On 2020-12-20 20:25:16, user Pablo Pablo wrote:

I can't explain why this result is in contradiction with this other one.eurosurveillance<br /> Could it be because of the Simpson paradox?

On 2021-01-05 09:55:32, user Disqus wrote:

In addition to the previuos comments I read, page 7 "SARS-CoV-2 positive incidence rates were calculated for staff and students attending an educational setting, irrespective of whether the infection was acquired within or outside the educational setting."

thus it is evident that if the incidence is higher among teachers than the general population, <br /> schools are not the safest places, with a perhaps low transmission rate among students but a <br /> greater transmission rate from students to teachers

On 2020-12-24 23:19:01, user Matthias Fax wrote:

By design, this study could only fail to meet its hypothesis. It only proves what was to be expected. They used inappropriate dosage in oral form. They accepted an inappropriate delay after onset of symptoms. They didn't mention the significance of 25OHD sufficiency for patient outcome, indicating that the oral dosage was given too late to be of any immunological use.

On 2020-11-18 12:16:47, user Robin Whittle wrote:

I did not see any mention about how long after supplementation the 25OHD levels were tested. D3 takes some days to be converted in the liver to circulating 25OHD.

Since the intervention was with already-hospitalised patients, on average 10 days after their symptoms began - and with 25OHD levels rising over a period days, with the average length of stay about 7 days, this intervention may have been too late, and perhaps too little.

In the Cordoba trial (Castillo et al. https://doi.org/10.1016/j.j... "https://doi.org/10.1016/j.jsbmb.2020.105751)") 0.532 mg 25OHD calcifediol would have raised 25OHD levels within a few hours, probably above 100ng/ml on average - if one extrapolates from the curve shown for 0.266mg (a single Hidroferol capsule, of which two were used in Cordoba) in this patent: https://patents.google.com/... This greatly reduced the need for intensive care and eliminated deaths.

Since hospitalised COVID-19 patients have an extremely urgent need for raised 25OHD levels, so the autocrine signaling systems of their immune cells and many other cell types can function properly (McGregor et al. https://www.biorxiv.org/con... "https://www.biorxiv.org/content/10.1101/2020.07.18.210161v1)"), a combination of 25OHD calcifediol with bolus D3 may prove more effective than either treatment alone. The bolus D3 would sustain 25OHD levels for weeks, and the D3 itself may protect the endothelium (Gibson et al. https://doi.org/10.1371/jou... "https://doi.org/10.1371/journal.pone.0140370)").

On 2020-12-28 23:51:01, user ErikCarter wrote:

You really ought to test for infectious virus, rather than just RNA. Otherwise you can't truthfully claim that the variant results in higher "viral load"...you've not measure viral load, you've measured RNA load. These are not the same thing.

On 2021-01-08 15:09:29, user Kevin McKernan wrote:

Can the authors explain the mess in Table 2? This dilution series is non-linear and any student delivering such data would be told to repeat it. If it is in 6 replica's, you should share the dispersion in that data. There should a clear 3.3 Ct shift in each 10X dilution. If you dont have linearity in your dilution series how can you make a Ct cutoff? The non-linearity is non-concordant across different amplicons? It is frightening this is being used as a diagnostic test. Are there any internal controls in the test to ascertain the sample prep variance? Dahdouh et al demonstrates 10-16 Ct variance in RNaseP signals (human gene) suggesting tests that lack internal controls to normalize for swab and sample prep variance are random number generators. Table 2 also looks like a random number generator.

On 2020-12-29 01:55:20, user F Alexander Diaz-Quijano wrote:

This article is now published in the Journal of Clinical Epidemiology.

Share Link:<br /> https://authors.elsevier.co...

On 2020-12-31 14:23:25, user Don Wheeler wrote:

Interesting. Additional research with a larger sample size featuring a broader cross section of the population will be most beneficial. Let's see where this leads.. Great work! @ComaRecoveryLab #covid19

On 2021-01-05 03:55:19, user mahejibin khan wrote:

Though air transmission of the virus has been suspected , swab samples collected in unsterile conditions for RT-PCR screening of human subjects continues to be a practice in many regions/countries. <br /> Two mass scale nasopharyngeal swabs of employees of an establishment in Mysore, Karnataka, India, collected under unsterile conditions in their premises, by seating them in an open ground and screened for SARS-CoV-2 infection by RT-PCR, identified a large number of asymptomatic SARS-CoV-2 positive cases. Thus the establishment forced a two-day campus lockdown, on both the occasions, in order to sanitize and break the virus transmission chain. Since most of the infected subjects remained asymptomatic through their home quarantined period, they were certified for fitness to resume work. Since reports have shown patients fighting SARS-CoV2 infection developing IgM and IgG antibodies between 6–15 days after disease onset. Blood samples of two RT-PCR positive asymptomatic subjects after 17 day home quarantine were analysed for the presence of IgM and IgG antibodies. Absence of detectable titres of antibodies to SARS-CoV-2 virus in the two blood samples suggested lack of acquired immunity due to asymptomatic patients unexposed to the virus. Nasopharyngeal swabs positive for the virus by RT-PCR inferences establishment of the Covid-19 pathogen infection in the host. Absence of prodromal symptoms for the disease in these subjects and some of them testing negative in a second Rapid Antigen Detection Test (RAD) opinion, when swabs were sampled in designated hospital rooms, suggested occurrence of air borne virus and swab contamination during sample collection under unsterile conditions. <br /> Droplets that are sneezed or coughed behave differently in the open air, according to environmental conditions like temperature, humidity, ventilation, and the amount of virus deposited.<br /> My observations on plausibility of air borne SARS-CoV2, RT-PCR determining their fairly high numbers and prevalence of asymptomatic subjects living in that environment provides leads for studies with reference to herd immunity from the purview of viral attenuation due to environment and/or innate immunity initiation through pattern recognition receptors

On 2021-01-05 15:56:38, user Ti wrote:

You write that "the best performing method is XRAI (AUPRC = 0.224 ± 0.240)". Meaning that the AUPRC ranges from -0.016 to 0.464. Surely, you cannot have a negative area under the curve.

On 2021-01-09 15:11:56, user Derek Enlander, MD, MRCS, LRCP wrote:

The "long Haul Post Viral" SARS 2 Covid19 effects, Fatigue, Myalgia, Cognative defect, insomnia etc are reminiscent of the symptoms reported historically by Melvin Ramsay in 1955 when he reported these symptoms in a cohort of young doctors and nurses in the Royal Free Hospital in London. He termed the outbreak as Post Viral Fatigue, renamed Myalgic Encephalomyelitis (ME) and later Chronic Fatigue Syndrome (CFS).

On 2021-01-10 22:17:22, user Wayne Griff wrote:

Single dose vaccine efficacy is not 90%. It's less than 50%, and that's after only 3 weeks. It would be even less effective at 6, 9, 12 weeks or more. More importantly, at 3 weeks the neutralizing ability of 1 dose is only 1/5th as much as Convalescent Plasma (NEJM)

On 2021-01-11 23:14:43, user Chaitanya wrote:

Excited to read the paper since its amazing that the authors have both in vitro and patient sample data. I a curious to read more with regard to false positive/negative and the role of NASBA amplification

On 2021-01-13 11:39:34, user carina brehony wrote:

hopefully a full published paper will acknowledge the laboratories, public health departments and the Health Protection Surveillance Centre that collected, validated and provided the data which was then made available publicly

On 2021-01-15 16:00:23, user Martin Reijns wrote:

Congratulations on this work. One comment though: I know it's difficult (if not impossible) to keep up with all the literature on SARS-CoV-2, but I just wanted to say that the statement "Currently, no test combines detection of widely used SARS-CoV-2 E- and N-gene targets and a sample control in a single, multiplexed reaction" is incorrect. Our paper on this has been on medRxiv since June:

https://www.medrxiv.org/con...

and was recently published in PLoS Biology

https://journals.plos.org/p...

All the best, Martin

On 2021-01-18 05:38:05, user Benjamin Seng wrote:

This paper has been published in General Hospital Psychiatry (journal) and is available at the following link: https://doi.org/10.1016/j.g...

On 2021-01-21 09:05:28, user Dominik wrote:

The conclusion drawn here is simply wrong: "suggesting that current SARS-CoV-2 vaccines will protect against the 20B/501Y.V1 strain" when in fact they didn't check for all 17 epitope changes of mentioned strain but only N501Y which was never thought to be immune evasive. The same erroneous conclusion was drawn in the paper of Uni Texas which also only tested against N501Y but not all mutations.

On 2021-01-24 20:42:28, user Thomas Arend wrote:

Just some short remarks to table 5:

(35-5)/35 is 85.71% and not 82.86%

If you round 32/23 = 91.4285.. you get 91.43 not 91.42

Typo? 34/35 = 97.1428 ~ 97.14 ... not 97.12

And I agree with Michel Schrader comments to the presicion of 4 digits.

The hypothesis H0: specifity = 94% / Ha: specifity <> 94% you can only be rejected for AgPOCT V. p=0,0168.

H0: Specifiy = 92% cant be rejected for any AgPOCT.

You should calculate a CI for the specifity.

On 2021-01-26 23:19:50, user Janet Aisbett wrote:

The analysis as presented does not appear to support the conclusion that “individuals discharged from hospital following COVID-19 face elevated rates of multi-organ dysfunction…..”. We can conclude that these individuals have elevated rates of multi-organ dysfunction, but we have no way of knowing whether these were ‘new-onset’ events after discharge or were factors contributing to the severity of the individual’s COVID episode. This is because ‘new-onset’ events are defined with respect to HES APC and GDPPR extracts over the ten years prior to 2020. It would help if counts of ‘new-onset events’ were provided, broken into those which first appear in 2020 but before discharge (e.g., as secondary diagnostic codes alongside the COVID primary) and those which first appear post-discharge.

Forgive me if I have missed something, but I also have concerns about the 1:1 matching of the COVID cases to controls. Supplementary Table 1 suggests very coarse matching criteria. The use of an age category 70+ is particularly striking, given the comparative age distributions of COVID versus all hospitalisations. The matching of clinical characteristics also deserves further explanation. As presented, it appears that an episode of skin cancer eight years ago could allow a match to an individual with metastasised tumours requiring palliative care. Since more serious comorbidities may be a factor in COVID hospitalisation, matching on coarse clinical characteristics may tend to select a healthier control group. Presenting frequencies of selected sets of ICD-10 codes by age for the COVID cohort versus the control group would help resolve this question. Also worthy of explanation are the decisions not to include dementia in the matched histories, and not to consider previous hospitalisation.

Finally, the Supplementary Table 3 shows quite different outcomes for controls matched to ICU COVID cases compared with controls matched to non-ICU cases. These differences are not reflected in the COVID cohort. Although numbers are small for the ICU control group, the discrepancy is worthy of comment.

On 2021-01-27 14:15:18, user Antonio Beltrão Schütz wrote:

I think that this article is important, considering that in spite of does not proof by mean RT-PCR test that ivermectin can turn negative viral load in patients with increased viral load of Covid-19, it decreased the mortality (4/112) patients. This data extrapolated to 100.000 or 1,000.000 cases is significant.

On 2021-04-12 15:56:55, user Philip Machanick wrote:

I am also curious how in a double-blind trial it comes about that someone in the control is given the test drug.

On 2021-02-04 14:21:22, user Peter Ray wrote:

The suggested reason for the increased case rates for the first 10 days or so after injection is a possible change in behaviours to being less cautious.

Another possible reason is the dramatic increase in Covid prevalence occurring in Israel generally at the start of the vaccination program (late December). Given that the positive case data is available in public it might be worthwhile including a comparison of general population case rate on the daily incidence chart.

On 2021-02-04 21:24:38, user Charles wrote:

I am a bit unsettled by the days they decide to pick to get their best (around 90%) estimate.

They use the daily rate from day 1 to 12. Day 1 it's .028% and average until day 12 is around .041% (there is a spike in infection from day 1 to 12). Day 21 is .004%, day 22 is .011%, day 24 .006%, i.e. there is some standard-error. <br /> Now, it's all about which days one picks. <br /> - if one calculates Expected as being day 1, the best effectiveness rate is 86% on day 21, but on day 22 it dwindles at 61%...<br /> - if you use day 1 to 12 as Expected, effectiveness rate is around 85% on day 24 and 73% on day 22. <br /> - to get the 90% in the paper, you need to pick day 21 (the lowest incidence, that went up again the next days) and the Expected as days 1 to 12 (the highest incidence).

It seems that efficiency estimate does improve over time, but reaching 90% depends on which days one picks, both in term of "actual" and "expected". This choice might very well explain fluctuations between 60% and 90%, i.e. the estimate is very sensitive to small numbers and differences. Differences with previous estimate might be methodological (no proper control group).

On 2021-02-06 02:50:56, user Crystal Sonia wrote:

How accurate and reliable is this SIR model? With the mco arent the cases still increasing? How are the beta and gamma estimated? What's the sensitivity and specificity of this model?

On 2021-02-15 15:10:33, user Paul Wolf wrote:

Near the end of the abstract, you say the data is "suggesting parallel evolution of a trait that may confer an advantage in spread or transmission." Why would this mutation be occuring in different parts of the US and nowhere else in the world? That doesn't suggest convergent evolution, but a common origin.

On 2021-02-16 20:47:03, user jiver wrote:

NHS are obsessed with over simplifying race. Why ask people to self report in only 3 categories? And two are skin colour but the other is geographical. Why on earth? What did you hope to achieve by only going this far? Already people are using this to discredit non whites. And NHSP have done it before, exactly the same, 3 categories allowed only.

On 2021-02-19 20:04:10, user Jacqueline J Clarke wrote:

Have you confused the symbols lesser than and greater than ? The occupying sentences don't make sense ?

On 2020-10-31 07:52:03, user Robert Eibl wrote:

This looks interesting, although there are a few caveats mentioned in the paper. Nevertheless, it should be possible almost everywhere, and even restrospectively, to check the vaccination status of Covid-19 patients, not only for influenza - and compare this with the average vaccination status of a whole country. Then it should be immediately clear, if there is a major benefit.

On 2020-11-16 06:38:23, user whitecat31 wrote:

At the 39th replicant when the exponential phase is basically over? Am I understanding that correctly? Seriously? Did you guys run a comparison standard curve with 39 points? Something like the 39th replicant would be considered below the limit of detection and LOQ. So yeah.. your sample was contaminated.

On 2021-06-02 11:15:56, user Philip Ward wrote:

The authors of the paper have now published a second study that states their earliest positive sewage sample is from January 15th 2020 and does not make any reference to this preprint:

https://journals.asm.org/do...

On 2021-06-01 09:39:20, user Facundo Muñoz wrote:

Very nice paper.

I'd just like to point out a minor mistake in the text. At the top of page 6 it is stated that alpha_lockdown has a Gamma prior of mean 0.1667 and standard deviation 1.

This didn't match the stated 50/50 chance for decreasing/increasing effects. And indeed, thanks to your open-science approach to publishing I could verify that in the code [1] you used a Gamma prior with shape and rate parameters of values 0.1667 and 1 respectively.

Best wishes.

[1] https://gitlab.in2p3.fr/bou...

On 2021-08-15 11:06:56, user Dorian Dale wrote:

The most obvious flaw is depending on honest self-declarations of educational status. Go to LinkedIn for innumerable examples of resume inflation. The huge disparity between masters at 8.3% and PhDs at 23.9%. We are now seeing much analysis of how pervasive are dishonest responses to polling. If one is an anti-vaxxer, why not claim PhD status to add cred at the expense of over-educated elite?

On 2021-08-14 17:36:32, user Matthias von Davier wrote:

Overclaiming, or the use of straight-lining is another option, as are other types of response biases.

In addition, the level of hesitancy for self-reported PhD (doctorate in the questionnaire) is at the same level of hesitancy seen in the group that chose to not answer the education question (missing education information also has 23.9% vaccine hesitancy).

On 2021-08-17 09:28:07, user One bird one cup wrote:

"Additionally, we assume the survey was completed in good faith." .... The assumption is what bothers me here. The people responding to a survey on Facebook aren't necessarily representative, as they're self-selected. This does not inspire confidence. Who's to say the respondents answered honestly about their education level? In addition -- apparently those who live in communities who were largely for Trump in 2020 appear to have been more vax-hesitant. I'm not a statistics person so I can't judge how the authors adjusted for this. But I feel hinky about it.

On 2021-06-01 18:07:54, user japhetk wrote:

This research is problematic. <br /> First, this clinical trial's primary outcome measures were as attached below. And authors did not mention 2 out of 3 primary outcome measures. These are not good omissions apparently.

Second even the primary outcome measure they used was not specified before the study. Authors used ct cut value of 30, which was arbitrary. Authors explain why they did not use 40, but they must have used 40 if that leads to the good results. And that is not a clinical trial.

They did not correct for multiple comparisons across three primary endpoints, either, which they should have done as there are three primary endpoints.

Second as the figure 2 and the table shows the ct values of two groups before the intervention are close to statistically significant differences (p = 0.10).<br /> And as the figure 2 shows this group difference did not show even a hint of change at day 6! The two groups were almost statistically different from the beginning and that did not change visually at day 6 apparently. I can't see any hints of effects of IVM from this study.

Lay persons are watching this study, and they say they love IVM and hate vaccine and let's use IVM instead of vaccine based on this study's result. We hope authors do the proper research. They should provide three primary endpoints, correct the preexisting differences, and correct multiple comparisons, and should provide apparent conclusions.

Primary Outcome Measures :

Viral clearance at day 6 [ Time Frame: Outcome will be determined till 6 days post intervention ]

The primary outcome will be the viral clearance at day 6 in the intervention group compared to placebo.

Viral shedding duration [ Time Frame: Outcome will be determined till 14 days post intervention ]

Secondary outcomes: viral shedding duration (time between first positive PCR to last of two consecutive negative tests)

Symptoms clearance time [ Time Frame: Outcome will be determined till 14 days post intervention ]

Time between drug treatment and symptoms resolution

On 2021-06-02 13:20:34, user Robert Clark wrote:

Thank you for this report. In fact, several earlier studies on HCQ failed to report the effectiveness in their own data of HCQ specifically for the case of patients on mechanical ventilation.

Since this was one of the cases with the highest fatality rates, it was truly unfortunate that this option for treatment was not presented to doctors treating ventilated patients.

See discussion here:

Rapid identification of effective treatments for COVID-19.<br /> https://exoscientist.blogsp...

Robert Clark

On 2021-06-25 10:48:30, user ScottK wrote:

Since this was an observational study with a small number of survivors, the fear is that you draw conclusions that are tied to survival and not to treatments. Simply put...<br /> MDs won't prescribe HCQ/Zithro to certain risk pools.<br /> Cumulative dose may rise with survival, not the other way around.<br /> You've got 15% of the total population in the study on this regimen and 20% of the population survived. If the MDs picked 10-15 of the 'least contraindicated' patients to do anything with, chances are that you'd see higher survival.

Double blind, controlled or explain the heck out of the pooling and selection criteria.

On 2021-06-08 00:40:20, user Daniel Bastian wrote:

"The available pharmacokinetic data from clinically relevant and excessive dosing studies indicate that the SARS-CoV-2 inhibitory concentrations are not likely to be attainable in humans."

Say it with me now: cell culture studies != controlled clinical data.

On 2021-06-11 03:08:36, user Kel Sigmund wrote:

1. Do you know if the relative lack of severe manifestations among the vaccinees with breakthrough covid 19 was associated with the occurrence of prior Covid 19 infection before undergoing vaccination?
2. Do you have data on the demographics of the vaccinee recipients, especially risk factors for severe disease and age because it would be interesting to know if the vaccine truly mitigated the severity of disease or the vaccinated population is younger and healthier than most being health care workers.

On 2021-06-15 23:51:09, user Drew wrote:

Anyone know an estimate as to when the article will be peer-reviewed? Thanks in advance!

On 2021-08-04 02:30:29, user Deplorably Black wrote:

Interesting. Does this still apply considering<br /> the new variants?

Has a study been conducted as to the vaccines effect on long COVID?

I suffered from daily headaches post COVID for 8 months. They stopped immediately after vaccination.

I know several others with the same experience.

That in itself made vaccination post COVID worthwhile for quality of life.

On 2021-08-09 20:32:15, user KS wrote:

I haven't scrutinized this paper but even if all the results are accepted, the one-line "Conclusions" at the beginning is highly problematic without qualifiers. "Unlikely to benefit"? This study is limited, so the conclusion can't be so broad. Are elderly or long-haulers unlikely to benefit? You've got a 42 day window (seems arbitrary) so if you got the disease 6 months prior are you unlikely to benefit from the vaccination? The study doesn't address any of these things, yet makes a huge leap in its conclusion. This is a pre-print so PLEASE make a conclusion that fits your experiment and data. Because it's likely the only thing the general public will read and it will become the basis for more misinformation.

On 2021-06-16 18:24:27, user David McAllister wrote:

Given that randomisation to budesonide was stopped in March, are we expecting the updated findings soon?

On 2021-06-16 21:14:35, user Amanda wrote:

hi - i think there is a typo! This says 8/174 -- yet earlier it said 175.

The overall prevalence of persistent symptoms was 1.7% (80/4678 children; 95% CI 1.4%, 2.1%), and 4.6% (8/174 children; 95% CI 2.0%, 8.9%) in children who had a history of SARS-CoV-2 infection before persistent symptom onset.

Also ages 2-11 were overrepresented versus 12-17

On 2021-06-19 11:30:14, user Will Turner wrote:

Is this small changes in most subjects, or large changes in a smaller subset of subjects? You could get insight into this by showing the distribution (histogram) of (xbefore - xafter) where x is any brain measure found to significantly decrease. Figure 1 is very helpful, but it’s unclear what percent of subjects stay the same, increase slightly, decrease slightly, or decrease dramatically.

A second question your data should be able to address but the paper doesn’t: what are the % changes here? I get that fig 1 necessarily shows an index on the y axis for comparison purposes. But shouldn’t it be possible to construct this index with an absolute 0, because any length or volume measurement can be compared to 0. Then you could understand the difference in means in terms of not just statistical significance but magnitude, which is important for understanding what kind of effects this could have.

On 2021-06-22 13:45:29, user ibamvidivici wrote:

Both, age and BMI is highly correlated for the risk of Covid-19. Can you add the data about Median-age and Median-BMI for the groups:<br /> - positive SARS-Covid-19-Test<br /> - negative SARS-Covid-19-Test<br /> for both (LTCF and HCW) cohorts?<br /> This would be necessary to measure the margin of error for this study.

On 2021-06-23 10:31:57, user Otto von Ruggins wrote:

As a retired High School English Teacher, my concern with this Pre-print release is that at times it reads very poorly for a would be scientific paper. There are numerous errors in syntax and sentences that are not properly formatted. As much as I appreciate the findings of the researchers, I am disappointed in the lack of editing prior to the pre-print. I am willing to go through this paper and make corrections, but I can also imagine a simple Word document Spelling and Grammer check would also be a place to start. As an example, just try reading the paragraphs prior to the endnotes from "4. Muller’s ratchet, 'mutational meltdown' and fundamental principle of natural selection" on. You will encounter 'led' which should be spelled 'lead', two non-sentences in a row, the word 'where' which was probably supposed to be 'were', which would have made one of those phrases an actual sentence and more. Sadly, as I read this informative document, every time I came across these errors, I cringed at how it ever reached this stage with so many stumbling blocks to a proper English read!

On 2021-06-23 15:59:54, user Alain Tremblay wrote:

Do the authors have more information regarding the seropositive cases. Are these believed to be seropositive due to late phase of acute illness, prior SARS-Cov-2 infection, or prior vaccination? Since the trial recruited well into the vaccination effort in the UK, vaccination status of participants should be reported as well. Thanks for this great effort!

On 2021-06-29 08:20:33, user Max Pietsch wrote:

For the peer-reviewed updated version of this paper, please see: https://www.sciencedirect.c...

On 2021-06-30 07:39:55, user ViralPseudotypeUnit wrote:

This has now been published in The Lancet Microbe: https://www.thelancet.com/j...

On 2021-07-03 19:27:03, user Janet Cunningham wrote:

This paper is now published in The American Journal of Psychiatry:

https://doi.org/10.1176/app...

On 2021-07-08 13:48:55, user Eric wrote:

So is there a study that backs up 6 or 8 weeks for young and middle aged adults?

In Germany, the recommendation until last week was to hit exactly six weeks to stay within EMA licence but spread out the vaccine. Now, with more vaccine available and Delta looming, the allow 3 - 6 weeks but without any recommendation as to which end of that window to prefer.

EMA recommends 19 - 23 days but their reasoning is that 93% of trial participants fell into that bracket. So maybe they have simply no data to say that six weeks are better?

Unlike with the AZ vaccine, there is no vector immunity to overcome so it is not clear why a longer interval should be better.

Back to those seniors in this study here, is it even good for them to have more antibodies and less T-cells? My understanding is that they are typically T-cell challenged, so is it not better to boost T-cells?

On 2021-05-19 18:42:43, user Fred Bass wrote:

Were the patients randomized into those getting usual and those getting 12 week delay? Having 99 in one group and 73 in the other does not seem like a random split of 172 people! A bias toward giving healthier seniors the longer interval might account for some or even all of the results.

On 2021-07-08 22:19:15, user Michael Plank wrote:

This paper has now been published in the New Zealand Medical Journal and is available open access at:https://www.nzma.org.nz/jou...

On 2021-07-09 09:39:24, user Alice Ka wrote:

Another possible interpretation for hesitation/reluctance to get vaccinated could be that people who did not get Covid do not see the interest of getting vaccinated since they managed to avoid Covid by using masks, washing hands, etc. This could be even more relevant for workers who attended their work as usual during the three lockdowns. It could be worth to look into this if you have access to these information.

Other interpretation: poorer people tend to travel less frequently and may have less interest in the vaccine since it is not mandatory for conducting daily activities.

On 2021-07-10 16:09:38, user mzprx wrote:

I read in another study the rouleaux formation can be by created by in inflamation acute-phase proteins..

On 2021-07-14 16:34:10, user Melissa Mallon wrote:

I was just doing research and found this article and this describes my nose sensation. It feels like I just used a nose spray and it is clear and dry. It is a little worrisome for sure. I was in Mexico and tested positive for Covid. Small cough, smell gone, headache that's it and now this nose spray feeling. I am about 7 days after test and probably about 10 days since first symptoms of cough and headache. By the way I was fully vaccinated.

On 2021-07-15 19:57:15, user Linsey Marr wrote:

The conclusions on cough samples, sputum, nasal secretions, hands, and high-touch surfaces seem sound, but I do not agree that this study can rule out speech as a source of virus because the sampling method was not appropriate for collecting aerosols (which might carry virus) generated by speech. Subjects spoke into a 18x19 cm or 27x27 cm polyethylene bag, to which "2 to 5 mL of DMEM+ was added and residual air was expelled." First, the ~1 L volume of air sampled, representing 1-2 breaths worth, is orders of magnitude too small to capture enough viruses to detect. Second, the act of expelling the air would push almost all aerosols out of the bag. An analogy is that it's like trying to catch a fish by dipping a hula hoop into the water. The authors should consider removing this portion of the study from the manuscript.

Linsey C. Marr, Ph.D.<br /> Charles P. Lunsford Professor<br /> Civil and Environmental Engineering<br /> Virginia Tech

On 2021-07-29 08:59:44, user Johannes wrote:

"We obtained the baseline risks for selected U.S. counties from the Johns<br /> Hopkins University dashboard and for selected states of India from the <br /> New York Times dashboard"

JHU has received well in excess of $100,000,000 from the BMGF.

Is this a potential conflict of interest ?

Many Thanks.

On 2021-07-31 22:45:18, user Matt Mauro wrote:

In Table S4 it lays out the causes of death. What's the difference between a COVID-19 pneumonia death and a COVID-19 death?

On 2021-08-15 17:21:01, user carbsane wrote:

Can someone PLEASE explain to me how there can be 850 cases of COVID among the placebo group through March 13th if most of that group was subsequently vaccinated?? <br /> According to Pfizer's website they began unblinding and vaccinating in December (pretty much after the EUA), as they reported that as of Jan 29th 3,624 placebos had been FULLY vaxxed. Their last reported numbers (before dropping the information from their weibsite) were on Feb 24th by which time 16,904 had received at least one dose of vaccine.

On 2021-08-18 18:30:23, user Steve Kirsch wrote:

There were two people in the placebo group who got the drug after the unblinding. The paper never talks about the cause of death from those two people. This is EXTREMELY important. Does anyone know?

On 2021-08-06 22:23:54, user Ewin Barnett wrote:

The government of Scotland reported that 5,522 have died as a result of being vaccinated. No other data released like what percentage had comorbidities or were low on vitamin D at their time of admission to hospital.. No data released as to the appropriate percentage of the national population had been vaccinated. For a nation of about 5.5 million, this represents at least 0.1% risk.

On 2021-08-07 20:35:21, user vinu arumugham wrote:

Table S4 shows 4 deaths in the vaccine arm and 1 death in the placebo arm due to cardiac arrest. <br /> The probability that this outcome is a chance occurrence is 1.5%.

(((21999÷22000)^21996)×((1÷22000)^4)×(22000!))÷(21996!×4!) =0.0153 or 1.5%. <br /> So 98.5% chance that the vaccine CAUSED the excess cardiac arrest deaths.

Table S4 also shows 1 excess COVID-19 related death in the placebo arm.<br /> So to prevent 1 COVID-19 related death, the vaccine causes at least 3 deaths due to cardiac arrest.

On 2021-08-03 16:04:04, user Daniel Keyes wrote:

Overall the study seems strong and has tremendous impact potential. Many parts of the world could potentially prevent hospitalizations and save lives by proper allotment of vaccine resources based on evidence of prior infection if the conclusion is correct.<br /> I felt that the first diagram/flowchart could be improved: there should be vaccinated/unvaccinated for each of the two groups: previously infected, not previously infected. Would envision a branch/fork for each of the aforementioned groups rather than continuous across the same line in the diagram.<br /> Given the time being taken to review this article (it seems long, but actually is probably not that long!), the reviewers might consider extending the data to the end of June, which could provide implications with respect to delta variant. As it is, the Midwest, where the study is located, already has a very high percentage of delta variant. But this was probably not the case for the period up to May 15, the end date for the study. Delta started to be present in mid-March, but was not substantial for the ensuing 2 months.. But, of course, that might delay the review process even longer, and would not be likely to change the conclusion.

On 2021-09-11 13:44:07, user Don Schott wrote:

First off, I tested positive, quarantined and received two jabs.<br /> This fills in some of the blanks of the Pfizer-BioNTech-19 submitted to the FDA that was approved by their Scientific Advisory Committee for experimental use. Tens of millions of jabs later and more to follow, we apparently know less.

The FDA Reviewers expressed specific concerns that the 40,000 plus in each Pfizer experimental and control groups did not show much difference-- no one died, 6 hospitalized in one group 1 in other group. But, they claimed there focus was on safety. The authors should be applauded for calling for more study of the effects of vaccines.

Sadly, FDA and CDC have little or no research (consensus doesn't count) before and since these approvals. The dissent that calls for more research is met with derision and insults, never data.

On 2021-09-12 11:21:41, user Shih-Hao Yeh wrote:

Let me assume your approach and data you used are all valid without any problems. <br /> 2 questions for your calculation and comparison in Fig 6 & 7.

(1) I'm confused in 44.4+210.5=255 in your Fig. 6. According to your context, 70% of children hospitalized for COVID-19 having medical comorbidity, and 30% don't. And in general, you estimate 33% of children in this age group have comorbidity based upon current data. So the likelihood of a CMB(comorbidity) kid get to hospital for COVID is 4.7 times more than a H(healthy) kid. [(0.67/0.33) / (0.3/0.7) = (0.67*0.7) / (0.33*0.3) = 0.469 / 0.099 = 4.7] That is correct.

Yet, what is the risks to be hospitalized for COVID for a H kid and a CMB kid respectively?<br /> Ans: <br /> Suppose in an average US medical area with 1 million adolescents, by your data, there will be 255 kids/1M hospitalized for COVID in 12 weeks supposed median prevalence . <br /> So, how many of them are H kids? How many CMB kids?<br /> 255*30%=76.5 H kids/1M kids<br /> 255*70%=178.5 CMB kids/1M kids<br /> Not 44.4 and 210.5.<br /> Yet, there are 670k H kids and 330k CMB kids per 1M kids.<br /> So, if you're healthy, in 1M healthy kids, your risk to be hospitalized for COVID within 12-week is<br /> 76.5/0.67=114/1M H kids<br /> if you have CMB, in 1M CMB kids, your risk to be hospitalized for COVID within 12-week is<br /> 178.5/0.33=541/1M CMB kids<br /> And yes, 541/114=4.7.

The risks to be hospitalized for COVID are actually larger than 44.4 and 210.5. Same mistake in high or low prevalence in the table. Tip: conditional probability. You don't include adult in the denominator of kid's risk, right? Same here.

(2) Further stratifying numbers into healthy and comorbidity groups to make the number smaller (by miscalculation) is a cunning move. Yet, it make sense. Comorbidity do contribute the severity of COVID.

However, since you stratify data for risks being hospitalized for COVID, why don't you stratify data for risks of vaccine-associated myocarditis (VAM)? I suppose that some medical comorbidity may also contribute to the risk of VAM?

I don't think these comparison in your paper are fair, meaningful comparison: <br /> P(healthy AND hospitalized for COVID) vs P(VAM)<br /> P(CMB AND hospitalized for COVID) vs P(VAM)<br /> These are meaningful comparison given same conditions:<br /> (a) P(hospitalized for COVID) vs P(VAM)<br /> (b) P(healthy AND hospitalized for COVID) vs P(H AND VAM)<br /> (c) P(CMB AND hospitalized for COVID) vs P(CMB AND VAM)

Taking these 2 problems into consideration, I don't think you can hold your original conclusion. If 255/1M can become 114 and 541 respectively, 162/1M can also become some numbers less than 114 and 541.

On 2021-09-21 11:11:33, user 4qmmt wrote:

Would any of you agree that rate of myocarditis/pericarditis due to the vaccine in youth is a) unknown and b) higher than the available data suggest?

On 2021-09-10 16:16:11, user bee researcher wrote:

To clarify, this work is not comparing the risk of myocarditis in vaccinated individuals with the risk of hospitalization in similarly aged COVID-positive individuals, but rather an age-matched demographic regardless of COVID infection status. Is that correct?

This seems misleading in terms of risk assessment, because it's comparing the risk after a specific event (vaccination) with the background level of risk over certain periods of time. Yet active spread of COVID appears likely to continue for at some level for years, and the risk of hospitalization in COVID-positive individuals in this age group is much higher than the risk of vaccine-related myocarditis. Indeed the risk of COVID-related myocarditis is higher in this age group than the risk of vaccine-related myocarditis. If eventual infection by a now-endemic COVID-19 is incredibly likely, than it seems more informative to compare the risks associated with such an infection with the risks of vaccination.

On 2021-09-10 19:11:30, user David Goldberg, MD, MSCE wrote:

Although the scientific question that is being address is an important one, I have concerns about the methodology used to adjudicate the outcome. In similar circumstances (e.g., the FDAs Mini-Sentinel Initiative), complex clinical outcomes like this (e.g., acute liver failure) were adjudicated independently by two experts, with a third person serving to break any ties. That seems not to have been done in this study, as there was only one cardiologist involved. Secondly, the clinical data to adjudicate the outcome of myocarditis seems to be insufficient in many cases. Although one could argue "this is the best data we have" sometimes that is not good enough. When the question is so important and politically charged, incomplete/invalid data is sometimes worse than no data. Unless the authors can have two-party adjudication with record review, and classification using standard techniques (e.g., definite vaccine-induced myocarditis, highly likely, probable, possible, not) then there are major methodological concerns with the outcome, and the overall validity of the study.

On 2021-09-10 21:38:17, user anime profile picture wrote:

This study completely misses the point of young kids getting vaccinated. COVID is infectious. Meaning when someone gets the virus, it can be passed on. Whether or not they are at high risk relative to the adverse side effects, they should be vaccinated to reduce the probability of older, more at-risk people from getting it. In short, young boys should get vaccinated to protect them, their parents, their teachers, and their grandparents. Consult with your doctor of course. I am no medical professional, but I understand that a vaccine does more than protect the person being vaccinated.

On 2021-08-21 06:05:58, user Dinofelis wrote:

Even though 10% is within the confidence interval 8.4% - 24.8%, what is hard to explain is that the number of severe cases per 100 cases decreases faster (16.6%) than the rate of vaccination increases (10%). It would actually mean that non-vaccinated people that do get covid, are less often severely ill because others got vaccinated. That's very hard to explain, unless several of them got infected by vaccinated people with a lower viral load, but that would then imply a lower effect on infection prevention than demonstrated in this article.

On 2021-08-23 08:57:48, user Isatou Sarr wrote:

Hi,

What is the self ''clearance efficacy'' of the immune attack complex as a result of re-infection after vaccination and is there a need for medication to boost up the clearance cycle? What is the half-life of the vaccine induced antibodies/immune cells? Most vaccine studies are majorly focused on immune end-points with little on debris clearance and it is important to understand the dynamics of immune ''mop up'' as well as not only the longevity of the generated antibodies/immune cells but their subsequent efficacy upon initial encounter with antigens. It is also critical to understand the clonal expansion pathway of immune cells generated as a result of specific vaccination both on an individual basis and on the wider population.

Thank you.

On 2021-08-24 10:14:29, user Mikaela Olsen wrote:

How I wish it was possible to contact the authors to ask a simple question. The study compares two groups but which groups? One group contains those who survived a SARS-cov-2 infection the second group contains vaccinated people who would and would not survive an in fection. Is it really possible to compare these two groups? What would waning of antibodies have looked like if it was possible to exclude those who would not survive an infection from the vaccinated group?

On 2021-08-23 13:36:18, user Leo G. wrote:

Oral and nasal hygiene with Povidone-Iodine is widely used in India & Bangladesh to prevent nosocomial transmission.

It is equally effective in community settings. This hygiene includes gargling, mouth rinsing, nasal drop or irrigation. They should be performed 2-5 times per day, and/or before visiting the clinical

Many Listerine & Crest mouthwash products can be used for gargling.

On 2021-08-24 15:12:55, user Maria Kozlova wrote:

Thank you for the research!<br /> But perhaps the descriptions for Figures 5 A and B in the text and in the picture are confused?

On 2021-08-25 10:47:17, user ibamvidivici wrote:

In Figure 1 c is a infectiouness profile startet ca. 10 days before symptoms onset. But Figure 3 shows, that the meassurement startet 4 days before symptoms onset. How is that possible?

The infectiousness profile is not the real infectivity, it is the viral load of the tested person, estimated from the Ct-Value. For real infectivity the viral load had to be transfered to another people. After symptoms onset this happens with cough and sneeze. I doubt, that this happens before symptoms onset, because the only possibility would be by breathing. But Aerosol size of breathing droplets ist smaller than 1 micron and is vaporized in less than 1 ms, so before it settles onto a desk or towards other people. It's not proofed, that the virus is still intact after vaporisation process of the aerosol droplet.

(only relatives could become infected from asymptomatic by kissing or shared cutlery.)

On 2021-08-26 20:37:24, user David Anfinrud wrote:

This is mostly common sense. But again to get people to understand that those that had COVID are better protected and do not need the vaccine you have to have a study. This information is just a repeat of science seen over the Decades. Vaccines help but the best protection is natural immunity

On 2021-08-27 06:06:45, user joseph harrison wrote:

I wonder how this study accounts for people who died from infection from covid, considering that people who die from covid may have some defect in immune response, which has been documented in serveral studies. These immunocompromised people are effectively removed from the infected pool but are still present in the vaccinated pool, where they may not have as strong of an immune response to the vaccine. Furthermore, we are talking about a relatively small increase in breakthrough infection rate 13%, or the difference between a 30% or a ~35% chance. While the study seems well done and interesting to evaluate, I am dissappointed to see it linked on drudge with a headline natural immunity is better than the vaccine, when there are many other ways to potentially explain the small increase in protection from breakthrough infections.

On 2021-08-27 16:48:32, user Edward wrote:

This study adds important previously unreported information comparing natural post-infection immunity to immunity after vaccination. Unfortunately, the study risks giving the false impression that it is better to go ahead and seek natural immunity over vaccine immunity. The study, for example, does not take into account covid-attributable excess deaths. Thus, by default, those with natural post-infection immunity considered in the study are covid survivors. Hence, they can be expected to have stronger immunity than those who died because of covid. While the basic premise that natural immunity is stronger than vaccine immunity in the abstract, I suspect it is better to get a milder case of breakthrough covid than to risk death in search of natural immunity. We need a much larger study, ideally prospective, and will have to measure the frequency of "long haul covid" cases between the vaccinated and unvaccinated.

On 2021-08-27 19:58:34, user Kryptos wrote:

Good research study. So is it necessary to risk vaccinating a billion children who have no underlying conditions, considering the risks of blood clots, vascular damage, etc.? Wouldn't it be better to let them acquire natural immunity?

On 2021-08-28 16:17:13, user Aaron Plummer wrote:

Doesn’t common sense already confirm this though. Natural immunity has already been proven to be the most effective in everything for hundreds and hundreds of years. The vaccine hasn’t even been around for a year yet. One is our natural survival instincts that have allowed humans to survive severe deadly and catastrophic events over hundreds of years, and one is man made in a lab based on hypothesis and trial and error experiments. Again common sense dictates that natural immunity will always win this debate. Too bad this administration doesn’t seem to recognize or acknowledge its effects.

On 2021-08-29 17:43:03, user Edison Wong wrote:

I looked at the raw #s. If you take model 1, the break thru infection rate for the twice vaccinated was 1.46%. This is actually a much higher rate of efficacy vs clinical trials abd other studies I have seen. When you look at breakthru infections for previously infected, this is 0.12%. I do not see this mentioned anywhere else. A 13-fold greater risk of infection does becomes less meaningful if the higher risk group is closer to 1% than 10%.

Perspective is important to determine how much of a public health response is reasonable. If the risk is for 100 people vs 1000,000 in a nation of 6 million, that should figure into any decision for lockdown & mask mandates.

On 2021-08-30 20:34:53, user Jason Anderson wrote:

I am from the opinion that this type of article being available before being peer-reviewed is slightly irresponsible due to the amount of news coverage it is likely to receive. After reading the manuscript, if I were reviewing, it would be a strong reject or major revisions (depending on the opinion of the handling editor). My expertise is certainly not medicine, but it is on data science and advanced statistical/econometric methods - the precise methodology the authors used here. To keep it short, splitting the data and generating separate models is not appropriate in this context based on the discrete outcomes the authors are modeling. IF, and big if, the authors are going to defend having separate models, there are a series of tests that need to be done to show that this is the appropriate approach. This is lacking. Also quickly, the authors have done their best at controlling for what they can, but there are still numerous unobservables that are not accounted for. Why is this important - it can bias parameter estimates, which leads to ORs (calculated from parameter estimates) that are not true representations of the population parameters. ORs can also be misleading; hence, the preferred inference is based on marginal effects.

If anybody, including the authors, are interesting in additional, more detailed comments, I'd be happy to discuss.

On 2021-09-02 18:28:43, user Kostas Damdas wrote:

Who were the participants? people vaccinated in january vs people covid positive recently? immunocompromised vs healthy?

On 2021-11-09 10:52:01, user Harry Wood wrote:

The Israeli study is flawed on many levels. This dissects the errors.

https://www.quora.com/A-rec...

On 2021-08-26 04:22:25, user brisalta wrote:

The paper does not clearly state which variant the subjects were previously infected with. If that data is available it may be useful to include that information.

On 2021-08-28 22:44:31, user Business wrote:

Have you considered comparing Covid-19 naïve vaccinated vs unvaccinated and Covid-19 previously infected vs Covid-19 naïves?

Also, is the data available for further analysis?

On 2021-09-11 16:32:55, user Chadwick wrote:

Red Flags all over the place, like there are 2.5x more immunocompromised in normalized comparison groups, being immunocompromised makes re- or breakthrough infection LESS likely, Economic class swings between comparison groups, being wealthier makes infections more likely...

On 2021-08-27 02:16:17, user Tom Hennessy wrote:

Phlebotomy.

"Reduction of the body iron stores can improve hyperandrogenemia and insulin resistance"<br /> "phlebotomy with consecutive reduction of body iron stores lowered blood pressure and resulted in improvements of markers of cardiovascular risk and glycemic control."<br /> "blood donation may prevent not just diabetes but also cardiovascular disease"<br /> “Our findings suggest that lower-end normal Hb levels are favorable for and maintenance of healthy metabolism involving mild chronic activation of the hypoxia response. Therefore modulation of Hb levels could serve as a novel strategy towards treatment of metabolic syndrome”<br /> “Our findings suggest that an increased Hb level is a predictor of elevated serum ALT in adolescent girls with dyslipidaemia. Our study also highlights the importance of further research to establish cut-off points for Hb and its utility in diagnosing and preventing the onset of dyslipidaemia in adolescents. ”<br /> "Our findings provide in vivo evidence of a relation between hyperinsulinaemia/insulin resistance, the main variables of insulin resistance syndrome and erythropoiesis. Increased red blood cell count could be considered as a new aspect of the insulin resistance syndrome that could contribute to the increased risk of developing cardiovascular problems."

On 2021-08-29 20:22:01, user Holger Lundstrom wrote:

"PCM received funding from the Wellcome Trust [110110/Z/15/Z]."

To quote from:<br /> https://www.bmj.com/content...

"An increasingly clear feature of the covid-19 pandemic is that the public health response is being driven not only by governments and multilateral institutions, such as the World Health Organisation, but also by a welter of public-private partnerships involving drug companies and private foundations."

"These advisory and media activities seem to overlap with Wellcome’s £28bn endowment, which has at least £1.25bn invested in companies working on covid-19 vaccines, therapeutics, and diagnostics: Roche, Novartis, Abbott, Siemens, Johnson & Johnson, and—through its holdings in the investment company Berkshire Hathaway—Merck, AbbVie, Biogen, and Teva.11"

"Yet charities such as Gates and Wellcome—and even drug companies—have generally been praised in the news media during the pandemic for their efforts to solve the public health crisis, with relatively little attention paid to their financial interests and with few checks and balances put on their work."

“What the pandemic is doing is buffing the reputation of organisations like Gates and Wellcome and the drug companies, when I don’t think they really deserve that buffing up,” says Joel Lexchin, professor emeritus of York University’s school of health policy and management in Toronto. “I think they’re acting the way they always have, which is, from the drug companies’ point of view, looking after their own financial interests, and from the point of view of the foundations is pursuing their own privately developed objectives without being responsible to anybody but their own boards of directors.”

On 2021-08-30 04:59:27, user William Brooks wrote:

The authors estimate that if the UK government's hadn't extended restrictions for another month, daily hospital admissions would have reached 3400, whereas they peaked at only 1400 due to restrictions being extended. However, according to Our World in Data, peak weekly admissions in July were higher in the UK than all mainland European countries except for Spain and considerably higher than in countries with fewer restrictions and smaller percentages of population vaccinated such as Sweden and Croatia.

To better assess the results of the UK government's decisions, it would be more informative to compare England's outcomes to the real-world outcomes of other European countries instead of models that may overestimate the effects of government actions.

On 2021-08-31 14:22:02, user Shungo Y@JOJOLANDS(?)????????? wrote:

The standardized mean difference is the difference in proportions between groups divided by the standard deviation for a binary variable. <br /> https://handbook-5-1.cochrane.org/chapter_9/9_2_3_2_the_standardized_mean_difference.htm

The authors seem to use SMD simply as the difference in proportions. Therefore, we do not know if the two groups are well balanced.

On 2021-09-01 09:50:45, user Till Bruckner wrote:

This paper usefully highlights and quantifies the scarcity of randomised trials of NPIs. Providing a precise definition of NPIs and more details on inclusion/exclusion criteria might add value.

A potential weak point is the claim that "it is unlikely that we have been unaware of pertinent results of further NPI trials, given their substantial impact on current debates and scarcity of the evidence." This appears to assume that all NPI trials were either (a) registered in a trial registry or (b) reported in the academic literature.

There may have been experiments meeting the inclusion criteria that were run by government bodies and research units such as "nudge units" that were neither registered nor made public in academic formats.

Performing a grey literature search and/or reaching out to key informants outside academia who may be able to comment on the likelihood of such research having been performed would help to provide assurance that no relevant studies have been missed, and strengthen the conclusions of the paper.

Till Bruckner

On 2021-09-03 13:39:22, user rbrine@msn.com wrote:

Since “each mRNA-1273 dose provides three times more mRNA copies of the Spike protein than BNT162b2”, why do recipients of mRNA-1273 require two doses for “full vaccination”, like recipients of BNT162b2, especially if the first mRNA-1273 dose caused a prolonged adverse reaction?

On 2021-08-11 15:23:39, user Rene Reeves Brandon wrote:

The study included a cohort of unvaccinated individuals, but only reported on outcomes of individuals fully vaccinated with either of the two vaccines. What did the data on the vaccinated reveal in comparison to the vaccinated regarding previous infection, illness, hospitalization, and death? That data is necessary to share, especially as mandatory vaccines are being discussed in several states.

On 2021-12-01 13:58:36, user Nudnik_de wrote:

I'm missing one parameter in the study. It seems there is no differentiation made under which condition people interact with each other. In other words, whats the impact of 3G and 2G rules? Vaccinated but not tested people meet Unvaccinated but tested folks... I'm concerned that the lack of considering such aspects could have a severe impact on the results and therefor lead to improper measures.

On 2021-12-01 21:40:48, user anedabei wrote:

The statement of the paper "unvacs drive it" ist not grounded in reality.

The weekly report of the RKI Report from Nov 25 compares vacs and unvacs in "Tabelle 3"

Unfortunately, it is in German, so some help: The row "Symptomatische COVID-19-Fälle¹ " shows symptomatic cases for the prior 4 weeks.<br /> Adding them up results in 289.953 cases for all age groups. 139.856 of them or 48 % are vac breakthru.

So both vacs und unvacs contribute about the same to drive the pandemia. However, vacs are of course somewhat better protected.

For VE, the vac rate needs to be considered. It is, taken from page 24, 12-17 years 43.0 %, 18-59 years 75.0 % and for 60+ years 87.8 %, resulting in an average rate of 68.1 % for the entire population.

On 2021-12-01 22:43:19, user Tom wrote:

Is the use of a 2005 Contact-Model feasable? It does not take the "2G"-Rules and the general fear of Covid into account. I Assume that stadiums full of vaccinated people thinking they are safe while the unvaccinated are not allowed to enter would skew the contact-matrix.

On 2021-12-02 08:51:52, user koen wrote:

This publication makes a number of hard claims, with a title that insinuates as such. These claims are based on a model that is proposed by the authors without proper validation and verification of the model. One of your claims is that your models shows that with a vaccination uptake of 80% of the total population the reproduction number r remains at 0.86 in the current situation in Germany. These claims could be verified by applying the model to the COVID situation in different countries (with higher and lower vaccination uptake). Furthermore, contact tracing results should be used in part to validate claims about the source of infection. Based on the comments above and the discussion in the article the subjective title seems inappropriate and suggestive to person viewpoints of the authors.The best of luck in publishing this article in the current state!

On 2021-12-08 20:50:16, user doc_fishoil wrote:

The theme of this paper is the poor attempt of turning absurd assumptions into golden scientific insights by algebraic mumbo-jumbo: <br /> Just take formula (7) to see that "base transmissibility" for the vaccinated and the unvaccinated (that represents their behaviour) produces any proportion of contributions of vaccinated and unvaccinated, as all other parameters are gauged somehow on data. <br /> However, the authors want to blame the unvaccinated, hence they chose to set them as equal although rather harsh testing rules only for unvaccinated were in place in Germany during the referred time ("3G"). Without any reason, comment, validation estimation of real word data, just by assumption in obvious and absurd contrast to everyday life experience. <br /> The result is delivery as ordered.

On 2021-12-03 05:20:19, user Alberto wrote:

"see Figure 1(b). The plots show the dramatic situation that would have occurred in the case of the lack of vaccines. Indeed, by looking at Figure 1 (a) and (b), we observe an increase of a factor 10 in severe infections. This scary increase would have generated a serious crisis in the Israeli health system."

A 10 times increase in severe cases and (therefor, presumably) deaths is indeed a scary scenario. So much so that it's incompatible with the reality we see everywhere (including, for example, Palestine), and incompatible with the previous year's numbers, when 0% of the population was vaccinated.

There is obviously a very strong confounding factor that must have not been taken into account in these calculations of vaccine efficacy. Finding that confounding factor would be essential for this and all other studies to give us correct estimations. Otherwise we're just speculating with unrealistic numbers.

On 2021-09-29 01:16:54, user Alberto wrote:

So on 2 September the total severe cases was 629 (hospitals and ICUs at the verge of collapse), and the estimation if 0% of the population had been vaccinated is that there would have been 5182 severe cases in the country. And this during the summer. I don't have the figure at hand of how many severe cases were on 2 September 2020, when 0% of the population was vaccinated, but it may have been around 250-300? It's hard to know what exact effect mass vaccination is having that leads to these kind of absurd results, but it would be worth looking at it in detail.

On 2021-12-03 21:49:24, user gwern wrote:

An incorrect result from the first version of this paper (about PhDs being the most reluctant to get vaccines, when really they are probably the least) is still being very widely shared on social media (I can see several instances on Twitter today alone). The error should be discussed explicitly, in more detail, not buried in a vague throwaway comment about some categories being 'higher'; not just so people reading it will understand it, but as an instructive lesson to other researchers about the perils of mischievous responders in surveys, particularly online ones.

On 2021-12-06 07:07:21, user neil Muller wrote:

While this study may raise important questions it is being interpreted in ways that are not justified by the analysis. The paper answers a very narrow technical question as to whether there is an increase in the hazard ratio of primary infection versus reinfection compared to the first wave. Given that the risk profiles of the groups subject to the risk of primary infection and reinfection are so different (by definition the group at risk of primary infections now consists of only 30 to 40 percent of the population who have either adopted behaviour that is less risky, live in communities that were bypassed by the previous waves, or are in the 26 million people vaccinated so far) while the previously infected include the population at higher risk of infection by definition amounting to as much as 70 percent of the population) one would simply expect this.

As the study notes, to date of the possibly 42 million South Africans who have survived Covid infection 36 000 of these people have been identified as reinfections. Naturally as only 3 million infections have been identified by a test so this will be a dramatic under-estimate. But even if it is off by a factor of 15 which identified cases may be this is still only about 500 000 reinfections from 40 million infections.

Natural immunity is highly effective against reinfection.

It is unclear how the estimated change in the hazard ratio changes the projected number of reinfections.

In addition as no information is provided on the risks of hospitalisation and death based on the 36 000 identified reinfections to date we don’t even know whether this has any meaningful policy implication.

But it is the use of this paper in the framing of social and health policy that suggests that if these implications are not spelled out that makes this article misinformation.

The analysts cannot be naive about the debate on vaccine mandates in South Africa. There is clearly a concerted push to demonise the unvaccinated and to make the path to Vaccine Mandates for Covid Acceptable.

The headlines in the popular press focus on the apparent implications of this paper for natural immunity. The claim is that it will not hold up for infection under omicron.

This is clearly NOT what the paper says. The authors need to take responsibility for the way in which this research is being presented and clarify exactly what the paper says about the likely number of people who will be infected by omicron and if so, the number that are likely to require hospitalisation and run the risk of death.

The fact of the matter is that the authors can’t say anything about this as we don’t know about omicron. They admit this.

But they can indicate the number of 42 million South Africans that have natural immunity are likely to be reinfected. They can say the number of these people who are likely to be hospitalised. They can say the number of reinfected people who are likely to die.

They can say that there is no evidence that vaccination will provide any more immunity against infection than previous infection. They can say that there is no evidence that vaccination will lead to less hospitalisations or deaths than natural immunity.

Absent this they remain silent on the calla to reimpose apartheid era strategies such as the population registration act, separate amenities act, and all the hate speech and violation of rights guaranteed in our constitution. This time it is not based on race but on the equally socially constructed and unscientific concept of the unvaccinated.

To sensitise oneself simply replace the term unvaccinated with the k or n word and see if the statements that are made so easily are acceptable.

On 2021-12-06 16:24:28, user Hank Black wrote:

What cycle levels were used in the PCR tests that were used to determine infections? If those levels we’re above 24, then this paper is irrelevant. If the authors can identify people who had symptomatic Covid disease, and compare that number to current persons symptomatic with Covid disease from the new variant, then this paper might have merit.

On 2021-12-06 18:05:54, user FACAGIRL wrote:

What were the CT values for the PCR confirmed cases. I ask because PCR test only test for presence of virus and not infectivity - yes? I found the following CEBM/Oxford systematic review on this and the detail suggesting a lower CT value is better to use as proxy for infection - was based on cultured and PCR samples. Reinfection would be subject to the same limitations associated with PCR tests.

Thanks

On 2021-12-06 15:18:05, user Jens Happel wrote:

Dear Robert,

thanks for the study. Is it possible to differentiate the group of the unvaccinated in unvaccinated and vaccinated between 1st dose and 2 weeks after infection?

In some studies they found the effect that between 1st and 2nd jab the likelihood of infection is significantly increased.

For example here

https://www.researchgate.ne...

see figure 2

Would be intressting to see what happens in this group.

Kind regards<br /> Jens Happel

On 2021-12-08 20:43:25, user Peka Bali wrote:

page 15 of the full text report reads: "Symptom probability time courses for participants with confirmed COVID-19 (n=1020, RT-PCR, antigen, or antibody tests) overlapped significantly with probability estimates from the whole population (Figure 7), except for “changes in sense of smell/taste."

How does this coincide with the conclusion of the report on the first page?!

"Conclusions. Patients with Long COVID report prolonged multisystem involvement and significant disability. Most had not returned to previous levels of work by 6 months. Many patients are not recovered by 7 months, and continue to experience significant symptom burden. "

I am simply flabbergasted by this pseudo-scientific conclusion, not to mention giving it a collective name!<br /> If anything, the only rational conclusion that can be drawn is that other than the altered sense of taste/smell there is NO correlation or causation whatsoever between Covid-19 and the other 65 symptoms as described!<br /> The insinuation as posed above in the publication, stating negative PCR and antibody tests as "suspected cases" is just absurd. What do you base this assumption on?!

This report makes no sense: when you have a control group and baptizing your control group to "suspected cases" to justify the conclusion, which is that these symptoms are Covid related when they are clearly not.

On 2021-12-13 14:29:16, user vepe wrote:

it looks like this study has a major flaw in the calculation of the covid cases

for example their data set contained 6846 cases in the cohort 12-17 (they applied the same logic for the other cohorts)

The 6846 number of covid cases for 12-17 was 2.5% of the total covid cases in their data set.<br /> Then they assumed the same infection rate as the adults at the time, 9.2% and normalized their total number of cases for 12-17 based on that:<br /> adjusted number covid cases = 6846*9.2/2.5 = 6846*3.7 = 25193

then they almost doubled the number of myocarditis cases on the premise that there would be cases that they would miss (e.g. people receiving care outside the TriNetX system)

so they end up with about ~12 myocarditis cases per 25193

so the biggest problem is that their estimated number of covid cases, is essentially the number of covid cases they were expecting to see in their data set and not the total number of covid cases associated with their data. Even if they were meant to estimate the number of covid cases they were expecting to see, this estimation is not accurate since the probability of a younger person ending up in the hospital is way smaller than adults.

In practice, based on that estimation of covid cases, the authors implicitly say that 2.5/9.2=27% of young people that get coronavirus, end up diagnosed/treated by health care provider. This looks like a big overestimation.

In practice, hospitalization rate for younger people looks like is closer to 2% as indicated below:<br /> https://www.aap.org/en/page...<br /> https://covid.cdc.gov/covid...

I think a more accurate estimation would have been to skip the normalization based on infection rates and estimate based on the probability a young person has to end up to a health care provider.<br /> example, covid cases = 6846*100/2 (instead of 6846*9.2/2.5)

Based on this estimation of covid cases, myocarditis risk would be higher in vaccination instead of infection for young people

On 2021-11-24 21:54:37, user Jens Happel wrote:

If the calculation and assumptions would be correct there would be a huge surge of Myocarditis during the Covid19 waves.

But that is clearly not tbe case.

https://jamanetwork.com/jou...

During the Covid19 waves the number of Myocarditis and Pericarditis was more or less constant.m, compared to 2019.

The surge started according to cited paper above in February, when most of the wave was over but vaccination rate started to pick up speed and was changing from elderly to the next younger groups where Myocarditis is more likely.

I guess your assumption about not detected Myocarditis is terrible over estimating that factor.

The charts in cited paper above show clearly that your paper has substantial flaws.

On 2021-09-04 04:21:48, user lifebiomedguru wrote:

Please describe how the groups "vaccinated" and "unvaccinated" defined? Were patients who were vaccinated consider "unvaccinated" until 14 days after their second dose for Moderna of Pfizer products, per CDC's definition? This would clearly bias the main result in favor of your conclusion. The fact that NYT cited this work as a subtext and the Editor chose your conclusions as their title confirms to me that this publishing preprints prior to peer review may be doing some damage to the long-term credibility of science.

On 2021-12-13 19:29:22, user Surya wrote:

Dear researchers,

It's stated in the text that : "However, a 1-42 day risk interval was also used, since this interval is often used in vaccine safety studies of GB S and other outcome."; also the text states "Results were similar when excluding Brighton level 4 cases and when using a 42-day risk window, with incidence rates ranging from 1.1 t o 2.1."

I'm wondering why the results of SCRI are not shown for the 42 days window at risk and mRNA vaccines.

On 2021-12-13 19:29:49, user Joseph Psotka wrote:

The study fails a basic test of good design: the HCW were only described as over 18. That's ridiculous! Full age and gender details should have been provided. Seems like a crummy study.

On 2023-08-30 17:56:46, user Caroline Lima wrote:

This review resulted from the graduate-level course "How to Read and Evaluate Scientific Papers and Preprints" from the University of São Paulo, which aimed to provide students with the opportunity to review scientific articles, develop critical and constructive discussions on the endless frontiers of knowledge, and understand the peer review process.

In this pre-print, the authors discuss the growing demand for ultra-processed foods and their harmful effects on human health. The presence of different oxidized substances and the low nutritional value is associated with chronic cardiometabolic diseases such as cancer, diabetes, Parkinson's, and Alzheimer's. This makes ultra-processed foods a subject of great interest and widely studied. This observation reinforces how important it is to study possible causes for the development of the aforementioned diseases and how research should be conducted to identify and possibly prevent them. It is also important to emphasize that the specific description of what leads these foods to develop oxidized substances is necessary in order to make a correct judgment of the causes and not classify all foods that have undergone some processing as equally containing oxidative substances.

Comments and questions:<br /> The authors prove that oxidative dietary substances and phytosterols are found in ready-to-eat foods and fast foods including those of animal or vegetable origin if preservatives/dyes were used, when high temperatures during the preparation process were used, and in a manner related to forms of storage and distribution.<br /> The use of different biomarkers has been suggested for both ready-to-eat foods and fast foods. Why use brassicasterol biomarkers for ready-to-eat foods and biomarkers (7?-OH and 7?-OH) for fast foods? Is there any specific reason for using these biomarkers? Are there other biomarkers that could be used?<br /> The use of different biomarkers for each food category is reccomended: dairy products (brassicasterol), eggs and derivatives (stigmasterol and ?-sitosterol), meat and poultry (7?-OH), seafood and baby food (?-sitosterol) and others (campesterol). What can each biomarker reveal for each food?<br /> How can the assessment of exposure to oxidative substances be established and what criteria should be considered and disregarded in this assessment? Would these values/results be enough for possible preventions and diagnoses?<br /> For biomarkers, is there any factor that interferes with this measurement and evaluation?

On 2021-12-20 23:18:35, user Nico wrote:

One more comment - it seems the survey was originally designed to look at impacts of covid itself on menstrual cycles - has that analysis been done? It would be useful to mention in this paper as well. If not already done - that seems like a good control: how do the effects of vaccination on menstrual cycles compare to covid itself? People get so focused on effects of vaccination, forgetting that in many cases effects of covid are far worse. Thanks. (Going to go and search now to see what I can find!)

On 2023-10-23 04:42:47, user CDSL JHSPH wrote:

Dear Dr. Bi et al,

This is a valuable paper that examines the potential influence of prior-season vaccination on the risk of clinical influenza infection. You recognized that past research has shown that prior-season influenza vaccination is associated with an increased risk of clinical influenza infection among vaccine recipients. A key limitation of these previous studies is their reliance on a test-negative design, which fails to consider the intra-season timing of vaccination and the individual's history of clinical infection in the preceding season.

A noteworthy finding in this paper is that individuals who receive repeat vaccinations tend to get their vaccines earlier in the season compared to non-repeat vaccinees. Remarkably, even when after adjusting for this discrepancy in timing, it does not significantly alter the observed higher probability of clinical infection in repeat vaccinees.

Clinical infection seems to play a dual role in influencing vaccination behavior. First, it serves as a motivator, prompting individuals to get vaccinated in the following season. Second, it also provides some degree of protection against clinical infection of the same subtype. However, even after accounting for recent clinical infections, the effect of prior-season vaccination on the current season's clinical infection risk remains not significantly different.

A potential mitigating factor, subclinical infection, is theoretically posited to attenuate the effect of prior-season vaccination. However, you were clear in the paper that this aspect is still largely theoretical and necessitates further investigation to determine its actual impact on vaccine efficacy.

The primary contribution of this pre-print lies in its careful consideration of confounding factors, specifically the intra-season timing of vaccination and the history of clinical infection in the previous season. By addressing these variables, it challenges the established findings of prior research, which suggest an elevated risk of clinical influenza infection associated with prior-season vaccination. These insights carry significant public health implications, particularly in the realm of vaccine policy and compliance.

The paper is methodologically robust, particularly in the sections that explore the impact of timing and previous clinical infection. However, the discussion of subclinical infection is less conclusive, as it relies on a theoretical model and a pseudo-population. The exact details are not in the main body of the paper and was referred to the supplemental section. As the explanation for the main findings of the paper is hinged on subclinical infection, it may be helpful to develop this idea further in the main text.

In terms of its presentation, the paper is well-structured with clear delineation of sections, and the text is appropriately complemented by the figures. The inclusion of the "infection block hypothesis" in the discussion aids in facilitating a deeper understanding of the research.

Overall, this paper marks a significant breakthrough by challenging the conventional approach to assessing vaccine efficacy, incorporating the roles of vaccination timing and previous clinical infection. It also highlights the potential importance of subclinical infections, opening important conversations and may lead to enhanced strategies for data collection in this context.

We truly appreciate you sharing your pre-print with us.

On 2021-12-22 17:55:54, user Thomas Gade Koefoed wrote:

Awesome work! I would perhaps consider rephrasing the sentence in the abstract: "However, the VE is significantly lower than that against Delta infection and declines rapidly over just a few months", since it can be read ambigously providing two opposite meanings. (Depending on what "that" refers to; the VE or the the statistics just mentioned in the previous sentence.)

On 2021-12-24 07:45:25, user Jeff H wrote:

So assume the results you like (high VE for recent vaccination) are causal, but hand wave confounders at results you don't like (negative VE for distant vaccination)? Science?

On 2021-12-24 21:34:35, user Robert Parker wrote:

So, these vaccines are, essentially, not effective against Omicron. The upside is that Omicron seems, at the moment, to be like getting a really bad cold. Very little hospitalization, and no deaths as far as I can find. This may be a Godsend. It is highly transmissible, with few bad effects. It may actually serve as a means to herd immunity, with few deaths. Hope springs eternal.

On 2021-12-22 22:56:01, user Richmond Heath wrote:

Have any of the authors considered a possible restorative role & purpose of the tremors & vibrations rather than simply seeing them as a pathaological? Could they be efforts to help down-regulate the ANS from the chronic hyper-arousal associated with long Covid similar to the 'neurogenic tremors' associated with the recovery cascade after shock & trauma seeking to restore the systems of the body to natural states of flexibility & variability?

On 2021-12-26 20:00:42, user Lee Jimmy wrote:

I read the preprint and could not find any mention of mask use/non use nor was the type of "activity" at this gathering spelled out. Anybody know anything about these details ? Do I need new reading glasses? Etc.

On 2022-01-06 14:06:14, user Maker innovations Tom maker wrote:

Notice they do not list the non pharmaceutical therapies

On 2022-01-09 16:15:33, user Greg wrote:

Here is my big bone with the study. The OR given for Omicron susceptibility is 1.04 for the unvaxxed, suggesting that the double-vaxxed were pretty much equally susceptible. Reading the Method, however, it stated that they counted the vaxxed with one dose as unvaxxed. What?! Would that not mean the true unvaxxed were less susceptible to Omicron? Likely!

Also, with vaccine protection waning rapidly, and even for the boosted, it would've been nice to know how vaccination timing was affecting susceptibility. This study did not consider that. Interestingly, the other prior Danish study suggested that after a few months of being double-vaxxed, there was a net negative protection against Omicron.

On 2022-01-31 21:33:01, user Anonymous wrote:

What do the "ref" mean in table 2. Trying to see the figures for fully vaccinated individuals.

On 2020-05-01 11:05:45, user Robin Whittle wrote:

Please see this report from Dr Mark Alipio, Davao Doctors College; University of Southeastern Philippines: Vitamin D Supplementation Could Possibly Improve Clinical Outcomes of Patients Infected with Coronavirus-2019 https://papers.ssrn.com/sol... . Hospitalised COVID-19 patients were classified into Mild (without pneumonia), Ordinary (CT confirmed pneumonia with fever and respiratory symptoms), Severe (hypoxia and respiratory distress) and Critical (respiratory failure).

Of the 55 patients with greater than 30ng/ml (20nmol/L) 25OHD, 47 had Mild symptoms, 4 Ordinary, 2 Severe and 2 Critical. Of the 157 patients with 30ng/ml or less, 2 had Mild symptoms, 55, Ordinary, 54 Severe and 46 Critical.

On this basis, if everyone had more than 30ng/ml 25OHD, very few people would be dying from COVID-19 and there would be no need for lockdowns, with their extremely high social, health and economic costs.

In this research, Gallagher et al. 2014 “Vitamin D supplementation in young White and African American women” https://www.ncbi.nlm.nih.go... , almost all the White women had less than 30ng/ml 25OHD. Those who took 2500IU vitamin D3 raised their levels significantly, but about 16% of them were still below 30ng/ml. 4000IU a day would improve on this considerably. African American women generally had lower levels.

4000IU is 0.1 milligrams a day. A gram would last for 27 years. The ex-factory price of vitamin D is USD$2.50 a gram, so the cost of this good, healthy, level of vitamin D supplementation is 9 cents a year, plus the cost of making and distributing and selling capsules. D3 need only be taken every week or two. My wife and I take a 50,000IU capsule three times a month.

Figure 3 at https://www.ncbi.nlm.nih.go... shows that normal weight people taking 4000IU a day will, on average, reach 47ng/ml (117nmol/L) which is about the average level of African herders and hunter gatherers reported in https://www.ncbi.nlm.nih.go... . Toxicity (https://www.ncbi.nlm.nih.go... "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6158375/)") may occur at levels three times this.

More links to research are at my page: http://aminotheory.com/cv19/

On 2021-10-26 17:10:43, user Stephane wrote:

Could you please explain why the effectiveness is lower between fully vaccinated people ? "Effectiveness of full vaccination of the index against transmission to fully vaccinated household contacts was 40%"

On 2021-11-09 13:00:01, user ingokeck wrote:

Dear Authors, two Questions:

(1) You state: "Partly vaccinated was defined as having received the first dose of a <br /> 2-dose schedule with a time since vaccination of at least 14 days." So you counted freshly vaccinated persons as not-vaccinated? IMHO this is a bad idea, because in the first 14 days after the 1. dose it is well known that the immune system is impacted by the vaccination and a high risk of testing positive for Covid19 exists. If you count these cases as not-vaccinated, this will skew your results towards higher vaccine effect.

(2) Thanks for plotting the case counts in figure 1. Did you check if there is some temporal imbalance in the cases? It seems the second part of your data interval has a substantial lower infection risk and may have higher vaccination numbers, i.e. you may have data that skews towards vaccinated in the lower risk time, also accounting for part of the measured vaccination effect. Could you please have a look at this as well? Thanks.

On 2021-11-02 05:39:54, user Athanasios Chalkias wrote:

The manuscript has been published in Surgery:<br /> https://www.surgjournal.com...

On 2021-11-02 11:57:57, user guy wrote:

Hopefully the reviewers will insist that the serological data and explicit discussion of their assumptions are brought into the main body of the text, most importantly this means table S4.<br /> From the abstract “We then use probabilistic risk assessment and data on [.., ]human SARSr-CoV seroprevalence, [..] to estimate that ~400,000 people (median: ~50,000) are infected with SARSr-CoVs annually in South and Southeast Asia. “ appears to be incorrect as the dataset used to approximate a distribution of SARSr-CoV seroprevalence, only 4% (1/27) of the positives are from this viral grouping (70% are from Nipah or Ebola viruses). The only data on SARSr-CoV comes from a single study (by the same authors (https://doi.org/10.1016/j.b...) "https://doi.org/10.1016/j.bsheal.2019.10.004))"), which if exclusively used in this study --as the abstract would imply-- would likely dramatically reduce striking numbers in the abstract. Using distributions to allow for uncertainty is a good approach if the data used to approximate them are valid, in this case any justification appears lacking.

The fact that the above cited article concluded that “Direct contact with bats was not identified as a risk factor [ in the transmission of coronaviruses to humans ]” should also be discussed in the current article, given that they now explicitly assume the opposite .

On 2021-11-07 19:23:55, user Eleutherodactylus Sciagraphus wrote:

It is relevant to note that this preprint (along with other two from the same group) includes data from human subjects that are under ethical scrutiny. The majority of patients enrolled were not informed nor agreed on participating in the study. The Brazilian National Comission for Research Ethics (CONEP) has been bypassed and is now investigating this case.

https://brazilian.report/li...<br /> https://www.emergency-live....<br /> https://www.dire.it/14-10-2...<br /> https://www.matinaljornalis...<br /> https://g1.globo.com/rs/rio...

On 2021-11-09 21:39:32, user Andy Bloch wrote:

Conclusions are contradicted by this paper: Waning of SARS-CoV-2 antibodies targeting the Spike protein in individuals post second dose of ChAdOx1 and BNT162b2 COVID-19 vaccines and risk of breakthrough infections: analysis of the Virus Watch community cohort.

On 2021-11-20 23:32:38, user Gordon V. Cormack wrote:

Were the previously infected also vaccinated, either before or after their infection?

Edit: I think I answered my own question: <br /> When we examined HCWs (n=423) with infections occurring before vaccination, no re-infection was observed, accumulating 74,557 re-infection-free person-days (starting 10 days after initial infection and censoring at the date of receiving their first vaccine dose). Further, after vaccination, previously infected HCWs did not contribute any breakthrough infection events among the vaccinated HCWs.

On 2021-11-26 03:56:31, user mike wrote:

I's really like to see this study continued until there is say 10 people who have been re-infected or a conclusion to say a million days without re-infection. 75k days is a lot, but they may be much more educated and a significant percent of these people do not want to get the virus a second time, therefore creating a dramatic improvement rate comparatively otherwise.

On 2020-04-22 08:46:06, user jaxthots wrote:

With self-selected subjects, sampling bias is always a major limitation for generalizing study findings. However, anxiety is probably a common emotional denominator in this sample over the focal existential issues of health and employment that are widely shared by the larger population. More significant bias, perhaps, are the unique characteristics of affluent, high-tech dominated Santa Clara County which, with median household incomes over $100,000 and median SFR values in the $1M range, is unrepresentative of America at large. But with a major airport and lots of foreign professionals visiting Silicon Valley, it has undoubtedly had more exposure to hitch-hiking viruses than most other US populations, which would predict above average infection rates. And similar findings from LA County, Germany and other populations sampled irrespective of varying subject samples and testing methods are providing a clear enough picture to establish a realistic denominator in rate calculations.

Unfortunately, the nominators are far less clear since US hospitals and doctors nationwide have been instructed and incentivized to identify covid-19 as cause of death irrespective of serious co-morbidities, which Italian doctors report are near-universal among the seriously ill and the deaths of presumed covid-19 patients, with definitive test results typically not returned until 2 weeks after the death certificates are signed and recorded. With no possibility before now of valid rate calculations, why are the media inflaming public panic that support draconian protective measures with severe economic consequences by reporting fabricated data?

In addition, many US doctors are reporting peculiar symptoms of "oxygen starvation" without the expected fluid lung congestion of pneumonia, suggesting a different, yet-unidentified disorder. These might possibly involve EMF interacting synergistically with the infection since the pandemic epicenters are in cities that rolled out 5G last year and EMF has been shown to alter voltage-gated calcium channels in hemoglobin molecules that deliver oxygen from the lungs into the circulatory system, as well as other compromising effects on pulmonary functioning.

There are one or more very serious, troubling and suspicious agendas at work here that beg more than perfunctory investigation.

On 2021-11-23 10:17:56, user Yixiang wrote:

Do you assume vaccine efficacy wane over time since the 2nd jab -- e.g. Antibody titer drops by 75% 6-12 months after 2nd jab? What about natural immunity?

On 2021-01-21 16:36:21, user Michael wrote:

DL+DiReCT including the trained model is also available on github: https://github.com/SCAN-NRA...

This method is mentioned in the manuscript and refers to https://doi.org/10.1002/hbm...

On 2021-01-27 15:14:20, user Florence Paré wrote:

How do you account for the possibility of COVID infections disproportionately occurring later in the period under study (due to rapidly rising numbers of infections), whereas influenza and respiratory tract infections may tend to slightly go down over the period due to distancing measures? This seems to risk introducing a confounding variable - mental health deterioration due to social distancing and pandemic-related anxiety. Did you or do you intend to make adjustments to the control cohorts to match the distribution of events over the period under study?

On 2021-01-28 19:01:30, user lbaustin wrote:

This leaves out two simple blood tests that are more predictive than any of the parameters on the list: initial blood sugar of >140 and 25(OH)D of less than 20ng/ml. Please add these to the model prior to publication.

On 2021-01-29 12:29:23, user stephan walrand wrote:

Nice correlation with the cloudiness and sun light insolation, but which is also compatible with vitamin D production!!! However, it is obvious that when comparing deaths from March to July, it is impossible to see any latitude correlation, because sun elevation averaged between March-July is almost equal for all countries.

On 2021-01-31 18:31:27, user Graeme Ackland wrote:

The statement

"we showed approximately 51% effectiveness of BNT162b2 COVID-19 vaccine against PCR-confirmed SARS-CoV-2 infection 13-24 days"

Is highly misleading. The data suggests more like "15% effectiveness 13-18 days, 85% effectiveness 19-24 days.". The most relevant day is day 21, when the second dose is meant to be given.

So their conclusion is that someone else should be deprived of 85% protective first dose, in order to give an 10% uplift with a second dose.<br /> I find that logic debatable

On 2021-01-31 21:31:27, user Ilya Zakharevich wrote:

The last two columns in the tables do not match each other (as they probably “should” for all developed countries, if one wants to get “meaningful comparison”; look for Lithuania vs Liechtenstein). I think that this is due to very different strategies to count child mortality.

Is it possible to replace the last column, dividing by the mortality (say) after age 1 year? As I said, it may be a “more interesting” number. (Less dependent on arbitrary accounting policies…)

On 2021-01-31 22:01:02, user Pablo Olavegogeascoechea wrote:

I have read this trial with great interest and I have some worries about some detalles: fist of all, the absolute risk reduction is quite low (1.4%) and the NNT for the primary outcome is 70 as it is for hospitalization. On the other hand there were more patient who developed pulmonary embolism in the Colchicine group (may be this issue needs more infromation)

On 2021-02-01 11:20:07, user Fjortoft9 wrote:

Given that the study is assuming the rate of vaccinations will be around 1m a week in January, rising to 2m by February I’m afraid it doesn’t seem to be very useful. <br /> We know now that the actual rate of vaccinations in January was more like double that and the rate in the last week is well over 2.5m. That difference would completely change the modelling and it’s disappointing that you didn’t model the impact of a faster vaccination rollout.

On 2021-02-01 15:11:35, user Alessandro Soria wrote:

Very interesting paper. To my knowledge, there are at least three other papers which look at the same topic (the effect of healthcare strain on COVID-19 mortality) from other perspectives: <br /> 1. doi.org/10.1371/journal.pon.... This is our recently published work, in which we tried to assess the impact of patient load on in-hospital mortality from COVID-19 based on hospital stress variables, such as the number of daily admissions, the number of total daily census, and the period before the peak, and we did find an independent harmful impact on mortality.<br /> 2. doi:10.1001/jamainternmed.2020.8193. In this analysis on the variation of COVID-19 mortality over 6 months in the US, the authors found that increased mortality reflects increasing numbers of cases in the community, possibly reflecting hospital burden.<br /> 3. doi:10.1001/jamanetworkopen.2020.34266. In this report on ICU in the US, there is a clear association between exceeding bed occupancy and increased mortality.

On 2021-02-01 21:30:19, user Igi Dano wrote:

As a Slovak citizen, I agree with most comments/notes presented here. I could as well add my own experience with "following testing procedure recommended by manufacturer..", where this testing procedure was conducted outside (of any premise, just an open tent) with temperature well below recommended range.

But that is not the point of my post here. The point is that Slovakia is currently (1.2.2021) ending the second round of another population-wide screening.<br /> I am desperately waiting for another study from the authors, confronting the newest results with original ones. <br /> Without that I would recommend potential readers of this study to use extreme carefulness with interpretations of it..

On 2021-02-02 12:13:10, user Miriam wrote:

Nobody in Slovakia was informed about this research. And it was not voluntary as they signed. There was and there is still strictly prohibited to go at work and to the nature if we are not tested. The final result of this mass testing is, that numbers of covid positive strongly increase. That is all. I am really afraid about my human rights in future.

On 2021-02-19 01:42:51, user Oliver Cudziš wrote:

Voluntary? "All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived - Yes" What is this, areu all blind or what. Slovak nation was like experimental rabbit without knowing, congratulations you just made stage for Slovak national uprising 2, good luck.

On 2021-02-11 16:06:09, user David McAllister wrote:

Congratulations on this excellent work. The potential for ICS therapy to improve outcomes for intermediate risk individuals not yet vaccinated is tantalising.

No doubt the paper is currently under peer-review, but if the authors have time it would be great to know the following:-<br /> 1. How many of the primary endpoint events included hospitalisation.<br /> 2. How was such a high proportion of positive tests for SARS-CoV-2 obtained? Was this based on subjective clinical judgement, or was there some other factor driving the high pre-test probability ?<br /> 3. How difficult was it to teach adequate inhaler technique?<br /> 4. Did any of the participants have wheeze or other signs of reversible airflow obstruction?<br /> 5. Were any steps taken to exclude participants who might have had a lobar pneumonia (eg by excluding individuals with purulent sputum)?<br /> 6. In the Guardian interview it was mentioned that at least 5 other trials were investigating this use of ICS. Is it possible to say when these are due to report?

On 2021-02-16 20:52:40, user Chris Cappa wrote:

Very interesting study. Interesting to see that exercise doesn't appear to increase the smaller particles but does the larger particles. In any case, two factors you might consider in revision. First is the differential dilution that will occur between different activities. Breathing and talking expiratory airflow rates differ substantially from coughing, from the various ventilatory therapies, and importantly from the OPC. Thus, there will be different levels of dilution associated with each activity that you might factor in to facilitate comparison between activities. It doesn't appear this was done (although I could be wrong). Or, at least note that this likely had an influence. The second issue relates to the comparability between the different activities. For example, talking was continuous whereas coughing was just 6 times in a minute. If a person had (for example) been asked to cough twice as often the number of particles measured would have doubled. Or, if there were more breaks in speech the number of particles would have differed. You might consider normalizing to per second of activity to allow for greater comparability.

On 2021-02-19 10:08:04, user Javier Mancilla-Galindo wrote:

This study is interesting, with robust analyses and a great effort to adequately report the model. Including predictors like S/F ratio, frailty score, and acidosis clearly differentiates this model from others and would make it a highly clinically relevant model. However, I am afraid it may lack any real clinical utility as long as the authors do not clearly explain in a simple way to clinicians how this model should be used in real-world settings (unless I somehow missed it).

Dichotomization of age (i.e. greater than cut-off age) may have led you to loose discrimination ability since too many studies have already shown that age is the main risk factor for mortality in patients with COVID-19. This may, however, not be an issue for such a shot-term (48-hour) mortality prediction, although I do strongly believe this model would have had a better mortality discrimination had you evaluated age differently (i.e. multiple age categories could be included with different weighted risks or coefficients, or perhaps allow age to be inputted as a continuous variable if at all compatible with your model).

The model shown in Supplementary Table 4 that includes CRP and not IL-6 could have a greater potential to be widely used even in moderately resource-strained hospitals. Thus, I found it more useful from a global perspective. Even when the model including IL-6 is better at predicting the outcome, it could have limited clinical applicability as correctly stated in the manuscript.

Lastly, you have adequately reported your manuscript according to the TRIPOD statement. However, the RECORD statement may also apply to this particular study since you have used routinelly-collected data in an observational study design. You could consider including this checklist, too, for the peer-review process.

Congrats for such a great work!

On 2021-02-21 14:31:34, user DMac wrote:

Good day. I've found this work immensely valuable as a reference for discussions in our office. With new variants developing and particularly the "UK" B.1.1.7 and "South African" or B.1.351 variant spreading, I wonder to what extent the changes they reflect would impact modeling results. I expect most variables are the same, but wonder if the added efficacy of transmission can be accounted for with the model. As an interim approach, might one adjust downwards the risk tolerance or other variable to approximate adjustment for the variants?

On 2021-02-23 23:14:07, user phil wrote:

Fig 1I - the plot is piecewise linear. Shouldn't it be a step function? The key dates mark the point where presumably R_t^eff changes, which should then be constant until the next key date?

On 2021-02-24 02:58:40, user Eric O'Sogood wrote:

1. The trial was stopped early and did not enroll enough subjects to meet its own initial power calculations. 2. Single dose ivermectin at this stage is not the recommended regimen. 3. Ivm arm had the highest d dimer (p 0.01) and I do not see any discussion of anticoagulant beyond thromboprophylaxis. 4. Absorbtion of ivm with food rises ~4 fold, was it given on an empty stomach or with food? 5. The authors write that this is the first trial of ivm vs placebo. There are already 5.

On 2021-02-24 22:37:41, user Sócrates Brasileiro wrote:

There are not two waves. It is the same pandemic, reaching different people. Countries population are more or less constant in one year. This means that infection and fatality rates should be computed by summing up (in the numerator) respective cases during the whole period. And not by splitting the numerator into two waves as if they were cases from different pandemics. If this was done, previous statements by one of the authors, such as "covid is as deadly as driving your car to work", would clearly be wrong, as they are indeed.

On 2021-02-25 19:02:42, user Lisa Mair wrote:

I'm so reassured that others are noticing that their conclusion does not match what their data showed. I've seen this in several of the pro mask studies. Like in the Lancet mask study, authors admit that the data is low certainty of evidence and that there were confounding variables, but they still strongly recommend masks. The WHO recommends masks but then admits data is weak. It's very common. Do you think it's because of encouragement of a specific conclusion due to funding? It is well known that research usually favors the desired result of the funder.

On 2021-02-28 00:56:39, user Kevin wrote:

Still, the vast majority of studies have shown significant increases in survival and with a drug generally as safe as Ivermectin waiting for perfect evidence is deadly and foolish. Remdisivir was approved with much less efficacy and much more side effects (many severe). I find it laughable that we are tip-toeing around with ivermectin but there was no problem at all pushing a drug through approval that hadnt shown a significant increase in survival but, hey, atleast it will help you get out of the hospital faster! - If your lucky enough to survive that is.

On 2021-02-28 12:37:41, user micro dentist wrote:

Many thanks for your effort. Very useful data, yet requires cautious interpretation.<br /> It is important not to aggrandise conclusions when the sample population is skewed due to disproportionate under-representation.

Such an aggrandisement potentially occurs here:<br /> “The observation that the seroprevalence amongst dental practice receptionists, who have no direct patient contact, was comparable to the general population, supports the hypothesis that occupational risk arose from close exposure to patients.’

Whilst in comparison to 16% of clinical staff 6% of receptionists were seropositive, it is important to also acknowledge that 21.6% of practice managers (also non-clinical) were seropositive.

Where significant conclusions may be derived through occupational comparisons, the effect of disproportionality should also be independently validated through careful examination of the internal validity of any inferred conclusions.

Here this would show lack of consistency with the derived conclusion. Should there still be a requirement a desire for an assumption, it may be worth considering combining any smaller similar samples (such as receptionists and practice managers in this case). In this study such combined group would show a seropositivity of 12.2% (n=131).

Through erroneously overlooking disproportionate occupational representation, there is the real potential of developing ludicrous conclusions: the most obvious being that seroprevalence is related to the amount of occupational administrative paperwork completed by each member of the team: practice managers>dentists>receptionists.

Clearly such a conclusion is neither desirable or valid.

On 2021-03-03 00:42:19, user James Gorley, PhD wrote:

In this ambitious study, the authors set out to show histological safety of low intensity FUS. A few key questions should be addressed by the authors. Namely, if the EEG was not usable, how is the claim of "temporal slowing" of one participant justified? Was any statistics or rigor applied to support this claim? Furthermore, two participants are excluded from the analysis, but the data is analyzed later anyway in the psych testing. Interested to see how this manuscript will evolve!

On 2021-03-09 14:22:05, user Alexis Alomoto wrote:

I would like to know, in which countries or where were the participants chosen to carry out this study?