Posts
Comments
Regarding the Danish hospitalization numbers:
The non-omicron variants column in the hospitalization table of the daily Danish omicron reports (which one can found on here, with the most recent one via the big "Download her" button and the older ones under "Arkiv") was changed from the previous one to the last report. Until the report from the December 15, they only reported cases for which a variant PCR test was carried out, i.e. hospitalizations of people who tested positive for coronavirus, but where no variant PCR test result is available was not counted (which makes sense, as it is then not known whether the case is omicron or not!). Starting with the current report from December 16 they are counting positive tests without a variant PCR result as "other variants". At least that is how I understand the explanation on page 1. So here is how I read the two tables:
| Report | Column | Which cases are counted |
|---|---|---|
| Dec 15 | Other variants | PCR test positive, variant PCR result negative for Omicron |
| Dec 15 | Omicron | PCR test positive, variant PCR result positive for Omicron |
| Dec 16 | Other variants | PCR test positive, variant PCR result negative for Omicron OR no variant PCR result available |
| Dec 16 | Omicron | PCR test positive, variant PCR result positive for Omicron |
The previous report had 95,245 other variant cases of which 715 were hospitalized with positive tested earlier than 48 hours after (abbreviated <48h from now on), and 4 with test after (48+h). Now, with one extra day of data plus the added cases that did not have a variant PCR done (or no result available yet), they report 115,017 other variant cases with 1,560 (<48h) and 222 (48+h) of those hospitalized cases. This doesn't make sense unless a large number of hospitalized cases did not have a variant-PCR test done? In particular, those could still turn out to be omicron, so this makes concluding things about virulence from the new table more difficult. It confuses me why (even though the total number of other variant cases went up only moderately by the change in definition for that column, corresponding to the fact that variant PCR test coverage in general is very high) so many hospitalized cases seem not to have a variant PCR result, with the difference particularly drastic in the 48+h category. The only explanation I can come up with that fits this is the following: Hospitals have their own labs to analyze covid tests they carry out. Originally, in February / early March 2020, I think tests were essentially done in those labs, now the mass testing program has two big central labs in which the PCR tests for the whole country is analyzed. But presumably hospitals still analyze their own PCR tests in their own labs. Perhaps hospitals tend to not be equipped with the variant PCR test? I think this would explain the data. Up to the report before the current one, the numbers in the 48+h category were not changing much for other variants and omicron, (eg. staying at 8-9 cases for omicron). I dimly remember in the early days of omicron (1-2 weeks ago) that it was said (in a press conference or interview for the news) by someone responsible for contact tracing that there had been an omicron outbreak at a hospital, so that is probably why there originally were a couple of omicron cases identified in the 48+h category. My conclusion is that the 48+h row in that table is essentially useless to compare variants, as there seems to be very little variant testing to be done for those cases so far, or with a big delay (I would assume that the hospital labs are either going to set up variant tests or forward positive tests to the central labs for variant testing, but this might introduce extra delays, so this is not really reflected in the data yet?). In the <48h row there also must be quite a few cases that don't have a variant result, though the difference is less drastic than for the 48+h row. Which makes sense because <48 hours includes tests done at the hospital (which I suspect correlates strongly with no variant PCR test result available) as well as tests done before admission (for which there seems to be good coverage of variant PCR tests, though with a little extra delay until results are ready).
The previous table from the December 15 report shows 0.6% of omicron cases being hospitalized in the <48h category and 0.8% of non-omicron cases being hospitalized in the <48h category, which is quite disappointing regarding virulence; as omicron is still rapidly rising and hospitalizations are delayed we would expect a lower rate currently even if virulence is the same, so this little difference in hospitalization is not good news.
The table from December 16 report still shows 0.6% of the omicron cases hospitalized (<48h) and 1.4% of the other variant cases (<48h). But by my discussion above I think the additional difference likely can be explained by people who were tested positive at the hospital and for which we don't (yet) have a variant PCR test result.
Thanks for writing these posts on corona Zvi, I really appreciate them!
The question of how much more infectious B.1.1.7 is is pretty useless without also referencing a generation time estimate. Different agencies/countries use different values for that, so the numbers for the relative R number R_B.1.1.7 / R_old they give are not directly comparable. I expanded on this in a comment a while ago.
In the meantime, the Danish SSI also published a report in which they also stress that the numbers of how much more infectious B.1.1.7 is can't be compared across countries due to in particular different generation times being used. This report is from January 21., and in it they estimate relative R to be 1.36 as of January 14. A newer report from February 3. mentions that SSI now estimates a relative R for B.1.1.7 of 1.55. The SSI uses a generation time of 4.7 days, the English PHE uses a generation time of 6.57 days.
The quoted conclusion of 37% increase in infectiousness from the original post is unfortunately a mistake, see my comment here.
This is an estimated 37% increase in infectiousness. Compared to 50%, that’s much much better. The difference is enough to give us a puncher’s chance of things not being so bad, both buying us time and reducing how bad it is when the time comes.
Unfortunately, this is an incorrect conclusion from the data referenced in the tweet. It seems the 37% number was obtained by dividing 1.07 by 0.78, which rounds to 1.37. However, while 1.07 is the R of the B.1.1.7 variant, the 0.78 is not the R of the other variants, but the overall R (it says so right in the tweet!), which includes B.1.1.7. As B.1.1.7 is a sizable portion of total cases, it already skews overall R upwards quite a bit, and this means that the 37% number is an underestimation.
The latest conclusion from the SSI that I am aware of is, as is also mentioned in the article linked in the post, that B.1.1.7 is 55% more infectious (using the Danish generation time estimate of 4.7 days).
SSI (Denmark) published a new report today, that makes some of the things I talked about in the parent comment clearer.
Bilag (=Appendix) B talks about estimating the relative growth rate for B.1.1.7. On page 14 they write:
Det mest interessante er den tidslige udvikling. For hver uge øges log(odds) med 0.077 per dag. Med den nuværende lave andel af cluster B.1.1.7 svarer dette til at hyppigheden af cluster B.1.1.7 blandt de smittede stiger med 71% (95% CI: [33%, 120%]) per uge.
My translation:
The most interesting is the temporal evolution. Every week log(odds) increases by 0.077 per day. With the current low share of cluster B.1.1.7, this corresponds to the frequency of cluster B.1.1.7 among the infected increasing by 71% (95% CI: [33%, 120%]) per week.
On the next page they consider the relative contact number (=Rt) Rt_B.1.1.7 / Rt_other. They clarify that Rt is taken with respect to an assumed generation time of 4.7 days for all variants, and estimate this quotient to be
1,36 (95% CI [1,19; 1,53])
Taking this to the power of 7/4.7 to get weekly rates as I did in my parent comment we would get a weekly factor of 1.58, which is different from the 71% increase per week that they had. I am not sure how to reconcile this. They write that they are using the SEIR model (which I am not familiar with) to convert between the data they consider on page 14 and the ratio of Rt's, so this might be the reason.
In the former control group, Sweden is throwing out the extra vaccine doses in Pfizer vials, because we might dislike the FDA but at least we don’t have to deal with the European Medicines Agency, who are totally Delenda Est Club members:
But good news, they could soon give the go ahead to stop throwing away vaccine doses.
Just for additional information / clarification, as it seems to me this could be interpreted to suggest that EU countries, after starting vaccinations on the 2020-12-27, threw away anything left over after taking 5 doses out of a vial until some time after 2021-01-07, whenever EMA approved using the extra doses:
While I am not familiar with EMA's role in this, and also do not know how Sweden handled it, it is certainly not true that every EU country threw away what was left over after 5 doses. In Denmark it was headline news on 2020-12-28 that more doses had been vaccinated than expected the previous day, the reason being that while 5 doses per vial was expected, they usually got 6 and often even 7 doses out of each vial. These extra doses were used from the start, and this was encouraged from the relevant Danish authorities[1], see for example this article or the evening news on public television of that day.
Using delivered vaccines seems to go reasonably well in Denmark as well, according to the daily report, the status today is that 88.7% of received doses have been used, though they are still assuming 5 doses per glas of the Pfizer BioNTech vaccine (two days ago they had a doses used quota of 126.1% because of this). ↩︎
Short version: I think it comes down to different generation times used, and the Danish reports, the English reports, as well as what the referenced tweet 1 is saying are consistent with (assuming for the moment cases of the other variants stay constant) B.1.1.7 cases increasing by something like 60% to 80% each week. I would be very happy about corrections from someone who understands this better, I am not an expert at all.
Long version:
(Note: I will think about the change in infectiousness between other, old variants and B.1.1.7 as multiplicative below.)
In interpreting these numbers I think it is highly relevant to understand what is used as a generation time. In the referenced tweet 1, it is stated that a generation time of 5.5 is used. However, PHE (Public Health England), in their first report on B.1.1.7 2 seem to use 6.57:
we calculate the week on week growth rate in both S-negative and S-positive cases by simply dividing the case numbers in week t+1 by the case numbers in week t. We correct these weekly growth factors by raising them to the power of 6.57 to ensure they can be interpreted as reproduction numbers (given the mean generation time of SARS-CoV-2).
I interpret this like this: The effective reproduction number Rt is the factor the cases multiply by in the timespan of a generation time, so here PHE uses 6.57 days, meaning with their Rt we can get the weekly increase as Rt^(7/6.57). By the way, I think they made a typo and meant "by raising them to the power of 6.57/7".
E.g. Rt for B.1.1.7 being 70% larger than for the other variants means a weekly factor of 1.7^(7/6.57) = 1.76, so if other variant's daily cases stay constant, then daily B.1.1.7 cases will multiply by 1.76 every week.
However, SSI (Statens Serum Institut, in Denmark) generally seems to use a generation time of 4.7 [1]. So where PHE would get a Rt-ratio Rt_B.1.1.7 / Rt_other of say 1.7, we would expect SSI to obtain around 1.7^(4.7/6.57) = 1.46. This obviously still corresponds to a weekly increase of around 1.76. If PHE has 1.5, then SSI should have 1.34.
Unfortunately this is all not very transparent, SSI's reports don't really make this clear, not even when they cite the PHE numbers... :(.
The latest Danish information on what Rt for B.1.1.7 is when compared to other variants current in Denmark is from a press conference two days ago (2021-01-13), see here, the important information being: SSI estimates (as of two days ago) Rt in general to be between 0.85 and 0.9, and for B.1.1.7 Rt is estimated to be 1.2.
As B.1.1.7 still is likely under 5%, and very likely not more than 10% of total cases, we can estimate Rt_other as roughly being the overall Rt, perhaps taking a value towards the lower end of the range. So with Rt_other = 0.85 and Rt_B.1.1.7 = 1.2 we would get as ratio roughly Rt_B.1.1.7 / Rt_other = 1.41. This should be interpreted with respect to a generation time of 4.7, converting it to PHE generation time of 6.57 we get 1.41^(6.57/4.7) = 1.62. Both correspond to a weekly factor of around 1.7.
It is unclear to me whether it is better to think of the change in infectiousness between other variants and B.1.1.7 multiplicatively (so assuming Rt_B.1.1.7 / Rt_other will stay roughly constant if Rt_other changes) or additively (so assuming Rt_B.1.1.7 - Rt_other). But this might be dependent on how exactly the virus spreads, how this interacts with how people behave etc... I have been thinking about it multiplicatively up to now, but if someone has data / arguments for why additively or some other model might be better I would be very interested.
See for example the last line on page 11 in 3. This report, in which they explain how they estimate the contact number (their terminology for Rt) is a bit older (2020-10-23), but I have also seen this in several other reports by SSI where generation time mattered and as far as I can remember never a different value, and am fairly confident that SSI uses 4.7 days as generation time. ↩︎