I don't understand your table or summary results. It has the same n as "symptomatic" (from part 4) which came in at 28% of all COVID but this subset of "acute symptomatic" comes in at 45%?
OK, but still, how does the subset represent a larger portion of the overall population in your dataset? If "symptomatic" COVID is 28%, surely, "acute symptomatic" COVID must be a smaller percentage?
As Joel may be mentioning, seems like there is some error here.
Proportions seem like a hopeless quest due to Trust not having testing rates. Seems like only country-wide evolution of IFR is doable - either through just lifting it from a paper, or constructing it some place where testing rates are known.
Since the common denominator here is folk who've been tested at least once and found to be positive then it's not so hopeless unless you argue that clinically symptomatic cases were subject to a battery of tests until they found a positive result, or that asymptomatic but suspected cases were tested ad infinitum compared to symptomatic until they obtained a positive result. All this rests on the assumption that a positive test result alone is driving the ICD10 coding but this is not necessarily the case. We also have to explain why test bias has changed over time in this manner, if it indeed has. If bias has remained just as problematic over time then hopelessness melts into a warm puddle.
p.s. there's no error - Joel didn't realise 'n' pertains to the number of weeks in the sample, a data record being a week and not a case. An additional column of summed case counts is needed - I may well revise the tables with these when I can find time.
Shouldn't proportion of acute asymptomatic covid deaths be lower overall than proportion of any symptomatic covid deaths by definition? You report the opposite.
We need RT-qPCR test results (Ct-values!), along with dates, stratified by age.
You will see that the proportion of "extremely low Ct-values" is correlated with the proportion of residents who recently received dose 1.
Small effect in the initial campaign
Larger effect during Alpha
Largest effect during Delta
Then a moderate effect during Omicron, because Omicron was NOT an adaption to vaccine immunity.
And again a very large effect during BA.2, which was Omicron adapted to vaccine immunity.
Oh the irony of the smoking gun (RT-qPCR test results) being discredited by the very critics who want these vaccines off the market. No coincidence the way I see it.
You know how much time I've spent on this and I am eagerly awaiting the time when the narratives start focussing on this.
This is what happens:
- Only first doses do this, or at least the effect is much more pronounced than after subsequent doses
- Only infections occurring within roughly 5 days of the dose are problematic.
- No antibodies at that time. Double spike exposure overwhelming innate immunity.
- This causes higher viral loads in the the recently first-dosed, which is possibly exacerbated as soon as ab's are being produced (by means of antibody-dependent enhancement).
This not only negatively affects outcomes, but also makes people more infectious. When first doses rise above a certain level in the early phase of an outbreak, when the ratio of daily infections and remaining susceptibles is still very high, it has a profound effect on transmission dynamics, increasing the severity of outbreaks.
There is a plethora of evidence supporting this and imho this is what all our efforts should focus on.
In England and Germany this effect was very weak, because no region really cranked up first doses during the critical phase, but some other countries suffered horribly:
I don't understand your table or summary results. It has the same n as "symptomatic" (from part 4) which came in at 28% of all COVID but this subset of "acute symptomatic" comes in at 45%?
n = weeks not people! The data record is the week and not the case. To get to n = people we have to sum counts over weeks.
OK, but still, how does the subset represent a larger portion of the overall population in your dataset? If "symptomatic" COVID is 28%, surely, "acute symptomatic" COVID must be a smaller percentage?
An error in my batch code - part 4 is now revised with the corrected figures!
Damn! Had to revise my article that referenced it toohttps://metatron.substack.com/p/covid-the-biggest-threat-faced-in then!
This is what happens if I don't stuff carbs and reduce my bacon ration. Your 50% guesstimate was right on the money!
I've stared at enough mortality series to do this stuff in my head!
I've added an extra slide at the end of part 5 so we can see the two series bouncing along, this being penance for my shoddy coding!
As Joel may be mentioning, seems like there is some error here.
Proportions seem like a hopeless quest due to Trust not having testing rates. Seems like only country-wide evolution of IFR is doable - either through just lifting it from a paper, or constructing it some place where testing rates are known.
Since the common denominator here is folk who've been tested at least once and found to be positive then it's not so hopeless unless you argue that clinically symptomatic cases were subject to a battery of tests until they found a positive result, or that asymptomatic but suspected cases were tested ad infinitum compared to symptomatic until they obtained a positive result. All this rests on the assumption that a positive test result alone is driving the ICD10 coding but this is not necessarily the case. We also have to explain why test bias has changed over time in this manner, if it indeed has. If bias has remained just as problematic over time then hopelessness melts into a warm puddle.
p.s. there's no error - Joel didn't realise 'n' pertains to the number of weeks in the sample, a data record being a week and not a case. An additional column of summed case counts is needed - I may well revise the tables with these when I can find time.
Shouldn't proportion of acute asymptomatic covid deaths be lower overall than proportion of any symptomatic covid deaths by definition? You report the opposite.
It should - just going to check the batch code...
Bottoms, bottoms, bottoms and bottoms - revised figures coming out shortly.
My batch code for part 4 had errors - a revised article is now ready for your eyeballs.
We need RT-qPCR test results (Ct-values!), along with dates, stratified by age.
You will see that the proportion of "extremely low Ct-values" is correlated with the proportion of residents who recently received dose 1.
Small effect in the initial campaign
Larger effect during Alpha
Largest effect during Delta
Then a moderate effect during Omicron, because Omicron was NOT an adaption to vaccine immunity.
And again a very large effect during BA.2, which was Omicron adapted to vaccine immunity.
Oh the irony of the smoking gun (RT-qPCR test results) being discredited by the very critics who want these vaccines off the market. No coincidence the way I see it.
I've added an extra slide at the end of part 5 so we can see the two series bouncing along, this being penance for my shoddy coding!
Awesome John.
You know how much time I've spent on this and I am eagerly awaiting the time when the narratives start focussing on this.
This is what happens:
- Only first doses do this, or at least the effect is much more pronounced than after subsequent doses
- Only infections occurring within roughly 5 days of the dose are problematic.
- No antibodies at that time. Double spike exposure overwhelming innate immunity.
- This causes higher viral loads in the the recently first-dosed, which is possibly exacerbated as soon as ab's are being produced (by means of antibody-dependent enhancement).
This not only negatively affects outcomes, but also makes people more infectious. When first doses rise above a certain level in the early phase of an outbreak, when the ratio of daily infections and remaining susceptibles is still very high, it has a profound effect on transmission dynamics, increasing the severity of outbreaks.
There is a plethora of evidence supporting this and imho this is what all our efforts should focus on.
In England and Germany this effect was very weak, because no region really cranked up first doses during the critical phase, but some other countries suffered horribly:
- the USA (particularly Southern Census Region)
- Bulgaria
- Romania
- Latvia
...
All about my observations of the US data here: https://vigilance.pervaers.com/p/us-summer-deaths-of-2021
I've added an extra slide at the end of part 5 so we can see the two series bouncing along, this being penance for my shoddy coding!