With the XEC variant on the way to dominance in most places, and XEC waves starting to show, it is time to ponder which variant will drive the next wave after XEC.
Here are the leading contenders: MV.1, XEC.2, XEM and XEK. They are shown here using a log scale, vs OG XEC.
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MV.1 is descended from JN.1.49.1 via MB.1.1.1. MV.1 adds the Spike:K478T mutation.
MV.1 showed some early success in India, reaching 22% frequency. Data from India has been sparse and lagging. The more recent data from Singapore shows it at an impressive 39% frequency.
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Here's an animated map showing the spread of MV.1. It was first reported in Maharashtra (India), in late June. It eventually spread to New York (USA) and then to all points of the compass.
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MV.1 has reached some impressively far-flung places including Mauritius, Hawkes Bay (New Zealand), Saskatchewan (Canada) and Wielkopolskie (Poland).
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Here's the latest variant picture for Singapore, showing the dominance of MV.1 over DeFLuQE KP.3.1.1. Note XEC does not even make the cut (top 8 lineages).
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Globally, MV.1 is showing a growth advantage of 2.4% per day (17% per week) vs XEC, so any crossover looks distant. Note the "global" variant data is dominated by North America and Europe.
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XEC.2 adds the ORF1a:I1367L mutation, and lacks ORF1a:A599T.
XEC.2 has been most successful in Australia, reaching 12% frequency. There are also some signs of growth in the UK, France and Sweden.
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Globally, XEC.2 is showing a growth dis-advantage of 0.2% per day vs XEC, so a crossover looks unlikely.
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XEM is a newly classified recombinant of KS.1 and KP.3.3.1. XEM has gained the Spike F59S and R346T mutations.
XEM has only been reported in Canada (Ontario and BC) so far, with 37 samples in under a month. It is showing impressive growth, but these could be related clusters.
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For Canada, XEM is showing a growth advantage of 5% per day (35% per week) vs XEC, which predicts a crossover in early November.
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XEK is a recombinant of KP.2.3 and XEC, with a very similar set of Spike mutations to XEC.
XEK has been most successful in France, reaching 3.5% frequency. There are also some signs of growth in the UK and around Europe.
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Globally, XEK is showing a growth advantage of 1.1% per day vs XEC, so a crossover looks very distant.
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So in summary, the most obvious contenders to challenge XEC at this point are MV.1 and XEC.2. I will back MV.1 at this point, with it's higher growth rate and it's success in undersampled regions.
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XEM is a "dark horse" which could sweep the field if it's early growth indications from Ontario are sustained on the global stage.
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The usual caveats apply - recent sample sizes are smaller which might skew these results.
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Of course, variant evolution continues unfettered, with billions of infected hosts and millions of chronic infections in a historically unprecedented global "flywheel" of momentum. Surprises could appear at any time, and might be well established before they are detected.
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Here's an outstanding thread on the evolutionary trajectory by Arijit Chakravarty and collaborators:
I am relying on the latest pango lineage designations, which currently go up to NL.1 and XEN. As always, there are many proposed lineages under consideration for designation. That important work is slowed and made more difficult by the uncertainty of limited data.
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Interactive genomic sequencing dataviz, code, acknowledgements and more info here:
A recent scientific paper compared long-term mortality by vaccination status.
I noticed that Table 2 drew a lot of attention, but was actually included in the paper as a static image. So I built a quick dataviz project to explore.
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On the first page, I've added a % Incidence change (vaccinated vs unvaccinated) and emphasised that with data bars. This is quicker for general readers to grasp than hazard ratios.
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You can click on any column header to sort the rows, e.g. as shown by % Incidence change. The starkest difference was deaths from COVID-19 at +372%.
Other causes with significant differences were diseases of the skin & blood, pregnancy and childbirth.
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A recent scientific paper included an antigenic map, comparing the immune status of individuals vaccinated with a range of vaccines "… vaccinated sera", against a collection of significant variants "Virus …".
The map was very informative, so I built a quick dataviz project.
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The map shows starkly that BA.3.2 "Cicada" is a wild outlier, way out on its own in the south-west corner. This suggests the current vaccines and/or disease-acquired immunity will not offer strong protection against infection.
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Another point of note is how the XFG "Stratus" variant is the furthest away from BA.3.2, at the extreme south, compared to other recent variants.
This might help explain how BA.3.2 has been able to drive significant waves in Europe, following their recent waves of XFG
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Here's the latest variant picture for Europe (excluding the UK), to late November.
BA.3.2.* "Cicada" is showing a very strong growth advantage of 7.9% per day (55% per week) over XFG.* "Stratus", which predicts a crossover in late December.
#COVID19 #EUR #BA_3_2 #Cicada #XFG
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To include the latest BA.3.2.* samples, I have rolled my reporting window forward an extra week or so. So the most recent data is even less representative than usual. The picture for those dates might change as more data is shared.
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Here are the leading European countries reporting BA.3.2.* .
The Netherlands leapfrogged Germany to report the highest frequency at 31%. Germany also grew sharply to 25%. Denmark grew to 16%.
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Here's the latest variant picture for the United Kingdom, to late November.
For the UK, BA.3.2.* "Cicada" is showing a strong growth advantage of 5% per day (35% per week) over XFG.* "Stratus", which predicts an imminent crossover.
#COVID19 #SARSCoV2 #UK #BA_3_2 #Cicada
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To include the latest BA.3.2.* samples, I have rolled my reporting window forward an extra week. So the most recent data is even less representative than usual. The picture for those dates might change as more data is shared.
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BA.3.2.* accelerated sharply in Scotland to 16% of recent samples.
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A recent scientific paper explored the impact of mass SARS-CoV-2 infections on Lymphocytes (crucial to the body’s immune system).
I noticed the authors had shared the data behind their charts in the Appendix Supplementary materials, so I built a quick dataviz project.
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Above, I’ve re-cast the data behind their Figures 3 and 5 in terms of % change from the baseline. Hopefully this is useful to help compare the subsets, whose results vary in scale.
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I added interactive filter controls and a trend line (dashed pink). You can use those to explore for example the trends in the last 12 months measured in the paper, for the CD3, 4 & 8 series.
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It became clear during November that a unusual second wave is underway in Australia, driven by the new "clade K" (H3N2 clade 2a.3a.1, subclade K).
Tasmania, New South Wales and South Australia are currently the hardest-hit.
#Influenza #Australia
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Western Australia had been spared the worst of this second wave until the last week or so. But now there’s a signal of a sharp change in case momentum there also.
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The latest Australian Respiratory Surveillance Report confirms my earlier speculation that the new "clade K" (H3N2 clade 2a.3a.1, subclade K) is driving the "unusual" second wave of influenza in Australia.