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The evolutionary war raging between humans and Covid-19

The race is sure. Vaccines against the virus that causes Covid-19 are spreading through shoulders around the world, the tip of the hypodermic spear of a year-long scientific triumph. But this protean virus, like all the things that infect humans and make them sick, stings and dodges.

Virology versus epidemiology. Vaccinology against evolution. Mutation versus mutation, transmission versus infection, virus versus vaccine. Start! Your! Engines! The past year (horrible, tragic, not good, very bad) might have seemed like a simple battle between scientists and a virus to find new drugs and vaccines. But it wasn’t just a standing fight; it was also a bug hunt – a subtle push-pull through a dozen different vectors. Viruses aren’t exactly alive, but they still follow the same rulebook as every living thing on Earth: adapt or die. Understanding these more occult forces – how viruses evolve in us, their hosts, and how they change the way they pass from person to person – will define the next phase of the pandemic.

It’s easy to panic about the new variants of the SARS-CoV-2 virus, with their science fiction nomenclature. There is B.1.1.7, which seems to be a genius at infecting new people. And you have B.1.351 and P.1 – maybe not better for host-to-host transmission, but better for evading an immune response (natural, or the type that a vaccine induces). A group of those who escape immunity share the same unique mutation, even if they are only distant. As the saying goes, that’s life. “The way the virus evolves, the fundamentals of evolution, are the same. What is different is that it is played out on a very, very large scale. There are so many people who are infected, and every person has a lot of viruses in them. So there are lots of opportunities for the virus to mutate and try new things, ”says Adam Lauring, a University of Michigan virologist who studies viral evolution. “Every now and then one of these takes off. This is a rare event, but when the virus has so many opportunities to fix this problem, it will happen more and more frequently. It is as much a game of epidemiology, in other words, as a game of evolutionary biology.

So while it may seem like these variants have some sort of evil intent – to make people sicker, to kill all humans! – that’s not what happens. Viruses don’t want anything; they are just verbs. Infect, reproduce, infect. A virus that kills too effectively does not become a virus for very long, as dead hosts cannot walk around breathing on uninfected but sensitive cups. So, one hypothesis says that these successful mutations are mainly changes in the way the virus infects. In other words, they improve the way the virus enters a human being, or enters a human cell, or reproduces in that cell (because the more virus a person produces, the more virus they emit and the more likely they are to reach another person).

This is probably why all these similar variations seem to appear at the same time and quickly. Viruses are just small spoonfuls of protein wrapped around large molecules of code, of genetic material. In SARS-CoV-2, this material is RNA. And some viruses mutate more frequently than others.

Viruses evolve because they reproduce – in fact, that’s about all of their shtick – and errors creep into that genetic material in the process. Over generations, sometimes these random or “stochastic” errors allow the virus to do its job better; sometimes they make things worse. That is, the circumstances of a virus’s life, or way of life, work against random changes in the code underlying its genes. (SARS-CoV-2 appears to mutate to at about the same rate as other RNA viruses, although, like other coronaviruses in his family, he has a built-in error correction mechanism. He needs it, because his genome is so large, relatively speaking, three times the size of the genome of HIV, the virus that causes AIDS, for example. “Without proofreading, it would probably create too many virus replication event mutations to remain viable,” says Katrina Lythgoe, an evolutionary epidemiologist at the Big Data Institute at the University of Oxford. This type of genomic suicide is called crossing the “error catastrophe threshold”.)

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