IID: The Coronavirus mRNA Vaccines Did Not Slow The Spread Of COVID-19

The vaccinated are showing viral loads (very high) similar to the unvaccinated (Acharya et al. and Riemersma et al.), and the vaccinated are as infectious. Riemersma et al. also report Wisconsin data that corroborate how the vaccinated individuals who get infected with the Delta variant can potentially (and are) transmit(ting) SARS-CoV-2 to others (potentially to the vaccinated and unvaccinated). [4]

Immunology and virology 101 have taught us over a century that natural immunity confers protection against a respiratory virus’s outer coat proteins, and not just one, e.g. the SARS-CoV-2 spike glycoprotein. There is even strong evidence for the persistence of antibodies. Even the CDC recognizes natural immunity for chicken-pox and measles, mumps, and rubella, but not for COVID-19. [4]

13 fold increased risk of breakthrough Delta infections in double vaccinated persons, and a 27 fold increased risk for symptomatic breakthrough infection in the double vaccinated relative to the natural immunity recovered persons…the risk of hospitalization was 8 times higher in the double vaccinated (para)…this analysis demonstrated that natural immunity affords longer lasting and stronger protection against infection, symptomatic disease and hospitalization due to the Delta variant of SARS-CoV-2, compared to the BNT162b2 two-dose vaccine-induced immunity. [5]

IID: The Coronavirus mRNA Vaccines Did Not Slow The Spread Of COVID-19

• CON would win if it has been proven that COVID mRNA vaccines did slow its spread.
  
• mRNA vaccines include any issued by the government, including such as Pfizer, etc.
  
• Showing that the mRNA vaccines sped up the spread of the virus does not yet prove the Pro position correct, as without proof of that the COVID vaccines is entirely incapable of slowing down the transmission, the same vaccines could speed up and slow down the transmission in different ways in different populations as side effects due to specifications and differences not specified yet.
  

Independent and Identically Distributed Data

• Record in many cases the standardized statistic of the percentage infected in samples of appropriate(at least 30 people per sample) and similar(or the same) size from identical settings(to eliminate external factors, which is to vindicate that the spreading speed is positively correlated to the percentage infected, in groups of people that are vaccinated and unvaccinated. The Delta percentage is the final infected percentage minus the initial. This statistic shall not be confused with the Delta variant.
  
• Assume both datasets are normally distributed(according to the Central Limit Theorem). Let's call the distribution of delta percentages of individuals infected in vaccinated groups C, and the distribution for the samples in the unvaccinated population D. The difference between the two, (C-D), is also normal, and represents how much faster the speed of transmission are in vaccinated samples than in unvaccinated ones.
  
• If reality is really as Pro said, then the percentage would be higher in C, in the vaccinated population. Therefore, suppose we use the variable X to represent the normal distribution C-D, unless P(X<0) is so small it is classified as a rare event, Pro fails.
  

Showing that the mRNA vaccines sped up the spread of the virus does not yet prove the Pro position correct,

Assume both datasets are normally distributed(according to the Central Limit Theorem).

Mathematical models usually have three basic parts. These are the variables and their definitions, the equations into which the variables are incorporated, and starting values for the variables. When mathematical models are applied to ecological situations, more information is required. For example, an ecological model requires the user to assign a meaning to the variables, to know the units of the variables, and to bind the ranges of values over which the variables are realistic. It might also be useful to know the way that the values of the variables are measured, the ecological context of the variables, the reproducibility of the values of the variables, among other information. [1]

Individual-level data of the study population included patient demographics, namely age, sex, socioeconomic status (SES) and a coded geographical statistical area (GSA, assigned by Israel’s National Bureau of Statistics, corresponds to neighborhoods and is the smallest geostatistical unit of the Israeli census). The SES is measured on a scale from 1 (lowest) to 10, and the index is based on several parameters, including household income, educational qualifications, household crowding and car ownership. Data were also collected on last documented body mass index (BMI) and information about chronic diseases from MHS’ automated registries, including cardiovascular diseases19, hypertension20, diabetes21, chronic kidney disease22, chronic obstructive pulmonary disease, immunocompromised conditions, and cancer from the National Cancer Registry23. [2]

The eligible study population was divided into three groups: (1)fully vaccinated and SARS-CoV-2-naïve individuals, namely MHS members who received two doses of the BioNTech/Pfizer mRNA BNT162b2 vaccine by February 28, 2021, did not receive the third dose by the end of the study period and did not have a positive PCR test result by June 1, 2021; (2) unvaccinated previously infected individuals, namely MHS members who had a positive SARS-CoV-2 PCR test recorded by February 28, 2021 and who had not been vaccinated by the end of the study period; (3) previously infected and vaccinated individuals, including individuals who had a positive SARS-CoV-2 PCR test by February 28, 2021 and received one dose of the vaccine by May 25, 2021, at least 7 days before the study period. The fully vaccinated group was the comparison (reference) group in our study. Groups 2 and 3, were matched to the comparison group 1 in a 1:1 ratio based on age, sex and residential socioeconomic status. [2]

Model 1 – previously infected vs. vaccinated individuals, with matching for time of first event

In model 1, we matched 16,215 persons in each group. Overall, demographic characteristics were similar between the groups, with some differences in their comorbidity profile (Table 1a).

During the follow-up period, 257 cases of SARS-CoV-2 infection were recorded, of which 238 occurred in the vaccinated group (breakthrough infections) and 19 in the previously infected group (reinfections). After adjusting for comorbidities, we found a statistically significant 13.06-fold (95% CI, 8.08 to 21.11) increased risk for breakthrough infection as opposed to reinfection (P<0.001). Apart from age ≥60 years, there was no statistical evidence that any of the assessed comorbidities significantly affected the risk of an infection during the follow-up period (Table 2a). As for symptomatic SARS-COV-2 infections during the follow-up period, 199 cases were recorded, 191 of which were in the vaccinated group and 8 in the previously infected group. Symptoms for all analyses were recorded in the central database within 5 days of the positive RT-PCR test for 90% of the patients, and included chiefly fever, cough, breathing difficulties, diarrhea, loss of taste or smell, myalgia, weakness, headache and sore throat. After adjusting for comorbidities, we found a 27.02-fold risk (95% CI, 12.7 to 57.5) for symptomatic breakthrough infection as opposed to symptomatic reinfection (P<0.001) (Table 2b). None of the covariates were significant, except for age ≥60 years. [2]

Mathematics proves that statistical estimates are increasingly accurate as the sample size grows, borne out in more precise estimates, as well as smaller standard errors that translate into increased statistical confidence. [3]

The Coronavirus mRNA Vaccines Did Not Slow The Spread Of COVID-19

The Coronavirus mRNA Vaccines Sped up The Spread Of COVID-19

Humans did send other humans to the moon.

You do not know the messages because we Xed him to tell you about it. (You do not know the messages because we didn't send him to tell you about it.)

I am an individual who was Xed to the moon.

The Coronavirus mRNA Vaccines Did Not Slow The Spread Of COVID-19 (In which "did not slow" is substituted with "sped")

The Coronavirus mRNA Vaccines Did Not Slow The Spread Of COVID-19

 "The Coronavirus mRNA Vaccines Slowed The Spread Of COVID-19" is a wrong claim

The Coronavirus mRNA Vaccines Sped up The Spread Of COVID-19

More evidence accumulated in March with a slew of studies about the mRNA vaccines. One with 9,109 healthcare workers in Israel found infections cut by 75 percent after two doses of the Pfize-BioNTech vaccine. Another revealed that the viral load fell fourfold in those who received one dose and then developed an infection.

Among more than 39,000 people screened for infection at the Mayo Clinic, patients had a 72 percent lower risk of infection 10 days after the first dose of either mRNA vaccine and 80 percent lower after both doses. The New England Journal of Medicine published research letters showing reduced infections in fully vaccinated healthcare workers at the University of Texas Southwestern Medical Center, the Hadassah Hebrew University Medical Center in Jerusalem, and the University of California in Los Angeles and San Diego.
The most persuasive evidence, according to Dean, came from an early April CDC study of 3,950 healthcare workers who were tested weekly for three months after receiving both doses of either mRNA vaccine. Full vaccination reduced infection—regardless of symptoms—by 90 percent, and a single dose reduced infection by 80 percent.
Then there’s the evidence all around us, Kindrachuk says.
“We’ve seen a pretty drastic decrease of transmission in the country,” he says. “That suggests not only are the vaccines protecting against severe disease but it suggests there’s a reduction in transmission.”
Taken together, the evidence shows that full vaccination with either mRNA vaccine cuts risk of infection by at least half after one dose, and by 75 to 90 percent two weeks after the second dose. Though less research is available on the Johnson & Johnson vaccine, the trial data suggest an infection reduction of more than 70 percent is likely. With the vaccines preventing this much infection, they’re also stopping the majority of vaccinated people from passing along the virus.

• There is no conflict between the vaccines "speeding up the spread" and "did not slow the spread" if the latter is true. There is no necessary conflict between the vaccine having "sped" and "slowed" the spread due to neither of the two concerning the subset of each other. There is necessary conflict between the vaccine "did" and "did not" slow the spread because for the latter to be true, the former shall have no instances existing.
  
• "Sped" cannot be a viable substitution of "did not slow" because "did not" is a viable verb in the sentence as opposed to "did not slow" as a whole due to pragmatic implications.
  
• To prove Con's position, 1 instance of the vaccine "did slow the spread" is all that is need. I have brought such example.
  
• The statement "The Coronavirus mRNA vaccines did slow the spread of Covid-19", like “We did send people on the moon", which requires only individual instances to prove as opposed to population statistics on balance, is proven due to presented individual instances even if they do not reflect the entirety of the population. On the other hand, "The Coronavirus mRNA vaccines did not slow the spread of Covid-19" concerns the full set and requires that out of every sample that exist and could have existed, none of them exhibit the slowing of the spread of the virus, was never met with sufficient proof by the opposing side.
  
• Thus, Pro is proven to be incorrect. Vote CON.
  

There is no "On balance" or "On average", nor is there any restrictions on what counts as samples and what are ruled out of consideration.

Slow The Spread: cause COVID-19 to ultimately spread to fewer people than in an unvaccinated population of the same size.

a body of persons or individuals having a quality or characteristic in common [1]

cause COVID-19 to ultimately spread to fewer people than in an unvaccinated [body of persons or individuals having a quality or characteristic in common] of the same size.

How to prove the adversary statement? Simple, to present one instance where the vaccines slowed the spread, as therefore, it would be true that the vaccines did slow the spread of the virus in that instance, if we set our set to specific considerations.

Heads up, there is no "On balance" or "On average".

cause COVID-19 to ultimately spread to fewer people

a smaller number of persons or things [2]

something that brings about an effect or a result [3]

For the most part you can trust respected medical authorities. . . I would stick with trusted medical authorities who have a track record of telling the truth, who have a track record of giving information and policy and recommendations based on scientific evidence and good data. If I were to give advice to you and your family and your friends and your [sic] family, I would say that's the safest bet to do. [5]

Immunology and virology 101 have taught us over a century that natural immunity confers protection against a respiratory virus’s outer coat proteins, and not just one, e.g. the SARS-CoV-2 spike glycoprotein. There is even strong evidence for the persistence of antibodies. Even the CDC recognizes natural immunity for chicken-pox and measles, mumps, and rubella, but not for COVID-19.

The vaccinated are showing viral loads (very high) similar to the unvaccinated (Acharya et al. and Riemersma et al.), and the vaccinated are as infectious. Riemersma et al. also report Wisconsin data that corroborate how the vaccinated individuals who get infected with the Delta variant can potentially (and are) transmit(ting) SARS-CoV-2 to others (potentially to the vaccinated and unvaccinated).

This troubling situation of the vaccinated being infectious and transmitting the virus emerged in seminal nosocomial outbreak papers by Chau et al. (HCWs in Vietnam), the Finland hospital outbreak (spread among HCWs and patients), and the Israel hospital outbreak (spread among HCWs and patients). These studies also revealed that the PPE and masks were essentially ineffective in the healthcare setting. Again, the Marek’s disease in chickens and the vaccination situation explains what we are potentially facing with these leaky vaccines (increased transmission, faster transmission, and more ‘hotter’ variants). [6]

• Dr. Harvey Risch, MD, PhD (Yale School of Public Health) 
  
• Dr. Howard Tenenbaum, PhD ( Faculty of Medicine, University of Toronto)
  
• Dr. Ramin Oskoui, MD (Foxhall Cardiology, Washington)
  
• Dr. Peter McCullough, MD (Truth for Health Foundation (TFH)), Texas
  
• Dr. Parvez Dara, MD (consultant, Medical Hematologist and Oncologist) [6]
  

PRO shall only argue that The Coronavirus mRNA Vaccines Did Not Slow The Spread Of COVID-19
CON shall only argue that The Coronavirus mRNA Vaccines DID Slow The Spread Of COVID-19

Population

3a: a body of persons or individuals having a quality or characteristic in common

Once again, the definition of "Slow The Spread" states:

cause COVID-19 to ultimately spread to fewer people

This is important because, without those qualifiers, CON would be correct.

More evidence accumulated in March with a slew of studies about the mRNA vaccines. One with 9,109 healthcare workers in Israel found infections cut by 75 percent after two doses of the Pfize-BioNTech vaccine. Another revealed that the viral load fell fourfold in those who received one dose and then developed an infection.

Among more than 39,000 people screened for infection at the Mayo Clinic, patients had a 72 percent lower risk of infection 10 days after the first dose of either mRNA vaccine and 80 percent lower after both doses. The New England Journal of Medicine published research letters showing reduced infections in fully vaccinated healthcare workers at the University of Texas Southwestern Medical Center, the Hadassah Hebrew University Medical Center in Jerusalem, and the University of California in Los Angeles and San Diego.

The most persuasive evidence, according to Dean, came from an early April CDC study of 3,950 healthcare workers who were tested weekly for three months after receiving both doses of either mRNA vaccine. Full vaccination reduced infection—regardless of symptoms—by 90 percent, and a single dose reduced infection by 80 percent.

• A population beyond data collection cannot prove either side(since there is no "on average" or "on balance) and a defined pair of population can be biased while still being eligible for the sake of this debate based on how Pro didn't even bother putting confinements on what a population cannot be.
  
• Therefore, any cases where transmission slowed due to vaccines, among any set of people, prove CON as any set of vaxxed/unvaxxed people count as populations.
  
• I have shown several authentic sets of these in Haelle's article with links. Pro didn't read them.
  
• Pro cannot define them in the next and last round due to rules on this site.
  
• As long as at any time at any collection of vaxxed/unvaxxed people it can be shown that the transmission is lower in the vaxxed population, Con wins.
  
• Pro has conceded my choice of language only with one condition, and that condition, which is the ultimate number of people spreaded to, can be easily resolved.
  
• Both parties agree what could be a population. That definition lead to THESE.
  
• Overall, VOTE CON!
  

It was never defined clearly to be any given one thing or any given set of things or even any given set of sets of things. 

Slow The Spread: cause COVID-19 to ultimately spread to fewer people than in an unvaccinated population of the same size.

1. They are either Vaccinated or Non-Vaccinated
   
2. They came into contact with COVID-19
   
3. They consist of groups of people equal to one-another.
   

As long as the difference of transmission rates is finite, there will be a nonzero chance that said value is negative, which means that no matter the conditions, as long as you don't survey the entirety of the population constantly over a period, you are never entirely sure that the vaccines never slowed down the transmission.

Mathematics proves that statistical estimates are increasingly accurate as the sample size grows, borne out in more precise estimates, as well as smaller standard errors that translate into increased statistical confidence. [1]

Alright, what is the population at play here?

Recognizing that careful consideration of statistical power and the sample size is critical to assuring scientifically meaningful results, protection of human subjects, and good stewardship of fiscal, tissue, physical, and staff resources, let's review how power and sample size are determined.

One-Sided Hypothesis Testing

Power is calculated with regard to a particular set of hypotheses. Often epidemiologic hypotheses compare an observed proportion or rate to a hypothesized value. The above hypotheses are one-sided, i.e. testing whether the proportion is significantly less in group 2 than group 1. An example of two-sided hypotheses would be testing equality of proportions as the null hypothesis; using as the alternative, inequality of proportions. [2] 

Statistical analysis

Two multivariate logistic regression models were applied that evaluated the four aforementioned SARS-CoV-2-related outcomes as dependent variables, while the study groups were the main independent variables. [3]

In all three models, we estimated natural immunity vs. vaccine-induced immunity for each SARS-CoV-2-related outcome, by applying logistic regression to calculate the odds ratio (OR) between the two groups in each model, with associated 95% confidence intervals (CIs). Results were then adjusted for underlying comorbidities, including obesity, cardiovascular diseases, diabetes, hypertension, chronic kidney disease, cancer and immunosuppression conditions. [3]

Logistic regression can describe the relationship between a categorical outcome (response variable) and a set of covariates (predictor variables).[4]

2. Indicating the need to control for effect modifiers:

Since an effect modifier changes the strength of the association under study, different study populations may yield different results concerning the association of interest. For instance, you might need to present separate models for men and women. This is important because, unlike potential confounders, modifying variables cannot create the appearance of an association where none exists, nor obscure an association where one does. But the proportion of the study population that has a greater susceptibility will influence the strength of the association. Therefore, to achieve comparability across studies, it is necessary to control for the effect of the modifying variables. [5] 

Therefore, if Pro can click on that, Pro can click on these.

The limitations of this study include the observational nature of the study design. Lack of active laboratory surveillance in the cohort might have resulted in an underestimation of asymptomatic cases. Data on vaccine efficacy in preventing asymptomatic SARS-CoV-2 infection are scarce, and our results of rate reductions in SARS-CoV-2 infections, which include asymptomatic HCWs, need further validation through active surveillance and sampling of vaccinated people and unvaccinated controls to ascertain the actual reduction of asymptomatic infection in vaccinated individuals. [6] 

Patients were excluded if they had a positive sample before vaccination; if they had a positive sample more than 21 d after the first dose of the vaccine but did not receive the second dose on day 21; or if they were over the age of 90 years (28 patients older than 90 were not included because it was not possible to match them with unvaccinated controls). For patients with multiple positive post-vaccination tests, only the first test was included. [7]

Those who were vaccinated were significantly younger and more likely to be female compared with those without prior vaccination, reflecting the early focus on vaccinating healthcare workers. We observed differences in the race, state of residence, and residence within the local HRR. Among the vaccinated group, median (interquartile range) time from first dose of vaccine to their molecular screening was 16 days (7–27 days), with 707 (23.5%) screening tests in the vaccinated group having occurred among individuals who had received their second dose. [8]

with infections in 234 of 8969 nonvaccinated employees (2.61%; 95% confidence interval [CI], 2.29 to 2.96), 112 of 6144 partially vaccinated employees (1.82%; 95% CI, 1.50 to 2.19), and 4 of 8121 fully vaccinated employees (0.05%; 95% CI, 0.01 to 0.13) (P<0.01 for all pairwise comparisons). [9]

From this definition, the populations are clearly defined:

1. They are either Vaccinated or Non-Vaccinated
   
2. They came into contact with COVID-19
   
3. They consist of groups of people equal to one-another.
   

• Populations the size of 1 each, one of them vaccinated and the other unvaxxed.
• Samples the size of 20, that are purposely biased, satisfying the 3 criteria above surely, that show the vaccines having a lower transmission velocity
  
• I could just bring a positive patient to a room with 199 other unvaccinated unmasked negative individuals and order the positive one to cough loudly in front of the whole room. Then I could repeat a "control" with 200 vaccinated individuals telling all of them to wear masks, not speak, not cough, etc. as of the moment as quickly as possible to show the conclusion that vaccines did slow down transmission in this case.
  
• These confounding factors that exist that obviously could skew the end results from what is expected does not terminate the experiment from being an experiment and the population from being a population if the population was being chosen in these three vague rules. TL:DR, My experiments can be done unfairly and you can't do anything about it because you failed to rule them out.
  

The populations in question might as well be 1 unvaccinated guy verses 1 vaccinated dude, and the rates would be the same undefined meaningless value. The populations might as well be two groups of 2,000 people, except the vaccinated 2,000 have recorded lower rates than the vaccinated people. Of course this is biased on purpose, but the thing is you can't call me out for it not being a population, because it is a possible population in every right.

CON, now, is arguing against his own case here. He claims that without a large enough sample size, we have no way of knowing the effectiveness of the vaccine.

This study is totally useless for our purposes because it excludes those who were naturally immune, who were above 90 years old, and had positive samples more than 21 days after the first dose AND didn't receive the second dose. This is not two equal populations at all. It is completely cherrypicked data.

The Hadassah one did not compare unvaccinated people to vaccinated people, Ditto for the cited University of California study, AND the CDC study. So these three studies are worthless since the discussion is between vaccinated and unvaccinated populations. There was no comparison between the two populations.

But now he is asserting we can't know the efficacy of the population studies or the definitions of them. As Perdue University pointed out, this is erroneous thinking. As a sample size grows, so does its reliability and authenticity toward real life. 

• Pro accepts my arguments on how the topic works in English as long as satisfactory populations exist
  
• According to that unrefuted argument, one instance of a population of vaxxed people spreading COVID slower than a population of unvaxxed people of the same size is all that is needed to disprove the topic. They exist as shown.
  
• Pro attempts to deny their worth using new points in the last round, while they violate the pass given for the last four rounds because these specific requirements are not mentioned until now.
  
• Considering a population larger than samplable is and never will arrive at absolutely accurate statements. Considering a small population with all of them able to be sampled, no matter how biased, counts as populations. With biased procedures that cannot be ruled out with organized points by Pro, they still count and these instances(with unbiased studies existing as well) do count.
  
• Since necessary instances to show that Con is true exists, vote CON!