A counterpoint to the alarm bells that are sounding over novel SARS-CoV-2 variants.
Is it possible that we are misinterpreting differences in human behavior as differences in the biological fitness of viral variants?
A thread to explain this hypothesis...
1. Infectious disease transmission is heterogeneous (overdispersed), largely due to human behavior.
Large "superspreading events", differences in behavior, and/or people who have many contacts generate an outsized number of transmission events.
2. This makes it easy for viral variants - even those with no inherent transmission advantage - to take over a population.
Imagine an infected person attending a large indoor gathering with hundreds of people. That viral strain will expand - because of behavior, not biology.
3. If we look for viral variants that are expanding in the population, and transmission is (behaviorally) heterogeneous, we will find certain variants that take over - even if those variants are not biologically more transmissible.
4. An example (based on a random number generator): Assume you have 10 people infected with 10 different strains. On average, each person infects one other person (R=1). If each transmission event is random (Poisson distributed), no one variant tends to take over.
5. Now, consider a biologically more transmissible variant. Not surprisingly, this strain will take over the population - as we saw with the B.1.1.7 variant in England, for example.
6. But, now consider transmission with "superspreader" events. What happens in this system looks very similar to the more transmissible strain above - it's just that one (orange) variant gets "lucky" to be involved in those events more than other variants.
7. So, if you just look retrospectively, it's impossible to tell the difference between a "fit" variant and a "lucky" one - and easy to interpret the "lucky" strain as being more biologically transmissible.
8. Additional evidence has been cited for the transmissibility of B.1.1.7. First, this variant has mutations that increase transmissibility in the lab. I agree, but would argue we as scientists should be humble & skeptical - we have a strong bias toward finding such evidence.
9. It's also true that the areas of England where the variant first took over also saw more rapid increases in case counts.
But this would be true whether the underlying mechanism was increased transmissibility of the virus, or more frequent superspreading events.
10. Also, contacts of people with B.1.1.7 were more likely to be infected than contacts of people w other variants.
But superspreading events (or people taking fewer precautions) would also be expected to affect contacts - again without having to invoke higher transmissibility.
11. The best way to evaluate increased transmissibility is to *prospectively* determine whether incidence continues to increase in areas with these new variants (or can take over new populations) - *after* the variant has been identified. "Lucky" strains won't persist forever.
12. So, what is happening in England now? I'll let the maps below speak for themselves - cases per 100,000 population in the week of Jan 7 (~2 generation times after the variant was first widely publicized) and the week of Jan 21 (2 weeks later).
13. It's true that this also reflects a strict lockdown in England - but worth noting that this decline was much more rapid than in March. Herd immunity tends not to generate rapid drops in incidence - and hard to believe this could be achieved if R0 had gone up 50% with B.1.1.7
14. It's also true that B.1.1.7 has been detected in many other countries - but despite this variant being in circulation since Sept., there's no evidence that it has taken over any other large-sized population. Again, difficult to square with a 50% increase in R0.
15. All this to say, the most parsimonious explanation for the rise of variants like B.1.1.7 might be simple overdispersion and human behavior, not increased viral transmissibility. It's not a scientifically sexy explanation, but imho one worth considering more carefully.
(16. And can we please stop calling these the "UK variant", "South African variant", etc? Countries should not be penalized/stigmatized for conducting good surveillance...it creates a strong incentive not to collect these data...)
(17. Also, I would argue that we need to check our own cognitive [political?] biases - arguments that augment the the pandemic are generally better received by scientists than arguments that we may be overstating the problem. This can have negative consequences.)
Sorry for a long thread.
Long story short, it's critical to study these new variants - and in general wise to proceed with caution.
But we also need to take care with concepts that can stoke panic, when cases are falling & the public may perceive scientists as biased.
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After some conversations with a trainee, I've recognized at least 7 "academic phenotypes" based on underlying core professional goals.
A thread, aimed primarily at junior researchers learning to navigate the academic world.
Take-home: know your phenotype, know your superiors'.
Phenotype -> core goal:
Politician -> power
Performer -> fame/pubs
Pragmatist -> things that work
Inquirer -> knowledge/insight
Idealist -> a better world
Epicurean -> pleasure/time off
Humanist -> relationship
We are all each of these to some extent. But more some than others.
Step 1: Recognize your (actual & ideal) phenotype by asking yourself which goals you would sacrifice for others.
Ex.: would you delay promotion to achieve an ideal?
Be honest w yourself about which phenotypes you (a) are, (b) want to be.
This may be controversial, but here's a thread on 5 problems I see with the #JohnSnowMemorandum.
I agree with the concept, but am worried about the message it sends.
I sympathize w/ those who have signed, submit this in the spirit of scientific debate.
First, I am no fan of surrender (aka "herd immunity") strategy articulated in #GreatBarringtonDeclaration. "Those...not vulnerable should immediately be allowed to resume life as normal" suggests vulnerable & non-vulnerable can be (a) identified & (b) kept apart. Both fallacies.
Second, full disclosure, I have a personal stake - an immediate family member has been in the hospital for months, with no visitation due to COVID restrictions. My pandemic life is not OK.
After sitting in study section last week reviewing proposals for K-series career development awards, thought I'd list my top 5 reasons why such proposals fail. (Not linked to any one submission.)
Junior scientists who might be interested in applying - avoid these pitfalls!!
1. The primary mentor(s) never read the proposal in detail.
Many applications have clear holes in logic that no mentor would let through.
Give your mentors enough time to review your proposal, and steer away from mentors who will not spend the time to offer you comments.
2. The candidate is not quite ready.
Reviewers like to see upward trajectory and (if K22/K99) near-independence.
Be strategic about when you apply. Not a bad idea to put in an initial submission before major papers come out, so you look like a "rising star" on resubmission.
It's tough to compose science-related tweets when a family member is hurting.
But here's a quick thread on 5 things I've tried at work to keep myself strong enough to support someone very special to me.
Keeping in mind that everyone's story is different and equally meaningful...
1. Put "self-care time" on the calendar.
It's easy to get caught up in my own thoughts and waste time as a result. But if I'm intentional about blocking specific times for self-care, I spend that time doing things (exercise, online bridge w/ my mom) that actually rejuvenate me.
2. Focus on others' projects.
I usually block time for writing/big-picture thinking. But when I'm low emotionally, I don't use that time well. Even if I feel like $#!+, I will show up for meetings and not let others' projects down. Which in turn helps me feel better about myself.
Another lesson for the COVID response from the TB world:
"Have we reached herd immunity?" is the wrong question.
If, by "herd immunity", we mean Rt<1, then we achieved herd immunity for TB a decade ago. And yet, 1.4 million people still die of TB every year.
What's the fallacy? There are two: 1. Herd immunity isn't a magic threshold to cross. A decline in cases doesn't mean a rapid decline, nor that the current case count is acceptable. Just as for TB, millions of people could still die of COVID after "achieving" herd immunity.
2. Many factors contribute to Rt<1; immunity is only one. If we ease the TB response, deaths will rise. Saying "we've achieved herd immunity to TB" is therefore problematic. Same for COVID: when herd immunity is reached, if we stop distancing, wearing masks, etc, people will die.