Just as we can decompose the US #COVID19 epidemic into a B.1.1.7 epidemic and a non-B.1.1.7 epidemic, we can further partition by variants of concern B.1.1.7, B.1.351 and P.1, where it's clear that P.1 has been gaining ground. 1/13
Here, using data from @GISAID, we see that in terms of frequencies across the US, P.1 has been undergoing more rapid logistic growth in frequency than B.1.1.7, while B.1.351 has been slower than B.1.1.7. 2/13
I'm plotting this with the unusual "logit" y-axis (with 1%, 10%, 50%, etc...) because a straight line in logit space is indicative of logistic growth. This sort of plot makes it easy to compare logistic growth rate of frequency between lineages with different frequencies. 3/13
We generally see more rapid logistic growth of P.1 across individual states as well. These 9 were chosen as those with the most data. All these plots only go to Apr 14, as there is a necessary lag to the genomic data as samples are processed. 4/13
The observation of faster logistic growth of P.1 compared to B.1.1.7 suggests that P.1 may have a transmission advantage over B.1.1.7 in the US and may continue to gain ground even as B.1.1.7 comes to dominate the virus population. 5/13
Given that P.1 frequency is growing rapidly, but overall cases are generally falling, the question becomes are vaccines and other control measures enough to curb spread of P.1 in absolute case counts? 6/13
To address this we can take these frequency estimates alongside case counts from @CDCGov and use frequencies to partition case counts by variant. Doing so results in this plot for the US, where the large majority of the epidemic is due to B.1.1.7 and non-B.1.351/P.1 viruses. 7/13
However, plotting partitioned case counts on a log-scale clearly shows the continued growth in absolute case counts of P.1. 8/13
We can use the same approach with state-level case counts and frequency data to estimate case counts for B.1.1.7, B.1.351 and P.1 variants. Here, we see that P.1 is still a minor (but growing) contribution but where some states like IL and WA have a larger P.1 share. 9/13
Looking at this same data on a log-scale makes the absolute growth of P.1 cases in CA, FL, IL, MA, MI, NY and WA quite clear. 10/13
Vaccination will continue bring down overall transmission rate, where I expect transmission chains of non-variant viruses to largely die out in the coming weeks. Variant viruses B.1.1.7 and P.1 raise the bar for the level of vaccination required to control the epidemic. 11/13
Mutations in P.1 suggest partial escape from antibody binding, which is borne out in drops in neutralization titer. I would expect P.1 to show some decrease in vaccine effectiveness, but remain largely effective. Figure from Wu et al (nejm.org/doi/full/10.10…). 12/13
Growth of P.1 alongside probable decrease in vaccine effectiveness suggests it's all the more important to get as many people vaccinated as possible as vaccination can still suppress circulation of P.1; it's just that the bar for herd immunity is higher. 13/13

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More from @trvrb

26 Apr
There are effectively two #COVID19 epidemics in the US at this moment; one largely resolving epidemic comprised of non-variant viruses and one growing epidemic of B.1.1.7. Together they have resulted in a near-plateau of cases throughout much of the spring. 1/10
If we look at virus frequencies in the US using data in @GISAID, we can see that the 7-day weighted frequency of B.1.1.7 has been growing consistently since January and is now over 50% in the US. 2/10
This pattern is repeated across individual states. These six were chosen as states with plentiful genomic data and to provide geographic diversity. B.1.1.7 is dominating throughout the US, except for New York and surroundings where B.1.526 is co-circulating. 3/10
Read 10 tweets
23 Apr
It's hard for me to infer the degree to which new variants are driving the surge in cases in India, but we are seeing rapid growth in frequency of multiple viral variants. 1/5
Here is a @nextstrain view of @GISAID data that focuses on viruses from India and highlights emerging lineages B.1.1.7 (in blue), B.1.351 (in green) and B.1.617 (in orange). Interactive version at nextstrain.org/ncov/asia?c=em…. 2/5
We can fit a logistic growth model to the full genomic dataset from India for these three lineages, where we see logistic growth as "linear on a logit scale". Each of these lineages is estimated to have similar logistic growth rates of ~0.3 per week. 3/5
Read 5 tweets
23 Apr
When variants of concern were first identified in late Dec, the US was not where it needed to be in terms of genomic surveillance. However, with considerable ramp up by the CDC, state labs and academic groups, we now have a remarkable genomic surveillance system. 1/14
My favorite metric for genomic surveillance is the number of cases that have been sampled, sequenced and shared publicly to @GISAID in the previous 30 days. By incorporating both sequencing volume and turnaround time, it tells you how much is known about current circulation. 2/14
Throughout the fall, the US had just 100-300 genomes available that were sampled, sequenced and shared in the previous 30 days. 3/14
Read 14 tweets
14 Apr
Since their recognition in the UK, South Africa and Brazil in Dec 2020 and Jan 2021, the variant of concern lineages B.1.1.7, B.1.351 and P.1 have continued to spread throughout the world with B.1.1.7 so far the most successful of the three. 1/15
These lineages first received attention due to large numbers of mutations to the spike protein along with rapid increases in frequency in the UK, South Africa and Manaus, Brazil, but much subsequent attention has focused on key mutations E484K and N501Y. 2/15
This figure shows genotype at sites 484 and 501 mapped onto a reference phylogeny of ~4k viruses sampled from all over the world with 484K viruses in light orange, 501Y viruses in blue (including B.1.1.7) and 484K+501Y viruses in dark orange (including B.1.351 and P.1). 3/15
Read 16 tweets
18 Feb
After a ~2 month plateau from mid-Nov to mid-Jan, the US #COVID19 epidemic has undergone a steady week after week decline and is now back to daily case counts last seen in late October. A thread on what we might expect going forwards. 1/13
Working with case counts from @COVID19Tracking and Rt estimates from epiforecasts.io, I'm showing US confirmed cases broken out by state alongside transmission rate as measured by Rt through time. 2/13
Generally, Rt > 1 in Nov and Dec corresponding to rising cases and drops below 1 in Jan corresponding to falling cases. We've seen a steady decline in Rt from Nov to Feb. Thus, current decline is not a sudden shift in circumstance, but resulted from reaching Rt < 1. 3/13
Read 13 tweets
3 Feb
With emerging variants of SARS-CoV-2 and initial evidence of antigenic evolution, I've seen comparisons here to seasonal influenza and its rate of evolution. In this thread, I want to ground these comparisons with some data. 1/18
If we follow a transmission chain of SARS-CoV-2 from person to person, we'll generally see one mutation occur across the viral genome roughly every two weeks. 2/18
Here I use data from @nextstrain and @GISAID to compare sampling date to the number of mutations across the SARS-CoV-2 genome relative to initial genomes from Wuhan. This shows a steady accumulation of mutations through time with the average virus now bearing ~24 mutations. 3/18 Image
Read 19 tweets

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