I mentioned that the Israel dashboard features two new segments on “recurrent morbidity,” i.e. reinfections. The “guide” page confirms that these are PCR positives that are more than 90 days separated from a previous PCR positive.
Reinfections, of course, should be in scare-quotes as well: Because these are rarer, they may be more prone to false positives; because they are believed to be rarer, they may go under-tested.
Either way, rates have been low in Israel and throughout every study setting in the era before Omicron, indicating that previously testing PCR positive confers a lower likelihood of testing positive again, regardless of whether the first or second positive is “false.”
This is no longer the case:
How does this translate to reinfection rate vs naive infection rate? Let’s wade into the numbers.
First, the Google-translated guide text for the definitions on these graphs:
“Repeat-verified” are those who have been tested and found positive once again for the COVID-19 virus in a PCR test, starting 90 days after the previous discovery, whether or not they have had symptoms, whether they are sick, recovered or have died1 […] The data are presented as a division between repeat verifiers who were vaccinated at least once on the day of the additional verification and repeat verifiers who were not vaccinated at all.
Absolute values are reported on the related graph. The combined view:
Absolute (by age) values can also be displayed for last-30 days alone (to be displayed below). The results are stunning. Crudely adding all ages together:
All-time reinfections: 24,700
Last 30 days: 12,900
That’s more reinfections in the last 30 days than there had been before (11,800).
Onto the per-100k rates. “Reinfections,” again, are PCR positives among Israelis in the “PCR positive over 90 days ago pool.” “90 days ago” changes each day, so I used the October 21 value as the halfway point for the previous 30 days. This gives us our two populations of “Omicron susceptibles” in Israel:
“Omicron Susceptibles” (in millions)
“90 Days+ Previously PCR positive,” crude: 1.3239*-.0118-.0082* = 1.3042
December 8 Naive, crude: 9.217**-1.348* = 7.869
“Omicron infections” (in millions)
All infections, last 30 days: 1.4632*-1.3483* = .1149
Reinfections, last 30 days: .0129
Naive infections, last 30 days: .1149-.0129 = .102
(*=Worldometers.3 **=Israel Population. Other values from MOH Dashboard)
30-day Reinfection rate, last 30 days:
(.0129 / 1.304) x 100k = 989 per 100k
30-day Naive infection rate, last 30 days:
(.102 / 7.869) x 100k = 1,296 per 100k
Hazard Ratio:
Pretty Similar!
The result appears to confirm the reports from the UK and (it must begrudgingly be acknowledged) the very shoddy South Africa study update.4 Omicron substantially circumvents natural immunity from previous infection.
While an increased number of reinfections in general can be expected as there are more previously infected, the relative per capita rate should not jump in kind. Obviously some of the “naive” will in fact have immunity from prior encounter with SARS-CoV-2 - but this was also true in the summer, and yet “Delta (and prior) reinfection” rates were only ~.1-.2 of naive infection rates.5
Of course, all of this could be an artifact of a change in false-positivity rates associated with Omicron; if all of these positives are “false,” then there are no true reinfections, to take the extreme case.
Since Omicron is mild, defining the corresponding, new meaning of a “false positive” is philosophically tricky. But as PCR positives were never a good proxy for “Severe Covid 19” to begin with, we must deal with them as nothing more than what they are. In that sense, Omicron has clearly increased “reoccurrence of PCR positive.” The meaning of that fact will only become clear later: Either the virus is evading prior immunity, or the test no longer tells us anything.
Or, it could be a bit of both.
Omicron has changed niche: It appears to replicate with more affinity in nasal airway cells,6 whereas prior iterations of SARS-CoV-2 seemed only to be detectable in nasal swabs after a certain threshold of replication had taken place.7 This could be due to a shift in dynamics in which Omicron no longer requires TMPRSS2 for binding with ACE2. As found by Peacock, et al. (emphasis added):
In [human nasal epithelial cultures] Omicron showed a large early replication advantage, yielding viral titres ~100-fold higher than Delta by 24 hours post- infection. […]
We propose that Omicron achieved this rapid replication rate by becoming less specialised in terms of its cellular tropism. Earlier SARS-CoV-2 isolates were limited in their tropism by their requirement for cell surface entry using the TMPRSS2 protease, and it appears subsequent variants, such as Alpha and Delta, became even more heavily reliant. However only a low proportion of cells in the upper respiratory tract express both ACE2 and TMPRSS2.8
This could represent a break in prior associations between PCR positivity, seroconversion (the appearance of new antibodies in the blood), and transmission - as well as natural immunity as defined in respect to all three of those.
If Omicron is more “teens egging the coach’s house” than “methed-out home invader,” we should make less of the similar attack rates between those who have or have not installed a security system.
What higher PRC re-positive rates in Omicron do not appear to represent, is any evidence that the Covid vaccines diminish post-infection immunity. Regardless of whether the original PCR positive was before or after receipt of the Covid vaccines, the “at least 1 dose” group seems to be outperforming the unvaccinated in the Omicron era:
The Covid-vaccinated + previously infected are apparently outperforming the unvaccinated + previously infected in every age group, even though naive per-100k rates are similar.
This could reflect a million different things - particularly a higher percentage of “previously PCR positive” in the unvaccinated pool; or perhaps a higher percentage of “previously PCR positive before the summer,” either of which would indicate that non-vaccination is not a proxy for “reinfection risk,” but merely for higher rate of prior infection or longer time since prior infection.
I think it also represents some flaws in the data collection - otherwise, at a glance, these results suggest that a re-calculation of PCR positive rates for just the unvaccinated + previously infected would reveal “negative natural immunity efficacy.” Additionally, the correlation between risk ratio and age seems explained by the secondary trend in “unvaccinated cohort size.” This could mean that the percentages for “overall unvaccinated” are inaccurately low in older groups (due to an exaggeration of the percent vaccinated).
These results, overall, are confusing and challenging; but they at least help clarify some of the apparent data over “negative efficacy” for the Covid vaccinated,9 and for Omicron’s apparent evasion of prior immunity: Neither is easily explained by “rampant reinfections” among the Covid-vaccinated. We can rule that out as a distortion to the signal.
The Covid vaccinated + previously infected do not seem to be driving Omicron infections to a particularly meaningful extent.
If you believe a deceased family member or coworker has been re-exposed to the virus, please have them tested immediately.
Pre-December 8 reinfections (.0118m, via MOH dashboard) and deaths (.0082m, Worldometers) are subtracted from the October 21 total case value (Worldometers), as they do not add new individuals to the “reinfection susceptible” pool.
See https://www.worldometers.info/coronavirus/country/israel/, total cases by date, 2021 October 21 (1.3239), December 8 (1.3483), and 2022 January 7 (1.4632); total deaths, 2021 December 8 (.0082)
Dissected in “Oh Nomicron!”
See NIII Pt. 2.
A highly-recommended report on two recent studies exploring Omicron’s changes in tropism and severity is at:
which reviews
Peacock, T. et al. “The SARS-CoV-2 variant, Omicron, shows rapid replication in human primary nasal epithelial cultures and efficiently uses the endosomal route of entry.” biorxiv.org.
Willet, B. et al. “The hyper-transmissible SARS-CoV-2 Omicron variant exhibits significant antigenic change, vaccine escape and a switch in cell entry mechanism.” medrxiv.org.
As discussed in “Travels to Maskladesh”:
This study is nonetheless interesting in another respect, which is the use of oral swabs (represented by the light blue squares) as one of the markers of “infection.” In fact, one of the subjects - who was 52 days post- second dose - never would have been detected on a normal (nasal) PCR test to begin with. An earlier paper by the same authors includes additional interesting findings, along similar lines, but for natural infection: The virus is detectable in the mouth before the nose (I haven’t yet reviewed the earlier paper itself). There appears to be a second realm of transitional, asymptomatic “infection” with this and probably other respiratory viruses, yet to be well-explored.
Similarly, Omicron likely “breaks” the association between (nasal PCR swab) cycle counts and “transmissibility,” if indeed there ever was one (the association was never more than an assumption to begin with).
Omicron transmission, after all, may have less to do with replication in humans than in mice, or some combination of the two. See “Mouse Party.”
See footnote 6
The way PCR tests have been used in this pandemic should have question marks attached:
1. What should threshold cycle be?
2. What does PCR test really tell you? Presence of live virus / disease? Or just mere
genetic material for the virus and we infer (correctly or not) that genetic material
is equal to live virus that then leads to disease.
But let's leave that aside for the moment. PCR test seems the best tool we have
at the moment. Although, I don't know why traditional approach (symptoms of some disease -> check for what disease / virus you have) was abandoned.
The questions you raise in this post are pretty much what I have been wondering for a while now:
1. "Natural immunity": we can start speculating whether infection from the old Wuhan strain or alpha/delta variants protect from omicron infection. You've analyzed this data in this post. But we have no idea whether infection from Omicron confers any long term immunity from Omicron itself or future variants. We know that for many coronaviruses immunity doesn't last very long. We repeatedly get colds all the time and immunity lasts only 6-8 weeks.
2. Is Omicron variant still SARS-Cov2 or is it really a different virus and it should be called SARS-Cov3? If it evades prior infection immunity (from OG Wuhan and alpha/delta) then it's really a different virus.
But as one of the other commenters pointed out, the data collection has been very disappointing. Whether this is done on purpose or because we are just simply incompetent is a separate question.
I am finding it rather frustrating that we are still splitting hair whether vaccines work or not, whether masks and other measures work, etc. The fact that it's not obvious
(whether they do or to what extent) pretty much answers the question. In any other circumstance or endeavor you pretty much know right away whether you are better off or not. Two years into "two weeks to flatten the curve" we
a) still haven't flattened the curve
b) we have no f**king idea where we are in this pandemic
Are we in a better place than in march 2020 (ie, it's over, Omicron will infect everybody and we have gained herd immunity), is this as good as it will get
(it's endemic, we will be getting seasonal waves just like with flu and this virus will compete with flu, but at least severity and mortality will be much much lower) or
is this just calm before the storm and we are all f**ked in a year or two (as per Vanden Bossche's hypothesis)?
I have a few questions for all the number specialists, table designers and statistics interpreters:
After two years of data accumulation in the context of the ''pandemic'', does it make any sense at all to extract any statement from this mountain of data? Does the result of a data evaluation still serve any truth at all?
Why do I ask? Well, in the meantime there are so many overlaps in the definition of unvaccinated and vaccinated, of recovered and diseased, to which constantly changing definitions differing from country to country are added, that there are no longer any possibilities for comparison with which any trends or conspicuous features can be uncovered. All of the data only creates a nebulous, ever-changing picture that, in its constant succession of snapshots, creates more confusion than clarity.
Does it still somehow make sense to put so much energy into data evaluation day after day, if no one is able to produce ONE clear picture from all the different evaluations that could really help us? I don't want to diminish in any way the efforts and expertise of many people who make their evaluation skills available to the general public on a daily basis, I just wonder what drives you? Do you feel that your efforts are really bearing fruit, that something good is coming out of your work for the community? Something that will expose all those who have been lying to us and leading us around by the nose for months now. Something that many would like to see happen, of course, but is always unlikely to happen - at least that's how I feel.
It seems to me that this is exactly the intention behind all the constantly changing definitions using non-standardized tests with ever questionable interpretations on their part. As if the aim is to generate as much confusion as possible in the data jungle so that the general public completely loses its orientation and at some point stops asking questions altogether.
I would be very interested in your opinion on this and I would like to thank all of you who are always putting so much energy into bringing light into the darkness, even if the darkness never really seems to go away.