This is a geek post, and if you’re familiar with my work, you might love it. If you’re not, well, good luck. You’ve been warned.
Why do we have such a hard time knowing at this time what the real rate of COVID-19 deaths are, as well as what the real infection rate is? NOTE — it looks from preliminary evidence to be 10x the flu, and if you get the disease with complicating factors, a terrible way to die — that we DO know. So don’t take this post as some odd minimization. It’s not. DO the right things!
The problem is that at the current time, we are attempting to understand this using people in hierarchical social structures (in this case, academic and industry virologists) whose main tool in the arsenal is algorithmic rule processing, which turns into our well-known empirical science. What that means is that they’re going to do some tabulation, a little data analysis, maybe plot things on an axis and make some guesses. That’s what folks have pretty much done. Work is improving — I liked this piece, and you can check this out. Make no mistake — while I talk about some of the science with questions, I HONOR our uber-geeks who are pouring themselves into this crisis. That’s one of the vectors to get to answers.
The truth is that this kind of thing works GREAT with what I call “closed systems” — where you can isolate a given population, subject them to a clinical trial of some sort, where you control the inputs, the population is also homogeneous, and there’s a start-and-end date that makes sense.
None of those things are present in the current problem. What we have here is an Open Systems problem — a society is infected from an unlikely source — a virus that has jumped from bats (we think) to humans (I actually think the evidence for this is reasonable) and is now exploding. We can’t draw any meaningful system boundaries YET on this, because we don’t really understand the system. We can guess on vagaries (animal -> human transmission, consumption of the virus through purchase at a wet market, etc.) but we just don’t know. And when I talk about system boundaries, I’m not just talking about the obvious ones — Wuhan city limits, Hubei province boundaries, etc. I’m talking about all the potential inputs and feedback loops present.
For example, we also don’t know a ton of other stuff about the impact to human health of people regarding their environment to resiliency against this virus. China’s air is absolutely execrable in most of the big cities. I have been in Beijing where the pollution fog is as thick as the dense fog that sometimes settles in on the Palouse — except ours is water vapor. And there are other open system factors that no one knows about. Chinese people smoke cigarettes. And they also have adopted about half of our own awful, sugar-based diet. I maintain, as a lone voice in the wilderness, that many of our problems are inflammatory-diet-related as far as overall health, psycho-social shift, etc. Especially in an epidemic. Here are a couple of pix — the daytime one is not so great showing the air — visibility was low, but trust me that the nighttime one is just air pollution, and you can see this through the lights. People had on masks and such.
What that means is we have a respiratory-handicapped, immune-compromised population who have watched this sweep through their population. And we have statistics associated with the virus, functioning under a combination of these exacerbating factors, along with age-related mortality. These things, at this time, are impossible to uncouple. Scientists may come up with a vaccine in a few months. But they will take years to untangle this mess.
What this means is virologists are observing a new phenomena with few guiding principles (up there in the Knowledge Structure stack) and a poor ability to guess at how exactly the virus messes you up. It is true that one can see the dynamics of transmission, and we can do things like count patients and symptoms, and make guesses based on intuition that vary on the guesser’s and their field’s past practice. Interestingly enough, it’s the virologists’ past experience with epidemics that really drives insights. Experience assembles tons of multi-dimensional data into complex narratives, and gives insights that are simply impossible to extract from a standardized data set. The brain’s demands for coherence from an expert lens can often drive insights that would be normally unavailable.
But it’s a tricky business (confirmation bias and all!), and as amenable to hunches as anything. It’s not that I don’t trust the virologists — actually, I do. But the public, and especially the media, like certainty. And we just aren’t going to have that for a while. As I said in the previous piece, you should act like this is the Big One. And hope that it isn’t.
Most important to understanding this whole deal are the people who got the virus and didn’t get sick at all, or only got moderately sick. But those people can’t meaningfully AT THIS POINT be included in any of the analysis. (This piece is an early attempt.) Later, down the pike, we might be able to do this with certain populations. Wuhan, China, has a population of 8.5M people, and Hubei province, where Wuhan is, is up around 58M! The different cordons that were drawn around these areas will, with intensive research, show the permeability of these boundaries and how it all works, as well as who actually likely got infected. Rapid contagion and fatality can go hand in hand. But there are also reasons why viruses like Ebola burn out quickly, and don’t go on to devastate countries.
But for now — with the knowledge structures we have — we just can’t know. And the best thing we can do is follow the precautionary principle (especially with the potential for hospital overload) and DO YOUR PART . But also realize that outcomes as far as pandemic deaths will likely be less than the straight multiplicative estimates we are seeing. That means your odds of surviving this pandemic, especially if you are in one of the low-impact groups, are extremely high. And the best course of action is to calm down, and think about how you might help others. That’s the empathy thing. We are all in this together.