(π§΅NO ONE COULD HAVE PREDICTED THIS): To answer the question "What does the future hold for πππ π-πππ-π?" it's worth examining how predictable its evolutionary trajectory has been so far. Evolution is stochastic, but stochastic processes can still yield predictions. (1/)
Paradoxically, while evolution is highly unpredictable at a molecular level, predicting its consequences and anticipating its risks is actually quite easy. We'll dive a lot deeper into this idea in a later TT, as it's a crucial one for understanding our current situation. (2/)
While "expert" prognostications from the early pandemic were wildly off-base, it was possible to reason deductively. We (my collaborators & I, h/t in particular @madistod & @debravanegeren) called out many of the risks within the first year, in the peer-reviewed literature. (3/)
In the fall of '20, we used a combination of structure/function analysis & evolutionary theory to predict the rapid evolution of resistance to neutralizing antibodies (nAbs), which are critical for vaccines & monoclonals, as well as natural immunity. (4/)
In Jan '21, we predicted that vaccines alone wouldnβt allow a return to pre-pandemic conditions, predicting hundreds of thousands of deaths following rollout. At the time, this was a highly contrarian position, with "experts" predicting an end to the pandemic with vaccines. (5/)
In May '21, we predicted relaxing restrictions too soon would lead to a variant-driven rebound. This was the same week the WH said "If you're vaccinated, the pandemic is over for you", as they lifted mask restrictions. The rebound we predicted was the Delta wave of Sep '21 (7/)
(although we guessed it'd be a different variant). All of that fall, PH kept referring to a "pandemic of the unvaccinated", which was factually incorrect. It was a pandemic of the unmasked & unvaccinated. "Vax & relax" was destined to fail from the get-go, as we predicted. (8/)
In Apr '21, we predicted intrahost evolution during long-term infections would drive the emergence of resistance. This is well proven at this point- many of the jumps made by the virus have been punctuated equilibrium events, as predicted by this paper. Punc eq is bad news. (9/)
The Omicron wave was caused by a punctuated equilibrium (punc eq) event, similar to what we've seen for other pandemics. Punc eq events are particularly dangerous for managing a pandemic, because they lead to huge & unpredictable jumps in sequence space (more on this later) (10/)
So here we are today, with several variant-driven waves of infection/yr, at unpredictable intervals. The virus is evolving faster than ever before, and vaccines aren't slowing its evolution. We haven't reached the endemic state (see screenshots). It's a volatile situation. (11/
All of this is what we predicted. This is not a "we told you so", although we, in fact, did. This work was intended as warnings, not predictions. These papers, which took some heat for being "FUD" at the time, were pointing out worst-case scenarios & flaws in the strategy. (12/)
It gives us no joy that these things came to pass. In fact, if our predictions had been heeded, they'd have been wrong & our lives would've been far better for it today. Some predictions we've made since then haven't come to pass (& hopefully never will). Let's take a look. (13/)
Our modeling predicts the average person will be infected 1-2x/yr if they take no precautions. We now know that post-acute damage from πππππ is common, and reported for a wide range of organ systems. The health burden of πππππ is not shrinking (see screenshots) (14/)
We showed in a paper a couple of years ago that transmission is minimally impacted by fatal COVID-19 outcomes. Put simply, the virus could evolve to kill everyone it infects and not take a hit to transmission. Small changes in IFR can have big impacts (see screenshot). (15/)
The promises made about stable protection vs severe disease being provided by T cells are ...debatable. The aspirational statements made by T cell experts have not materialized - ~5yrs into the pandemic, there are no T cell treatments on the horizon. (18/)
We pointed out in a paper from last year that the risk of co-circulation of multiple strains (serotype formation) remains on the table. Such a development would further complicate vaccine development efforts, which are already struggling to keep up with viral evolution. (19/)
New variants emerge from prior lineages not widely circulating at the time of emergence. Our work suggests this is due to punctuated equilibrium, happening as a result of prolonged intrahost evolution during persistent infections (see screenshots, manuscript in progress). (20/)
This creates an engine of viral evo that's capable of indefinitely throwing us "curve balls". It's popular among experts to claim that the "worst is behind us". Our work suggests that's far from guaranteed. Each new variant is a fresh draw from the viral evolution lottery. (21/)
In addition to sudden reversions to early pandemic IFRs caused by immune evasion, unrestrained viral evolution brings the risk of altered viral pathology. We pointed out in a preprint from a couple of years ago that there are many evolutionary pathways to higher virulence (22/)
As with other viruses, CoV fatality rates can vary. It's far from clear that CoVs emerge into human populations with low IFRs. A competing hypothesis is that virulence falls over time with the evolution of host resistance. (Optimism is not a good basis for risk management). (23/)
There is, in fact,a genomic signature of selection from an ancient CoV pandemic in East Asia 25k yrs ago. Those who would argue "that'll never happen with πππππ" should explain what makes them so confident in the trajectory of virulence evolution for πππ π-πππ-π (24/)
Five yrs in at this point & most of the early reassurances from experts were wrong. They were, in fact, inconsistent with what science predicted at the time. Failing to mitigate those risks has created a fresh set of downside risks that should be addressed, not dismissed.(25/25)
H/t again to my collaborators, @madistod & @DebraVanEgeren & many others for the work shown here. Also H/T to @TRyanGregory & @madistod for the discussions that led to this 𧡠& @0bj3ctivity & @gckirchoff for helpful feedback.
This is π§΅3 /5 in a series, earlier π§΅s below (1/2):
@madistod @DebraVanEgeren @TRyanGregory @0bj3ctivity @gckirchoff Part 0: foreword x.com/arijitchakrav/β¦
(π§΅2/5, HISTORY): What does history teach us about pandemics?
This is a topic that's been covered by others, but much of what's been said is worth taking a closer look at, in context.
Let's look at some historical pandemics/epidemics & see what we can learn. (1/)
It's worth starting by defining what a pandemic is- and isn't. To quote Michael Osterholm (in '09): β(A) pandemic is basically aβ¦novel agent emerging with worldwide transmission.β
It's an epidemiological, not a social, construct. Pandemics don't go away if you ignore them. (2/)
In the last π§΅, we looked at what biology tells us about emergent pathogens.
The key take-home: the evolution of their virulence is unpredictable- it often increases.
Host & pathogen are locked in a Red Queen's Race (3/). It's not a stable equilibrium.
(π§΅1/5, EMERGENCE): What happens to virulence after a new pathogen emerges? Popular thinking on the subject is that pathogens evolve become less virulent over time when they co-exist with their host species, based on the logic that virulent pathogens don't spread effectively.(1/)
This perception is occasionally echoed by experts as well, for example in this Science article: βπππ π-πππ-π is going to become a common cold. At least thatβs what we want.β (If wishes were horses, then zoonotic spillover would be nothing to worry about, I guess?) (2/)
The idea dates back to the "Law of Declining Virulence", propounded by medical doctor Theobald Smith in the 19thC (far from the last MD to confidently hold forth on the topic of evolution). Unfortunately, it's not supported by experimental data (see screenshots for example). (3/)
It's been ~5yrs since πππ π-πππ-π, the virus that causes πππππ, made its fateful jump into humans. Now seems as good a time as any to ask "is it over yet?" (For the 10th time, but who's counting?)
Let's talk about how this ends, shall we? (1/)
Every few months over the past 5 yrs, we've been reminded that the pandemic is over now, or perhaps it ended a long time ago, no one really knows.
The important thing is that it'll never go away, so we have to learn to live with it.
But not to worry, it's all very mild. (2/)
The dead moth buried in that word salad is the belief that newly emergent pathogens must eventually become endemic, that this process is about managing our own feelings about the situation.
A seven-stages-of-grief thing that we must all eventually accept. For our own good. (3/)
Been doing some thinking about how the pandemic will end (@TRyanGregory & @madistod have been great sounding boards).
In particular, focusing on two questions relevant for sc2:
1. What does biology teach us about emergent pathogens? 2. What can past pandemics teach us?
(1/)
TL; DR is weβre all gonna die.
Just kidding. (Actually true if you wait long enough, but that thought is not an original one).
Some interesting titbits, details to follow): (2/)
1. There is a wealth of biology literature on pathogen emergence & what happens to virulence.
Itβs a very well studied problem and the stuff you hear βexpertsβ say on the topic is quite different from what the literature says on it. The βexpertsβ are using 1980s textbooks. (3/)
In a recent paper, my colleagues and I assessed the effectiveness of contact tracing during the early stages of the Covid pandemic. We found that contact tracing identified 1-2% of all transmission events. (2/)
So, if the CDCβs is as successful with monkeypox contact tracing as it was with Covid, you would expect pretty much the same finding (1 out of 113) even if every one of those people had contracted it. (3/)
@KonLontos @GidMK Talk of a βplateauβ of risk comes from a fundamental misunderstanding about the underlying math.
The statcan data is consistent with a fixed probability of LC per infection. Such a fixed probability will give you a curved line asymptotically approaching a plateau (1/)
@KonLontos @GidMK Unfortunately, that βplateauβ is not useful, because such a risk function plateaus at 100%.
@gckirchoff and I explain the math in this blog post (with an interactive tool that you can play with) (2/): thedataquill.com/posts/understaβ¦
@KonLontos @GidMK @gckirchoff Iβve heard it said that there exists a subset of people who are uniquely vulnerable to LC, while LC risk in the βgeneralβ population is low. This is not consistent with the science on the subject. (3/)