Everyone knows that a barrel of something flammable is dangerous. We keep barrels full of gasoline away from heat and sparks. But an empty barrel?
An empty barrel that once held gasoline can be even more dangerous. The barrel has been emptied of its liquid contents, but it will be full of flammable vapors – weightless, invisible, and deadly.
Contemporary safety labels have helped – fuel drums are required to have warnings that stress the danger of “empty” containers – but still, people die every year after assuming that an empty barrel will be harmless.
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We often refer to the places where civilizations once thrived as ruins. People inhabited these places long ago and carried out all the bustle of living – they slept, cooked, ate, worked, talked, and traded within the boundaries of a community that then slid into disuse and disrepair.
From the vantage of the present, ruins often seem to belong to a single era: the past. We live now, and people lived there then.
But the past is almost incomprehensibly vast. Humans have been living in most regions of North America for 10,000 to 20,000 years. (Which is an enormous margin of error – for more on the debate about when humans first settled in various regions of the Americas, you should read Jennifer Raff’s Origin, which I reviewed for The ABT.)
Long, long ago, there were already ancient ruins. At times – perhaps because the local climate had shifted enough to make an area less habitable – these ruins were completely abandoned. At other times, new communities were established atop the remnants of the prior era, and elements of the ruins were restored or repurposed by new inhabitants.
It’s likely that these people all preserved stories of when those ruins had been actively populated – a recent study by Patrick Nunn & Margaret Cook argues that Polynesian oral tradition preserved stories of particularly dramatic climate change events over thousands of years, not just hundreds. The returning people likely felt a personal, spiritual connection to the prior inhabitants.
And ruins – places that are empty of the bustle of current life – are full of spirits.
Such ruins may have been kept intentionally separate from day-to-day activities, the better to preserve their sacred status. Contemporary people make similar choices: we maintain separate spaces for contemplation or prayer; we reserve seemingly functional objects, like special sets of plates or decorations, for the holy days or holidays that punctuate our calendars.
But when European colonialists arrived in the Americas – people who did not feel a personal or spiritual connection to anyone who’d inhabited these lands before them – they asserted that the ruins were merely empty. That there was no reason not to take, to fence, to farm (in the process, often depleting soil quality and biodiversity that had been cultivated through centuries or even millennia of active Indigenous management).
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Southern Florida is an enticing place to live. Indeed, this region has been an enticing place to live for many thousands of years, with sufficiently abundant wild food sources to support intensely hierarchical civilizations like the Calusa – a rarity among non-farming cultures. Researchers have found evidence that the Tequesta settlement in modern-day Miami was populated for at least 2,000 years – from about 1,500 BCE to 500 CE – even though this region’s climate is particularly bad for preserving archaeological evidence.
But now – after the arrival of European colonialists, influenza, and the worldview of capitalism – the Tequesta settlement is “empty.” No one is working, eating, or sleeping there. And so – despite the archaeological relics and burial grounds that lie beneath layers of more-recent dirt; despite the spirits that currently fill the space– a wealthy developer wants to build.
Presumably there will be court cases. But the outcome isn’t in much doubt. Almost inevitably, the luxury high rises will rise.
The land will not stay empty.
Although, actually, most luxury high rises in Miami are empty – most units are purchased simply as a commodity to store wealth.
And in time – perhaps very little time – the climate will shift again. Then these new high rises, too, will crumble into ruin.
As best we know, every particle in our universe follows the exact same physical laws.
These laws are not “deterministic.” We wouldn’t know what would happen next even if we could somehow measure everything about the state of our universe right now. But the unpredictable parts of each particle’s motion – due to each particle possessing a probabilistic mix of perhaps contradictory properties, which sounds strange in metaphorical languages (like English, Spanish, Mandarin, etc.) but not when expressed in mathematics – are totally outside of our control.
As best we know, humans shouldn’t have free will. Our future behaviors will unfold from the present positions and momenta of all the particles in our brains and bodies and the environments around us. Our thoughts will result from cascades of salt atoms crossing neuronal membranes. These salt atoms – like all other particles – are simply following physical laws that are, ahem, totally outside our control.
As best we know, we can make no choices.
As best we know, it’s still totally reasonable for the collections of particles inside our brains and bodies to experience an emergent phenomenon like consciousness. The particles inside of us collaboratively form neurons which collaboratively form minds. These minds can feel. But these minds still follow physical laws.
We can experience choices, not make them.
As best we know, we should experience our lives only passively, as though watching extremely immersive television shows. At times our minds would feel as though they had made choices, but that would just be a plot device. Cinematographic trickery! The choices are actually made by the positions and momentums of particles inside of us, which always result from their positions and momentums a moment before, and so on.
The math all works out.
So, for people who understand the math and the underlying physics, there’s a choice to be made (or perhaps I should say, “the person will passively feel as though they have made a choice”): should they believe in the laws of physics, or should they believe in free will?
Free will certainly feels real. But the sun also feels like it revolves around our planet. Our feelings have been wrong before.
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InExistential Physics, Sabine Hossenfelder professes not to believe in free will. But Hossenfelder’s disbelief feels unconvincing. For instance, in describing how we can react to immoral behavior without referencing free will, Hossenfelder writes that:
“We evaluate which actions are most likely to improve our lives in the future.”
This is nonsense, of course. Without free will, there is nothing to evaluate– “evaluate” is an active verb that implies choice. Without free will, we would have no way to“improve our lives,” because this phrasing also implies action and choice. If the entire course of the future depends solely on the current positions and momenta of particles, then our lives will simply happen. The future isn’t predetermined – the mathematics of quantum mechanics injects randomness into the future – but we have no way to influence it. The future course of our lives is not up to us.
The particles will act as they must. Our minds will only watch.
As best we know, the laws of physics tell us that each and every moment in which we feel like an active participant in our lives is simply an illusion.
Personally, I believe the laws of physics are wrong. So does Hossenfelder, most of the time. In her day to day life, she contemplates cognitive biases – for example, the “sunk cost fallacy,” that makes it easy for people to continue making a bad choice so that they don’t feel bad about the bad choices they’ve already made, like when Hossenfelder further delays enrolling in a frequent flier program because she has already missed out on some benefits – and in her better moments, Hossenfelder chooses to overcome them. Hossenfelder also believes that she chose to study physics (and she believes that more people would make a similar choice if introductory physics were taught with a different mathematically formulation).
Hossenfelder discusses the ways that poverty and childhood trauma caninfluence the choices that we make as adults – some decisions feel easier than others because we are always sailing through a headwind of our past experiences – but in every passage of the book, Hossenfelder conveys her belief in free will.
And for good reason! We do have free will. Everyone agrees – even people who, for professional reasons, claim that free will can’t exist.
Honestly, there’d be no other way to live. Human brains couldn’t fathom existence without choice.
So, where does that leave us?
Either our belief in free will is wrong, or our current understanding of physics is wrong. As Hossenfelder meticulously explains, the two belief systems are incompatible.
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Personally, I think our current understanding of physics is wrong. And I felt surprised that Hossenfelder never even mentions a major assumption that underlies her work. Occasionally, her chapters will include descriptions of theories that she doesn’t favor (usually followed by a curt dismissal), but the entire text of Existential Physics ignores the most glaring flaw in Hossenfelder’s arguments.
For instance, Hossenfelder writes that “We are all ultimately made of particles, and these particles follow computable equations.” And maybe this is true! But we have no evidence to suggest that it is.
All computation is digital. We can perform digital calculations at various levels of precision – for instance, if we’re trying to predict the behavior of a marble inside a pinball machine, we might measure the marble’s position down to the nearest inch, or tenth of an inch, or hundredth of an inch – but computation can never handle infinite precision. You can’t write the exact square root of two in decimal notation. You can’t write down the exact solution for the behavior of particles in any system with three or more – we can perform excellent calculations for the electronic structure of a hydrogen atom floating in an otherwise empty universe, but for atoms like helium, or for anything more complicated, we couldn’t come up with exact solutions even if we found empty universes for them to exist inside.
Possibly, our universe is digital, too. The mathematics of contemporary physics works best if we believe that our universe exists on a lattice of positions spaced approximately a Planck length apart: this would be a bit like a digital picture, where you can zoom in so far that eventually you’ll see that a red pixel can be either here or there but not anywhere in between.
Many of Hossenfelder’s claims presuppose that our universe is digital. In a digital universe, the amount of information in any particular volume of space would be finite. Decimal mathematics could correctly express everything. We could solve three-body problems, and the chaotic glitches** caused by rounding errors in our computations would be mirrored by chaotic glitches caused by rounding errors in the universe itself!
Wouldn’t that be grand!
But the only “evidence” we have so far that our universe might be digitized – pixelated, voxelated – is that it makes computation easier. That’s not compelling evidence.
It is testable. Consider a hydrogen atom held at a specific location with its electron in an excited orbital. When its electron collapses back to the ground state, the atom emits a photon that zooms off in a random direction. We might then kick the hydrogen’s electron back into an excited state, let it relax to the ground state again, and send another photon zooming off in another random direction. Again and again, photons zoom away!
If physical space were continuous, then the photons produced by this experiment could hit every possible location on detectors placed at any distance away – the probability distribution for photon collisions would be smooth over a sphere. But if physical space were digital, then photons could fly off in straight paths starting only at lattice points adjacent to the hydrogen atom (after accounting for the superposition of possible hydrogen positions). A graph of the probability distribution of photon strikes over a large sphere would show dark regions where photons couldn’t reach – locations where a photon’s path would’ve needed to pass between two lattice points to get there.
As best we know, the spacing between lattice points – if our universe were digital – would be ten to the minus thirty-fifth meters, which is like taking a yardstick and slicing it into a billion pieces, then slicing that piece into a billion pieces, and slicing that into a billion pieces, and slicing that into a billion, until you’ve taken just one billionth part four times over. This is very tiny! Which means that we wouldn’t notice a dark region unless our detector was very far away, and we would have to repeat this experiment with many photons to reveal it.
But – unlike several theories in contemporary physics – this is testable. It’s just an excruciating engineering problem.
Until we test this, though, Hossenfelder’s ardent claims – such as her claim that we can’t have free will – are a matter of belief. Although Hossenfelder doesn’t address this in her text, her worldview presupposes a digitized universe. There simply isn’t any evidence for this.
Until then, I’m perfectly content believing in free will. Even if my belief presupposes that our universe is continuous and is therefore not computable. I mean, computers are fun and all. But the way they work might not mirror our world. Even if that would make the math look prettier.
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** Note: often, numerical approximations of a solution will approach the real answer. If we were working on a problem that involved the number pi, we might treat pi as being equal to 3.14 and we’d get an answer, and then we could go through the math again while setting pi equal to 3.14159, and we’d often get an answer that was very similar and slightly more accurate. But certain systems exist at the cusp of very different behaviors – for example, if we were studying a neuron that was close to the threshold of either firing or not, small changes in our understanding of the present would lead to large changes in our predictions for the future. Sometimes rounding errors don’t matter much; sometimes they do.
When I was a child, my parents gave me a toy walrus to sleep with. While cuddling this walrus, I’d twist my fingers through a small looped tag on its back, until one day I knotted the tag so thoroughly that I cut off my circulation. I screamed; my finger turned blue; my parents rushed in and wanted to cut off the tag.
“No!” I apparently screamed. “The soft tag is the best part!”
I continued to refuse their help until they offered a compromise, merely slicing the loop in half so we could save my throbbing finger and prevent any future calamity.
I continued to sleep with that toy walrus until I was midway through high school. As I fell asleep, my parents would sometimes peer inside my bedroom and see me lying there, eyes closed, breath slow, my fingers gently stroking that soft tag.
Yes, kids with autism are sometimes quite particular about sensory stimulation. But I am not alone! Baby monkeys also love soft fabric.
So do their mothers.
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After biologist Margaret Livingstone published a research essay, “Triggers for Mother Love,” animal welfare activists and many other scientists were appalled. In the essay, Livingstone casually discusses traumatic ongoing experiments in which hours-old baby monkeys are removed from their mothers. The babies are then raised in environments where they never glimpse anything that resembles a face, either because they’re kept in solitary confinement and fed by masked technicians or because the babies’ eyes are sutured shut.
After the babies are removed from their mothers, Livingstone offers the mothers soft toys. And the mothers appear to bond with these soft toys. When one particular baby was returned to its mother several hours later, Livingstone writes that:
“The mother looked back and forth between the toy she was holding and the wiggling, squeaking infant, and eventually moved to the back of her enclosure with the toy, leaving the lively infant on the shelf.”
Although I dislike this ongoing research, and don’t believe that it should continue, I find Livingstone’s essay to be generally compassionate.
Livingstone discusses parenting advice from the early twentieth century – too much touch or physical affection will make your child weak! – that probably stunted the emotional development of large numbers of children. Livingstone expresses gratitude that the 1950s-era research of Harry Harlow – the first scientist to explore using soft toys to replace a severed maternal bond – revealed how toxic these recommendations really were.
Harlow’s research may have improved the lives of many human children.
Harlow’s research intentionally inflicted severe trauma on research animals.
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To show that the aftereffects of trauma can linger throughout an animal’s life, Harlow used devices that he named “The Rape Rack” and “The Pit of Despair” to harm monkeys (whom he did not name).
Harlow did not justify these acts by denigrating the animals. Indeed, in Voracious Science and Vulnerable Animals, research-scientist-turned-animal-activist John Gluck describes working with Harlow as both a student and then professorial collaborator, and believes that Harlow was notable at the time for his respect for monkeys. But this was not enough. Gluck writes that:
“The accepted all-encompassing single ethical principle was simple: if considerations of risk and significant harm blocked the use of human subjects, using animals as experimental surrogates was automatically justified.”
“Harlow showed that monkeys could be emotionally destroyed when opportunities for maternal and peer attachment were withheld. He argued that affectionate relationships in monkeys were worthy of terms like love.”
“In his work on learning in monkeys … [he offered] abundant evidence that monkeys develop and evaluate hypotheses during attempts to develop a solution.”
“Everything that Harlow learned from his research declared that monkeys are self-conscious, emotionally complex, intentional, and capable of substantial levels of suffering.”
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For my own scientific research, I purchased cow’s brains from slaughterhouses. I used antibodies that were made in the bodies of rabbits and mice who lived (poorly) inside industrial facilities. For my spouse’s scientific research, she killed male frogs to take their sperm.
We’re both vegan.
I’d like to believe that we’d find alternative ways to address those same research questions if we were to repeat those projects today. But that’s hypothetical – at the time, we used animals.
And I certainly believe that there are other ways for Livingstone to study, for instance, the developmental ramifications of autistic children rarely making eye contact with the people around them – without blinding baby monkeys. I believe that Livingstone could study the physiological cues for bonding without removing mothers’ babies (especially since Harlow’s work, from the better part of a century ago, already showed how damaging this methodology would be).
Personally, I don’t think the potential gains from these experiments are worth their moral costs.
But also I recognize that, as a person living in the modern world, I’ve benefited from Harlow’s research. I’ve benefited from the research using mice, hamsters, and monkeys that led to the Covid-19 vaccines. I’ve benefited from innumerable experiments that caused harm.
Livingstone’s particular research might not result in any benefits – a lot of scientific research doesn’t – but unfortunately we can’t know in advance what knowledge will be useful and won’t won’t.
And if there’s any benefit, then I will benefit from this, too. It’s very hard to avoid being helped by knowledge that’s out there in the world.
To my mind, this means I have to atone – to find ways to compensate for some of the suffering that’s been afflicted on my behalf – but reparations are never perfect. And no one can force you to recognize a moral debt.
You will have to decide what any of this means to you.
Like many people, I have a weak grasp on long times. My family often visits a nearby pioneer reenactment village where the buildings and wooden gearworks of its water-powered corn mill are about two hundred years old; I feel awed. In Europe, some buildings are a thousand years old, which sounds incredible to me.
These are such small sips of evolutionary time.
Humans have roamed our world for hundreds of thousands of years. Large dinosaurs ruled our planet for hundreds of millions of years. Animals whom we’d recognize as Tyrannosaurus rex prowled for the final 2.5 million years of that, with their last descendants dying about 66 million years ago.
My mind struggles to comprehend these numbers.
I found myself reflecting on this after a stray remark in Oded Galor’s The Journey of Humanity: The Origins of Wealth and Inequality: “Why is such a powerful brain so rare in nature, despite its apparent advantages?”
Galor’s question seems reasonable from the vantage of the present. We live on a planet where 96% of the mammalian biomass is either our own species or prey animals we’ve raised to eat. The total mass of all surviving wild dinosaurs – otherwise known as “birds” – is less than a thirtieth the mass of humans. We’ve clearly conquered this world. Our dominance is due to our brains.
And this moment – right now! – feels special because we’re living through it. From a geological or evolutionary perspective, though, the present is a time much like any other. If we represent the total lifespan of our sun as a 24-hour day (which is much more sensible than representations with the present moment at the end of the day), the current time would be 10:58 a.m., and our sun will become so hot that it boils away all our planet’s liquid water at 7:26 p.m. Between now and then, though, we have a whole workday’s time for life to continue its beautiful, chaotic evolutionary dance. Perhaps quite soon – maybe just a million years from now, or 10 million, which is less than two minutes of our total day – the descendants of contemporary parrots, crows, or octopuses could become as intelligent as contemporary Homo sapiens.
As a human, I’m biased toward thinking that parrots and crows would have a better chance than octopuses – after all, these birds face a similar evolutionary landscape to my own ancestors. They’re long-lived, social species that invest heavily in childcare, are anatomically well-suited for tool use, and face few risks from predators.
Or rather, parrots would face few risks if humans weren’t around. Unfortunately them, a voracious species of terrestrial ape is commandeering their homeland and kidnaps their young to raise as pets. But crows can thrive in a human-dominated landscape – some crows even use our cars as tools, cracking nuts by placing them in urban crosswalks then retrieving their snack after the light turns red.
Octopuses, however, are short-lived and antisocial. They’re negligent parents. Their brief lives are haunted by nightmarish predators. And yet. Some octopuses are already quite intelligent; their intelligence appears to confer a reproductive advantage (if only by virtue of survival); their bodies are well-suited for tool use. Certain types of tools, like flaked stone, would be more difficult to create underwater, but many octopuses are capable of brief sojourns into open air. So I wouldn’t rule them out. Sometimes evolution surprises us – after all, the world has a lot of time to wait.
Which means that powerful brains like ours might not be rare in the future. Especially if our species does something stupid – like engaging in nuclear war, succumbing to global pandemic, or ruining crop yields with climate change – and the animal kingdom’s future intelligentsia don’t have to compete with 8 billion Homo sapiens for space and resources.
Also, it’s surprisingly difficult to assess whether powerful brains like ours were rare in the past. Intelligent, tool-crafting, fire-wielding, language-using species have gone extinct before – consider the Neanderthal. Our own ancestors nearly went extinct during past episodes of climate change, like after a volcanic eruption 70,000 years ago. And even if some species during the age of dinosaurs had been as intelligent as modern humans, we might not recover much evidence of their brilliance.
Please note that I’m not arguing that Tyrannosaurus rex wove baskets, wielded fire, or built the Egyptian pyramids. For starters, the body morph of T-Rex is ill-suited for tool use (as depicted in Hugh Murphy’s T-Rex Trying comics). But simply as a thought experiment, I find it interesting to imagine what we’d see today if T-Rex had reached the same level of technological and cultural sophistication as humans had from 100,000 to 10,000 years ago.
If T-Rex made art, we wouldn’t find it. The Lascaux paintings persisted for about 20,000 years because they were in a protected cave, but as soon as we found them, our humid exhalations began to destroy them. Millions of years would crush clay figurines, would cause engraved bone to decompose.
If T-Rex crafted tools from wood or plant fibers, we wouldn’t find them. We can tell that ancient humans in the Pacific Northwest of North America caught an annual salmon harvest by analyzing radioactive isotopes, but we’ve never found evidence of the boats or nets these ancient people used. After a few more radioactive half-lives passed – much sooner than a million years – this would have become invisible to us.
If T-Rex crafted tools from stone, we’d find remnants, but they’d be difficult to recognize. Evidence for human tool use often comes in three types – sharp flakes (usually 1-3 inch blades used as knives or spear tips), a hammer (often just a big round stone), and a core (a hunk of good rock that will be hit with the hammer to knock knife-like flakes off its surface). We’re most likely to realize that a particular rock was a human tool if it’s near a human settlement or if it’s made from a type of sediment rare in the location where contemporary archaeologists found it (which is why we think that an ancient primate took particular interest in the Makapansgat pebble).
Still, time is a powerful force. 66,000,000 years can dull the edges of a flake, or produce sharp rocks through mindless geological processes. It’s been difficult for archaeologists studying submerged sites in ancient Beringia – a mere 30,000 years old! – to know for certain whether any particular rock was shaped by human hands or natural forces. Other stone tools used by ancient humans look a lot like regular rocks to me, for example this 7,000-year-old mortar from Australia or these 9,000-year-old obsidian knives from North America. Ten million more years of twisting, compressing, and chipping might deceive even a professional.
And then there’s the rarity of finding anything from that long ago. Several billion T-Rex have tromped across the land, but we’ve only found as much as a single bone from a hundred of them. 99.999996% of all T-Rex vanished without a trace.
From those rare fossils, we do know that T-Rex brains were rather small. But not all neurons are the same. Work from Suzana Herculano-Houzel’s research group has shown that the number of neurons in a brain is a much better proxy for intelligence than the brain’s total size – sometimes a bigger brain is just made from bigger neurons, with no additional processing power. And the brains of our world’s surviving dinosaurs are made quite efficiently – “Birds have primate-like numbers of neurons in the forebrain.” **
We humans are certainly intelligent. And with all the technologies we’ve made in the past 200 years – a mere millisecond of our sun’s twenty-four hour day – our presence will be quite visible to any future archaeologists, even if we were to vanish tomorrow. But we do ourselves no favors by posturing as more exceptional than we are.
Animals much like us could have come and gone; animals much like us could certainly evolve again. Our continued presence here has never been guaranteed.
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** A NOTE ON NEURON COUNTS: many contemporary dinosaurs have brains with approximately 200 million neurons per gram of brain mass, compared to human brains with approximately 50 million neurons per gram of brain mass. A human brain has a much higher total neuron count, at about 80 billion neurons, than dinosaurs like African Gray Parrots or Ravens, which have about 2 billion neurons, but only because our brains are so much more massive. If the brain of a T-Rex had a similar composition to contemporary dinosaurs, it might have twice as many neurons as our own.
Of course, elephant brains also have three times as many neurons as our own — in this case, researchers then compare neuron counts in particular brain regions, finding that elephant brains have about a third as many neurons specifically in the cerebral cortex compared to human brains. For extinct species of dinosaurs, though, we can only measure the total size of the cranial cavity and guess how massive their brains would have been, with no indication of how these brains may have been partitioned into cerebellum, cerebral cortex, etc.
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Header image: a photograph of Sue at Chicago’s Natural History Museum by Evolutionnumber9 on Wikipedia.
Recently my spouse & I reviewed Jennifer Raff’s Origin: A Genetic History of the Americas for the American Biology Teacher magazine (in brief: Raff’s book is lovely, you should read it! I’ll include a link to our review once it’s published!), which deftly balances twin goals of disseminating scientific findings and honoring traditional knowledge.
By the time European immigrants reached the Americas, many of the people living here told stories suggesting that their ancestors had always inhabited these lands. This is not literally true. We have very good evidence that all human species – including Homo sapiens, Homo neaderthalensis, and Homo denisovans among possible others – first lived in Africa. Their descendants then migrated around the globe over a period of a few hundred thousand years.
As best we know, no lasting population of humans reached the Americas until about twenty thousand years ago (by which time most human species had gone extinct – only Homo sapiens remained).
During the most recent ice age, a few thousand humans lived in an isolated, Texas-sized grassland called Beringia for perhaps a few thousand years. They were cut off from other humans to the west and an entire continent to the east by glacial ice sheets. By about twenty thousand years ago, though, some members of this group ventured south by boat and established new homes along the shoreline.
By about ten thousand years ago, and perhaps earlier, descendants of these travelers reached the southern tip of South America, the eastern seaboard of North America, and everywhere between. This spread was likely quite rapid (from the perspective of an evolutionary biologist) based on the diversity of local languages that had developed by the time Europeans arrived, about five hundred years ago.
So, by the time Europeans arrived, some groups of people had probably been living in place for nearly 10,000 years. This is not “always” from a scientific perspective, which judges our planet to be over 4,000,000,000 years old. But this is “always” when in conversation with an immigrant who believes the planet to be about 4,000 years old. Compared with Isaac Newton’s interpretation of Genesis, the First People had been living here long before God created Adam and Eve.
If “In the beginning …” marks the beginning of time, then, yes, their people had always lived here.
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I found myself reflecting on the balance between scientific & traditional knowledge while reading Gabriel Andrade’s essay, “How ‘Indigenous Ways of Knowing’ Works in Venezuela.” Andrade describes his interactions with students who hold the traditional belief in partible paternity: that semen is the stuff of life from which human babies are formed, and so every cis-man who ejaculates during penetrative sex with a pregnant person becomes a father to the child.
I appreciate partible paternity because, although this belief is often framed in misogynistic language – inaccurately grandiose claims about the role of semen in fetal development, often while ignoring the huge contribution of a pregnant person’s body – the belief makes the world better. People who are or might become pregnant are given more freedom. Other parents, typically men, are encouraged to help many children.
Replacing belief in partible paternity with a scientifically “correct” understanding of reproduction would probably make the world worse – people who might become pregnant would be permitted less freedom, and potential parents might cease to aid children whom they didn’t know to be their own genetic offspring.
Also, the traditional knowledge – belief in partible paternity – might be correct.
Obviously, there’s a question of relationships – what makes someone a parent? But I also mean something more biological — a human child actually can have three or more genetic contributors among their parents.
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Presumably you know the scientific version of human reproduction. To wit: a single sperm cell merges with a single egg cell. This egg rapidly changes to exclude all the other sperm cells surrounding it, then implants in the uterine lining. Over the next nine months, this pluripotent cell divides repeatedly to form the entire body of a child. The resulting child has exactly two parents. Every cell in the child’s body has the same 3 billion base pair long genome.
No scientist believes in this simplified version. For instance, every time a cell divides, the entire genome must be copied – each time, this process will create a few mistakes. By the time a human child is ready to be born, their cells will have divided so many times that the genome of a cell in the hand is different from the genome of a cell in the liver or in the brain.
In Unique, David Linden writes that:
Until recently, reading someone’s DNA required a goodly amount of it: you’d take a blood draw or a cheek swab and pool the DNA from many cells before loading it into the sequencing machine.
However, in recent years it has become possible to read the complete sequence of DNA, all three billion or so nucleotides, from individual cells, such as a single skin cell or neuron. With this technique in hand, Christopher Walsh and his coworkers at Boston Children’s Hopsital and Harvard Medical School isolated thirty-six individual neurons from three healthy postmortem human brains and then determined the complete genetic sequence for each of them.
This revealed that no two neurons had exactly the same DNA sequence. In fact, each neuron harbored, on average, about 1,500 single-nucleotide mutations. That’s 1,500 nucleotides out of a total of three billion in the entire genome – a very low rate, but those mutations can have important consequences. For example, one was in a gene that instructs the production of an ion channel protein that’s crucial for electrical signaling in neurons. If this mutation were present in a group of neurons, instead of just one, it could cause epilepsy.
No human has a genome: we are composite creatures.
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Most scientists do believe that all these unique individual genomes inside your cells were composed by combining genetic information from your two parents and then layering on novel mutations. But we don’t know how often this is false.
Pluripotent (“able to form many things”) cells from a developing human embryo / fetus / baby can travel throughout a pregnant person’s body. This is quite common – most people with XX chromosomes who have given birth to people with XY chromosomes will have cells with Y chromosomes in their brains. During the gestation of twins, the twins often swap cells (and therefore genomes).
At the time of birth, most humans aren’t twins, but many of us do start that way. There’s only a one in fifty chance of twin birth following a dizygotic pregnancy (the fertilization of two or more eggs cells released during a single ovulation). Usually what happens next is a merger or absorption of one set of these cells by another, resulting in a single child. When this occurs, different regions of a person’s body end up with distinct genetic lineages, but it’s difficult to identify. Before the advent of genetic sequencing, you might notice only if there was a difference in eye, skin, or hair color from one part of a person’s body to the next. Even now, you’ll only notice if you sequence full genomes from several regions of a person’s body and find that they’re distinct.
For a person to have more than two genetic contributors, there would have to be a dizygotic pregnancy in which sperm cells from unique individuals merged with the two eggs.
In the United States, where the dominant culture is such that people who are trying to get pregnant are exhorted not to mate with multiple individuals, studies conducted in the 1990s found that at least one set of every few hundred twins had separate fathers (termed “heteropaternal superfecundication”). In these cases, the children almost certainly had genomes derived from the genetic contributions of three separate people (although each individual cell in the children’s bodies would have a genome derived from only two genetic contributors).
So, we actually know that partible paternity is real. Because it’s so difficult to notice, our current estimates are probably lower bounds. If 1:400 were the rate among live twins, probably that many dizygotic pregnancies in the United States also result from three or more genetic contributors. Probably this frequency is higher in cultures that celebrate rather than castigate this practice.
Honestly, I could be persuaded that estimates ranging anywhere from 1:20 to 1:4,000 were reasonable for the frequency that individuals from these cultures have three or more genetic contributors.** We just don’t know.
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I agree with Gabriel Andrade that we’d like for medical students who grew up believing in partible paternity to benefit from our scientific understanding of genetics and inheritance – this scientific knowledge will help them help their patients. But I also believe that, even in this extreme case, the traditional knowledge should be respected. It’s not as inaccurate as we might reflexively believe!
The scientific uncertainty I’ve described above doesn’t quite match the traditional knowledge, though. A person can only receive genetic inheritance from, ahem, mating events that happen during ovulation, whereas partible paternity belief systems also treat everyone who has sex with the pregnant person over the next few months as a parent, too.
But there’s a big difference between contributing genes and being a parent. In Our Transgenic Future: Spider Goats, Genetic Modification, and the Will to Change Nature, Lisa Jean Moore discusses the many parents who have helped raise the three children she conceived through artificial insemination. Even after Moore’s romantic relationships with some of these people ended, they remained parents to her children. The parental bond, like all human relationships, is created by the relationship itself.
This should go without saying, but: foster families are families. Adopted families are families. Families are families.
Partible paternity is a belief that makes itself real.
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** A note on the math: Dizygotic fertilization appears to account for 1:10 human births, and in each of these cases there is probably at least some degree of chimerism in the resulting child. My upper estimate for the frequency that individuals have three or more genetic contributors, 1:20, would be if sperm from multiple individuals had exactly equal probabilities of fertilizing each of the two egg cells. My lower estimate of 1:4,000 would be if dizygotic fertilization from multiple individuals had the same odds as the 1:400 that fraternal twin pairs in the U.S. have distinct primary genetic contributors. Presumably a culture that actively pursues partible paternity would have a higher rate than this, but we don’t know for sure. And in any case, these are large numbers! Up to 5% of people from these cultures might actually have three or more genetic contributors, which is both biologically relevant and something that we’d be likely to overlook if we ignored the traditional Indigenous knowledge about partible paternity.
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header image from Zappy’s Technology Solution on flickr
The shape of things determines what they can do. Or, as a molecular biologist would phrase it, “structure determines function.”
In most ways, forks and spoons are similar. They’re made from the same materials, they show up alongside each other in place settings. But a spoon has a curved, solid bowl – you’d use it for soup or ice cream. A fork has prongs and is better suited for stabbing.
In matters of self defense, I’d reach for the fork.
On a much smaller scale, the three-dimensional shapes of a protein determines what it can do.
Each molecule of hemoglobin has a spoon-like pocket that’s just the right size for carrying oxygen, while still allowing the oxygen to wriggle free wherever your cells need it. A developing fetus has hemoglobin that’s shaped differently – when the fetal hemoglobin grabs oxygen, it squeezes more tightly, causing oxygen to pass from a mother to her fetus.
Each “voltage-gated ion channel” in your neurons has a shape that lets it sense incoming electrical signals and pass them forward. Voltage-gated ion channels are like sliding doors. They occasionally open to let in a rush of salt. Because salts are electrically charged, this creates an electric current. The electrical current will cause the next set of doors to open.
Every protein is shaped differently, which lets each do a different job. But they’re all made from the same materials – a long chain of amino acids.
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Your DNA holds the instructions for every protein in your body.
Your DNA is like a big, fancy cookbook – it holds all the recipes, but you might not want to bring it into the kitchen. You wouldn’t want to spill something on it, or get it wet, or otherwise wreck it.
Instead of bringing your nice big cookbook into the kitchen, you might copy a single recipe onto an index card. That way, you can be as messy as you like – if you spill something, you can always write out a new index card later.
And your cells do the same thing. When it’s time to make proteins, your cells copy the recipes. The original cookbook is made from DNA; the index-card-like copies are made from RNA. Then the index cards are shipped out of the nucleus – the library at the center of your cells – into the cytoplasm – the bustling kitchen where proteins are made and do their work.
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When a protein is first made, it’s a long strand of amino acids. Imagine a long rope with assorted junk tied on every few inches. Look, here’s a swath of velcro! Here’s a magnet. Here’s another magnet. Here’s a big plastic knob. Here’s another magnet. Here’s another piece of velcro. And so on.
If you shake this long rope, jostling it the way that a molecule tumbling through our cells gets jostled, the magnets will eventually stick together, and the velcro bits will stick to together, and the big plastic knob will jut out because there’s not enough room for it to fit inside the jumble.
That’s what happens during protein folding. Some amino acids are good at being near water, and those often end up on the outside of the final shape. Some amino acids repel water – like the oil layer of an unshaken oil & vinegar salad dressing – and those often end up on the inside of the final shape.
Other amino acids glue the protein together. The amino acid cysteine will stick to other cysteines. Some amino acids have negatively-charged sidechains, some have positively-charged sidechains, and these attract each other like magnets.
Sounds easy enough!
Except, wait. If you had a long rope with dozens of magnets, dozens of patches of velcro, and then you shook it around … well, the magnets would stick to other magnets, but would they stick to the right magnets?
You might imagine that there are many ways the protein could fold. But there’s only a single final shape that would allow the protein to function correctly in a cell.
So your cells use little helpers to ensure that proteins fold correctly. Some of the helpers are called “molecular chaperones,” and they guard various parts of the long strand so that it won’t glom together incorrectly. Some helpers are called “glycosylation enzymes,” and these glue little bits and bobs to the surface of a protein, some of which seem to act like mailing addresses to send the protein to the right place in a cell, some of which change the way the protein folds.
Our cells have a bunch of ways to ensure that each protein folds into the right 3D shape. And even with all this help, something things go awry. Alzheimer’s disease is associated with amyloid plaques that form in the brain – these are big trash heaps of misfolded proteins. The Alzheimer’s protein is just very tricky to fold correctly, especially if there’s a bunch of the misfolded protein strewn about.
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Many human proteins can be made by bacteria. Humans and bacteria are relatives, after all – if you look back in our family trees, you’ll find that humans and bacteria shared a great-great-great-grandmother a mere three billion years ago.
The cookbooks in our cells are written in the same language. Bacteria can read all our recipes.
Which is great news for biochemists, because bacteria are really cheap to grow.
If you need a whole bunch of some human protein, you start by trying to make it in bacteria. First you copy down the recipe – which means using things called “restriction enzymes” to move a sequence of DNA into a plasmid, which is something like a bacterial index card – then you punch holes in some bacteria and let your instructions drift in for them to read.
The bacteria churn out copies of your human protein. Bacteria almost always make the right long rope of amino acids.
But human proteins sometimes fold into the wrong shapes inside bacteria. Bacteria don’t have all the same helper molecules that we do,.
If a protein doesn’t fold into the right shape, it won’t do the right things.
If you were working in a laboratory, and you found out that the protein you’d asked bacteria to make was getting folded wrong … well, you’d probably start to sigh a lot. Instead of making the correctly-folded human protein, your bacteria gave you useless goo.
Shucks.
But fear not!
Yeast can’t be grown as cheaply as bacteria, but they’re still reasonably inexpensive. And yeast are closer relatives – instead of three billion years ago, the most recent great-great-grandmother shared between humans and yeast lived about one billion years ago.
Yeast have a few of the same helper proteins that we do. Some human proteins that can’t be made in bacteria will fold correctly in yeast.
So, you take some yeast, genetically modify it to produce a human protein, then grow a whole bunch of it. This is called “fermentation.” It’s like you’re making beer, almost. Genetically modified beer.
Then you spin your beer inside a centrifuge. This collects all the solid stuff at the bottom of the flask. Then you’ll try to purify the protein that you want away from all the other gunk. Like the yeast itself, and all the proteins that yeast normally make.
If you’re lucky, the human protein you were after will have folded correctly!
If you’re unlucky, the protein will have folded wrong. Your yeast might produce a bunch of useless goo. And then you do more sighing.
There’s another option, but it’s expensive. You can make your human protein inside human cells.
Normally, human cells are hesitant to do too much growing and dividing and replicating. After all, the instructions in our DNA are supposed to produce a body that looks just so – two arms, two eyes, a smile. Once we have cells in the right places, cell division is just supposed to replace the parts of you that have worn out.
Dead skin cells steadily flake from our bodies. New cells constantly replace them.
But sometimes a cell gets too eager to grow. If its DNA loses certain instructions, like the “contact inhibition” that tells cells to stop growing when they get too crowded, a human cell might make many, many copies of itself.
Which is unhelpful. Potentially lethal. A cell that’s too eager to grow is cancer.
Although it’s really, really unhelpful to have cancer cells growing in your body, in a laboratory, cancer cells are prized. Cancer cells are so eager to grow that we might be able to raise them in petri dishes.
Maybe you’ve heard of HeLa cells – this is a cancer cell line that was taken from a Black woman’s body without her consent, and then this cell line was used to produce innumerable medical discoveries, including many that were patented and have brought in huge sums of money, and this woman’s family was not compensated at all, and they’ve suffered huge invasions of their privacy because a lot of their genetic information has been published, again without their consent …
HeLa cells are probably the easiest human cells to grow. And it’s possible to flood them with instructions to make a particular human protein. You can feel quite confident that your human protein will fold correctly.
But it’s way more expensive to grow HeLa cells than yeast. You have to grow them in a single layer in a petri dish. You have to feed them the blood of a baby calf. You have to be very careful while you work or else the cells will get contaminated with bacteria or yeast and die.
If you really must have a whole lot of a human protein, and you can’t make it in bacteria or yeast, then you can do it. But it’ll cost you.
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Vaccination is perhaps the safest, most effective thing that physicians do.
Your immune system quells disease, but it has to learn which shapes inside your body represent danger. Antibodies and immunological memory arise in a process like evolution – random genetic recombination until our defenses can bind to the surface of an intruder. By letting our immune system train in a relatively safe encounter, we boost our odds of later survival.
The molecular workings of our immune systems are still being studied, but the basic principles of inoculation were independently discovered centuries ago by scientists in Africa, India, and China. These scientists’ descendants practiced inoculation against smallpox for hundreds of years before their techniques were adapted by Edward Jenner to create his smallpox vaccine.
If you put a virus into somebody’s body, that person might get sick. So what you want is to put something that looks a lot like the virus into somebody’s body.
One way to make something that looks like the virus, but isn’t, is to take the actual virus and whack it with a hammer. You break it a little. Not so much that it’s unrecognizable, but enough so that it can’t work. Can’t make somebody sick. This is often done with “heat inactivation.”
Heat inactivation can be dangerous, though. If you cook a virus too long, it might fall apart and your immune system learns nothing. If you don’t cook a virus long enough, it might make you sick.
In some of the early smallpox vaccine trials, the “heat inactivated” viruses still made a lot of people very, very sick.
Fewer people got very sick than if they’d been exposed to smallpox virus naturally, but it feels different when you’re injecting something right into somebody’s arm.
We hold vaccines to high standards. Even when we’re vaccinating people against deadly diseases, we expect our vaccines to be very, very safe.
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It’s safer to vaccinate people with things that look like a virus but can’t possibly infect them.
This is why you might want to produce a whole bunch of some specific protein. Why you’d go through that whole rigamarole of testing protein folding in bacteria, yeast, and HeLa cells. Because you’re trying to make a bunch of protein that looks like a virus.
Each virus is a little protein shell. They’re basically delivery drones for nasty bits of genetic material.
If you can make pieces of this protein shell inside bacteria, or in yeast, and then inject those into people, then the people can’t possibly be infected. You’re not injecting people with a whole virus – the delivery drone with its awful recipes inside. Instead, you’re injecting people with just the propeller blades from the drone, or just its empty cargo hold.
These vaccine are missing the genetic material that allow viruses to make copies of themselves. Unlike with a heat inactivated virus, we can’t possibly contract the illness from these vaccines.
This is roughly the strategy used for the HPV vaccine that my father helped develop. Merck’s “Gardasil” uses viral proteins made by yeast, which is a fancy way of saying that Merck purifies part of the virus’s delivery drone away from big batches of genetically-modified beer.
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We have a lot of practice making vaccines from purified protein.
Even so, it’s a long, difficult, expensive process. You have to identify which part of the virus is often recognized by our immune systems. You have to find a way to produce a lot of this correctly-folded protein. You have to purify this protein away from everything else made by your bacteria or yeast or HeLa cells.
The Covid-19 vaccines bypass all that.
In a way, these are vaccines for lazy people. Instead of finding a way to make a whole bunch of viral protein, then purify it, then put it into somebody’s arm … well, what if we just asked the patient’s arm to make the viral protein on its own?
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Several of the Covid-19 vaccines are made with mRNA molecules.
These mRNA molecules are the index cards that we use for recipes in our cells’ kitchens, so the only trick is to deliver a bunch of mRNA with a recipe for part of the Covid-19 virus. Then our immune system can learn that anything with that particular shape is bad and ought to be destroyed.
After learning to recognize one part of the virus delivery drone, we’ll be able to stop the real thing.
We can’t vaccinate people by injecting just the mRNA, though, because our bodies have lots of ways to destroy RNA molecules. After all, you wouldn’t want to cook from the recipe from any old index card that you’d found in the street. Maybe somebody copied a recipe from The Anarchist Cookbook – you’d accidentally whip up a bomb instead of a delicious cake.
I used to share laboratory space with people who studied RNA, and they were intensely paranoid about cleaning. They’d always wear gloves, they’d wipe down every surface many times each day. Not to protect themselves, but to ensure that all the RNA-destroying enzymes that our bodies naturally produce wouldn’t ruin their experiments.
mRNA is finicky and unstable. And our bodies intentionally destroy stray recipes.
So to make a vaccine, you have to wrap the mRNA in a little envelope. That way, your cells might receive the recipe before it’s destroyed. In this case, the envelope is called a “lipid nanoparticle,” but you could also call a fat bubble. Not a bubble that’s rotund – a tiny sphere made of fat.
Fat bubbles are used throughout cells. When the neurons in your brain communicate, they burst open fat bubbles full of neurotransmitters and scatter the contents. When stuff found outside a cell needs to be destroyed, it’s bundled into fat bubbles and sent to a cellular trash factories called lysosomes.
For my Ph.D. thesis, I studied the postmarking system for fat bubbles. How fat bubbles get addressed in order to be sent to the right places.
Sure, I made my work sound fancier when I gave my thesis defense, but that’s really what I was doing.
Anyway, after we inject someone with an mRNA vaccine, the fat bubble with the mRNA gets bundled up and taken into some of their cells, and this tricks those cells into following the mRNA recipe and making a protein from the Covid-19 virus.
This mRNA recipe won’t teach the cells how to make a whole virus — that would be dangerous! That’s what happens during a Covid-19 infection – your cells get the virus’s whole damn cookbook and they make the entire delivery drone and more cookbooks to put inside and then these spread through your body and pull the same trick on more and more of your cells. A single unstopped delivery drone can trick your cells into building a whole fleet of them and infecting cells throughout your body.
Instead, the mRNA recipe we use for the vaccine has only a small portion of the Covid-19 genome, just enough for your cells to make part of the delivery drone and learn to recognize it as a threat.
And this recipe never visits the nucleus, which is the main library in your cells that holds your DNA, the master cookbook with recipes for every protein in your body. Your cells are tricked into following recipes scribbled onto the vaccine’s index cards, but your master cookbook remains unchanged. And, just like all the mRNA index cards that our bodies normally produce, the mRNA from the vaccine soon gets destroyed. All those stray index cards, chucked unceremoniously into the recycling bin.
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The Johnson & Johnson vaccine also tricks our cells into making a piece of the Covid-19 virus.
This vaccine uses a different virus’s delivery drone to send the recipe for a piece of Covid-19 into your cells. The vaccine’s delivery drone isn’t a real virus – the recipe it holds doesn’t include the instructions on how to make copies of itself. But the vaccine’s delivery drone looks an awful lot like a virus, which means it’s easier to work with than the mRNA vaccines.
Those little engineered fat bubbles are finicky. And mRNA is finicky. But the Johnson & Johnson vaccine uses a delivery drone that was optimized through natural selection out in the real world. It evolved to be stable enough to make us sick.
Now we can steal its design in an effort to keep people well.
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Lots of people received the Johnson & Johnson vaccine without incident, but we’ve temporarily stopped giving it to people. Blood clots are really scary.
You might want to read Alexandra Lahav’s excellent essay, “Medicine Is Made for Men.” Lahav describes the many ways in which a lack of diversity in science, technology, and engineering fields can cause harm.
Cars are designed to protect men: for many years, we used only crash test dummies that were shaped like men to determine whether cars were safe. In equivalent accidents, women are more likely to die, because, lo and behold, their bodies are often shaped differently.
Women are also more likely to be killed by medication. Safety testing often fails to account for women’s hormonal cycles, or complications from contraceptives, or differences in metabolism, or several other important features of women’s bodies.
Although more than half our population are women, their bodies are treated as bizarre.
For most people, the Johnson & Johnson vaccine is safe. But this is a sort of tragedy that occurs too often – causing harm to women because we’re inattentive to the unique features of their bodies.
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I haven’t been vaccinated yet, but I registered as soon as I was able – my first dose will be on April 26th. Although I’ve almost certainly already had Covid-19 before, and am unlikely to get severely ill the next time I contract it, I’m getting the vaccine to protect my friends and neighbors.
If you’ve been reading about Covid-19 in the New York Times, you’ve probably learned that reinfection is very unlikely.
What you’ve learned is incorrect.
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Don’t get me wrong – I love the New York Times. Within the spectrum of United States politics, I am very far to the left. Anti-consumerist, prison abolitionist, environmentalist, feminist, climate activist, etc., etc. I fit into all those categories.
I’m also a scientist. I am staunchly pro-vaccine. I don’t like pesticides, but I’m a huge fan of GMO crops. (Honestly, I wish there was a category at the grocery store where you could pay to support genetically-modified organisms grown without environmental toxins – “organic” doesn’t have the nuance I’d like.)
So my goal here isn’t to rag on the New York Times. I’m including screenshots of their headlines only to give us a common frame of reference.
This is what the news is saying. And it’s wrong.
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It was going to be very difficult to demonstrate reinfection with Covid-19.
Why?
In general, reinfection with any virus will produce a milder illness the second time.
Most people’s first infection with Covid-19 is so mild that they don’t realize they have it – perhaps 80% of infections are “asymptomatic,” in which a person has been infected with the virus, is probably shedding the virus (thereby infecting other people), but feels totally fine. So, people’s second infection? Some percentage higher than 80% are likely to feel totally well, even though they might be shedding virus.
When people develop severe complications from Covid-19, the illness can linger for weeks or even months.
I don’t know for certain whether my family contracted Covid-19 in February, because there were no tests available here at the time. All I know is that we were two close contacts removed from someone who had just returned from China, that this close contact tested negative for influenza, that my family had been vaccinated for influenza, and that our symptoms precisely mirrored the common suite for Covid-19. But in any case, we felt horrible for about three weeks, and we experienced lingering fatigue with occasional coughing for about two months.
Lengthy recovery is so common that there’s a colloquial name for it: “long-haulers.” If we’re trying to identify whether someone was re-infected, we’d need to make sure that we weren’t looking at continued viral shedding during a lengthy recovery.
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To demonstrate that someone was re-infected with Covid-19, the following would have to happen:
A person gets tested for Covid-19 during their first infection.
The genome of the virus is sequenced after that first infection.
The person is re-infected.
The person happens to get a Covid-19 test during the second infection (even though it’s highly likely that this person feels well at the time).
The genome of the virus is sequenced after the second infection.
The genome of the virus that infected the person on the second occasion is noticeably different from the first (even though Covid-19 includes a proofreading enzyme that slows genetic drift).
That’s all very unlikely!
There are just so many coincidences involved – that you happen to get infected with an easily distinguishable virus the second time, that you happen to get a test the second time, that anyone took the (significant) trouble and expense to sequence both genomes.
And what I mean is, proving re-infection is very unlikely. Which is totally independent of the likelihood of re-infection itself.
And yet, even though it’s so unlikely we’d be able to prove that re-infection is occurring, we have.
We know, with 100% certainty, that people can be reinfected. We’ve documented it.
Given how unlikely it was that we’d be able to document reinfection, the fact that we’ve seen this at all indicates that it’s probably quite common. As you would expect based upon our bodies’ responses to other coronaviruses.
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Given that re-infection definitely occurs, and is probably quite common, why have you read that it’s unlikely?
The underlying probably is language usage. When my father – an infectious diseases specialist – talks about re-infection, he’s thinking about contracting severe symptoms during a second infection. Which is reasonable. He’s a medical doctor. He cares about helping sick people get better.
But when we’re thinking about how to respond, as a nation, to this pandemic, we’re thinking about the dynamics of transmission. We’re trying to answer questions like, “Can kids go to school without people dying?”
From this perspective, we’re thinking about who is going to spread the virus, and where. We need to know whether a person who is protected from severe disease – either from prior recovery or vaccination – might shed viral particles. Will that individual register as a positive case on a PCR test? Will that individual get classmates or co-workers sick?
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Re-infections are probably the underlying cause of the current rise in cases in New York City.
70% or more of the population of New York City was infected with Covid-19 during April. That’s a huge percentage, well above what most researchers consider the “herd immunity threshold” for similar respiratory viruses.
For there to be another spike in cases now, many of those 70% would need to have lost their initial immunity. That’s also why you’d expect to see a higher “test positivity rate” – if many of the current cases are reinfections, then they’re likely to be milder. People with milder (or asymptomatic) infections are less likely to seek out a test.
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For general audiences, the phrasing I’d recommend is to say “Severe illness is unlikely during Covid-19 reinfections” as opposed to “Reinfection is unlikely.”
There have been a few cases of people’s second infection being more severe than the first, but these cases indeed appear to be quite rare.
But re-infection itself?
The fact that we’ve documented any instances of re-infection suggests that it’s quite common. Which we could have predicted from the beginning – indeed, I did. And that’s why I’ve been recommending – for months – policies very different from what we’ve done.
We’re fast approaching flu season, which is especially harrowing this year.
We, as a people, have struggled to respond to this calamity. We have a lot of scientific data about Covid-19 now, but science is never value-neutral. The way we design experiments reflects our biases; the way we report our findings, even more so.
For example, many people know the history of Edward Jenner inventing the world’s first vaccine. Fewer are aware of the long history of inoculation in Africa (essentially, low-tech vaccination) that preceded Jenner’s work.
So it’s worthwhile taking a moment to consider the current data on Covid-19.
Data alone can’t tell us what to do – the course of action we choose will reflect our values as a society. But the data may surprise a lot of people – which is strange considering how much we all feel that we know about Covid-19.
Indeed, we may realize that our response so far goes against our professed values.
Spoiler: I think we shouldn’t close in-person school.
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Since April, I’ve written severalessays about Covid-19. In these, I’ve made a number of predictions. It’s worthwhile to consider how accurate these predictions have been.
This, after all, is what science is. We use data to make an informed prediction, and then we collect more data to evaluate how good our prediction was.
Without the second step – a reckoning with our success or failure – we’re just slinging bullshit.
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I predicted that our PCR tests were missing most Covid-19 infections, that people’s immunity was likely to be short-lived (lasting for months, not years), and that Covid-19 was less dangerous than seasonal influenza for young people.
In my essays, I’ve tried to unpack the implications of each of these. From the vantage of the present, with much more data at our disposal, I still stand by what I’ve written.
But gloating’s no fun. So I’d rather start with what I got wrong.
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My initial predictions about Covid-19 were terrible.
I didn’t articulate my beliefs at the time, but they can be inferred from my actions. In December, January, and February, I made absolutely no changes to my usual life. I didn’t recommend that travelers be quarantined. I didn’t care enough to even follow the news, aside from a cursory glance at the headlines.
While volunteering with the high school running team, I was jogging with a young man who was finishing up his EMT training.
“That new coronavirus is really scary,” he said. “There’s no immunity, and there’s no cure for it.”
I shrugged. I didn’t know anything about the new coronavirus. I talked with him about the 1918 influenza epidemic instead.
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I didn’t make any change in my life until mid-March. And even then, what did I do?
I called my brother and talked to him about the pizza restaurant – he needed a plan in case there was no in-person dining for a few months.
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My next set of predictions were off, but in the other direction – I estimated that Covid-19 was about four-fold more dangerous than seasonal influenza. The current best estimate from the CDC is that Covid-19 is about twice as dangerous, with an infection fatality ratio of 0.25%.
But seasonal influenza typically infects a tenth of our population, or less.
We’re unlikely to see a significant disruption in the transmission of Covid-19 (this is the concept of “herd immunity”) until about 50% of our population has immunity from it, whether from vaccination or recovery. Or possibly higher – in some densely populated areas, Covid-19 has spread until 70% (in NYC) or even 90% (in prisons) of people have contracted the disease.
Population density is hugely important for the dynamics of Covid-19’s spread, so it’s difficult to predict a nation-wide threshold for herd immunity. For a ballpark estimate, we could calculate what we’d see with a herd immunity threshold of about 40% in rural areas and 60% in urban areas.
Plugging in some numbers, 330 million people, 80% urban population, 0.25% IFR, 60% herd immunity threshold in urban areas, we’d anticipate 450,000 deaths.
That’s about half of what I predicted. And you know what? That’s awful.
Each of those 450,000 is a person. Someone with friends and family. And “slow the spread” doesn’t help them, it just stretches our grieving to encompass a whole year of tragedy instead of a horrific month of tragedy.
If we don’t have a safe, effective vaccine soon enough, the only way to save some of those 450,000 people is to shift the demographics of exposure.
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Based on the initial data, I concluded that the age demographics for Covid-19 risk were skewed more heavily toward elderly people than influenza risk.
I may have been wrong.
It’s difficult to directly compare the dangers of influenza to the dangers of Covid-19. Both are deadly diseases. Both result in hospitalizations and death. Both are more dangerous for elderly or immunocompromised people, but both also kill young, healthy people.
Typically, we use an antigen test for influenza and a PCR-based test for Covid-19. The PCR test is significantly more sensitive, so it’s easier to determine whether Covid-19 is involved a person’s death. If there are any viral particles in a sample, PCR will detect them. Whereas antigen tests have a much higher “false negative” rate.
Instead of using data from these tests, I looked at the total set of pneumonia deaths. Many different viruses can cause pneumonia symptoms, but the biggest culprits are influenza and, in 2020, Covid-19.
So I used these data to ask a simple question – in 2020, are the people dying of pneumonia disproportionately more elderly than in other years?
I expected that they would be. That is, after all, the prediction from my claims about Covid-19 demographic risks.
For people under the age of 18, we’ve seen the same number of deaths (or fewer) in 2020 as in other years. The introduction of Covid-19 appears to have caused no increased risk for these people.
But for people of all other ages, there have been almost three times as many people dying of these symptoms in 2020 compared to other years.
In most years, one thousand people aged 25-34 die of these symptoms; in 2020, three thousand have died. In most years, two thousand people aged 35-44 die of these symptoms; in 2020, six thousand have died. This same ratio holds for all ages above eighteen.
Younger people are at much less risk of harm from Covid-19 than older people are. But, aside from children under the age of eighteen, they don’t seem to be exceptionally protected.
Of course, my predictions about the age skew of risk might be less incorrect than I’m claiming here. If people’s dramatically altered behavior in 2020 has changed the demographics of exposure as compared to other years – which is what we should be doing to save the most lives – then we could see numbers like this even if Covid-19 had the risk skew that I initially predicted.
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I predicted that four or more years would pass before we’d be able to vaccinate significant numbers of people against Covid-19.
I sure hope that I was wrong!
We now know that it should be relatively easy to confer immunity to Covid-19. Infection with other coronaviruses, including those that cause common colds, induce the production of protective antibodies. This may partly explain the low risk for children – because they get exposed to common-cold-causing coronaviruses so often, they may have high levels of protective antibodies all the time.
Several pharmaceutical companies have reported great results for their vaccine trials. Protection rates over 90%.
So the problem facing us now is manufacturing and distributing enough doses. But, honestly, that’s the sort of engineering problem that can easily be addressed by throwing money at it. Totally unlike the problem with HIV vaccines, which is that the basic science isn’t there – we just don’t know how to make a vaccine against HIV. No amount of money thrown at that problem would guarantee wide distribution of an effective vaccine.
We will still have to overcome the (unfortunately significant) hurdle of convincing people to be vaccinated.
For any individual, the risk of Covid-19 is about twice the risk of seasonal influenza. But huge numbers of people choose not to get a flu vaccine each year. In the past, the United States has had a vaccination rate of about 50%. Here’s hoping that this year will be different.
Covid-19 spreads so fast – and so silently, with many cases of infected people who feel fine but are still able to spread the virus – that it will almost certainly be a permanent resident of the world we live in. We’re unlikely to eradicate Covid-19.
Which means that elderly people will always be at risk of dying from Covid-19.
The only way to protect people whose bodies have gone through “age-related immunosenence” – the inevitable weakening of an immune system after a person passes the evolutionarily-determined natural human lifespan of about 75 years – will be to vaccinate everybody else.
Depending on how long vaccine-conferred immunity lasts, we may need to vaccinate people annually. I worry, though, that it will become increasingly difficult to persuade people to get a Covid-19 vaccine once the yearly death toll drops to influenza-like levels – 50,000 to 100,000 deaths per year in the United States.
(Note: you may have seen articles in the New York Times suggesting that we’ll have long-lasting protection. They’re addressing a different question — after recovery, or vaccination, are you likely to become severely ill with Covid-19? And the answer is “probably not,” although it’s possible. When I discuss immunity here, I mean “after recovery, or vaccination, are you likely to be able to spread the virus after re-infection?” And the answer is almost certainly “yes, within months.”)
And I wrote about the interplay between short-lived immunity and the transmission dynamics of an extremely virulent, air-born virus.
This is what the Harvard public health team got so wrong. When we slow transmission enough that a virus is still circulating after people’s immunity wanes, they can get sick again.
For this person, the consequences aren’t so dire – an individual is likely to get less sick with each subsequent infection by a virus. But the implications for those who have not yet been exposed are horrible. The virus circulates forever, and people with naive immune systems are always in danger.
It’s the same dynamics as when European voyagers traveled to the Americas. Because the European people’s ancestors lived in unsanitary conditions surrounded by farm animals, they’d cultivated a whole host of zoogenic pathogens (like influenza and this new coronavirus). The Europeans got sick from these viruses often – they’d cough and sneeze, have a runny nose, some inflammation, a headache.
In the Americas, there were fewer endemic diseases. Year by year, people wouldn’t spend much time sick. Which sounds great, honestly – I would love to go a whole year without headaches.
But then the disgusting Europeans reached the Americas. The Europeans coughed and sneezed. The Americans died.
And then the Europeans set about murdering anyone who recovered. Today, descendants of the few survivors are made to feel like second-class citizens in their ancestral homelands.
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In a world with endemic diseases, people who have never been exposed will always be at risk.
That’s why predictions made in venues such as the August New York Times editorial claiming that a six- to eight-week lockdown would stop Covid-19 were so clearly false. They wrote:
Six to eight weeks. That’s how long some of the nation’s leading public health experts say it would take to finally get the United States’ coronavirus epidemic under control.
For proof, look at Germany. Or Thailand. Or France.
Obviously, this didn’t work – in the presence of an endemic pathogen, the lockdowns preserved a large pool of people with naive immune systems, and they allowed enough time to pass that people who’d been sick lost their initial immunity. After a few months of seeming calm, case numbers rose again. For proof, look at Germany. Or France.
Case numbers are currently low in Thailand, but a new outbreak could be seeded at any time.
And the same thing is currently happening in NYC. Seven months after the initial outbreak, immunity has waned; case numbers are rising; people with mild second infections might be spreading the virus to friends or neighbors who weren’t infected previously.
All of which is why I initially thought that universal mask orders were a bad idea.
We’ve known for over a hundred years that masks would slow the spread of a virus. The only question was whether slowing the spread of Covid-19 would cause more people to die of Covid-19.
And it would – if a vaccine was years away.
But we may have vaccines within a year. Which means that I may have been wrong. Again, the dynamics of Covid-19 transmission are still poorly understood – I’ll try to explain some of this below.
In any case, I’ve always complied with our mask orders. I wear a mask – in stores, at school pickup, any time I pass within six feet of people while jogging.
To address global problems like Covid-19 and climate change, we need global consensus. One renegade polluting wantonly, or spewing viral particles into the air, could endanger the whole world. This is precisely the sort of circumstance where personal freedom is less important than community consensus.
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The transmission dynamics of Covid-19 are extremely sensitive to environment. Whether you’re indoors or outdoors. How fast the air is moving. The population density. How close people are standing. Whether they’re wearing masks. Whether they’re shouting or speaking quietly.
Because there are so many variable, we don’t have good data. My father attended a lecture and a colleague (whom he admires) said, “Covid-19 is three-fold more infectious than seasonal influenza.” Which is bullshit – the transmission dynamics are different, so the relative infectivity depends on our behaviors. You can’t make a claim like this.
It’s difficult to measure precisely how well masks are slowing the spread of this virus.
But here’s a good estimate: according to Hsiang et al., the number of cases of Covid-19, left unchecked, might have increased exponentially at a rate of about 34% per day in the United States.
That’s fast. If about 1% of the population was infected, it could spread to everyone within a week or two. In NYC, Covid-19 appear to spread to over 70% of the population within about a month.
(To estimate the number of infections in New York City, I’m looking at the number of people who died and dividing by 0.004 – this is much higher than the infection fatality rate eventually reported by the CDC, but early in the epidemic, we were treating people with hydroxychloraquine, an unhelpful poison, and rushing to put people on ventilators. We now know that ventilation is so dangerous that it should only be used as a last resort, and that a much more effective therapy is to ask people to lie on their stomachs – “proning” makes it easier to get enough oxygen even when the virus has weakened a person’s lungs.)
Masks dramatically slow the rate of transmission.
A study conducted at a military college – where full-time mask-wearing and social distancing were strictly enforced – showed that the number of cases increased from 1% to 3% of the population over the course of two weeks.
So, some math! Solve by taking ten to the power of (log 3)/14, which gives an exponential growth rate of 8% per day. Five-fold slower than without masks.
But 8% per day is still fast.
Even though we might be able to vaccinate large numbers of people by the end of next year, that’s not soon enough. Most of us will have been sick with this – at least once – before then.
I don’t mean to sound like a broken record, but the biggest benefit of wearing masks isn’t that we slow the rate of spread for everyone — exponential growth of 8% is still fast — but that we’re better able to protect the people who need to be protected. Covid-19 is deadly, and we really don’t want high-risk people to be infected with it.
I’ve tried to walk you through the reasoning here — the actual science behind mask policies — but also, in case it wasn’t absolutely clear: please comply with your local mask policy.
You should wear a mask around people who aren’t in your (small) network of close contacts.
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I’m writing this essay the day after New York City announced the end of in-person classes for school children.
A major problem with our response to Covid-19 is that there’s a time lag between our actions and the consequences. Human brains are bad at understanding laggy data. It’s not our fault. Our ancestors lived in a world where they’d throw a spear at an antelope, see the antelope die, and then eat it. Immediate cause and effect makes intuitive sense.
Delayed cause and effect is tricky.
If somebody hosts a party, there might be an increase in the number of people who get sick in the community over the next three weeks. Which causes an increase in the number of hospitalizations about two weeks after that. Which causes people to die about three weeks after that.
There’s a two-month gap between the party and the death. The connection is difficult for our brains to grasp.
As a direct consequence, we’ve got ass-hats and hypocrites attending parties for, say, their newly appointed Supreme Court justice.
But the problem with school closures is worse. There’s a thirty year gap between the school closure and the death. The connection is even more difficult to spot.
Even if you have relatively limited experience reading scientific research papers, I think you could make your way through this excellent article from Chistakis et al.
The authors link two sets of existing data: the correlation between school closures and low educational achievement, and the correlation between low educational achievement and premature death.
The public debate has pitted “school closures” against “lives saved,” or the education of children against the health of the community. Presenting the tradeoffs in this way obscures the very real health consequences of interrupted education.
These consequences are especially dire for young children.
The authors calculate that elementary school closures in the United States might have (already!) caused 5.5 million years of life lost.
Hsiang et al. found that school closures probably gave us no benefit in terms of reducing the number of Covid-19 cases, because children under 18 aren’t significant vectors for transmission (elementary-aged children even less so), but even if school closures had reduced the number of Covid-19 cases, closing schools would have caused more total years of life to be lost than saved.
The problem – from a political standpoint – is that Covid-19 kills older people, who vote, whereas school closures kill young people, who are intentionally disenfranchised.
And, personally, as someone with far-left political views, it’s sickening for me to see “my” political party adopt policies that are so destructive to children and disadvantaged people.
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So, here’s what the scientific data can tell us so far:
We will eventually have effective vaccines for Covid-19. Probably within a year.
Covid-19 spreads even with social distancing and masks, but the spread is slower.
You have no way of knowing the risk status of people in a stranger’s bubble. (Please, follow your local mask orders!)
Schools – especially elementary schools – don’t contribute much to the spread of Covid-19.
School closures shorten children’s lives (and that’s not even accounting for their quality of life over the coming decades).
An individual case of Covid-19 is about twice as dangerous as a case of seasonal influenza (which is scary!).
Underlying immunity (from prior disease and vaccination) to Covid-19 is much lower than for seasonal influenza, so there will be many more cases.
Most people’s immunity to Covid-19 probably lasts several months, after which a person can be re-infected and spread the virus again.
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So, those are some data. But data don’t tell us what to do. Only our values can do that.
I was walking my eldest child toward our local elementary school when my phone rang.
We reached the door, shared a hug, and said goodbye. After I left, I called back – it was a friend of mine from college who now runs a cancer research laboratory and is an assistant professor at a medical school.
“Hey,” I said, “I was just dropping my kid off at school.”
“Whoa,” he said, “that’s brave.”
I was shocked by his remark. For most people under retirement age, a case of Covid-19 is less dangerous than a case of seasonal influenza.
“I’ve never heard of anybody needing a double lung transplant after a case of the flu,” my friend said.
But our ignorance doesn’t constitute safety. During this past flu season, several young, healthy people contracted such severe cases of influenza that they required double lung transplants. Here’s an article about a healthy 30-year-old Wyoming man nearly killed by influenza from December 2019, and another about a healthy 20-year-old Ohio woman from January 2020. And this was a rather mild flu season!
“One of the doctors told me that she’s the poster child for why you get the flu shot because she didn’t get her flu shot,” said [the 20-year-old’s mother].
These stories were reported in local newspapers. Stories like this don’t make national news because we, as a people, think that it’s normal for 40,000 to 80,000 people to die of influenza every year. Every three to five years, we lose as many people as have died from Covid-19. And that’s with vaccination, with pre-existing immunity, with antivirals like Tamiflu.
Again, when I compare Covid-19 to influenza, I’m not trying to minimize the danger of Covid-19. It is dangerous. For elderly people, and for people with underlying health issues, Covid-19 is very dangerous. And, sure, all our available data suggest that Covid-19 is less dangerous than seasonal influenza for people under retirement age, but, guess what? That’s still pretty awful!
You should get a yearly flu shot!
A flu shot might save your life. And your flu shot will help save the lives of your at-risk friends and neighbors.
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For a while, I was worried because some of my remarks about Covid-19 sounded superficially similar to things said by the U.S. Republican party. Fox News – a virulent propaganda outlet – was publicizing the work of David Katz – a liberal medical doctor who volunteered in a Brooklyn E.R. during the Covid-19 epidemic and teaches at Yale’s school of public health.
The “problem” is that Katz disagrees with the narrative generally forwarded by the popular press. His reasoning, like mine, is based the relevant research data – he concludes that low-risk people should return to their regular lives.
You can see a nifty chart with his recommendations here. This is the sort of thing we’d be doing if we, as a people, wanted to “follow the science.”
And also, I’m no longer worried that people might mistake me for a right-wing ideologue. Because our president has once again staked claim to a ludicrous set of beliefs.
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Here’s a reasonable set of beliefs: we are weeks away from a safe, effective Covid-19 vaccine, so we should do everything we can to slow transmission and get the number of cases as low as possible!
Here’s another reasonable set of beliefs: Covid-19 is highly infectious, and we won’t have a vaccine for a long time. Most people will already be infected at least once before there’s a vaccine, so we should focuson protecting high-risk people while low-risk people return to their regular lives.
If you believe either of those sets of things, then you’re being totally reasonable! If you feel confident that we’ll have a vaccine soon, then, yes, delaying infections is the best strategy! I agree! And if you think that a vaccine will take a while, then, yes, we should end the shutdown! I agree!
There’s no right answer here – it comes down to our predictions about the future.
But there are definitely wrong answers. For instance, our current president claims that a vaccine is weeks away, and that we should return to our regular lives right now.
That’s nonsense. If we could get vaccinated before the election, then it’d make sense to close schools. To wait this out.
If a year or more will pass before people are vaccinated, then our efforts to delay the spread of infection will cause more harm than good. Not only will we be causing harm with the shutdown itself, but we’ll be increasing the death toll from Covid-19.
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On October 14th, the New York Times again ran a headline saying “Yes, you can be reinfected with the coronavirus. But it’s extremely unlikely.”
This is incorrect.
When I’ve discussed Covid-19 with my father – a medical doctor specializing in infectious diseases, virology professor, vaccine developer with a background in epidemiology from his masters in public health – he also has often said to me that reinfection is unlikely. I kept explaining that he was wrong until I realized that we were talking about different things.
When my father uses the word “reinfection,” he means clearing the virus, catching it again, and becoming sicker than you were the first time. That’s unlikely (although obviously possible). This sort of reinfection happens often with influenza, but that’s because influenza mutates so rapidly. Covid-19 has a much more stable genome.
When I use the word “reinfection” – and I believe that this is also true when most laypeople use the word – I mean clearing the virus, catching it again, and becoming sick enough to shed the viral particles that will make other people sick.
This sense of the word “reinfection” describes something that happens all the time with other coronaviruses, and has been documented to occur with Covid-19 as well.
The more we slow the spread of Covid-19, the more total cases there will be. In and of itself, more cases aren’t a bad thing – most people’s reinfection will be milder than their first exposure. The dangerous aspect is that a person who is reinfected will have another period of viral shedding during which they might expose a high-risk friend or neighbor.
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If our goal is to reduce the strain on hospitals and reduce total mortality, we need to avoid exposing high-risk people. Obviously, we should be very careful around nursing home patients. We should provide nursing homes with the resources they need to deal with this, like extra testing, and preferably increased wages for nursing home workers to compensate them for all that extra testing.
It’s also a good idea to wear masks wherever low-risk and high-risk people mingle. The best system for grocery stores would be to hire low-risk shoppers to help deliver food to high-risk people, but, absent that system, the second-best option would be for everyone to wear masks in the grocery store.
Schools are another environment where a small number of high-risk teachers and a small number of students living with high-risk family members intermingle with a large number of low-risk classmates and colleagues.
Schools should be open – regions where schools closed have had the same rates of infection as regions where schools stayed open, and here in the U.S., teachers in districts with remote learning have had the same rates of infection as districts with in-person learning.
Education is essential, and most people in the building have very low risk.
A preponderance of data indicate that schools are safe. These data are readily accessible even for lay audiences – instead of reading research articles, you could read this lovely article in The Atlantic.
Well, I should rephrase.
We should’ve been quarantining international travelers back in December or January. At that time, a shutdown could have helped. By February, we were too late. This virus will become endemic to the human species. We screwed up.
But, given where we are now, students and teachers won’t experience much increased risk from Covid-19 if they attend in person, and schools aren’t likely to make the Covid-19 pandemic worse for the surrounding communities.
That doesn’t mean that schools are safe.
Schools aren’t safe: gun violence is a horrible problem. My spouse is a teacher – during her first year, a student brought weapons including a chainsaw and some pipe bombs to attack the school; during her fourth year, a student had amassed guns in his locker and was planning to attack the school.
Schools aren’t safe: we let kids play football, which is known to cause traumatic brain injury.
Schools aren’t safe: the high stress of grades, college admissions, and even socializing puts some kids at a devastatingly high risk for suicide. We as a nation haven’t always done a great job of prioritizing kids’ mental health.
And the world isn’t safe – as David Katz has written,
“If inclined to panic over anything, let it be climate change … Not the most wildly pessimistic assessment of the COVID pandemic places it even remotely in the same apocalyptic ballpark.”
At the end of “Just Use Your Thinking Pump!”, a lovely essay that discusses the evolution (and perhaps undue elevation) of a particular set of practices now known as the scientific method, Jessica Riskin writes:
Covid-19 has presented the world with a couple of powerful ultimatums that are also strikingly relevant to our subject here. The virus has said, essentially, Halt your economies, reconnect science to a whole understanding of yourself and the world, or die.
With much economic activity slowed or stopped to save lives, let us hope governments find means to sustain their people through the crisis.
Meanwhile, with the din of “innovation” partially silenced, perhaps we can also use the time to think our way past science’s branding, to see science once again as integral to a whole, evolving understanding of ourselves and the world.
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True, the world has presented us with an ultimatum. We must halt our economies, reconnect science to a whole understanding of ourselves and our world, or die.
Riskin is a professor at Stanford. Her skies are blackened with soot. In the words of Greta Thunberg, “Our house is on fire.”
For many years, we’ve measured the success of our economy in terms of growth. The idea that we can maintain perpetual growth is a delusion. It’s simple mathematics. If the amount of stuff we manufacture – telephones, televisions, air conditioners – rises by 3% each and every year, we’ll eventually reach stratospheric, absurd levels.
In the game “Universal Paperclips,” you’re put in control of a capitalist system that seeks perpetual growth. If you succeed, you’ll make a lot of paperclips! And you will destroy the planet.
Here in the real world, our reckless pursuit of growth has (as yet) wrought less harm, but we’ve driven many species to extinction, destroyed ancient forests, and are teetering at the precipice of cataclysmic climate change. All while producing rampant inequality with its attendant abundance of human misery.
We must reconnect science to a whole understanding of ourselves and the world, or die.
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We are in danger. But Covid-19 isn’t the major threat we’re facing.
I consider myself to be more cautious than average – I would never ride a bicycle without a helmet – and I’m especially cautious as regards global pandemic. Antibiotic resistance is about to be a horrific problem for us. Zoogenic diseases like Covid-19 will become much more common due to climate change and increased human population.
I’m flabbergasted that these impending calamities haven’t caused more people to choose to be vegan. It seems trivial – it’s just food – but a vegan diet is one of our best hopes for staving off antibiotic resistant plagues.
A vegan diet would have prevented Covid-19. Not that eating plants will somehow turbocharge your immune system – it won’t – but this pandemic originated from a meat market.
And a vegan diet will mitigate your contribution to climate change, which has the potential to cause the full extinction of the human race.
Make our planet uninhabitable? We all die. Make our planet even a little less habitable, which leads to violent unrest, culminating in warring nations that decide to use nukes? Yup, that’s another situation where we all die.
By way of contrast, if we had made no changes in our lives during the Covid-19 pandemic – no shutdown, no masks, no social distancing, no PCR tests, no contact tracing, no quarantines – 99.8% of our population would have survived.
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Indeed, we often discuss the Covid-19 crisis in a very imprecise way. We say that Covid-19 is causing disruptions to learning, that it’s causing domestic violence or evictions. On the front page of Sunday’s New York Times business section, the headline reads, “The Other Way that Covid Kills: Hunger.”
Covid-19 is a serious disease. We need to do our best to avoid exposing high-risk people to this virus, and we should feel ashamed that we didn’t prioritize the development of coronavirus vaccines years ago.
But there’s a clear distinction between the harms caused by Covid-19 (hallucinogenic fevers, cardiac inflammation, lungs filling up with liquid until a person drowns, death) and the harms caused by our response to Covid-19 (domestic violence, educational disruption, starvation, reduced vaccination, delayed hospital visits, death).
Indeed, if the harms caused by our response to Covid-19 are worse than the harms caused by Covid-19 itself, we’re doing the wrong thing.
In that New York Timesbusiness article, Satbir Singh Jatain, a third-generation farmer in northern India, is quoted: “The lockdowns have destroyed farmers. Now, we have no money to buy seeds or pay for fuel. …. soon they will come for my land. There is nothing left for us.”
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Covid-19 is awful. It’s a nasty disease. I’m fairly confident that I contracted it in February (before PCR tests were available in the United States), and my spouse says it’s the sickest she’s ever seen me.
Yes, I’d done something foolish – I was feeling a little ill but still ran a kilometer repeat workout with the high school varsity track team that I volunteer with. High intensity workouts are known to cause temporary immunosuppression, usually lasting from 3 to 72 hours.
My whole family got sick, but I fared far worse than the others.
It was horrible. I could barely breathe. Having been through that, it’s easy to understand how Covid-19 could kill so many people. I wouldn’t wish that experience on anyone.
And I have very low risk. I don’t smoke. I don’t have diabetes. I’m thirty-seven.
I wish it were possible to protect people from this.
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Obviously, we should have quarantined all international travelers beginning in December 2019. Actually, ten days probably would have been enough. We needed to diecitine all international travelers.
By February, we had probably allowed Covid-19 to spread too much to stop it.
By February, there were probably enough cases that there will always be a reservoir of this virus among the human species. 80% of people with Covid-19 feel totally fine and don’t realize they might be spreading it. By talking and breathing, they put viral particles into the air.
By the end of March, we were much, much too late. If you look at the numbers from New York City, it’s pretty clear that the preventative measures, once enacted, did little. Given that the case fatality rate is around 0.4%, there were probably about 6 million cases in New York City – most of the population.
Yes, it’s possible that New York City had a somewhat higher case fatality rate. The case fatality rate depends on population demographics and standard of care – the state of New York had an idiotic policy of shunting Covid-19 patients into nursing homes, while banning nursing homes from using Covid-19 PCR tests for these patients, and many New York doctors were prescribing hydroxychloroquine during these months, which increases mortality – but even if the case fatality rate in New York City was as high as 0.6%, a majority of residents have already cleared the virus by now.
The belated public health measures probably didn’t help. And these health measures have caused harm – kids’ schooling was disrupted. Wealthy people got to work from home; poor people lost their jobs. Or were deemed “essential” and had to work anyway, which is why the toll of Covid-19 has been so heavily concentrated among poor communities.
The pandemic won’t end until about half of all people have immunity, but a shutdown in which rich people get to isolate themselves while poor people go to work is a pretty shitty way to select which half of the population bears the burden of disease.
I am very liberal. And it’s painful to see that “my” political party has been advocating for policies that hurt poor people and children during the Covid-19 pandemic.
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Because we did not act soon enough, Covid-19 won’t end until an appreciable portion of the population has immunity – at the same time.
As predicted, immunity to Covid-19 lasts for a few months. Because our public health measures have caused the pandemic to last longer than individual immunity, there will be more infections than if we’d done nothing.
The shutdowns, in addition to causing harm on their own, will increase the total death toll of Covid-19.
Unless – yes, there is a small glimmer of hope here – unless we soon have a safe, effective vaccine that most people choose to get.
This seems unlikely, though. Making vaccines is difficult. And we already know that most people don’t get the influenza vaccine, even though, for younger people, influenza is more dangerous than Covid-19.
Look – this is shitty. I get an influenza vaccine every year. It’s not just for me – vaccination protects whole communities.
Yes, there are clear positive externalities to vaccination, but I think this sounds like a terrible idea. Ethically, it’s grim – the Covid-19 vaccines being tested now are a novel type, so they’re inherently more risky than other vaccines. By paying people to get vaccinated, we shift this burden of uncertainty onto poor communities.
We already do this, of course. Drug trials use paid “volunteers.” Especially phase 1 trials – in which drugs are given to people with no chance of medical benefit, only to see how severe the side effects are – the only enrollees are people so poor that the piddling amounts of money offered seem reasonable in exchange for scarfing an unknown, possibly poisonous medication.
Just because we already do an awful thing doesn’t mean we should make the problem worse.
And, as a practical matter, paying people to do the right thing often backfires.
To illustrate, consider the recent introduction, in many Indian states, of schemes of cash incentives to curb sex-selective abortion. The schemes typically involve cash rewards for the registered birth of a girl child, and further rewards if the girl is vaccinated, sent to school, and so on, as she gets older.
These schemes can undoubtedly tilt economic incentives in favor of girl children. But a cash reward for the birth of a girl could also reinforce people’s tendency to think about family planning in economic terms, and also their perception, in the economic calculus of family planning, that girls are a burden (for which cash rewards are supposed to compensate).
Further, cash rewards are likely to affect people’s non-economic motives. For instance, they could reduce the social stigma attached to sex-selective abortion, by making it look like some sort of ‘fair deal’ — no girl, no cash.
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What happens if it takes a few years before there are sufficient doses of an effective vaccine that people trust enough to actually get?
Well, by then the pandemic will have run its course anyway. Masks reduce viral transmission, but they don’t cut transmission to zero. Even in places where everyone wears masks, Covid-19 is spreading, just slower.
I’ve been wearing one – I always liked the Mortal Kombat aesthetic. But I’ve been wearing one with the unfortunate knowledge that masks, by prolonging the pandemic, are increasing the death toll of Covid-19. Which is crummy. I’ve chosen to behave in a way that makes people feel better, even though the science doesn’t support it.
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We, as a people, are in an awful situation right now. Many of us are confronting the risk of death in ways that we have not previously.
More than 37 percent of deaths in 1900 were caused by infectious diseases, but by 1955, this had declined to less than 5 percent and to only 2 percent by 2009.
Of course, this trend will still hold true in 2020. In the United States, there have been about 200,000 Covid-19 deaths so far, out of 2,000,000 deaths total this year. Even during this pandemic, less than 1% of deaths are caused by Covid-19.
And I’m afraid. Poverty is a major risk factor for death of all causes in this country. Low educational attainment is another risk factor.
My kids am lucky to live in a school district that has mostly re-opened. But many children are not so fortunate. If we shutter schools, we will cause many more deaths – not this year, but down the road – than we could possibly prevent from Covid-19.
Indeed, school closures, by prolonging the pandemic (allowing people to be infected twice and spread the infection further), will increase the death toll from Covid-19.
School closures wouldn’t just cause harm for no benefit. School closures would increase the harm caused by Covid-19 and by everything else.
Frank Brown Cloud 