I looked at this a few months ago. I went through the moderna deaths, for which the lot coding is pretty simple, and manually corrected most of the data entry errors. I ended up dropping a few that were garbage but it was probably not more than 10 lot codes that didn't resolve.
Now, if these were random occurrences, this distribution should be a normal distribution. But, it appears to be exponential (see the second figure which plots the log of the count).
This tells me something is wrong here.
It could be bias in the reporting. Maybe small towns with lower counts report with higher frequency than large cities with too many to report. There could be other bias against reporting that is not uniform. Perhaps as time went on fewer reports were made because it was more "normalized" or there were increased efforts to stifle it. Or it could be backlog of reporting.
If you plot the data as a function of manufacture date, there are fewer death reports for newer product.
This could be because there are more "fallow" vials out there for newer product.
It could also be problems in manufacturing or distribution / cold chain.
It could be that a manufacturing problem did exist and got worked out after the first people started dying - the plot ordered by manufacture date shows an improvement very shortly after the rollout started. That is, maybe they saw an issue and fixed something but almost half the product had already gone out the door (that is, the drop is correlated to manufacture date, NOT injection date)
From my view, THERE IS A PROBLEM.. what it is exactly is less clear, but so far there a lot of possible answers and none are particularly good.
You are right about the tail of garbage lot #'s through typo entries etc and ForkInSocket is right about the exponential. My plot here starts at a place well into the garbage tail of Moderna (batches need to have at least 10 events) and gives lots sequential x locations sorted by low to hi events L->R. Then for each lot TIME and events is vertical with 1/1/2020 at y=0 which makes things more viewable. Then I have used earliest report date for each batch as a line (basically linear -- lines don't appear in garbage data LOL) so you can see the early batches that are going exponential are the ones given to the oldest first (also have an average age of eventee for each batch line) and the date line is ~linear up and to the left! This is all for Moderna only but I can select the others -- it's just that Moderna stands out. I added markers for the two batches some friends of mine took and they happen at the dates you would expect as shots were opened up from just the elderly. I also marked what batch numbers I could find as valid from web searches and all of those are on the right side as you would expect also and none are in the left "semi garbage" tail. You can contact me through my substack email if you want to leverage efforts. I have some time to work on this now since I retired to skip the shot. (BTW, this makes sense about data entry typos as I also did a menstruation search and came up with ~1% of eventees as male in just that single character field LOL) https://baizuobu.substack.com/p/incoming-series-vaers-manufacturer
I greatly appreciate your plot ForkinSocket, and baizuobo’s too, not to mention Jessica’s work here.
Jessica is right that we need precision, and to not extrapolate to conclusions beyond the data. The Daily Expose claim that 100% of Covid-19 deaths are caused by just 5% of the batches is exaggerated. However, Craig Paardekooper’s claim, as made in this video, https://www.bitchute.com/video/CB49QokMgGV5/, that a mass experiment is taking place, should be seriously considered.
You offer several hypotheses for the adverse event distributions Forkin, all of which are alternatives to Paardekooper’s claim, and some of them may be right. The hypotheses in your comment all belong to a set of possibilities that do not imply intent to kill or harm. Paardekooper’s claim entails intent.
The difference is important. Assuming that Robert Malone and Mattias Desmet are correct that mass formation (psychosis) is underway (See https://rwmalonemd.substack.com/p/mass-formation-deployed-on-you-after) then it may be possible to dispel the illusions by keeping on like Jessica, Steve Kirsch, Matthew Crawford and many others do. But if there is a calculated hidden agenda in which some elite is directing all this it may require some additional strategies. Is this a deliberate low intensity war we’re unaware of? Is it a depopulation agenda - not necessarily one which is aimed at killing most of us right away, but perhaps one of experimenting with mRNA technology to figure out how to lower the birth rate for an extended period of time?
I think it hard to know at this point if there is a hidden agenda, and if so, what it is. However, to be aware of the possibilities may help us know what to do. And the work of Jessica and others may help us estimate the probability of what these possibilities are.
I have worked with psychotic individuals. I saw Dr. Desmet on Corona Ausschuss Sitzung 87 today, the 14th of Jan. He made it clear the he does not use the term Psychosis,He uses Mass Formation. It is Dr. Mark McDonald that uses the term Mass Formation Psychosis.
Psychotic Delusions are fixed false beliefs; they are based on incorrect (false) inferences about reality external to, or about, oneself and maintained firmly (fixed) despite the presentation of evidence that obviously and incontrovertibly contradicts the belief. My short Zen answer is evidence smevidence. At the point of the needle is a point view.
Dr. Malone, who is brilliant , could save himself some grief if he would stick to his field. Forbes ripped him one recently on his use of MFP after his Rogan appearance. The point the author makes on the recent creation of the term is warranted . The theory has been put forth for centuries . New is not necessarily better in theories and vaccines.
Forbes feeds both sides of the debate , as true divisional propaganda does by having an article on this Jan. 12th titled "No, Fauci’s Records Aren’t Available Online. Why Won’t NIH Immediately Release Them?
We are all psychotic in a world that produces diametrical opposition. Which of us end up in an institution is based on the ones who possess the delusion of having the legitimate use of force. Let us focus on the data.
Thanks for posting your list. I notice that 51 of the 186 items in kah452.csv don't appear on the Modera expiry page json of 367 (if that is really all their lots). I tried the first 6 or so manually to confirm "that number does not exist". The unmatched lot codes are: 002B21B,002M20A,003H20A,003M20A,004M20B,006B21B,006M20B,007G21A,009C21B,011I20A,012I20A,013G20A,013I20A,015B21A,018B21B,022L20A,022N20A,029I20A,031I20A,032K20A,033L20A,038K20B,039K20B,040M20A,041A21A,041G20A,041M20A,042A21A,043A21A,046B23A,047A20A,048B211A,059A21A,061B21A,063L20A,070M20A,073K20A,077L20A,085A21A,201A21A,202A21A,203A21A,204A21A,205A21A,205C21A,206A21A,207A21A,212C21A,309K20A,310M20A,402A21A,623M20A The 2--A21A codes look like Jansen lot numbers. Data cleanup is a pain. Good lists of valid lot numbers will sure help.
Unfortunately VAERS is "real world" data with ~1/3 missing or mis-spelled information in almost every field, even the dates. Many of the typos seem obvious but of course data cleaning is not an exact science either. If I am understanding Jessica's post she indicated almost 50% of the domestic VAERS records didn't have a confirmed Lot code.
So far the best overall list I've found is at https://howbad.info Of special interest on the howbad site are the lot sizes info for 33 Pfizer batches. The largest 27 range from ~130K to ~305K vials, and representing about 32.7 million doses (@6 doses/vial). Manufactured lot sizes are a big step in the right direction, but actual doses injected (by week) would be ideal. :) Right now I've got only 125 Phizer, 367 Moderna (271+96 json), and 42 Jansen codes that are "confirmed" from independent (hopefully official) sources. Jansen also has an expiry lookup page. Are there any other official sources for valid lot codes?
Jessica - if your confirmed list is bigger can you share it?
Thanks for the feedback.. Do you have a link for the Moderna expiry data? I hadn't seen it.
My cleaning of lot codes was based on assuming the format of the code was NNNMYYS where NNN seemed to be a serial number within a month, M was an alphabetic code that appeared to represent months or 4 week intervals, and YY was the last two digits of the year. S seemed to be A or B. I then went through and fixed case errors, L/I/O/0/1 errors, and then by sorting by month and year I was able to see duplicates that were due to typos and reclassify them.
I wasn't aware that the lot sizes vary - I thought they were all the same. If not, that's important information. That would definitely alter the distribution.
DEFAZ posted the Moderna site expiry data (json) in the 10th reply to the original post. The howbad site also has a spreadsheet with a slightly older version with 356 lot codes.
The Pfizer lot size info is under https://howbad.info/lotsize.html , link at the bottom to "Lot Sizes Doc..." a Pfizer doc from the recent release by the looks of it. Table 13 at ~page 24, "Manufacturing Scale (vials)" takes a little parsing, but here are the essentials:
Lot,DOM,Lot Vials,Vol(ml)
ED3938,16-Jul-20,19010,0.2
EE3813,29-Jul-20,30193,0.2
EE8492,5-Aug-20,67665,0.45
EE8493,5-Aug-20,68445,0.45
EJ0553,25-Sep-20,164580,0.45
EJ1685,5-Oct-20,159315,0.45
EJ1686,7-Oct-20,147615,0.45
EK1768,16-Oct-20,141960,0.45
EG5411,3-Sep-20,201258,0.45
EJ0701,26-Sep-20,200265,0.45
EH9978,23-Sep-20,304869,0.45
EJ0724,29-Sep-20,39195,0.45
EH9899,7-Oct-20,179400,0.45
EJ1688,12-Oct-20,150345,0.45
EK4176,16-Oct-20,131625,0.45
EK4175,12-Oct-20,145275,0.45
EJ1691,16-Oct-20,133575,0.45
EK2808,19-Oct-20,48945,0.45
EK5730,22-Oct-20,191295,0.45
EL0140,29-Oct-20,155610,0.45
EL0142,29-Oct-20,138060,0.45
EL0141,29-Oct-20,156195,0.45
EL0725,30-Oct-20,272073,0.45
EK9231,4-Nov-20,230685,0.45
EK4237,5-Nov-20,140985,0.45
EL0739,3-Nov-20,294239,0.45
EL1484,4-Nov-20,277608,0.45
EL1283,11-Nov-20,245895,0.45
EL1284,17-Nov-20,214305,0.45
EL3246,19-Nov-20,204360,0.45
EJ6795,12-Nov-20,282645,0.45
EJ6796,13-Nov-20,293828,0.45
EJ6797,17-Nov-20,293526,0.45
Thats only 33 of (at least) 125 Pfizer Lots, but it gives us a sense. It would be great to have something for the other jab makers too.
Expiration dates could be tied to supply chain. So if “Substrate X” gets sent out to 5 vendors and, due to different rates of meeting quality benchmarks, they finish cooking their lots at different dates, the expiration date is still pinned to X.
Ironically, this could also signal that the uniformity of Pfizer dates means there is 0 quality control in their chain. Or maybe their vendors are all working with immortal inputs.
Great and highly diligent work as always. Numbers as text, zeroes as the letter "O" , missing values, leading spaces etc. are all problems we have seen with other data sets. These take dozens of hours to clean up as Jessica has tried to do. The quasi-vax manufacturers do seem to have some major problems with stability, which could account for why some lots may be associated with slightly FEWER number of AEs etc., but in the absence of the full details of batch sizes, distribution etc., we cannot fully know ths. The important message from Jessica is a caution that one should not jump to conclusions that 5% of the lots cause 100% of the problems or that the data prove the existence of some dastardly plan. Stick to the science as Jessica is doing. Thank you again Jessica.
Thank you! This makes more sense than anything else I have read on the subject so far. I have no way to contact you privately, and I am not looking for anything in return, but I am a semi-retired database developer experienced in cleaning government health data in order to work with it relationally (ETL processes). Contact me if there is any way I can help.
I work in medical devices so am somewhat familiar with lot dating -- at least in devices. You could have lots manufactured on different dates that are queued for sterilization at the same time. In devices, you could have a DOM (date of mfr) -- sometimes that is used to calculate expiration date (based on logic Expiry = DOM plus Shelf life days). Some times you can have an expiry that is based on Date of sterilization - so the label may list DOM, but the system the label pulls from calculates expiry as Expiration Date = Date of Sterilization + Shelf life in days. In med devices, they don't degrade so we use sterility as the driver of when they expire (for example large pieces of capital equipment that aren't sterilized like lasers don't have expiration date). But with a drug, I would think sterility is not the key concern and you should have chemical/material degradation concerns that drive true product expiration - meaning your Expiration should consistently by DOM + shelf life (based on validation that would have been included in submission)
This is very interesting and intriguing post. Are you working with Dr Mike Yeadon? Great questions and wondering if you have considered the way these lots are stored and administered once they’ve been delivered. And are these vaccines being administered exactly the same per injection (I.e. CVS delivers precisely the same amount in an arm as Rite Aid? Doubtful). Anyway, look forward to your follow up
Good grief! Ballzzz! What am I doing here sleepless at 0300 hrs Alaska time reading, that is attempting to comprehend the magnitude of the crime of the century (so far) ? I think because I'm hooked as the extent of this tragic farce becomes factually clearer due to your amazing skill set. You are the protagonist in a crime novel in the truest sense of the genre and these updates are right next to vicariously being there on the scene, even before the police tape closes off the area. Jayzus H! I might be reclining next to my snoring cat but I'm on the edge of my seat nonetheless!
My guess as to Rochie's response to your Top Ten?
"Um, this may sound, I mean, car crashes and...(breaking down now).... well... (long pause)
My dog ate the requested data. No! Really!" That is the extent of her credibility. Methinks she fidgets too much. Always.
In looking at their deaths by lot number, I see the deaths tied more to an early time frame. Maybe when the most at-risk were getting their second dose.
The other crazy thing with VAERS though, if you follow it day to day, is the data doesn't come in FIFO. Why, for instance does new data contain events from across the entire time range? How are they processing the events and what is the back log?
Also -- based on Code of Federal regulations, product traceability has to be absolutely guaranteed. from a med device standpoint, we can trace all mfg data for a product given a lot/part number combination. That should be maintained and quickly determinable (in the event of a recall) and is a requirement of devices and I assume drugs. Required traceability is not just mfg location, it's also specific equipment, specific employees that assembled it, inspections, testing results, etc. Basically the entire lot history record has to be available in the event of audit or need to analyze product quality signals to support a recall.
I’d also be curious how the lots were distributed. For example, a lot may have 6000 vials, but did all 6000 vials get sent to the same site? Did only 4000 vials go out and 2000 remain in the warehouse?
I’ve also seen a theory that the “later” dated lots’ adverse events may suffer from data suppression. The CDC caught on after a few months and started suppressing the AE reports.
Given how awful the entry process is reported to be by multiple docs, and the general state of gov info systems, I can't say I'm surprised. GIGO.
As others have noted, without data as to quantities and distribution of lots it could be anything or nothing. I'm reluctant to give it much weight.
Frankly, to my mind it's plenty clear these treatments are a seriously bad idea as designed. I don't see much need to crawl down this particular rabbit hole.
Dr. Jane Ruby says that hackers got more information regarding batches. She said that the batches appeared to be coordinated and she believes that they represent testing usually done on animals before human trials. That is, one set of batches were for what she called “Lethal dose testing,” where they kill %50 of animals to find an upper limit and then work their way down to human testing, and the next set represented randomized trials with placebos. She also said that Red states in the US were given more toxic batches than Blue states. Here is the link. The batches are discussed starting at about 40:25 through 50:1. https://rumble.com/vsubzc-blood-deceit-and-exposure.html
Great work, even though it complicates the picture relative to the Daily Expose article series on the subject. I think perhaps the best thing to do is analyze a significant number of the dud lot #s to see if in fact they are just saline. The work of Drs Young, Madej, Botha, La Quinta Columna et al would then have some background context.
Hi Jessica,
I looked at this a few months ago. I went through the moderna deaths, for which the lot coding is pretty simple, and manually corrected most of the data entry errors. I ended up dropping a few that were garbage but it was probably not more than 10 lot codes that didn't resolve.
Here is the CSV file if you are curious:
https://files.catbox.moe/kah452.csv
cols are
year of manufacture,
month of manufacture (based on parsing the lot code),
lot within month,
original lot code,
vaers death count (as of sometime over the summer i think)
I then plotted the distribution of counts for each lot, sorted by ascending count.
heres' the plot:
https://files.catbox.moe/8okcyc.png
Now, if these were random occurrences, this distribution should be a normal distribution. But, it appears to be exponential (see the second figure which plots the log of the count).
This tells me something is wrong here.
It could be bias in the reporting. Maybe small towns with lower counts report with higher frequency than large cities with too many to report. There could be other bias against reporting that is not uniform. Perhaps as time went on fewer reports were made because it was more "normalized" or there were increased efforts to stifle it. Or it could be backlog of reporting.
If you plot the data as a function of manufacture date, there are fewer death reports for newer product.
This could be because there are more "fallow" vials out there for newer product.
It could also be problems in manufacturing or distribution / cold chain.
It could be that a manufacturing problem did exist and got worked out after the first people started dying - the plot ordered by manufacture date shows an improvement very shortly after the rollout started. That is, maybe they saw an issue and fixed something but almost half the product had already gone out the door (that is, the drop is correlated to manufacture date, NOT injection date)
From my view, THERE IS A PROBLEM.. what it is exactly is less clear, but so far there a lot of possible answers and none are particularly good.
agreed.
You are right about the tail of garbage lot #'s through typo entries etc and ForkInSocket is right about the exponential. My plot here starts at a place well into the garbage tail of Moderna (batches need to have at least 10 events) and gives lots sequential x locations sorted by low to hi events L->R. Then for each lot TIME and events is vertical with 1/1/2020 at y=0 which makes things more viewable. Then I have used earliest report date for each batch as a line (basically linear -- lines don't appear in garbage data LOL) so you can see the early batches that are going exponential are the ones given to the oldest first (also have an average age of eventee for each batch line) and the date line is ~linear up and to the left! This is all for Moderna only but I can select the others -- it's just that Moderna stands out. I added markers for the two batches some friends of mine took and they happen at the dates you would expect as shots were opened up from just the elderly. I also marked what batch numbers I could find as valid from web searches and all of those are on the right side as you would expect also and none are in the left "semi garbage" tail. You can contact me through my substack email if you want to leverage efforts. I have some time to work on this now since I retired to skip the shot. (BTW, this makes sense about data entry typos as I also did a menstruation search and came up with ~1% of eventees as male in just that single character field LOL) https://baizuobu.substack.com/p/incoming-series-vaers-manufacturer
That's quite a chart!
After my post, it occurred to me that the age stratified rollout could possibly explain the distribution.
I greatly appreciate your plot ForkinSocket, and baizuobo’s too, not to mention Jessica’s work here.
Jessica is right that we need precision, and to not extrapolate to conclusions beyond the data. The Daily Expose claim that 100% of Covid-19 deaths are caused by just 5% of the batches is exaggerated. However, Craig Paardekooper’s claim, as made in this video, https://www.bitchute.com/video/CB49QokMgGV5/, that a mass experiment is taking place, should be seriously considered.
You offer several hypotheses for the adverse event distributions Forkin, all of which are alternatives to Paardekooper’s claim, and some of them may be right. The hypotheses in your comment all belong to a set of possibilities that do not imply intent to kill or harm. Paardekooper’s claim entails intent.
The difference is important. Assuming that Robert Malone and Mattias Desmet are correct that mass formation (psychosis) is underway (See https://rwmalonemd.substack.com/p/mass-formation-deployed-on-you-after) then it may be possible to dispel the illusions by keeping on like Jessica, Steve Kirsch, Matthew Crawford and many others do. But if there is a calculated hidden agenda in which some elite is directing all this it may require some additional strategies. Is this a deliberate low intensity war we’re unaware of? Is it a depopulation agenda - not necessarily one which is aimed at killing most of us right away, but perhaps one of experimenting with mRNA technology to figure out how to lower the birth rate for an extended period of time?
I think it hard to know at this point if there is a hidden agenda, and if so, what it is. However, to be aware of the possibilities may help us know what to do. And the work of Jessica and others may help us estimate the probability of what these possibilities are.
I have worked with psychotic individuals. I saw Dr. Desmet on Corona Ausschuss Sitzung 87 today, the 14th of Jan. He made it clear the he does not use the term Psychosis,He uses Mass Formation. It is Dr. Mark McDonald that uses the term Mass Formation Psychosis.
Psychotic Delusions are fixed false beliefs; they are based on incorrect (false) inferences about reality external to, or about, oneself and maintained firmly (fixed) despite the presentation of evidence that obviously and incontrovertibly contradicts the belief. My short Zen answer is evidence smevidence. At the point of the needle is a point view.
Dr. Malone, who is brilliant , could save himself some grief if he would stick to his field. Forbes ripped him one recently on his use of MFP after his Rogan appearance. The point the author makes on the recent creation of the term is warranted . The theory has been put forth for centuries . New is not necessarily better in theories and vaccines.
Forbes feeds both sides of the debate , as true divisional propaganda does by having an article on this Jan. 12th titled "No, Fauci’s Records Aren’t Available Online. Why Won’t NIH Immediately Release Them?
We are all psychotic in a world that produces diametrical opposition. Which of us end up in an institution is based on the ones who possess the delusion of having the legitimate use of force. Let us focus on the data.
Thanks for posting your list. I notice that 51 of the 186 items in kah452.csv don't appear on the Modera expiry page json of 367 (if that is really all their lots). I tried the first 6 or so manually to confirm "that number does not exist". The unmatched lot codes are: 002B21B,002M20A,003H20A,003M20A,004M20B,006B21B,006M20B,007G21A,009C21B,011I20A,012I20A,013G20A,013I20A,015B21A,018B21B,022L20A,022N20A,029I20A,031I20A,032K20A,033L20A,038K20B,039K20B,040M20A,041A21A,041G20A,041M20A,042A21A,043A21A,046B23A,047A20A,048B211A,059A21A,061B21A,063L20A,070M20A,073K20A,077L20A,085A21A,201A21A,202A21A,203A21A,204A21A,205A21A,205C21A,206A21A,207A21A,212C21A,309K20A,310M20A,402A21A,623M20A The 2--A21A codes look like Jansen lot numbers. Data cleanup is a pain. Good lists of valid lot numbers will sure help.
Unfortunately VAERS is "real world" data with ~1/3 missing or mis-spelled information in almost every field, even the dates. Many of the typos seem obvious but of course data cleaning is not an exact science either. If I am understanding Jessica's post she indicated almost 50% of the domestic VAERS records didn't have a confirmed Lot code.
So far the best overall list I've found is at https://howbad.info Of special interest on the howbad site are the lot sizes info for 33 Pfizer batches. The largest 27 range from ~130K to ~305K vials, and representing about 32.7 million doses (@6 doses/vial). Manufactured lot sizes are a big step in the right direction, but actual doses injected (by week) would be ideal. :) Right now I've got only 125 Phizer, 367 Moderna (271+96 json), and 42 Jansen codes that are "confirmed" from independent (hopefully official) sources. Jansen also has an expiry lookup page. Are there any other official sources for valid lot codes?
Jessica - if your confirmed list is bigger can you share it?
Hi Wizdum,
Thanks for the feedback.. Do you have a link for the Moderna expiry data? I hadn't seen it.
My cleaning of lot codes was based on assuming the format of the code was NNNMYYS where NNN seemed to be a serial number within a month, M was an alphabetic code that appeared to represent months or 4 week intervals, and YY was the last two digits of the year. S seemed to be A or B. I then went through and fixed case errors, L/I/O/0/1 errors, and then by sorting by month and year I was able to see duplicates that were due to typos and reclassify them.
I wasn't aware that the lot sizes vary - I thought they were all the same. If not, that's important information. That would definitely alter the distribution.
DEFAZ posted the Moderna site expiry data (json) in the 10th reply to the original post. The howbad site also has a spreadsheet with a slightly older version with 356 lot codes.
The Pfizer lot size info is under https://howbad.info/lotsize.html , link at the bottom to "Lot Sizes Doc..." a Pfizer doc from the recent release by the looks of it. Table 13 at ~page 24, "Manufacturing Scale (vials)" takes a little parsing, but here are the essentials:
Lot,DOM,Lot Vials,Vol(ml)
ED3938,16-Jul-20,19010,0.2
EE3813,29-Jul-20,30193,0.2
EE8492,5-Aug-20,67665,0.45
EE8493,5-Aug-20,68445,0.45
EJ0553,25-Sep-20,164580,0.45
EJ1685,5-Oct-20,159315,0.45
EJ1686,7-Oct-20,147615,0.45
EK1768,16-Oct-20,141960,0.45
EG5411,3-Sep-20,201258,0.45
EJ0701,26-Sep-20,200265,0.45
EH9978,23-Sep-20,304869,0.45
EJ0724,29-Sep-20,39195,0.45
EH9899,7-Oct-20,179400,0.45
EJ1688,12-Oct-20,150345,0.45
EK4176,16-Oct-20,131625,0.45
EK4175,12-Oct-20,145275,0.45
EJ1691,16-Oct-20,133575,0.45
EK2808,19-Oct-20,48945,0.45
EK5730,22-Oct-20,191295,0.45
EL0140,29-Oct-20,155610,0.45
EL0142,29-Oct-20,138060,0.45
EL0141,29-Oct-20,156195,0.45
EL0725,30-Oct-20,272073,0.45
EK9231,4-Nov-20,230685,0.45
EK4237,5-Nov-20,140985,0.45
EL0739,3-Nov-20,294239,0.45
EL1484,4-Nov-20,277608,0.45
EL1283,11-Nov-20,245895,0.45
EL1284,17-Nov-20,214305,0.45
EL3246,19-Nov-20,204360,0.45
EJ6795,12-Nov-20,282645,0.45
EJ6796,13-Nov-20,293828,0.45
EJ6797,17-Nov-20,293526,0.45
Thats only 33 of (at least) 125 Pfizer Lots, but it gives us a sense. It would be great to have something for the other jab makers too.
Expiration dates could be tied to supply chain. So if “Substrate X” gets sent out to 5 vendors and, due to different rates of meeting quality benchmarks, they finish cooking their lots at different dates, the expiration date is still pinned to X.
nice
Ironically, this could also signal that the uniformity of Pfizer dates means there is 0 quality control in their chain. Or maybe their vendors are all working with immortal inputs.
exactly
Great and highly diligent work as always. Numbers as text, zeroes as the letter "O" , missing values, leading spaces etc. are all problems we have seen with other data sets. These take dozens of hours to clean up as Jessica has tried to do. The quasi-vax manufacturers do seem to have some major problems with stability, which could account for why some lots may be associated with slightly FEWER number of AEs etc., but in the absence of the full details of batch sizes, distribution etc., we cannot fully know ths. The important message from Jessica is a caution that one should not jump to conclusions that 5% of the lots cause 100% of the problems or that the data prove the existence of some dastardly plan. Stick to the science as Jessica is doing. Thank you again Jessica.
Thank YOU for the lovely comment. It's getting more interesting as I dig and fix and I found a nerd to help me with clean up using code!
Thank you! This makes more sense than anything else I have read on the subject so far. I have no way to contact you privately, and I am not looking for anything in return, but I am a semi-retired database developer experienced in cleaning government health data in order to work with it relationally (ETL processes). Contact me if there is any way I can help.
I work in medical devices so am somewhat familiar with lot dating -- at least in devices. You could have lots manufactured on different dates that are queued for sterilization at the same time. In devices, you could have a DOM (date of mfr) -- sometimes that is used to calculate expiration date (based on logic Expiry = DOM plus Shelf life days). Some times you can have an expiry that is based on Date of sterilization - so the label may list DOM, but the system the label pulls from calculates expiry as Expiration Date = Date of Sterilization + Shelf life in days. In med devices, they don't degrade so we use sterility as the driver of when they expire (for example large pieces of capital equipment that aren't sterilized like lasers don't have expiration date). But with a drug, I would think sterility is not the key concern and you should have chemical/material degradation concerns that drive true product expiration - meaning your Expiration should consistently by DOM + shelf life (based on validation that would have been included in submission)
yes!
This is very interesting and intriguing post. Are you working with Dr Mike Yeadon? Great questions and wondering if you have considered the way these lots are stored and administered once they’ve been delivered. And are these vaccines being administered exactly the same per injection (I.e. CVS delivers precisely the same amount in an arm as Rite Aid? Doubtful). Anyway, look forward to your follow up
yes. he's a nice man and supremely interested in nabbing these a-holes.
Good grief! Ballzzz! What am I doing here sleepless at 0300 hrs Alaska time reading, that is attempting to comprehend the magnitude of the crime of the century (so far) ? I think because I'm hooked as the extent of this tragic farce becomes factually clearer due to your amazing skill set. You are the protagonist in a crime novel in the truest sense of the genre and these updates are right next to vicariously being there on the scene, even before the police tape closes off the area. Jayzus H! I might be reclining next to my snoring cat but I'm on the edge of my seat nonetheless!
My guess as to Rochie's response to your Top Ten?
"Um, this may sound, I mean, car crashes and...(breaking down now).... well... (long pause)
My dog ate the requested data. No! Really!" That is the extent of her credibility. Methinks she fidgets too much. Always.
i think i saw her dog driving her car with fauci
Very comprehensive; thanks for your work on this!
Jessica just checking if you have the Moderna lot file expiry.json, from their expiration lookup tool?
https://lnl-dl.s3.amazonaws.com/Moderna/expiry.json
In looking at their deaths by lot number, I see the deaths tied more to an early time frame. Maybe when the most at-risk were getting their second dose.
The other crazy thing with VAERS though, if you follow it day to day, is the data doesn't come in FIFO. Why, for instance does new data contain events from across the entire time range? How are they processing the events and what is the back log?
so many questions... i have as well
Also -- based on Code of Federal regulations, product traceability has to be absolutely guaranteed. from a med device standpoint, we can trace all mfg data for a product given a lot/part number combination. That should be maintained and quickly determinable (in the event of a recall) and is a requirement of devices and I assume drugs. Required traceability is not just mfg location, it's also specific equipment, specific employees that assembled it, inspections, testing results, etc. Basically the entire lot history record has to be available in the event of audit or need to analyze product quality signals to support a recall.
damned straight!
I’d also be curious how the lots were distributed. For example, a lot may have 6000 vials, but did all 6000 vials get sent to the same site? Did only 4000 vials go out and 2000 remain in the warehouse?
I’ve also seen a theory that the “later” dated lots’ adverse events may suffer from data suppression. The CDC caught on after a few months and started suppressing the AE reports.
Thanks for all you do!
Given how awful the entry process is reported to be by multiple docs, and the general state of gov info systems, I can't say I'm surprised. GIGO.
As others have noted, without data as to quantities and distribution of lots it could be anything or nothing. I'm reluctant to give it much weight.
Frankly, to my mind it's plenty clear these treatments are a seriously bad idea as designed. I don't see much need to crawl down this particular rabbit hole.
I wonder which VAX LOT Fauci got.
the one with saline
Dr. Jane Ruby says that hackers got more information regarding batches. She said that the batches appeared to be coordinated and she believes that they represent testing usually done on animals before human trials. That is, one set of batches were for what she called “Lethal dose testing,” where they kill %50 of animals to find an upper limit and then work their way down to human testing, and the next set represented randomized trials with placebos. She also said that Red states in the US were given more toxic batches than Blue states. Here is the link. The batches are discussed starting at about 40:25 through 50:1. https://rumble.com/vsubzc-blood-deceit-and-exposure.html
Great work, even though it complicates the picture relative to the Daily Expose article series on the subject. I think perhaps the best thing to do is analyze a significant number of the dud lot #s to see if in fact they are just saline. The work of Drs Young, Madej, Botha, La Quinta Columna et al would then have some background context.
yup. but people need to be very diligent with what they are claiming. precision now is vital. we are at war.
the dud lots don't mean anything. it's just bad data entry. there is no way to know what vax lot they were. one of the joys of vaers.
can you share your code? looks like ggplot in R? maybe myself and others can chip in on a repo.
can you send me your email address? jessicarose1974@protonmail.com
simitpatel@protonmail.com