Instigator / Pro
7
1589
rating
18
debates
69.44%
won
Topic
#3938
IID: The Coronavirus mRNA Vaccines Did Not Slow The Spread Of COVID-19
Status
Finished
The debate is finished. The distribution of the voting points and the winner are presented below.
Winner & statistics
Better arguments
3
0
Better sources
2
2
Better legibility
1
1
Better conduct
1
1
After 1 vote and with 3 points ahead, the winner is...
Public-Choice
Tags
Parameters
- Publication date
- Last updated date
- Type
- Standard
- Number of rounds
- 4
- Time for argument
- One week
- Max argument characters
- 10,000
- Voting period
- One week
- Point system
- Multiple criterions
- Voting system
- Open
Contender / Con
4
1731
rating
167
debates
73.05%
won
Description
STANCES:
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
* * *
DEFINITIONS:
All terms shall first be defined from MedicineNet's Medical Dictionary available here:
https://www.medicinenet.com/script/main/alphaidx.asp?p=a_dict
And if MedicineNet's Medical Dictionary cannot provide a definition, then Merriam Webster's Online Dictionary available at merriam-webster.com will be used for all other words.
Specific definitions for debate:
COVID-19: SARS-Coronavirus-2019 and all variants.
Slow The Spread: cause COVID-19 to ultimately spread to fewer people than in an unvaccinated population of the same size.
mRNA vaccines: All of the combined mRNA vaccines as approved by government health departments around the world.
* * *
RULES:
1. Burden of Proof is shared.
2. No Ignoratio Elenchis.
3. No trolls.
4. Forfeiting one round = auto-loss.
Round 1
Pro
#1
Thank you to Intelligence_06 for accepting this debate.
As the total carnage of COVID-19 has reached more than 641,000,000 cases worldwide, many science deniers continue to spread COVID-19 disinformation about vaccine efficacy, which jeopardizes the lives of everyone in a population.
The United States Center for Disease Control and Prevention's latest statistics for April officially reported that 58% of all deaths from COVID-19 occurred in the vaccinated. [2]
Moreover, in a testimony before European Parliament, Pfizer's President of Developed Markets, Janine Small, told European Parliament in a COVID-19 vaccine hearing that preventing transmission of COVID-19 was never tested and also was not even required for emergency approval. [1]
Additionally, a groundbreaking study involving tens of thousands of participants that took place in Israel concluded that vaccine-induced immunity not only waned after mere months, but, around month 9, participants incurred what is known as "negative efficacy," or a negative immune response to COVID-19, meaning the vaccine effectively caused them to have a worse immune response than if they were never vaccinated at all. [3]
These disturbing facts show one clear truth: not only was the COVID-19 vaccine NOT designed to curb transmission of the virus, but, in the long run, it even makes a person significantly more likely to catch COVID-19.
But why do vaccines cause more transmission of COVID-19 than in an unvaccinated population? After all, if the vaccine supposedly prevented against infection, the CDC's official April statistics would not have shown a pandemic of the vaccinated.
Dr. Paul Elias Alexander, an epidemiologist whose resume includes working at Johns Hopkins University's world-renowned research hospital and consulting work for the World Health Organization, published an article at the Brownstone Institute explaining the breakdown of more than 150 studies into vaccine efficacy and natural immunity; reporting some shocking results:
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]
According to the research, the vaccines did absolutely nothing to prevent viral load and did nothing to reduce transmissibility of COVID-19. Dr. Alexander further states that natural immunity provides additional immune defenses than what the mRNA vaccines could offer:
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]
So vaccinated individuals not only have inferior protection to COVID-19 compared to naturally immune individuals, but they also confer WORSE immunity over time, are just as viral, and MORE likely to spread COVID-19 compared to naturally immune individuals.
Coupled with the negative efficacy of the vaccines, it is apparent that the COVID-19 mRNA vaccine results in further infection rates than in an unvaccinated population. This due to the inferior immunity offered in the beginning stages and the negative immunity offered in the later stages of mRNA vaccine partial-immunity.
This assertion was conclusively proven in a 2021 study that found:
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]
Considering the facts, it is apparent the mRNA vaccine did not slow the spread of COVID-19. In fact, it exacerbated it, causing variants to spread faster in vaccinated and unvaccinated populations alike due to a weakened immunity from COVID-19 in the vaccinated population.
Had no vaccine been offered, natural immunity would have provided superior protection and not led to dangerous pandemics of COVID-19 variants. Which stufies have shown spread significantly faster in vaccinated populations. There would not have been negative efficacy from the natural immunity, unlike with mRNA vaccine partial-immunity, and COVID-19 would have therefore spread much more slowly in an unvaccinated population, which would have saved more lives.
SOURCES:
Con
#2
IID: The Coronavirus mRNA Vaccines Did Not Slow The Spread Of COVID-19
It should be noted and inferred from this title:
- 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.
IID stands for:
Therefore, we assume that the results need to come from such distributions. How should such experiments be held?
- 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.
Pro has failed to provide anything statistically even though he put this in the prompt himself. A larger portion of the Pro R1 is saying that the transmission is just "in some cases, the transmission is faster in vaccinated people" but has never validated exactly in what probability it really IS faster.
The topic itself is an absolute, and we know only a sith would deal in those. Just kidding, but really, without saying "On net balance" or anything else clearly, the title just proposes that in almost all cases, the vaccination makes the virus spread quicker rather than slower. This exact thing was not proven as of yet. I rest my case.
Round 2
Pro
#3
Showing that the mRNA vaccines sped up the spread of the virus does not yet prove the Pro position correct,
P1: mRNA vaccines sped up transmissibility.
P2: More people got sick in vaccinated groups than unvaccinated.
C1: Therefore mRNA vaccines sped up transmissibility of COVID-19
Anything to the contrary is rationally incoherent.
Assume both datasets are normally distributed(according to the Central Limit Theorem).
CON's method of modeling violates basic rules of creating epidemiological models.
Assuming normal distribution fails to take into consideration different variables. Variables are the difference between a good model and a bad model, as per Cengage's Environmental Modeling: In Context:
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]
According to Centgage Research's overview of environmental models, the very first step in designing a model is to NOT assume normal distribution but instead define the variables.
This is important because, when building models based on data, data scientists define the characteristics of the population before drawing any conclusions. CON has chosen to ignore this foundational first step toward proper data science and has instead opted to use an unquantified, undefined, and nonspecific variable called "normal."
In contrast to CON's unscientific methods, the study I cited built mathematical models controlling for variables (which Centgage Research proved is proper modeling data science, especially for biology) and found that mRNA vaccinated individuals were significantly more likely to become infected with COVID-19 compared to the unvaccinated.
Here is the methodological breakdown of the model the scientists constructed:
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]
So, unlike CON's unscientific method of assuming transmission rates based on an undefined characteristic of "normal," the scientists in the study built their model based on properly defining, qualifying, and quantifying the population based on complete health profiles. They followed proper foundations of data science.
This was done IN ADDITION TO vaccination status, to make sure the main variable being studied was vaccination status and its affect on different populations, particularly unvaccinated, partially vaccinated and naturally immune, and only naturally immune. The study authors confirm this when they state:
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]
Based on their very thorough model that followed proper data science methods, and accounts for different people groups within the vaccinated, partially vaccinated, and naturally immune camps, the authors found:
Model 1 – previously infected vs. vaccinated individuals, with matching for time of first eventIn 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]
In a statistically representative sample size of more than 673,000 adults, the results clearly indicate that mRNA induced immunity is inferior to natural immunity and also caused COVID-19 to spread more quickly in vaccinated populations. Therefore, According to the science, the mRNA vaccine did not slow the spread of COVID-19.
As previously explained, this is because of two main factors. The first being mRNA immunity's inherently inferior form of immunity compared to natural immunity, as previously explained by renowned epidemiologist Dr. Paul Elias Alexander. But also due to negative immunity, where, over time, the vaccine causes the host to receive a negative immune state against COVID-19, as the second supplemented study of Israel's population in round one proved.
It is worth noting that the study examining vaccine inferior immunity to natural immunity was a statistically representative sample. Statistical relevance is just as important as proper modeling, as Purdue University's Center For Food And Agriculture Business states:
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 study's authors note that their data effectively covers 1 out of every 4 Israelis, which is a statistically representative sample size. This is important because, without such a large sample size, the study risks being statistically unreliable, and therefore cannot be used as a strong determiner of the vaccine's effect on transmissibility of COVID-19.
Therefore, this proves that the mRNA vaccine exasorbated the spread of COVID-19, causing more people to be infected with COVID-19 than in an unvaccinated population. This is the science.
SOURCES:
Con
#4
Since time is running out, this is going to be a short one.
1. Topic
Ignoring the "IID", the topic is
The Coronavirus mRNA Vaccines Did Not Slow The Spread Of COVID-19
Which is not the same as, as Pro thought:
The Coronavirus mRNA Vaccines Sped up The Spread Of COVID-19
If in a samplable population, out of it, 1% of the samples shows an increased infection rate of Covid-19 among vaccinated people, and 99% showed that the rate decreased in those samples, the second statement in the quotes would be correct, as instances of vaccines speeding up the transmission of the virus therefore indeed exists. 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. Considering everything, It is true that the vaccine can simultaneously speed up and slow down the transmission speed in different instances, however it is also true that the vaccine cannot simultaneously "slow" and "did not slow" the transmission.
The real topic is the first statement in the quotes. That statement would be false given the conditions. "Slow the spread of COVID-19" was placed after "Did Not", meaning that were Pro to really fulfill the burden Pro set up, it would be needed to be proven that not only a majority of samples within the population exhibits increased transmission between vaccinated people, but also that in ALL possible samples, this is the case. What is also required as well is that it must be shown that the transmission among vaccinated people needs to be proven to be always faster(or at least the same speed) at all samplable locations.
Why? Because the sentence containing "Did Not", for obvious reasons, is the opposite of "The Coronavirus mRNA Vaccines Slowed The Spread Of COVID-19". 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".
Let's look at other statements.
Humans did send other humans to the moon.
If we blatantly add an "On balance" before the statement without changing the statement itself, the statement may be proven "false" by saying that over 99.99% of people did not participate in NASA's space programs, according to Pro's logic. However, the general concensus, if the Apollo mission was a success which it is, is that "Humans did send other humans to the moon" is proven true by individual instances. Therefore, "Humans did not send other humans to the moon" is false.
If we are to define a verb that explicitly means "did not send" as "Xed", this would be a grammatically correct sentence.
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.)
If we are to keep the same definitions, this would also be true.
I am an individual who was Xed to the moon.
Why? Because by the time I was born, the Apollo mission ended. I was never on the moon.
Then we look back at the topic. The "sent-Xed" relationship is not dissimilar to the "sped-slowed" relationship in terms of linguistical meaning. While it would be plausible to conclude that "sped" contained the meaning of "did not slow down", keep in mind how humans perceive sentences.
The verb within is bolded. People would not interpret the title as:
The Coronavirus mRNA Vaccines Did Not Slow The Spread Of COVID-19 (In which "did not slow" is substituted with "sped")
They would interpret it as:
The Coronavirus mRNA Vaccines Did Not Slow The Spread Of COVID-19
With pragmatic examples, it is pretty evident that almost no users of English would interpret the title with "did not slow" as the verb within. Therefore, the title ought to be interpreted as:
"The Coronavirus mRNA Vaccines Slowed The Spread Of COVID-19" is a wrong claim
instead of
The Coronavirus mRNA Vaccines Sped up The Spread Of COVID-19
And here to restate the burden according to the interpretation of the prompt consistent with grammatical preferences utilized by the majority.
Pro: Has to prove that in ALL samples that have existed, it has been shown that Covid Vaccines did not slow the spread of the virus.
Con: Only has to bring up one case in any instance in which Covid Vaccines slowed down the spread of the virus since proving that "The Covid mRNA vaccines did slow down the spread of Covid-19" is enough.
In this case, I have numerous instances. Due to character constraints, I will only use one source that links to more.
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.
Conclusions
- 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.
>>>>>>>>>>>>>>>>>>>>>>>
Round 3
Pro
#5
R1: CON Is Trying To Weasel Out Of The Definitions He Agreed To
CON stated:
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.
CON's definition of sample violates the definition of "Slow the Spread" in the description:
Slow The Spread: cause COVID-19 to ultimately spread to fewer people than in an unvaccinated population of the same size.
CON is desperately trying to rewrite the topic of this debate by claiming there were no stipulations as to what does and does not count as a sample or a population, but, according to the definition of "Slow The Spread", there are two populations at play. One is the vaccinated, and the other is the unvaccinated, and they also must be the same size to each other for either side to prove their case.
As per Merriam-Webster, population means:
a body of persons or individuals having a quality or characteristic in common [1]
This means that debating "slow the spread" is debating:
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.
Why does this matter? Because CON inaccurately stated that there were "no restrictions as to what counts as samples and what are ruled out of consideration." Clearly, according to the definition of Slow The Spread, this is not the case, as it is abundantly clear the two populations that are necessarily being considered are vaccinated and unvaccinated.
CON, however, continues to make this error when he argues his case:
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.
Considering the definition of "population" necessarily includes a group of individuals with a common characteristic (in this case vaccination or non-vaccination status), CON's efforts to subvert the definitions for this debate can be ignored, since they violate the very definitions CON agreed to when he agreed to this debate. He is claiming that he does not need a population to prove slowing the spread when the definition of "Slow The Spread" necessarily includes a population.
CON Also Wrongly States An "On Balance" Or "On Average" Is Required:
Heads up, there is no "On balance" or "On average".
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. But the definition of "fewer," According to Merriam Webster:
a smaller number of persons or things [2]
Likewise for" cause":
something that brings about an effect or a result [3]
Therefore, what is being debated is if the COVID-19 vaccine brought about fewer transmissions in a population of the same size between vaccinated and nonvaccinated people. Any arguments stating there is no "on balance" or "on average" are pointless, because we are debating raw numbers of transmissions in two equally-sized populations, not whether there was a higher statistical average of spreading or a change between two individuals. It is one group vs. another group of the same size, and which group had fewer or more COVID transmissions.
CON's rebuttal, therefore, flagrantly ignores the established definitions for this debate that he agreed to. He is attempting to change the debate rather than adhere to what he agreed with (which is also banned under Rule 2).
As I previously proved with the meticulous research I cited, two COVID populations of the same size WERE investigated, and the results proved that the unvaccinated had fewer transmissions than the vaccinated. Therefore, I have proven that the vaccine did not slow the spread of COVID-19 in accordance with the debate definitions.
Since "Slow The Spread" means cause fewer cases, I had to argue that there were not fewer cases. I have sufficiently done so. The study of hundreds of thousands of Israelis conclusively found that there were not fewer cases in the vaccinated population of the same size as the unvaccinated population. Therefore, I have proven the COVID-19 vaccines did not slow the spread of COVID-19, since there were not fewer cases.
R2: CON Uses An Article Written By A Person With No Background In Science Or Epidemiology To "Prove" Vaccines Slowed The Spread
The National Geographic article was written by Tara Haelle. Tara Haelle has no background in science nor epidemiology. She is an English major from the University of Texas At Austin and has her masters in... photojournalism. She has never worked in a laboratory, for any reputable epidemiological or virology organization or institute, and has never had any formal training in medicine. [4] Hardly an expert in anything related to vaccine efficacy.
Dr. Anthony Fauci, the director of the National Institute of Allergy and Infectious Diseases, noted it is dangerous to follow the opinions of non-experts:
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]
Unlike CON's source, Dr. Paul Elias Alexander, the person whom I cited, is an epidemiologist who consulted for both the World Health Organization and the Department of Health and Human Services as part of their COVID-19 response team. Additionally, he also got his degree in epidemiology and medical research and evidence-based medicine. [6]
In his article, he stated:
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]
According to the body of medical research and one of the foremost experts in epidemiology, the vaccines have done nothing to curve transmissibility. At minimum, this proves my case correct, because I simply have to prove that the COVID-19 vaccines did not cause fewer cases of COVID-19. The research and experts seem to agree on this one.
One more thing worth noting, Dr. Paul Elias Alexander cites more than 160 studies that he compiled along with:
- 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]
So the combined studies were added by respected medical doctors, epidemiologists, professors of medicine and public health, and others. In other words, the experts disagree with a random National Geographic writer with no experience in epidemiology or virology. This is in addition to the study which looked at more than 600,000 people who has COVID-19 and separated them into equal (and equally-represented) populations of vaccinated and non-vaccinated that corroborated the results that the vaccines did not provide additional benefits to slowing the spread of COVID-19.
SOURCES:
Con
#6
1. Population
Alright, what is the population at play here? 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. Despite Pro citing a large number of reputable sources in their own right, their "populations", which in reality are just samples, are different. They survey different sets of people, which is inevitable.
To estimate the population parameter from a sample mean, a sample error is used along with a confidence interval. For example, for a normally distributed sample(which by no means am I assuming it is relatable to the case at play here), a z* value of 1.645 is used for an estimation of 90% confidence level. As the confidence level gets larger, the Z* value gets larger assuming an identical distribution regardless of anything else. If the confidence level gets to 100% actually, the margin of error would therefore be infinite. 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.
Obviously, an undefined population outside the confinements of samples would bring neither party any benefits, since it would be equally impossible to uphold, absolutely as true, that the vaccines did/did not at any point slowed down the transmission. Surely, the two are mutually exclusive, but we would not know which one it actually is, because that is how statistics works.
As nicely pointed out, both Pro and Con has a BoP here:
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
So both parties ought to find ground on a defined and confined population(or a pair of populations, as how Pro worded it) with every single individual in the population recorded by accessible data in samples. However, Pro never defined either the vaccinated or the unvaccinated population to be anything. Is it the world population, the US population, the Chinese population? None of them, because Pro never gave any confinements on what the population should be(Pro just stated that there needs to be a population). Therefore, the population can be as large as any sample(and only as large as the sum of related samples, because were it any larger, it devolves into a middle ground where neither party can absolutely prove their side, in this debate where both sides hold a separate BoP). The population
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.
You might ask, but Mr. Intel, doesn't a population require...(BLAH BLAH BLAH)?
3a: a body of persons or individuals having a quality or characteristic in common
Click on the blue link to see this entry. As long as the vaccinated population is all vaccinated and the unvaxxed population unvaxxed, it qualifies, and yes, again, to whoever it may concern, since 1 is a number, 1 vaxxed/unvaxxed individual technically qualify as a population. This definition is agreed by my opponent.
2. Only 1 Instance... AT ANY TIME!
Suppose if I ask you, "did you do your homework?"
You answer: "Was that the one you gave out last month?"
I respond: "Yes."
You respond: "Then surely, yes."
For the instance above, as long as you have done your homework at any point from the moment it is being offered down to the moment I ask you about it, the answer to the bolded one question would be yes. Yes, at any time. I could do my project today or I could do it at December the 31th if the deadline is February 15th and it is open now. Likewise, if the COVID transmission did get slowed by vaccines at any moment within consideration, Con's claim comes true.
The resolution statement gave no explicit time marks, so I am going to assume the maximum time extent possible to be considered, which is from the moment COVID vaccines existed to now. Heads up that the next round is the last one, so the opponent cannot define what the population in question is nor the time worth the consideration.
Unless at all moments, even down to the the Plankth of a second, it is possible to be shown that the transmission between vaccinated individuals in all possible populations(which is just any collection of individuals based on whether they are vaccinated or not), Pro's claim which Pro defends automatically fails. If just in 1 possible pair of set of people, COVID was spread to more people among the unvaccinated population than the vaccinated one in an equal time frame, that would mean Pro's claim fails.
Once again, the definition of "Slow The Spread" states:cause COVID-19 to ultimately spread to fewer peopleThis is important because, without those qualifiers, CON would be correct.
Exactly, and with those qualifiers, CON would still be correct due to how Pro lacked bounds on several crucial concepts.
3. "Sources"
Pro accused the article writer of the article I provided last round to be unprofessional in terms of the field. Notice how I embed the link of the article in blue in text without giving the URL in text form. Therefore, if Pro can click on that, Pro can click on these.
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.
Many of these are written by authentic origins and Haelle just collected them because that is what a journalist does. Just 1 pair of set of people is enough to prove Con to be correct and I have several. The fact I have all these highlighted in blue waiting to be clicked on only to be met by Pro's disappointment for the collector of these data to not be a medical professional shows that Pro did not read these things seemingly.
Also, comparing the same group of people before and after vaccination should count as "two populations" if we are juxtaposing them. Pro's studies have included this kind of method too.
Conclusions
- 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!
I rest my case.
Round 4
Pro
#7
R1 - CON Continues To Wiesel Out Of The Established Definitions
CON stated:
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.
The description clearly states:
Slow The Spread: cause COVID-19 to ultimately spread to fewer people than in an unvaccinated population of the same size.
From this definition, the populations are clearly defined:
- They are either Vaccinated or Non-Vaccinated
- They came into contact with COVID-19
- They consist of groups of people equal to one-another.
These are clearly defined characteristics of the populations at play here.
CON stated:
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.
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. I would like to remind readers of Purdue University's Center For Food And Agriculture Business's article on statistical analysis, which states:
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]
Why does this matter? Because all of CON's ignoratio elenchi rebuttals (CON is not supposed to argue there is nothing to argue about, he must argue that the vaccines DID slow the spread of Coronavirus, something which he repeatedly sidesteps in his rebuttals) hinge on the idea of uncertainty. First he wrongly asserted that there were no stipulations on the population size.
Now he has backtailed on that, as evidenced here when he says:
Alright, what is the population at play here?
So he openly admits we are arguing populations and that his first rebuttal saying he didn't need one was wrong.
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.
In fact, the PennState Eberly College Of Science provides a complete rebuttal to CON's claims on their textbook for Epidemiological Research Methods:
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 TestingPower 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]
What does this show? It shows that the testing used in epidemiological models is based upon a set of clearly defined hypotheses and populations. Unlike what CON is stating, real data scientists make statistical models of populations through defining the groups, controlling for equality of proportions, and getting a good sample size.
Let's re-examine the original study comparing two equal, well-defined populations with a 1-to-1 ratio that I cited earlier. They explain their methodology here:
Statistical analysisTwo 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]
They also state:
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]
The researchers conformed to the proper methods for data science. They defined the variables, they used a representative sample size, and they also strove for a high confidence interval. This is unlike CON's methods based on assumptions, rejecting populations, and the opinions of magazine writers.
The study authors used logic regression, which is inline with best practices for data science. As PennScience's Eberly College of Science explains:
Logistic regression can describe the relationship between a categorical outcome (response variable) and a set of covariates (predictor variables).[4]
However, the study authors even went a step further and controlled for covariables, which is precisely what all good data scientists do, as PennState's Eberly College of Science notes:
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]
As I previously showed in earlier rounds, the study authors created equally-representative populations and adjusted for comorbidities, age, and more to create populations that were equal to each other. This, therefore, as PennState's Eberly College of Science tells us, shows us stronger associations than if they did not follow proper data science methods, unlike CON.
R2 - CON's source used studies with incomplete data.
Therefore, if Pro can click on that, Pro can click on these.
The first study linked to, which claimed "cut by 75%" stated:
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]
What a surprise. A study that did not have a sampling of vaccinated and unvaccinated controls leads to incomplete data. Unlike the study I cited, which had controls for both population groups.
In the "fourfold" link, the study authors state:
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]
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 39,000 screened study also makes clear they did not have equal populations:
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]
Once again, they did not norm for age, making the populations different. The population sizes are also not the same, thus violating the debate description.
The University of Texas study used radically different population sizes:
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]
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.
Once again, this is why Dr. Anthony Fauci noted it is important to follow the opinions of experts and not magazine writers with no background in the subject they are writing about. Dr. Alexander is a noted epidemiologist, and he compiled more than 160 studies along with other respected medical experts. These studies found that the vaccine did not slow the spread of COVID-19.
SOURCES:
Con
#8
Rebuttal: Populations
From this definition, the populations are clearly defined:
- They are either Vaccinated or Non-Vaccinated
- They came into contact with COVID-19
- They consist of groups of people equal to one-another.
Exactly. However, Pro did not specify, at any of the four rounds, a sufficient boundary for chosen populations. Pro could have set the population a minimum of 10,000 and disable anything sample/population under this number from amounting to any effectiveness in the context of this debate. Pro did not. Therefore, a pair of populations chosen from the world in any number, in any preference, in any size, as long as one of them is vaccinated and the other is unvaxxed, both of them live in a society with COVID, and both are equal in size.
Pro's qualifications, no matter how hard he has tried, has failed to rule out cases such as:
- 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.
Recite my R3:
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.
Even if we eliminate where the two groups are significantly different(1 vs 1000, etc.), Even if we treat both populations with the exact same standard, there is still an almost infinite number of choices. We can have unbiased or biased populations sized 1, 100, 10,000, or even 4,000,000,000 and they would all count as long as one is vaxxed and one is not, both live in a society with COVID, and both are the same size. Obviously, scientists would not do such biased experiments because if the population was chosen with bias, the peers reviewing it will not buy it. However, this debate has not ruled out such populations(and Pro's 3 criteria actually enabled them, being final in the last round) so they count as much as an actual population as what Pro gave.
We need a population, however, the population is a variable, not a fixed parameter unless Pro specifies. Are we arguing the world population, the Asian population or what? Pro does not say. Therefore, we keep the assumption that a variable is everything said above and can be anything said above, including a pair of populations sized 1.
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.
Wrong. What I am saying is Pro's approach of using samples to estimate a larger population can never be absolute. If the confidence interval is 100%, the interval would be infinitely long. Therefore, giving an example where a larger population is chosen than could be measured accurately would yield a middle ground amounting to nothing for Pro. You, for example can't just survey every single person in America right now, so the entire American population cannot be drawn on absolute conclusions. We could be 95% sure that vaccines sped up transmission or even 99%, but unless it is 100%, it can never be claimed to be true that "The vaccines did not slow down transmission" due to uncontended points on how English has worked.
What can we conclude with 100% certainty? If the sample IS the population. If we measured the entirety of, say, 30 people, we can conclude with certainty, 100%, that the transmission between the times surveyed is what. Actually, Pro dominated his sources sections with studies that cannot conclude 100% what the transmission speeds, attempting to estimate the parameter of a larger population than the sample surveyed. The absolute inconclusivity in these studies would be dismissed as weighing barely anything in this debate.
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 two samples are randomly similar in quality and quantity, which fits Pro's criteria, For Pro saying this after providing nothing on what other attributes a population must have, this is at best nonsensical and at worse contradictory. Because these samples and populations are randomly similar to each other and obviously satisfy Pro's 3 criteria listed, I extend them as one of at least one examples that supports COVID slowing transmission down.
Not only that, this is the FIRST TIME Pro has brought up possible attributes a "bad" study posesses, mentioning nothing about it in the before rounds and despite all these sticked with only these simple criteria of what a population could be in this debate. This may have as well violated the unspoken rules of making new arguments in the last round, and may have also raised contradictions, as Pro says something is a valid population in one place and not one in another place in the same document sent, resulting in blatant contradiction.
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.
Are employees people? Oh gosh, are you perhaps claiming that employees are not people, how heartless! Jokes aside, picking relative ratios within the study that did not make the populations exactly the same(such as picking 500 vaxxed people out of 625 if only 500 unvaxxed people are here), making it however biased, still does not defeat the definition of what a population is for this discussion. They are still instances in the flow of time.
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.
You are right, but during the Topic Interpretation argument the last round which you appeared to show no objection to for these two rounds(with conditions easily satisfied), the topic could be simply proven wrong by providing one counterexample regardless of the power it brings. Pro never denied the existence of such populations. They exist.
Conclusions
- 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!
I rest my case. Thank you, random voter, for reading this.
Criterion
Pro
Tie
Con
Points
Better arguments
3 point(s)
Better sources
2 point(s)
Better legibility
1 point(s)
Better conduct
1 point(s)
Reason:
I thought I might have to do a longer RFD for this, but honestly, there's not much to talk about. Pro's argument goes largely unrefuted, and Con's argument is largely built on arguing that Pro cannot affirm the resolution if certain conditions aren't met. So, let's talk about those conditions.
He starts off by arguing that the topic is an absolute, i.e. that any individual instances of "slowing the spread of COVID-19" are sufficient to negate. This is a nitpicky argument that I'm having trouble getting on board with from the start, and it devolves into semantics. The inclusion of an "on balance" or "on average" would have helped, but just because the topic lacks those doesn't mean that you can insert the term "in any instance" into the start of it. The topic pretty plainly addresses the whole of the issue: what have the vaccines done as regards to the spread of COVID-19? Did they slow the spread or did they not? If they sped up the spread of the virus, then on the whole, they did not slow the spread. That's a pretty straightforward interpretation, and while Con claims that Pro never addressed all this in his final round, it's pretty clear that he did. The absolutist interpretation only spreads so far, and frankly, Con doesn't do enough to support it. Simply saying that it's possible to interpret the topic in that way, as far into the debate as R2, is not enough. There's some discussion before that about whether speeding up the rate of transmission would count in Pro's favor, and frankly, it does. The rate of spread needs to go down for it to slow. If its rate goes up, then the rate required to be called "slowing" requires a higher threshold. I think this debate could have done with some clear burdens up front that examined this, but that's how I'm interpreting it.
It doesn't help that Con's support for his side is relatively light. Con provides a single source in R2, which includes a number of internal sources. Pro responds to all of them by the end of R4, and yes, he is allowed to address those sources going into the final round. It was still up to Con to defend them, which he barely does. I buy that the research presented complicates Pro's conclusions, but Con doesn't seem concerned in the slightest with the possibility that winning on that level may not be sufficient to win the debate. A lot of the rest of Con's case is built on introducing more uncertainty. Uncertainty is fine, but you still have to win that your studies are demonstrating that uncertainty and not so flawed that they should be dismissed out of hand. I don't agree that they should be dismissed as readily as Pro seems to believe, but Con had to do the work to support them. Even if he had, though, the absence of any direct comparisons between studies just leads me to believe that, with all things taken into consideration (the speed ups of transmission presented by Pro and the slow downs presented by Con), it's more likely that things lean in Pro's direction, meaning that the vaccine in aggregate didn't slow the spread of COVID-19 based on the presented data. That's enough for me to vote Pro.
Con, I think relying on skewed interpretations of the resolution is only ever going to work if your opponent isn't actively arguing back the point. If you both have interpretations of the resolution, then the judge will decide which is more reasonable, and when you're arguing that the topic should only be interpreted in the most absolute of terms, that's not going to help your case. You did a lot of semantic work here, but frankly, it seemed mostly nitpicky and dedicated to pointing out how Pro could have specified the resolution better rather than how the topic should actually be interpreted. You might have had a better chance convincing me if it didn't feel like every round you were moving onto a new semantic problem rather than engaging in the debate before you.
Well, considering at least 8 people were hospitalized after receiving the Johnson and Johnson vaccine, and considering thousands were hospitalized with heart attacks and heart problems due to the mRNA vaccine, I'd say putting yourself at risk of dying by taking a vaccine just to have an inferior vaccine immunity is also not a great tradeoff.
God shows no subjective intentions. Even if God exists, whenever an antivaxxer dies, he probably would sigh, "another one bites the dust."
Prominent Christian televangelist and anti-vaccine advocate Marcus Lamb died after being hospitalized with Covid-19. Isn't God showing us that he wants us to be vaccinated?
Past infection may better protect against Delta than vaccine, but "Putting yourself at risk of dying to have natural immunity is not a great tradeoff" says expert.
Also, fwiw, I am not a "vaccine doubter." The data does show that if you are 65+, getting the vaccine has almost no serious side effects and is successful at preventing COVID-19 compared to other age groups. So, scientifically, the COVID vaccine is practically harmless for the elderly.
But what remains to be seen is whether negative immunity from the vaccine also extends into the elderly.
But mRNA vaccines in general are still experimental. They are different from traditional vaccines, in particular inactive virus vaccines, which were used to fight smallpox successfully.
It's a hard sale to tell me vaccines don't work, particularly because I've been vaccinated myself in the past. It is just this particular vaccine that has not really been shown to work all that well in studies and been shown to have an unusually high prevalence of dangerous side effects.
Considering COVID-19 has a 99.5%+ survival rate in non-immunocompromised adults under the age of 65, why take an experimental vaccine that comes with serious side effects like heart attacks, bell palsy, blood clots, and more? I've also already had both original COVID and Omicron. So I am naturally immune. There's no reason to get a vaccine if you are immune already. That's what the point of vaccines were supposedly for, making someone immune.
But the study I cited was a 1 to 1 ratio between vaccinated and unvaccinated people controlling for comorbities to make the populations the same. This is completely different than doing what the WaPo epidemiologist is saying people are doing.
What the WaPo epidemiologist is saying is people sre looking at raw, un-normed case data and making unquantified observations. This isn't what the study did. The study quantified the data, defined the data, and made it as similar as possible to the unvaccinated so a really accurate model was drawn. And they found the vaccines were the only difference between the infection rates of the groups. All other variables were controlled.
The WaPo epidemiologist is right that many are just looking at the raw case numbers and not going deeper. They did it with the unvaccinated for 2 years, now they are doing it with the vaccinated. But the study did not do this. It actually controlled for variables and built two similar populations of the same size in a 1 to 1 ratio as much as possible, even getting the same distribution of age and comorbidities.
From the Washington Post: Vaccine doubters’ strange fixation with Israel
Analysis by Aaron Blake
It’s true that most new cases are coming from the vaccinated community, but that’s in large part because of how relatively big that community is in Israel. The latest numbers show that 85 percent of Israeli adults are vaccinated, meaning there are more than five times as many of them as unvaccinated people.
Epidemiologist Katelyn Jetelina last week explained this misleading use of data, which is known as a base rate fallacy — or base rate bias in epidemiology:
The more vaccinated a population, the more we’ll hear of the vaccinated getting infected. For example, say there’s a community that’s 100% vaccinated. If there’s transmission, we know breakthrough cases will happen. So, by definition, 100% of outbreak cases will be among the vaccinated. It will just be 100% out of a smaller number.
The debate prompt is not "what does Geon arbitrarily feel is honest debating."
Intelligence and I are debating whether the vaccine was effective. This requires proof, since we both share the burden of proof.
Don't think I'm allowed to say till after the debate is done right? but you haven't been honest-very naughty
I won't comment on how effective Intelligence's delineation is between those two topics (I'll save that for the RFD), though I will say that I understand where Intelligence is coming from, whether it affects the outcome of this debate or not.
If something speeds up the transmission of it, it does not slow it down. My argument is that it did not slow the spread because it sped up the spread. That isn't an ignoratio elenchi. It is completely on-topic. An ignoratio elenchi would be if I argued it sped up the spread in lab rats. Since we are talking about human beings, that would be off topic. But it necessarily follows that the spread was not slowed if it was sped up. You need one to have the other. Therefore not off topic.
After glossing at the description after R2 submission, yes, pretty sure you have confused "did not slow" with "sped". I am pretty sure that is a form of ignoratio elenchi.
And yes, "CON shall only argue that The Coronavirus mRNA Vaccines DID Slow The Spread Of COVID-19", I always argue EXACTLY what the phrased topic wants me to argue.
Having written several literature reviews and continuing to work in a field where examining existing research is a required part of the job, paywalls are the bane of my existence, so I wholeheartedly agree.
I'll say one thing, it sure was nice to have almost all COVID research in almost every journal available for free. That made it great to learn a lot about COVID-19 and immune responses specific to COVID-19 and the vaccine efficacy in general. Normally that stuff is paywalled behind $500/month subscription rates lol
Needless to say, this is my jam, so I’m looking forward to it as well.
I can't wait. It'll be interesting to debate a microbiologist on this subject.
Yes, I would be arguing that they did slow the spread, though there are other facets we could choose to explore if you wish. Worth discussing this after the debate ends.
awesome. When this one is over we can establish terms in a forum post and re-debate this. Are you taking the CON or PRO stance? e.g. will you be arguing that COVID vaccines did not slow the spread or that they did? I figure you will be arguing that they did slow the spread, right?
I'd be down for that.
I can re-debate this one with you if you'd like. We can discuss terms in the forum.
What is dishonest about citing research?
i guess i cant comment exactly what till after, but pro seems to be pretty dishonest... just like, well.. guess i have to wait.
I'll vote on this debate and do my best to leave my personal opinions that I'll try to leave out of my RFD, though I'll admit that some of these interpretations of the research are... interesting.
FWIW, IID is abbreviated for "It Is Decided." I guess I need to make that clear in future debates.
There is actually a way that your argument is absolutely undefeatable, I will not tell you how but it exists.
Ah. I see I managed to be matched with one of the best on this website. This will be fun.