Pandemic 03: Survivability Traits

by Shane L. Larson

Over the millions of years that natural selection produced modern humans, countless traits were selected becasue they were somehow advantageous to our suvival. Ultimately, some 40,000 generations ago, modern humans began walking the lands of Earth; experiments that Nature had made as it grew our branch of the Tree of Life were terminated without a second thought. Today, there are no archaic humans left — gone are those that came before us, erased but for a few fragments and bones that rise from the tomb of the Earth.

A skull of homo rhodesiensis, an ancient ancestor of humans. The Universe has long experimented with what makes humans good survivors; today there are no homo rhodesiensis left. [Wikimedia Commons]

One might ponder what it is about humans that made us the fittest in our long line of ancestors? The Latin name for our species gives a clue to what we think the advantage is: homo sapiens means “wise man.” More often than not, our intelligence, our brains, are regarded as the prominent trait that made our survival most likely. The ability to make tools, to solve problems, and to plan for possible futures are all powers of the brain that suggest its development was a good survival trait.

But for those of us who think about life in the Cosmos, we eventually ask whether or not human intelligence is a survival trait or not? Look at the utter disregard our species has for the finite resources on our planet, or the fact that we are willfully ignoring the accelerating climate crisis, or any of a hundred other existential global threats we are ignoring. It makes one question whether our intelligence is being used for survival at all.

Interestingly, the brain is just like every other physiological trait we have — it was built by Nature through a long chain of experiments in survival. The earliest parts of the human brain to develop, the paleomammalian cortex (or limbic system), is the core of human emotion and response to external stimuli, particularly danger or threats. It evolved over time, like all of your biological systems, to protect you and give you a better chance at survival. One of its safety responses is to control your psychological response to threats. Sometimes that response is designed to protect you from very tangible direct harm; at other times it is designed to protect you from very tangible threats, but ones which may harm you by overwhelming your reactions until you are completely debilitated.

We see both of these deeply ingrained threat responses playing out right now in the ongoing crises that have ensnared the world.

Death has always preoccupied humans, in biological imperatives, deep psychology, and art. This 17th Century painting from Philippe de Champaigne is often associated with the Stoic philosophies surrounding Memento Mori: “remember that you will die.” [Wikimedia Commons]

Consider how we humans perceive and deal with death. A single death can transform your worldview — the death of a close friend or a loved one has profound impact on your mental state, precisely because of the deep personal relationship you shared. Death acutely focuses your attention on the fact the memories you carry with you will be the last ones you have with that person. It also acutely focuses your attention on your own mortality.

But you don’t have to be personally related to a person, or even know them, to feel grief at the loss of life. You feel the same pain, as if it were a friend or a loved one, precisely because you understand the deep personal loss from the death of a single person. Your brain has been wired from your personal experiences to understand how single people change one another’s lives. You extrapolate those experiences to people you don’t know when you hear of their death. The result is you are devastated, tortured by grief when they die. The deaths of famous people are a curious mix of the two, since you often ascribe deep personal evolution to your exposure to music, writing, sports, and film.

As a result, the loss of David Bowie knocks you down, because you remember driving in your car with friends listening to “Scary Monsters” over and over again, and those powerful memories are inextricably melded with your knowledge of Bowie. Chadwick Boseman’s death sent you into a paroxysm of tears, not just because you admired him in 42, but because your own family has been ravaged by cancer. Your rage at the murder of Breonna Taylor was stoked by the fact that she was murdered in her own home, a place of perceived safety and sanctuary.

Tragically, our brains behave in the exact opposite way when the scale of the tragedy expands beyond numbers easily related to your own personal experiences. Word of a family dying in a car crash or an apartment fire invokes a terrible sense of tragedy. News of an airliner going down may fuel your fear of flying, but large groups of people being overwhelmed by disaster becomes, for the most part, abstract to your brain. The reason is your brain is defending itself in a rather peculiar way. You absolutely can imagine the tragedy of the deaths of thousands of people — but multiplying the agony of grief for a single person a thousand-fold would destroy your psychological balance, and your brain knows that. It clings to the abstractness of large, anonymous numbers, and lets your thoughts gloss over the fine-scale human details of the tragedy. This effect is called psychological numbing.

Map of confirmed COVID-19 infections per capita (total divided by local population) as of 17 Sept 2020. The global scale of this crisis is beyond normal, everyday human experience. [Wikimedia Commons]

Which brings us to the current crisis. Without fail, the coronavirus Pandemic is a global crisis, not to be shirked and ignored. It kills people — 948,000 worldwide, and 202,000 in the United States (as of today, 17 September 2020). For virtually everyone who contracts the disease, there are long term consequences that we are only now beginning to understand — cardiovascular damage, fatigue, deterioration of your joints, and damage to your nervous system. The dire effects are why scientists and public health experts are so adamant about controlling the spread of the disease.

But unless you or a family member or a close friend have had (or died) from COVID-19, your brain protects itself. The psychological numbing associated with the scale of the pandemic takes over, and underpins all your thinking, regulating your personal behaviour as well as guiding your response to widespread social safety measures designed to cap the disease. Numbing can dull your sense of danger, leading to you not being as safe as you can be. An unfortunate lack of perceived danger might convince you that everyone who is responding with great caution are being silly, and it could lead you to rebel against social safety measures like a teenager against curfew. Your brain is protecting itself by convincing you it isn’t as serious as it is, but it is lying to you. You can control such responses, but only through diligent practice and self-reflection, and fearless trust in what the scientific data is really saying, not what we want it to say.

And so, our conversation returns to where it began. The brain of homo sapiens, with its capacity for abstract thinking and predictive speculation is the product of millions of years of evolution. Each stage in the long chain of natural selection helped our ancestors survive a ruthless and dangerous world, leading to us today.

So are our brains a trait that makes us fit for survival? The Universe developed our brains because along the way it seemed to be protective. But psychological numbing exposes us to threats that can decimate our species, like coronavirus to be sure, but other existential threats are on the horizon: pressures of population on limited natural resources, human wasting of natural environments, and the catastrophic collapse of the climate at the hands of humans. 

One could easily conclude that on the scale of our civilization, psychological numbing is not a survival trait, and the great experiment known as “humanity” will terminate, and fade into oblivion. It has happened before, with megalodons, sabre-tooth tigers, and trilobytes. That termination has happened to humans too — gone are our ancestors, Australopithecus, homo erectus, and the Neanderthals. But it has happened to our civilizations before too — gone are the ancient cultures of the Indus Valley civilization and Mesopotamia, and only fragments of the ancient Anasazi remain in the American Southwest, all erased by droughts that destroyed their supportive, agricultural systems. Humans are not immune to being erased by the Universe.

The Tree of Life is vast and tangled, but many more species have died than have lived, unable to survive the challenges the Universe throws at them. [Image: Pixbay]

But on the other hand the Universe has stirred another ability into the experiment — our capacity for reason, the ability to look at the Universe, figure out and predict what is happening and why, and doing something to protect ourselves. In some fashion, we have learned to utilize that trait and act in complete contraction to other biological imperatives our brain would like us to respond to. The Universe is testing out the idea that software updates, designed to circumvent hardware weaknesses and previous programming, might be a good survival trait.

Whether or not our reason adds to our survivability in the long term remains to be seen. We have yet to come to the end of this crisis, and do not yet know if our civilization can collectively shore up our defenses, or if we will continue to capitulate our future on the basis of wishful thinking. 

Either way, the Universe does not care. The Universe is callous, ruthless, unflinching. It is no mere tyrant, it simply has no reservations about terminating experiments that cannot survive in the face of adversity. Perhaps homo sapiens will sink into extinction; perhaps there will be some new strain of humans, homo postero, that will not be so fact resistant, and can survive more adversity than we.

As a brilliant fictional scientist once observed, “Life finds a way.” The Universe will find a strain of humans fit for survival, even if we are not.

————————-

This is the third in a series of posts about scientific reasoning, instigated by the Pandemic of 2020. The first post and links to the rest of the posts in this series are:

• Pandemic 01: Learning in a Time of Crisis
• Pandemic 02: Numeracy and Understanding Data
• Pandemic 03: Survivability Traits (this post)

Pandemic 02: Numeracy and Data

by Shane L. Larson

Let’s talk about “numeracy” — to be clear, I define this to be the ability to use and understand numbers and data. It is similar to “literacy” but related to quantifiable things, things that can be measured, and things that obey well defined and incontrovertible rules. Specifically, I want to talk about your numeracy.

I have spoken with many thousands of you at public lectures, and many more thousands of you have sat through my introductory physics and astronomy classes. I certainly hope there are many more thousands of you reading this. At this moment, my former students have a certain advantage over all of you — they know better than to utter the words “I’m not good at math,” lest I get out an actual soapbox (I’m quite short), stand up on it, and wax on and on about how that simple phrase, “I’m not good at math” is a lie you have been taught to repeat. You might call such a soapbox speech a “rant,” or so my teenage daughter tells me (she also knows to never utter the words “I’m not good at math”).

Many people like to opine that they are “bad at math” because they struggled at one point in the past, or because someone told them they were, or because it is cool to say you are “bad at math.” In fact, most of you aren’t bad at math at all — you just aren’t practiced at recognizing that fact.

I can already hear your objections. I know you will insist that you are bad at math based on a poor past experience in a calculus class, or the fact that you struggled with solving cubic equations, or because you don’t have a foggy clue what tensors are. Fair enough. But I don’t remember any of the soliloquies Shakespeare wrote in Hamlet; I can’t diagram a sentence, nor do I precisely remember the definition of a transitive verb, and I suspect the only reason I know about conjunctions is from singing Schoolhouse Rock songs. But I would not deem myself “bad at English” or “illiterate” as a result (my former English teachers might disagree; sorry Billie Wright!).

But this is precisely my point about math. You are not “innumerate” and “bad at math” — there are sophisticated and complex advanced topics you may have been taught at one point in your life and perhaps didn’t fully grasp and may not remember today. They were taught to you in order to develop the neural and cognitive framework of your brain, just like you were taught about diagramming sentences and iambic pentameter. Today, decades after you were last in a classroom, you may not remember all the details but you still have that cognitive framework. You are perfectly capable of using it. You are “numerate” and I know you are because I see you be numerate every day.

You very seldom fail to order the correct amount of pizza. Calculating incorrectly in either direction can be disastrous; you are numerate enough to do this.

For example, suppose your book club is going to meet next Saturday and you need to order pizza. What do you do? You count up how many people are coming, you estimate how many pieces of pizza each person will eat (based on prior observations), you add a few extra pieces in that your partner and kids will take, and based on the number of slices in a pizza, you place an order. You pretty much get it right every time. Seldom have you ordered 78 pizzas for 7 people because you are “bad at math.”

What if you are trekking across the country on your National Parks road trip. Your dependable late 90’s economy car gets maybe 28 miles per gallon, you’ve got 3/4 of a tank and are heading out onto the long stretch of I-90 between Sioux Falls and Rapid City and quickly calculate when to stop for gas so you don’t run out. You pretty much get it right every time and have seldom been stranded out in the middle of South Dakota.

You regularly and successfully calculate how much gas you need to make long trips. You are numerate enough that you have seldom, if ever, run out of gas in the middle of South Dakota.

You are perfectly capable of assimilating data (data are things like previously knowing how many pizzas are eaten or how far you drive on a tank of gas) and using current conditions (things like how many people are coming to your club meeting, or how much gas is left in your tank) to make a numerical calculation (how many pizzas to get or how much gas to buy). You are fine at math. More to the point, you are numerate. You don’t think about it, of course, because the risk associated with over-ordering pizza is low; you seldom have to make a 400 mile run in your car without hope of seeing another gas station soon. It doesn’t change my point that you are, in fact, numerate.

The value of being numerate cannot be overstated in the face of the crisis the world faces today. Understanding what COVID-19 numbers are telling you, and perhaps more importantly what they imply about your personal risk, is critical to safely weathering the Pandemic so you can emerge on the other side. In the sea of numbers we hear each day, how do we absorb those numbers and use them?

Numbers have a certain implacable relentlessness to them, a modicum of unassailable truth that is regularly at odds with the distinctly human need to rationalize.  That being said, a number’s implication for how it impacts your life requires context, otherwise it’s just a number devoid of how it relates to the world. We can use your numeracy to illustrate how context is important, and then apply it to understanding the current crisis.

Let’s begin with a learning experiment — a simple example that illustrates how information combines together to inform you about the world. Imagine I have a carpeted living room with a nice square grid pattern in the carpet, 20 squares by 20 squares (400 squares total). For some extravagant reason beyond the scope of this blog post, imagine I have dropped some nickels on the carpet; a lot of nickels. You are enjoying your lunch, and trying to decide if it is worth interrupting your delicious sandwich to go pick up all the nickels before someone else does. So you send me in to check the situation out.

An experiment in dropping nickels on a carpet. Each square of carpet is the same size, and the same number of nickels were dropped each time. What is shown is what landed and stayed in each square.

CASE A: I come back with two nickels. Do you go pick up nickels or not? Without any context you really can’t decide. Unhappy with me, you send me back into the room and I come back with five more nickels! Now you have a total of seven nickels, or 35 cents! Do you go pick up the nickels or not? This is all about context of the data — what do the nickels I brought back to you represent? Did I bring you all the nickels, or just a fraction of the nickels? How many more nickels might there be? 

CASE B: This time when I come back, I bring you 7 nickels, but I tell you they were all the nickels in one square of carpet.  This is context. Context allows you to start figuring things out, because you are numerate. In particular, if every square has 7 nickels on it, and the room is 20 by 20 squares (400 total squares), then the room would have 400 x 7 = 2800 nickels, or $140! This is good context, but we could still do better.

CASE C: In the last example, you made an assumption. Assumptions are neither good nor bad — assumptions are limited. The important thing about assumptions is that when you make them, you try to be clear about what the assumption is, so if your understanding of the situation improves (you get more data), you know how to update what you think is going on. Above, you assumed every square had 7 nickels. Is that true? You send me back into to find out.  I come back and tell you I looked at three more squares, and they had 23, 18, and 20 nickels in them respectively. This is greatly improved context, because you have many pieces of data. There are simple and complex ways of looking at data, even when you have only a few bits of information. One of the easiest is the average.  What is the average number of nickels on a square?  Based on our observed data:

   Average = (7 + 18 + 20 + 23)/4 = 17 nickels per square (on average)

So now you can estimate that in the room there would be 400 x 17 = 6800 nickels, or $340 dollars! It is definitely looking like you should be collecting those nickels.

A simulation dropping 8000 nickels on a carpet that is 20 x 20 squares wide. Note the highlighted random square — this one has 7 nickels, the first square we talked about in our discussion. [Image: S. Larson]

This image above shows the data this example was drawn from — a 20 x 20 carpet grid, with 8000 nickels ($400) dropped on it.  The first experiment where I brought you only 7 nickels told you something, but by collecting more data you developed a clearer picture of what was going on in the living room.

Now let’s use this example to help us understand something about the Pandemic.

As the coronavirus Pandemic has surged in the United States, considerable noise has arisen around testing and what the number of tests and results mean. Fortunately, you can use your numeracy to understand what the data is telling you. Two common testing numbers are reported for most states: 

• The number of tests administered
• The number of new daily cases (number of positive tests)

In and of themselves, these numbers have no context, except that most of us have some rudimentary knowledge of our state to provide context — the critical knowledge here is the population. Population provides a simple way to understand how widespread the disease is: 500 cases in a county with 20,000 residents has different implications than 500 cases in a state with 1 million residents.

One of the most common points of discussion in COVID-19 testing is whether or not the number of cases is rising just because we are testing more. At the heart of this talking point is the more fundamental question, the question we really want to know the answer to: how do we know if the coronavirus is spreading and growing in our state or not?

Testing is just like our nickel example above, and you can use the nickel example to help guide you in your thinking. 

NICKELS: Each square has some random number of nickels in it. If I look at one square, I get some sense of how many nickels there might be. If I randomly look at many different squares, I get a better, more reliable picture of how many nickels there are in the entire area of the carpet. If I get 7, then 23, then 18, then 20 nickels, there are on average (7 + 18 + 20 + 23)/4 = 17 nickels per square.

COVID-19: Take a fixed number of people, say 100. If I test those 100 people, I get some sense of how many COVID-19 infections there might be. If I randomly pick many different groups of people, I get a better, more reliable picture of how many COVID-19 infections there might be. If I get 7, then 23, then 18, then 20 infections, there are on average (7 + 18 + 20 + 23)/4 = 17 infections per 100 people.

Reporting the number of infections together with the number of tests given is called  the positivity (or, more correctly, the positivity rate), and is a way of giving context to the data. Another way to give context is to report the total number of cases divided by the population (typically reported per 100,000 people, rather than the full population; this is more similar in size to a typical community and helps personal visualization about how widespread COVID-19 might be in a small city. Cases per 100,000 also is easier to talk about without making arithmetic errors!). Most state health departments and most major COVID-19 tracking sites that report daily data report both of these important numbers, giving you a better way to understand the risk.

So how do you tell if things are improving, holding steady, or getting worse? You watch how a number like the positivity changes over time. The number of known cases does increase with time. The number of known cases does increase with the amount of testing deployed. But the positivity rate accounts for that fact by always thinking about the data in fixed, similar sized chunks. In our examples above, deploying more tests means more groups of 100 tests to include in the average. Just like counting more squares on the carpet gives a better idea of the number of nickels, increasing the number of tests improves how well we know the positivity rate, which more accurately captures how COVID-19 is spreading in our communities. So the rule of thumb is:

• If the positivity rate is increasing, then for any random group of people you pick, more of them are sick with COVID-19
• If the positivity rate is holding steady, then for any random group of people you pick, the disease is not increasing rapidly
• If the positivity rate is decreasing, then for any random group of people you pick, the disease is slowly being eradicated

You could also replace “positivity rate” in these rules of thumb with “cases per 100,000” if that is an easier number for you to relate to. The story the data is telling you will be the same either way.

Now keeping all of this in your head can be hard, even for those of us who “do numbers” every day. Use your mental examples, like the nickels on the carpet, to keep you grounded. Tactile, hands on examples that you could actually recreate on your living room floor are often easier for your brain to work with, since they are easily visualized or even created, making it easier to stick in your mind.

We will come back to using simple mental models to keep our reasoning grounded in some more of our upcoming discussions. Until then, be safe, be well.

————————-

This is the second in a series of posts about scientific reasoning, instigated by the Global Pandemic of 2020. The first post and links to the rest of the posts in this series are:

• Pandemic 01: Learning in a Time of Crisis
• Pandemic 02: Numeracy and Data (this post)