53% of Israel’s 9.3M population has received both Pfizer doses
Infection rates, severe illness and hospitalisations dropping sharply
Of the few breakthrough infections (in immunised) proportionally more are the B.1.351 (first detected in South Africa) reut.rs/3mB02yc
B.1.351 made up 1% of all the COVID19 cases studied, but in patients who had received two doses of the vaccine, the variant's prevalence rate was eight times higher than those unvaccinated - 5.4% versus 0.7%.
This suggests the vaccine may be less effective against the B.1.351 than B.1.1.7 (the dominant variant in Israel, see image)
This doesn’t mean ‘ineffective’
The pre-print’s authors say
“Given the low frequency of B.1.351 across time, our results overall suggest that selection does not strongly favour the B.1.351 variant in the particular conditions in Israel”
“In view of this low frequency of B.1.351 (across all groups of study herein...) ... it is possible that (a) vaccine effectiveness coupled with enacted non-pharmaceutical interventions remain sufficient to prevent its spread, and/or (b) B.1.1.7 outcompetes B.1.351”.
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The team at @PHE_uk begin by explaining that on 8 Dec they investigated the surge in cases in the South of England. Only 4% (255/6130) of Kent cases had genetic sequence data, but of these 117 genetically similar cases had been collected between 10-18 Nov
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When looking at national data, these 117 cases were part of a larger cluster of 962 (to 8 Dec) in Kent, NE London, plus a few in rest London, Anglia, Essex
The new “Kent” strain of SARS-CoV-2 (officially named VUI 202012/01 for Variant Under Investigation, year 2020, month 12, variant 01) was identified as have multiple spike protein mutations
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These include deletion 69-70, deletion 144-145, N501Y, A570D, D614G, P681H, T716I, S982A, D1118H
What’s the “gold standard” to determine what is a true case?
It depends on the condition in question, but for a viral RT-PCR a range of concentrations of RNA or virus can be added to a negative sample to help determine the limit of detection
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Specificity = proportion of unaffected ppl that a test correctly identifies as not having the disease
99% specific means 99 out of 100 truly unaffected ppl will be correctly labelled as being negative.
Many known negative samples will be examined to determine this value.
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1860s: nucleic acids discovered
1940-50s: the concept that “DNA makes RNA makes protein” is developed (& is called the central dogma)
1960s: messenger RNA discovered
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Path to the vaccine
1989: use of lipid nanoparticles to get mRNA into cells
1990: RNA injected into muscle can cause local synthesis of a protein
1994-9: RNA vaccines shown to induce immune response
2008-11: early phase trials
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2003-2012: studies to generate a vaccine against 2 new severe coronavirus diseases SARS and MERS identify the spike protein as a good target for protective antibodies
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Observations are made.
A hypothesis is generated.
Experiments are performed to test (try to disprove) the hypothesis.
This cycle is repeated many many times until the experiments are unable to disprove the hypothesis.
The results are shared at talks or posters at conferences or as preprints, so others can comment & criticise.
The results are then published as papers (a gruelling process when the paper is assessed by tough anonymous scientists who point out every error, big or small, which must be corrected).
Afterwards, scientists try to reproduce those results to see if they’re real.