Coronavirus COVID-19

[SeattleGriz]

C'mon. You're smarter than that and should be able to see StOnge has nothing other than an appeal to authority that is not warranted.

No, I got a BS in Microbiology with a minor in Chemistry. Worked as an OR Tech in the military and sold clinical laboratory testing and pathology for six years. In addition to two military national cycling championships in which I got invited to the Olympic Training Center, I also taught Nutrition, Business Development and Anatomy and Physiology at the trade school level. Performed the PCR test as a bench tech and worked in an RNA lab in college. Lastly I worked with two time Mr Universe, Lance Dreher, as a personal trainer for five years.

I know a thing or two about health, wellness and the medical industries.

So disprove his stats with some of your own, show us where he's gone wrong

Bro. I'm about done here if you don't stop to read what Ive said. StOnge posted no stats in his reply. He simply stated Brownstone was "right leaning" and the FDA and CDC are the best ever. There are no stats in this argument, only assumptions put into a model.

Read the Brownstone article for yourself and see. I'll even help you with the first assumption. The model was "programmed" to only count natural immunity as lasting one year and then done. That has been demonstrably proven incorrect. Almost egregious in its assumption.

[UNI88]

You seem a little touchy about called out on your biased source, SG.

Curious, your degree is in Computer Science?

C'mon. You're smarter than that and should be able to see StOnge has nothing other than an appeal to authority that is not warranted.

No, I got a BS in Microbiology with a minor in Chemistry. Worked as an OR Tech in the military and sold clinical laboratory testing and pathology for six years. In addition to two military national cycling championships in which I got invited to the Olympic Training Center, I also taught Nutrition, Business Development and Anatomy and Physiology at the trade school level. Performed the PCR test as a bench tech and worked in an RNA lab in college. Lastly I worked with two time Mr Universe, Lance Dreher, as a personal trainer for five years.

I know a thing or two about health, wellness and the medical industries.

But did you win a junior high science fair?




Sent from my iPhone using Tapatalk

[SeattleGriz]

C'mon. You're smarter than that and should be able to see StOnge has nothing other than an appeal to authority that is not warranted.

No, I got a BS in Microbiology with a minor in Chemistry. Worked as an OR Tech in the military and sold clinical laboratory testing and pathology for six years. In addition to two military national cycling championships in which I got invited to the Olympic Training Center, I also taught Nutrition, Business Development and Anatomy and Physiology at the trade school level. Performed the PCR test as a bench tech and worked in an RNA lab in college. Lastly I worked with two time Mr Universe, Lance Dreher, as a personal trainer for five years.

I know a thing or two about health, wellness and the medical industries.

But did you win a junior high science fair?




Sent from my iPhone using Tapatalk

no

[kalm]

This will not affect SeattleGriz. He will just say, again, the you can't trust mediabiasfactcheck. But I do consult mediabiasfactcheck. And I thought the rest of you may be interested in what it has to say about the source SeattleGriz chose to rebut the modeling study on COVID-19 vaccinations:

https://mediabiasfactcheck.com/brownsto ... tute-bias/

Overall, we rate the Brownstone Institute Right Biased based on editorial positions that favor a conservative-libertarian perspective. We also rate them Mixed for factual reporting due to a failed fact check and the promotion of misinformation regarding Covid-19.

It's just ridiculous. Don't believe credible sources like the CDC, the FDA, the American Medical Association, the Infectious Diseases Society of America, the American Society for Virology, etc. Believe a political think tank called the Brownstone Institute. Right.

Poor Seagriz…

[Baldy]

This will not affect SeattleGriz. He will just say, again, the you can't trust mediabiasfactcheck. But I do consult mediabiasfactcheck. And I thought the rest of you may be interested in what it has to say about the source SeattleGriz chose to rebut the modeling study on COVID-19 vaccinations:

https://mediabiasfactcheck.com/brownsto ... tute-bias/



It's just ridiculous. Don't believe credible sources like the CDC, the FDA, the American Medical Association, the Infectious Diseases Society of America, the American Society for Virology, etc. Believe a political think tank called the Brownstone Institute. Right.

Poor Seagriz…

It's cute that you and StWronge believe that the CDC, FDA, AMA, et. al. aren't politically biased and motivated.

[SeattleGriz]

Poor Seagriz…

It's cute that you and StWronge believe that the CDC, FDA, AMA, et. al. aren't politically biased and motivated.

It's like a version of Stockholm syndrome.

[SeattleGriz]

Lawl, looks like Kalm just got dunked on…

Did I?

Guess I’ll have wait for the actual studies that paint a different picture.

Yes, you did. You asked and I delivered. You're welcome! By the way, it was a model, not a study.

[kalm]

Did I?

Guess I’ll have wait for the actual studies that paint a different picture.

Yes, you did. You asked and I delivered. You're welcome! By the way, it was a model, not a study.

Hmmm…I must have missed it. I thought it was just your speculation or something from a biased anti-vax source. Feel free to post again.

[SeattleGriz]

About right.

Twitter monkeypox "experts" analysis. Quote below is not from the study, but gives you the bottom line.

Inaccurate claims were 4.6X more frequent than accurate ones and when adjusted for follower count this led to 974X more exposure for false claims.

Let that one sink in. if you grabbed a tweet on schools and monkeypox from “an expert” at random on a follower weighted basis, it had a 99.897% chance of being wrong.

https://www.medrxiv.org/content/10.1101 ... 1.full.pdf

[JohnStOnge]

About right.

Twitter monkeypox "experts" analysis. Quote below is not from the study, but gives you the bottom line.

Inaccurate claims were 4.6X more frequent than accurate ones and when adjusted for follower count this led to 974X more exposure for false claims.

Let that one sink in. if you grabbed a tweet on schools and monkeypox from “an expert” at random on a follower weighted basis, it had a 99.897% chance of being wrong.

https://www.medrxiv.org/content/10.1101 ... 1.full.pdf

If their assessment is accurate, that is another reason for not relying on Tweets for your information. Have you happened to notice that there is a lot of reliance on Tweets in this threat?

[JohnStOnge]

Any substantive rebuke from a legitimate source of the Lancet Journal’s findings.

You obviously haven’t read up much on the whole history of Mann’s research and litigation. Apply the same scrutiny you show with defending Trump and Putin but again, use unbiased sources. His work was validated quite awhile ago regardless of the obvious smear campaign by Nat. Review, CEI and others.

This has been discussed before.

But don’t take my word or Heartland Institute’s word for it. Here’s a relatively recent update from the National Academy of Science….

https://www.pnas.org/doi/10.1073/pnas.2112797118

https://brownstone.org/articles/more-fl ... ves-saved/

Here ya go. Some of the incorrect assumptions that were incorporated into the model. It's a table and looked horrible when I tried to paste.

By the way, while it doesn't make me an expert on the topic, I worked as a Product Manager for a team of data scientists, so I know a thing or two about assumptions and what it can do to a model.

Switching topics. How come all the current weather models are incorrect and don't match reality? Proxy data (calculated) and assumptions.

I meant to comment on that Brownstone institute thing some more earlier. I just looked at the first two assertions in the table. The first assertion is:

Immunity after natural infection is robust and long-lasting; protection against infection lasts much longer than for the jabbed; protection from severe disease is likely lifelong.

That is not relevant to the assumption it purports to rebut. Especially the assertion that natural immunity last longer. Whether it does or not has nothing to do with the assumption being discussed.

The second assertion is:

The study cited for this 27% number is interpreted incorrectly. In the cohort study, 27% of the participants showed a decline in antibodies followed by an increase. Rather than meaning that these individuals became susceptible again, it means that these individuals were re-exposed and their immune system worked exactly as it was supposed to.

The problem is that, if you click on the link to the referenced study, you see that it is clear that the interpretation of the authors of the modeling study is consistent with the interpretation of the authors of the referenced study while the interpretation of the guy critiquing the modeling study is not.

[SeattleGriz]

https://brownstone.org/articles/more-fl ... ves-saved/

Here ya go. Some of the incorrect assumptions that were incorporated into the model. It's a table and looked horrible when I tried to paste.

By the way, while it doesn't make me an expert on the topic, I worked as a Product Manager for a team of data scientists, so I know a thing or two about assumptions and what it can do to a model.

Switching topics. How come all the current weather models are incorrect and don't match reality? Proxy data (calculated) and assumptions.

I meant to comment on that Brownstone institute thing some more earlier. I just looked at the first two assertions in the table. The first assertion is:

Immunity after natural infection is robust and long-lasting; protection against infection lasts much longer than for the jabbed; protection from severe disease is likely lifelong.

That is not relevant to the assumption it purports to rebut. Especially the assertion that natural immunity last longer. Whether it does or not has nothing to do with the assumption being discussed.

The second assertion is:

The study cited for this 27% number is interpreted incorrectly. In the cohort study, 27% of the participants showed a decline in antibodies followed by an increase. Rather than meaning that these individuals became susceptible again, it means that these individuals were re-exposed and their immune system worked exactly as it was supposed to.

The problem is that, if you click on the link to the referenced study, you see that it is clear that the interpretation of the authors of the modeling study is consistent with the interpretation of the authors of the referenced study while the interpretation of the guy critiquing the modeling study is not.

No. I'll go with faculty in the Department of Computer Science and Engineering at IIT Bombay. You, someone who admittedly has never taken a Virology nor Epidemiology class, in addition to NOT teaching at a University in Computer Science has proven you misinterpret a large amount on the topic.

If you think you're right, let's hear your logic. Simply saying,"no" doesn't cut it.

[Skjellyfetti]

Has Bhaskaran Raman taken a Virology or Epidemiology class?



Have you read his super serious publication?

[JohnStOnge]

Writing with some updated numbers for those who think Florida has done a good job with COVID-19. Note that Florida's governor has appointed out of the mainstream public health officials who undermine confidence in the vaccines and has, himself, also worked to do that.

As of the day before the first COVID-19 vaccination was administered in the United States, 12/13/2020, New Jersey ranked #1 among states in cumulative COVID-19 death rate (deaths per million population) and New York ranked #2. Florida was in the middle of the pack at #20.

I checked where the three States ranked on 9/14/2022. New Jersey had dropped to #8 in cumulative COVID-19 death rate and Florida had climbed to #13. New York had fallen below Florida to #16, Also, when only the period starting with the start of vaccination (12/14/2020 - 9/14/2022) was considered, Florida climbed to #11 while New Jersey dropped to 33rd and New York dropped to 35th.

Latest update: As of the end of the day yesterday, Florida was at #10 in cumulative death rate since the beginning of the pandemic, New Jersey was at #13, and New York was at #15. And, when only the period starting with the start of vaccination in the United States was considered, Florida was at #8, New York was at #31, and New Jersey was at #37.

The point is that, since the vaccination tool became available, Florida has continued to move up the list in terms of COVID-19 mortality.

I don't think it's just the vaccine thing either. That is something that can be clearly identified. But I think Florida has generally failed to follow mainstream public health recommendations. I also think that Florida was fortunate in that it did not get hit hard with COVID-19 hard early on like New York and New Jersey did. When New York and New Jersey got hit, medical and public health officials were totally unfamiliar with the problem and there were no tools such as vaccines.

I think that, as time went on, New York and New Jersey followed mainstream recommendations while Florida did not. I think that, as a result, Florida's death rate toll now exceeds those of New York and New Jersey.I think that the perception that Ron DeSantis has done and is doing a good job with respect to COVID-19 is a false one and also a very unfortunate phenomenon.

All of the numbers I used came from https://www.worldometers.info/coronavir ... e_vignette .

[BDKJMU]

Writing with some updated numbers for those who think Florida has done a good job with COVID-19. Note that Florida's governor has appointed out of the mainstream public health officials who undermine confidence in the vaccines and has, himself, also worked to do that.

As of the day before the first COVID-19 vaccination was administered in the United States, 12/13/2020, New Jersey ranked #1 among states in cumulative COVID-19 death rate (deaths per million population) and New York ranked #2. Florida was in the middle of the pack at #20.

I checked where the three States ranked on 9/14/2022. New Jersey had dropped to #8 in cumulative COVID-19 death rate and Florida had climbed to #13. New York had fallen below Florida to #16, Also, when only the period starting with the start of vaccination (12/14/2020 - 9/14/2022) was considered, Florida climbed to #11 while New Jersey dropped to 33rd and New York dropped to 35th.

Latest update: As of the end of the day yesterday, Florida was at #10 in cumulative death rate since the beginning of the pandemic, New Jersey was at #13, and New York was at #15. And, when only the period starting with the start of vaccination in the United States was considered, Florida was at #8, New York was at #31, and New Jersey was at #37.

The point is that, since the vaccination tool became available, Florida has continued to move up the list in terms of COVID-19 mortality.

I don't think it's just the vaccine thing either. That is something that can be clearly identified. But I think Florida has generally failed to follow mainstream public health recommendations. I also think that Florida was fortunate in that it did not get hit hard with COVID-19 hard early on like New York and New Jersey did. When New York and New Jersey got hit, medical and public health officials were totally unfamiliar with the problem and there were no tools such as vaccines.

I think that, as time went on, New York and New Jersey followed mainstream recommendations while Florida did not. I think that, as a result, Florida's death rate toll now exceeds those of New York and New Jersey.I think that the perception that Ron DeSantis has done and is doing a good job with respect to COVID-19 is a false one and also a very unfortunate phenomenon.

All of the numbers I used came from https://www.worldometers.info/coronavir ... e_vignette .

Now adjust for age since Florida has one of the 2 or 3 oldest populations in the country..

[JohnStOnge]

I meant to comment on that Brownstone institute thing some more earlier. I just looked at the first two assertions in the table. The first assertion is:



That is not relevant to the assumption it purports to rebut. Especially the assertion that natural immunity last longer. Whether it does or not has nothing to do with the assumption being discussed.

The second assertion is:



The problem is that, if you click on the link to the referenced study, you see that it is clear that the interpretation of the authors of the modeling study is consistent with the interpretation of the authors of the referenced study while the interpretation of the guy critiquing the modeling study is not.

No. I'll go with faculty in the Department of Computer Science and Engineering at IIT Bombay. You, someone who admittedly has never taken a Virology nor Epidemiology class, in addition to NOT teaching at a University in Computer Science has proven you misinterpret a large amount on the topic.

If you think you're right, let's hear your logic. Simply saying,"no" doesn't cut it.

The first assumption I referenced that he tried to discredit is:

loss of infection-derived immunity.. follows an Erlang distribution with a mean duration of one year

The rebuttal offered is:

Immunity after natural infection is robust and long-lasting; protection against infection lasts much longer than for the jabbed; protection from severe disease is likely lifelong.

I am not going to pay to look through the whole modeling study for the reference to the assumption of an Erlang distribution with a mean duration of one year, but it doesn't matter. The quoted assumption does not include anything about the relative durations of natural and vaccine induced protection.

I would have to pay to read the nature article he linked to support saying natural infection likely provides lifelong protection against severe disease. But that also has nothing to do with the specific assumption he is supposed to be rebutting.

With respect to the broader picture: The idea that natural infection is robust and long lasting does not nix the idea that a lot of lives would be saved through vaccination. You have heard the rationale before. There is more risk associated with acquiring some level of immunity through natural infection than there is associated with acquiring it through vaccination.

The second assumption I mentioned is:

Immune evasion for infection-derived immunity occurs for 27% of the previously infected population.

His rebuttal is:

The study cited for this 27% number is interpreted incorrectly. In the cohort study, 27% of the participants showed a decline in antibodies followed by an increase. Rather than meaning that these individuals became susceptible again, it means that these individuals were re-exposed and their immune system worked exactly as it was supposed to.

The abstract of the paper he linked within his effort at rebuttal includes this:

Seropositivity of an employee and family cohort increased from 42% to 87.5% between March and July 2021, with 27% reinfections, as judged by increased antibody concentration after a previous decline. The likely high transmissibility and partial evasion of immunity by the Delta variant contributed to an overwhelming surge in Delhi.

That is not saying "...their immune system worked exactly as it was supposed to." The authors were interpreting the increase after a previous decline as evidence of reinfection and, yes, evasion of immunity.

[JohnStOnge]

Writing with some updated numbers for those who think Florida has done a good job with COVID-19. Note that Florida's governor has appointed out of the mainstream public health officials who undermine confidence in the vaccines and has, himself, also worked to do that.

As of the day before the first COVID-19 vaccination was administered in the United States, 12/13/2020, New Jersey ranked #1 among states in cumulative COVID-19 death rate (deaths per million population) and New York ranked #2. Florida was in the middle of the pack at #20.

I checked where the three States ranked on 9/14/2022. New Jersey had dropped to #8 in cumulative COVID-19 death rate and Florida had climbed to #13. New York had fallen below Florida to #16, Also, when only the period starting with the start of vaccination (12/14/2020 - 9/14/2022) was considered, Florida climbed to #11 while New Jersey dropped to 33rd and New York dropped to 35th.

Latest update: As of the end of the day yesterday, Florida was at #10 in cumulative death rate since the beginning of the pandemic, New Jersey was at #13, and New York was at #15. And, when only the period starting with the start of vaccination in the United States was considered, Florida was at #8, New York was at #31, and New Jersey was at #37.

The point is that, since the vaccination tool became available, Florida has continued to move up the list in terms of COVID-19 mortality.

I don't think it's just the vaccine thing either. That is something that can be clearly identified. But I think Florida has generally failed to follow mainstream public health recommendations. I also think that Florida was fortunate in that it did not get hit hard with COVID-19 hard early on like New York and New Jersey did. When New York and New Jersey got hit, medical and public health officials were totally unfamiliar with the problem and there were no tools such as vaccines.

I think that, as time went on, New York and New Jersey followed mainstream recommendations while Florida did not. I think that, as a result, Florida's death rate toll now exceeds those of New York and New Jersey.I think that the perception that Ron DeSantis has done and is doing a good job with respect to COVID-19 is a false one and also a very unfortunate phenomenon.

All of the numbers I used came from https://www.worldometers.info/coronavir ... e_vignette .

Now adjust for age since Florida has one of the 2 or 3 oldest populations in the country..

I did that a lot when I was looking at associations between State by State rates of being up to date on vaccination recommendations vs. COVID-19 cases and deaths. The point here is that the relative positions are changing over time. When all this started, before anybody knew what to do or had tools, New York and New Jersey were way ahead of Florida in terms of death rates. Now they are not.

[JohnStOnge]

No. I'll go with faculty in the Department of Computer Science and Engineering at IIT Bombay. You, someone who admittedly has never taken a Virology nor Epidemiology class, in addition to NOT teaching at a University in Computer Science has proven you misinterpret a large amount on the topic.

Did you look at the affiliations of the paper authors he was critiquing? I don't know if I ever did it but I was thinking about that when I was responding earlier. If you're going to go with credentials that indicate superior expertise in the subject matter, you are going to go with the authors of the modeling study.

[houndawg]

Did I?

Guess I’ll have wait for the actual studies that paint a different picture.

Yes, you did. You asked and I delivered. You're welcome! By the way, it was a model, not a study.

You deliver some political think tanks opinion and present it as the gospel. Probably has some bearing on how you're able to present the Russian invasion of Ukraine as "denazification".

.......an Olympic athlete who trains Navy SEALs means a lot of time spent in oxygen debt...

[houndawg]

No. I'll go with faculty in the Department of Computer Science and Engineering at IIT Bombay. You, someone who admittedly has never taken a Virology nor Epidemiology class, in addition to NOT teaching at a University in Computer Science has proven you misinterpret a large amount on the topic.

If you think you're right, let's hear your logic. Simply saying,"no" doesn't cut it.

The first assumption I referenced that he tried to discredit is:

loss of infection-derived immunity.. follows an Erlang distribution with a mean duration of one year

The rebuttal offered is:

Immunity after natural infection is robust and long-lasting; protection against infection lasts much longer than for the jabbed; protection from severe disease is likely lifelong.

I am not going to pay to look through the whole modeling study for the reference to the assumption of an Erlang distribution with a mean duration of one year, but it doesn't matter. The quoted assumption does not include anything about the relative durations of natural and vaccine induced protection.

I would have to pay to read the nature article he linked to support saying natural infection likely provides lifelong protection against severe disease. But that also has nothing to do with the specific assumption he is supposed to be rebutting.

With respect to the broader picture: The idea that natural infection is robust and long lasting does not nix the idea that a lot of lives would be saved through vaccination. You have heard the rationale before. There is more risk associated with acquiring some level of immunity through natural infection than there is associated with acquiring it through vaccination.

The second assumption I mentioned is:

Immune evasion for infection-derived immunity occurs for 27% of the previously infected population.

His rebuttal is:

The study cited for this 27% number is interpreted incorrectly. In the cohort study, 27% of the participants showed a decline in antibodies followed by an increase. Rather than meaning that these individuals became susceptible again, it means that these individuals were re-exposed and their immune system worked exactly as it was supposed to.

The abstract of the paper he linked within his effort at rebuttal includes this:

Seropositivity of an employee and family cohort increased from 42% to 87.5% between March and July 2021, with 27% reinfections, as judged by increased antibody concentration after a previous decline. The likely high transmissibility and partial evasion of immunity by the Delta variant contributed to an overwhelming surge in Delhi.

That is not saying "...their immune system worked exactly as it was supposed to." The authors were interpreting the increase after a previous decline as evidence of reinfection and, yes, evasion of immunity.

Ouch.

SG, you okay? Did you get the license number of that truck?

[kalm]

No. I'll go with faculty in the Department of Computer Science and Engineering at IIT Bombay. You, someone who admittedly has never taken a Virology nor Epidemiology class, in addition to NOT teaching at a University in Computer Science has proven you misinterpret a large amount on the topic.

If you think you're right, let's hear your logic. Simply saying,"no" doesn't cut it.

The first assumption I referenced that he tried to discredit is:

loss of infection-derived immunity.. follows an Erlang distribution with a mean duration of one year

The rebuttal offered is:

Immunity after natural infection is robust and long-lasting; protection against infection lasts much longer than for the jabbed; protection from severe disease is likely lifelong.

I am not going to pay to look through the whole modeling study for the reference to the assumption of an Erlang distribution with a mean duration of one year, but it doesn't matter. The quoted assumption does not include anything about the relative durations of natural and vaccine induced protection.

I would have to pay to read the nature article he linked to support saying natural infection likely provides lifelong protection against severe disease. But that also has nothing to do with the specific assumption he is supposed to be rebutting.

With respect to the broader picture: The idea that natural infection is robust and long lasting does not nix the idea that a lot of lives would be saved through vaccination. You have heard the rationale before. There is more risk associated with acquiring some level of immunity through natural infection than there is associated with acquiring it through vaccination.

The second assumption I mentioned is:

Immune evasion for infection-derived immunity occurs for 27% of the previously infected population.

His rebuttal is:

The study cited for this 27% number is interpreted incorrectly. In the cohort study, 27% of the participants showed a decline in antibodies followed by an increase. Rather than meaning that these individuals became susceptible again, it means that these individuals were re-exposed and their immune system worked exactly as it was supposed to.

The abstract of the paper he linked within his effort at rebuttal includes this:

Seropositivity of an employee and family cohort increased from 42% to 87.5% between March and July 2021, with 27% reinfections, as judged by increased antibody concentration after a previous decline. The likely high transmissibility and partial evasion of immunity by the Delta variant contributed to an overwhelming surge in Delhi.

That is not saying "...their immune system worked exactly as it was supposed to." The authors were interpreting the increase after a previous decline as evidence of reinfection and, yes, evasion of immunity.

So IE: get infected, don’t die from infection, then you’ll be less likely to to get infected again and die?

[SeattleGriz]

No. I'll go with faculty in the Department of Computer Science and Engineering at IIT Bombay. You, someone who admittedly has never taken a Virology nor Epidemiology class, in addition to NOT teaching at a University in Computer Science has proven you misinterpret a large amount on the topic.

If you think you're right, let's hear your logic. Simply saying,"no" doesn't cut it.

The first assumption I referenced that he tried to discredit is:

loss of infection-derived immunity.. follows an Erlang distribution with a mean duration of one year

The rebuttal offered is:

Immunity after natural infection is robust and long-lasting; protection against infection lasts much longer than for the jabbed; protection from severe disease is likely lifelong.

I am not going to pay to look through the whole modeling study for the reference to the assumption of an Erlang distribution with a mean duration of one year, but it doesn't matter. The quoted assumption does not include anything about the relative durations of natural and vaccine induced protection.

I would have to pay to read the nature article he linked to support saying natural infection likely provides lifelong protection against severe disease. But that also has nothing to do with the specific assumption he is supposed to be rebutting.

With respect to the broader picture: The idea that natural infection is robust and long lasting does not nix the idea that a lot of lives would be saved through vaccination. You have heard the rationale before. There is more risk associated with acquiring some level of immunity through natural infection than there is associated with acquiring it through vaccination.

The second assumption I mentioned is:

Immune evasion for infection-derived immunity occurs for 27% of the previously infected population.

His rebuttal is:

The study cited for this 27% number is interpreted incorrectly. In the cohort study, 27% of the participants showed a decline in antibodies followed by an increase. Rather than meaning that these individuals became susceptible again, it means that these individuals were re-exposed and their immune system worked exactly as it was supposed to.

The abstract of the paper he linked within his effort at rebuttal includes this:

Seropositivity of an employee and family cohort increased from 42% to 87.5% between March and July 2021, with 27% reinfections, as judged by increased antibody concentration after a previous decline. The likely high transmissibility and partial evasion of immunity by the Delta variant contributed to an overwhelming surge in Delhi.

That is not saying "...their immune system worked exactly as it was supposed to." The authors were interpreting the increase after a previous decline as evidence of reinfection and, yes, evasion of immunity.

So let me get this straight. You don't even have access to the papers, but you're correct...over a computer science professor who fully understands models AND who has access to the papers. Got it.

Let's hear how this guy is wrong too please.

Assistant Professor of Clinical Neurobiology (Psychiatry) at Columbia University. He is also Research Scientist at the New York State Psychiatric Institute.

https://brownstone.org/articles/model-m ... ves-saved/

The study uses a generative model of COVID-19 transmission, infection, and mortality dynamics that includes 20-25 assumed parameters based on select literature (i.e. vaccine effectiveness against transmission, infection, and death, age-mixtures of each country, age-stratified infection fatality rates etc.) that is fitted to reported excess deaths in order to infer (but not validate) virus transmissibility across time in 185 countries.

The study compares actual 2021 excess deaths to simulations (counterfactuals) that are supposed to predict the trajectory of excess deaths in each country if no vaccines had been introduced (i.e. by running multiple simulations of the above fitted models after removing the effects of vaccines). The difference between these counterfactual curves and actual excess deaths result in the estimated deaths averted due to vaccination.

The authors’ models do not appear to account for evolution of the infectivity or lethality of the virus, other than explicitly modeling an increase in infection hospitalization rates due to the Delta variant (see 1.2.3 Variants of Concern section in the Supplement). The primary assumption in the counterfactual simulations is that excess deaths are explained by the “natural” evolution of the virus as reflected in its time-varying transmissibility, which can only be inferred (fitted) and not validated.

In addition, virtually every study of vaccine efficacy or effectiveness either excludes or lumps symptomatic cases within 21 days of 1st dose or within 14 days of second dose with the “unvaccinated” comparator groups. (You had to be corrected on this multiple times) This is problematic in light of evidence that COVID infectivity may increase almost 3-fold during the first week post-injection (see Figure 1 in our commentary of the study). This suggests that reported vaccine effectiveness estimates that are based on lower case rates observed >6 weeks post-injection may (at least partially) be accounted for by infection-, not vaccine-, induced immunity due to short-term increases in COVID-19 infectivity immediately following vaccination.

While the models in Watson et al. include a latency period between vaccination and when protection kicks in, they do not account for a potential increase in vaccine-induced infectivity and transmissibility during this period. (You've been corrected on this as well) Not accounting for this effect in the models would overestimate naturally evolving and time-varying virus transmissibility and thus inflate excess deaths in the counterfactual simulations that exclude vaccination effects.

Finally, the authors explored the impact of immune evasion from infection-derived immunity by conducting a sensitivity analysis to estimate the deaths averted by vaccinations with different immune escape percentages ranging from 0% to 80% (see Supplementary Figure 4 in the original article). In these models, the authors make clear that they assume a constant (non-waning) vaccine protection which is an unrealistic assumption (see above paragraph). However, the authors do not appear to do a similar sensitivity analysis of immune evasion from vaccine-derived immunity, which is important given the point raised in the above paragraph.

Link to his submission to the Lancet, which has not replied, as they say they are "busy".

https://www.researchgate.net/publicatio ... ltext=true

[JohnStOnge]

Writing with some updated numbers for those who think Florida has done a good job with COVID-19. Note that Florida's governor has appointed out of the mainstream public health officials who undermine confidence in the vaccines and has, himself, also worked to do that.

As of the day before the first COVID-19 vaccination was administered in the United States, 12/13/2020, New Jersey ranked #1 among states in cumulative COVID-19 death rate (deaths per million population) and New York ranked #2. Florida was in the middle of the pack at #20.

I checked where the three States ranked on 9/14/2022. New Jersey had dropped to #8 in cumulative COVID-19 death rate and Florida had climbed to #13. New York had fallen below Florida to #16, Also, when only the period starting with the start of vaccination (12/14/2020 - 9/14/2022) was considered, Florida climbed to #11 while New Jersey dropped to 33rd and New York dropped to 35th.

Latest update: As of the end of the day yesterday, Florida was at #10 in cumulative death rate since the beginning of the pandemic, New Jersey was at #13, and New York was at #15. And, when only the period starting with the start of vaccination in the United States was considered, Florida was at #8, New York was at #31, and New Jersey was at #37.

The point is that, since the vaccination tool became available, Florida has continued to move up the list in terms of COVID-19 mortality.

I don't think it's just the vaccine thing either. That is something that can be clearly identified. But I think Florida has generally failed to follow mainstream public health recommendations. I also think that Florida was fortunate in that it did not get hit hard with COVID-19 hard early on like New York and New Jersey did. When New York and New Jersey got hit, medical and public health officials were totally unfamiliar with the problem and there were no tools such as vaccines.

I think that, as time went on, New York and New Jersey followed mainstream recommendations while Florida did not. I think that, as a result, Florida's death rate toll now exceeds those of New York and New Jersey.I think that the perception that Ron DeSantis has done and is doing a good job with respect to COVID-19 is a false one and also a very unfortunate phenomenon.

All of the numbers I used came from https://www.worldometers.info/coronavir ... e_vignette .

Now adjust for age since Florida has one of the 2 or 3 oldest populations in the country..

Today I adjusted for demographic factors, including age, that are not among those related to COVID-19 interventions (such as vaccination). I adjusted for population density as reported at https://worldpopulationreview.com/state ... -densities . I adjusted for poverty rate as reported at https://worldpopulationreview.com/state ... e-by-state . I adjusted for age based on the statistics reported at https://worldpopulationreview.com/state ... est-states . I adjusted for percent Black population as reported at https://worldpopulationreview.com/state ... n-by-state . I got state by state COVID-19 death rates reported as of the end of the day yesterday (6/2/2023) from https://www.worldometers.info/coronavir ... e_vignette .

The end of the story is that Florida ranks worse in COVID-19 deaths than one would expect based on its demographics.

First a correlation matrix:



Coefficients highlighted yellow are signficant at > 95% confidence for a two tailed test. Those highlighted in blue don't reach that level but are significant at > 95% confidence for a one tailed test. One tailed tests are reasonable in this case. The only age factor that is significant either way is the percent population 75+.

Next I plugged the data into the multiple regression calculator at https://statskingdom.com/410multi_linea ... ssion.html . I did it using % population 75+ as the indicator of population age. When I did that, whether I used two tailed tests or one tailed tests. the only two variables identified as significant at 95% confidence when other variables were accounted for were Population Density and % in Poverty. Age did not make the cut. I also did it including both median age and % population 75+ even though that is squirrelly. It didn't make a difference.

Finally, I looked at where Florida would be predicted to rank according to its demographics to where it actually ranked as of the end of the day yesterday. Florida would be predicted to rank 17th in terms of death rate according to its demographics and it's actual rank is 10th.

Since comparing Florida to New York has been popular, I'll note that New York would be ranked 9th according to its demographics and its actual rank is 15th.

So, yes, New York has done better than Florida has in terms of the death rate one would expect based on demographics as compared to the death rate observed.

I should note that I know from past analyses that percent population 65+ ( which is what I looked at in the past) does emerge as a factor if you control for vaccination rate. I think that what is going on when you do something like what I did today is that there is a very high vaccination rate among those 65+. Vaccination rates haven't been tracked as much for a while but according to the site I used in the past at https://usafacts.org/visualizations/cov ... ker-states, more than 93% of people in the US 65+ have been fully vaccinated at least.

[JohnStOnge]

So let me get this straight. You don't even have access to the papers, but you're correct...over a computer science professor who fully understands models AND who has access to the papers. Got it.

As I wrote, being able to see what's in the paper (and actually it was a supplement) doesn't matter for the first one. The guy was saying that an assumption of mean immunity based on natural infection with a certain uncertainty distribution around that estimate was contradicted by the idea that natural immunity lasts longer than vaccine induced immunity. If we accept that premise that natural immunity lasts longer than vaccine induced immunity, that does not contradict the assumption of a mean immunity with a certain uncertainty distribution.

I didn't need to be able to see the entire paper to assess his comments on the second assumption I talked about. The authors of the paper he linked clearly stated in their abstract that they interpreted the situation in the same way as the authors of the modeling study did and not in the way that your computer scientist guy did.

[JohnStOnge]

Let's hear how this guy is wrong too please.

Assistant Professor of Clinical Neurobiology (Psychiatry) at Columbia University. He is also Research Scientist at the New York State Psychiatric Institute.

https://brownstone.org/articles/model-m ... ves-saved/

The study uses a generative model of COVID-19 transmission, infection, and mortality dynamics that includes 20-25 assumed parameters based on select literature (i.e. vaccine effectiveness against transmission, infection, and death, age-mixtures of each country, age-stratified infection fatality rates etc.) that is fitted to reported excess deaths in order to infer (but not validate) virus transmissibility across time in 185 countries.

The study compares actual 2021 excess deaths to simulations (counterfactuals) that are supposed to predict the trajectory of excess deaths in each country if no vaccines had been introduced (i.e. by running multiple simulations of the above fitted models after removing the effects of vaccines). The difference between these counterfactual curves and actual excess deaths result in the estimated deaths averted due to vaccination.

The authors’ models do not appear to account for evolution of the infectivity or lethality of the virus, other than explicitly modeling an increase in infection hospitalization rates due to the Delta variant (see 1.2.3 Variants of Concern section in the Supplement). The primary assumption in the counterfactual simulations is that excess deaths are explained by the “natural” evolution of the virus as reflected in its time-varying transmissibility, which can only be inferred (fitted) and not validated.

In addition, virtually every study of vaccine efficacy or effectiveness either excludes or lumps symptomatic cases within 21 days of 1st dose or within 14 days of second dose with the “unvaccinated” comparator groups. (You had to be corrected on this multiple times) This is problematic in light of evidence that COVID infectivity may increase almost 3-fold during the first week post-injection (see Figure 1 in our commentary of the study). This suggests that reported vaccine effectiveness estimates that are based on lower case rates observed >6 weeks post-injection may (at least partially) be accounted for by infection-, not vaccine-, induced immunity due to short-term increases in COVID-19 infectivity immediately following vaccination.

While the models in Watson et al. include a latency period between vaccination and when protection kicks in, they do not account for a potential increase in vaccine-induced infectivity and transmissibility during this period. (You've been corrected on this as well) Not accounting for this effect in the models would overestimate naturally evolving and time-varying virus transmissibility and thus inflate excess deaths in the counterfactual simulations that exclude vaccination effects.

Finally, the authors explored the impact of immune evasion from infection-derived immunity by conducting a sensitivity analysis to estimate the deaths averted by vaccinations with different immune escape percentages ranging from 0% to 80% (see Supplementary Figure 4 in the original article). In these models, the authors make clear that they assume a constant (non-waning) vaccine protection which is an unrealistic assumption (see above paragraph). However, the authors do not appear to do a similar sensitivity analysis of immune evasion from vaccine-derived immunity, which is important given the point raised in the above paragraph.

Link to his submission to the Lancet, which has not replied, as they say they are "busy".

https://www.researchgate.net/publicatio ... ltext=true

Trying to run down everything that guy wrote to see if it is accurate and see if it has an impact on the validity of the model would take forever. But did you do something to determine that it IS accurate? Or are you just assuming he is because of his credentials?

And one is going to go with credentials, one should note neither one of the Brownstone Institute guys we are talking about has a background in infectious disease. One is a computer scientist and one is a psychiatrist. I just looked up some info on the first three authors listed on the modeling study they attacked. Here are some links:

https://academicmedicaleducation.com/oliver-watson-phd - His background is in modeling the transmission of infectious disease.

https://scholar.google.com/citations?us ... AAAJ&hl=en - His background is in statistics, infectious disease modeling and infectious disease epidemiology.

https://www.londonntd.org/research/rese ... preet-toor - Her background is in epidemiological modeling.

I would love to see the two Brownstone guys we've been talking about sit down with the study authors I just linked and debate the validity of the study. We would have a couple of guys with credentials and experience in computer science and psychiatry debating three people with experience and credentials in infectious disease epidemiology modeling about infectious disease epidemiology modeling. I think I know who I would bet on to
come out on top.

I can't even believe we have people out there seriously challenging the idea that the vaccines saved many millions of lives. And why? Why is it important to be in denial about something like that?