I realize this is rather late to the party, but I wanted to provide a look at the prospects of Mu variant virus. I believe we can conclude that Mu appears more transmissible than all circulating variants except for Delta, but Delta is substantially fitter than Mu. 1/9
If we look within Colombia, we see Mu becoming predominant around May 2021, outcompeting other endogenous South American variants Gamma and Lambda. However, recent sequencing suggests that Delta is successfully invading on this Mu background (nextstrain.org/ncov/gisaid/so…). 2/9 Image
In neighboring Ecuador, we see a heterogeneous mix of Alpha, Gamma, Iota, Lambda and Mu by June 2021. Delta has been successfully displacing most of this diversity since July 2021, while Mu has remained relatively stable (nextstrain.org/ncov/gisaid/so…). 3/9 Image
A more rigorous analysis of variant-specific Rt by @marlinfiggins that partitions @CDCgov case counts across states in the US by sequencing data from @GISAID shows that Mu was maintaining Rt > 1 in most states in July 2021 when Alpha, Beta and Gamma had dropped below 1. 4/9 Image
However, Rt of Delta is substantially greater than that of Mu and if we estimate variant-specific transmission advantage relative to ancestral non-variant viruses we see that Delta > Mu > other variant viruses. 5/9 Image
Here, each point is an estimate for a particular state analyzed independently, so that consistency of these estimates across states gives some degree of confidence in the results. 6/9
Model results by @ftzo et al (medrxiv.org/content/10.110…) show a similar pattern with Mu viruses assigned a higher fitness than non-Delta variants (nextstrain.org/ncov/gisaid/gl…). 7/9 Image
A couple broader thoughts here:
1. I still expect Delta to sweep through the global virus population.
2. Mu's estimated transmission advantage suggests that it would have spread widely had Delta not interfered
At this point, focus of genomic surveillance should be to identify sub-lineages of Delta bearing mutations that contribute to further transmissibility or to antigenic drift. So far, there is little signal here, but I expect such sub-lineages to emerge in the coming months. 9/9

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More from @trvrb

13 Oct
I've meaning to write a "COVID endgame" thread for a while and I apologize this is somewhat delayed compared to media interviews like science.org/content/articl… and statnews.com/2021/09/20/win… and to recent seminars like . 1/17
Here, I've been trying to think about what COVID will look like in its endemic state, ie once the (more or less entire) population has immunity to the virus, blunting transmission and disease relative to the pandemic state. 2/17
I expect endemicity to be achieved at different times throughout the world due to inequities in vaccine distribution and I expect this to be a soft transition rather than a sudden flip of a switch. 3/17
Read 17 tweets
28 Sep
I'm honored and completely overwhelmed by the recognition from @macfound and @HHMINEWS. Flexible funding with a multi-year commitment is the professional scientist's dream and I'm incredibly grateful for this opportunity. 1/4
For me, like others in the field, responding to the pandemic has been a ceaseless and exhausting endeavor. But I'm immensely proud of what the teams at @nextstrain, @fredhutch and the @seattleflustudy, as well as the larger scientific community, have accomplished. 2/4
That said, it's difficult for me to sort out my feelings about these awards, as they are so intertwined with the pandemic. It feels perhaps uncomfortable to be professionally rewarded for doing something that felt like a moral imperative. 3/4
Read 4 tweets
13 Sep
New (not yet peer-reviewed) work by Katie Kistler and @huddlej in the lab assessing adaptive evolution in SARS-CoV-2 across the viral genome. 1/12
We measure adaptive evolution by correlating mutations in different regions of the genome with growth of clade frequency. For this, we use a viral phylogeny of ~10k genomes sampled equitably through space and time across the pandemic (nextstrain.org/groups/blab/nc…). 2/12
If mutations to a region result in fitter viruses, clades bearing these mutations should expand more rapidly. We find that the S1 domain of spike accumulates protein-coding (nonsynonymous) changes rapidly and that clades with more S1 mutations tend to grow in frequency. 3/12
Read 12 tweets
7 Sep
It looks like we're about at the peak of the Delta SARS-CoV-2 wave in the US (figure based on @CDCGov data). A thread on current circulation patterns and the impact of Delta. 1/14
This inflection point in case loads at the country-level is due to decline in some states (such as FL and LA) and growth in others (such as OH and WV). Figure shows cases per 100k population on a log axis to emphasis state-level growth and decline. 2/14
Using previously described method to split cases by variant frequency (), we see a striking pattern in which most states have a moderate spring wave comprised of a mix of Alpha and other variants, but show a large Delta wave in the summer. 3/14
Read 17 tweets
31 Aug
I'm not an immunologist, but I've been trying to read into the literature on waning immunity to SARS-CoV-2 and to understand the recent NIH, CDC, FDA booster recommendation (hhs.gov/about/news/202…). I'll share some takeaways here. 1/14
Previous studies in other viruses found that the potency and the concentration of circulating antibodies in an individual is often predictive of their protection to infection or illness after exposure. 2/14
This potency + concentration is commonly quantified as the titer required to neutralize 50% of viral plaques in a lab assay. These assays are run by diluting sera from an individual and seeing what dilution causes loss of neutralization. 3/14
Read 14 tweets
30 Jun
How big of a wave of #COVID19 do we expect in the US from the Delta variant? Here I describe a simple approach to this question and attempt a rough back-of-the-envelop estimate. 1/16
First off, epidemic size is determined by two primary factors:
1. Efficiency of onward transmission from an index case, commonly quantified as R0
2. Size of susceptible pool
Given a specified R0, we can calculate final epidemic size in a simple SIR model with the following equation where Z is final epidemic size. For initial R0 of 1.1, an epidemic is expected to infect 18% of the susceptible population. 3/16
Read 18 tweets

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