I know a lady who tested positive when she went in for her c-section and was then subject to the covid measures whilst in hospital. No symptoms thankfully.
She had a terrible case of covid either right before she got pregnant or during early pregnancy and still looked absolutely dreadful weeks later. Her sister had it at the same time and it really affected her lungs. She’s a great runner (I am not!) and I beat her up the stairs next to the escalator in the shopping centre without any difficulty. Covid is a real leveller….
Great analysis and easy to understand, but can you clarify something please? Are these raw case numbers (which I think you have previously shown to be 90-odd % related to the number of tests being done)? If so I am not sure what is going on. Or should we be looking at correlating %-positives or something else?
Thank you kind sir! They are indeed raw case numbers and will be indeed be subject to test activity hence part 2, in which I've run the same analysis for COVID cases per 100 viral tests. That'll be launched 6am tomorrow, with part 3 being written this morning. For that I've chosen 60-day windows from national dose onset (defined as >100,000 doses per day).
Brilliant John! Just to clarify your comment "Money speaks louder in medical research circles than people realise." After 33 years training and working in the job people should know that money is so damn loud it drowns out EVERY other voice in medical research.....
The data for vaccinations follows an s-shaped sigmoid growth cureve for each age group with different ages at different times, I have data for 1st and 2nd doses). I wonder if the sinewave comes from shifting the vaccination date day be day?
Well, we are essentially talking about wave interference between two periodic signals. I have an idea but want to run a small simulation study just to check. One feature of CCF that I recall from way back is that sine waves correlate to produce a sine function and that there sigmoid growth curve is exactly that.
I've got to the bottom of matters. If you are able to open SPSS SPV output files I can give you a sneak preview. In a nutshell we've got strong positive correlation when both waveforms are in phase but delayed (cases behind doses), this being a key feature of the winter seasonal peak. Either side of this doses rise as cases drop to give the antiphase components. In the run up to the winter peak case peaks are 2 days behind dose peaks with the regularity of clockwork.
No SPSS file reader, might cost. So I'm happy with your explanation. With the trajectory of cases (a bit like gaussian) and vacc sigmoid and significant shift in timing it is interesting that your method is generating results. Hope I havent delayed your projects too much.
I've pulled the dose 3 trajectory apart into three phases and each display vaccine harm as well as benefit, with harm yielding the strongest correlations; it's a bit like bouncing on a trampoline! I've been distracted from a large pile of washing-up so no harm done :-)
I know a lady who tested positive when she went in for her c-section and was then subject to the covid measures whilst in hospital. No symptoms thankfully.
She had a terrible case of covid either right before she got pregnant or during early pregnancy and still looked absolutely dreadful weeks later. Her sister had it at the same time and it really affected her lungs. She’s a great runner (I am not!) and I beat her up the stairs next to the escalator in the shopping centre without any difficulty. Covid is a real leveller….
Great analysis and easy to understand, but can you clarify something please? Are these raw case numbers (which I think you have previously shown to be 90-odd % related to the number of tests being done)? If so I am not sure what is going on. Or should we be looking at correlating %-positives or something else?
Thank you kind sir! They are indeed raw case numbers and will be indeed be subject to test activity hence part 2, in which I've run the same analysis for COVID cases per 100 viral tests. That'll be launched 6am tomorrow, with part 3 being written this morning. For that I've chosen 60-day windows from national dose onset (defined as >100,000 doses per day).
Very interesting, and highly amusing! I think you knocked some weebles down. Keep on shooting those peas.
Brilliant John! Just to clarify your comment "Money speaks louder in medical research circles than people realise." After 33 years training and working in the job people should know that money is so damn loud it drowns out EVERY other voice in medical research.....
Just a brief very current issue showing this - significant original research indicating Alzheimer's caused by beta amyloid, based on which 99% of research for treatments has been directed at, turns out to be a FRAUD .... oooops!! https://www.science.org/content/blog-post/faked-beta-amyloid-data-what-does-it-mean
Why thank you kind sir, and thanks for fleshing out the sorry story for us.
What is causing the sine wave pattern expecially for 3 dose/delay with a slight negative mean CC? Also sign of sine in the 2 dose data.
A good question. I am still cogitating.
The data for vaccinations follows an s-shaped sigmoid growth cureve for each age group with different ages at different times, I have data for 1st and 2nd doses). I wonder if the sinewave comes from shifting the vaccination date day be day?
Well, we are essentially talking about wave interference between two periodic signals. I have an idea but want to run a small simulation study just to check. One feature of CCF that I recall from way back is that sine waves correlate to produce a sine function and that there sigmoid growth curve is exactly that.
I've got to the bottom of matters. If you are able to open SPSS SPV output files I can give you a sneak preview. In a nutshell we've got strong positive correlation when both waveforms are in phase but delayed (cases behind doses), this being a key feature of the winter seasonal peak. Either side of this doses rise as cases drop to give the antiphase components. In the run up to the winter peak case peaks are 2 days behind dose peaks with the regularity of clockwork.
No SPSS file reader, might cost. So I'm happy with your explanation. With the trajectory of cases (a bit like gaussian) and vacc sigmoid and significant shift in timing it is interesting that your method is generating results. Hope I havent delayed your projects too much.
IBM issue a freebie called Smartreader here...
https://community.ibm.com/community/user/datascience/viewdocument/downloads-for-ibm-spss-statistics?CommunityKey=886b6874-0fb1-402c-8243-c70ef8179a99&tab=librarydocuments
I've pulled the dose 3 trajectory apart into three phases and each display vaccine harm as well as benefit, with harm yielding the strongest correlations; it's a bit like bouncing on a trampoline! I've been distracted from a large pile of washing-up so no harm done :-)
Thank you, Downloading Smartreader now.
Another thought - does the data lend itself to any form of age analysis?
That would be lovely but I don't have access.
Could the vaccine batches be responsible for these seemingly odd results. I’m referring to the findings of “how bad is my batch” reports.
Thanks for enlightening us