we v…

Caveat: I am not an epidemiologist.  My fields are oncology, immunology, and to a lesser extent microbiology.  This whole situation is changing rapidly anyway, so uncertainty reigns during times like these.  I'm more interested in data analysis than I am in diagnosing conditions on the ground.

We should be careful - suspicious even - when we see data and think it's talking to us.  Especially in times like these, I often hear people say they're not doing anything fancy, they "just look at what the data say."  That's false.  Data don't say anything.  People say things about data.  Sometimes data can look very convincing, but then as you ask more questions and look at the data in a different way you can come away with different interpretations.  That's not what people mean when they say thy're just looking at what the data say.

By all means look at the data, but remember that if you're hearing something it's not because the data are talking t…

I pulled this data from Google Trends of searches for "coronavirus".  I had to set a range that excluded everything from mid-December 2019 to the present, because they dwarfed all the other searches and compressed the scale.



As you can see, there's a natural die-off of coronavirus-related searches by mid-March, which is good news for those concerned with the present crisis.  I've heard a lot of speculation about the current coronavirus, Covid-19, and I want to talk about a few ideas from epidemiology to get at more general principles.  First, the reproduction number (R0) of Covid-19 looks like it's about 2.2.  Meaning each infected person is likely to infect 2.2 additional people before they clear the virus or die and are no longer infectious.  That means it's likely to spread quickly - or more accurately exponentially.

In fact, given an R0 of that magnitude, it's unlikely we'll be able to stop it from spreading, no matter how good our quarantine is. …

An old friend told me about a conversation he had with his daughter over breakfast, where they mused about which cereal has the most sugar.  That led him to create a simple diagram comparing about a dozen different cereals against their sugar content.  While I was reviewing his graph, I noticed it had a few major design limitations.  The most glaring was that he used the serving sizes listed on the cereal boxes.  I decided to make my own comparison graph, using weight and percentages by weight instead of measurements by volume.  This led to many more comparisons, and was part of the inspiration for my recent book.  The first graph below is the result for different cereals, sorted in order of most to least amount of sugar.



As I was looking at this graph, I thought about how sugar and carbohydrates are basically the same thing.  (A carbohydrate is just a bunch of sugars chained together.  The body breaks these down into their component sugars and moves on from there.  There's more o…