On 2020-11-09 11:47:26, user Thomas Grünebaum wrote:
I wonder if there is an influence on adults having childeren in kindergarden versus adults who do not have.
The reason I am asking this is becaus I assume that the immune system of parents is more trained and has a better response to covid.
Is there any study realated to this? I fully agree this might be too specific and quite dificult to determin due to lack of data.
On 2021-06-04 19:54:51, user Nick Tustison wrote:
On 2021-08-29 07:53:30, user Swami Ganesh wrote:
It is not clear how the average IFR (e.g. 0.21%) was obtained. Report says it was based on reported Covid fatalities. There is also mention of estimating IFR based on government mortality data for the subject area. How do you derive that? The standard way is to divide the cumulative death for the representative population (on, say, the date the sero survey collection ended), by the infected population (average seroprevalence X population). Of course, if one doesn't believe the reported fatalities, then some use excess mortality data, but that is a can of worms and strains credibility because the baseline mortality from pre-Covid years are equaally unrelaible if one doesn't trust the reported Covid deaths. Will appreciate your clarification. Thanks
Swami Ganesh (PHD, MBA) retired engineering professional, NY, USA
On 2020-04-23 02:46:06, user Raspee wrote:
(1) There appears to be a statistically significant imbalance in the arms with regard to disease severity.
“However, hydroxychloroquine, with or without azithromycin, was more likely to be prescribed to patients with more severe disease, as assessed by baseline ventilatory status and metabolic and hematologic parameters.”
The base line pulse oximetry data and baseline line absolute lymphocyte count (Table 2) - indicates a statistical difference at p = 0.024 - the subjects that received hydroxychloroquine had a worse baseline respiratory status - and a worse absolute lymphocyte count p = 0.021.
This is an inherent bias in the design that has not been adequately addressed. The analysis should compare treatment in subjects with the same disease severity.
(2) If we look at table 4 - (HC + AZ) - 82% were discharged without ventilation vs. 77% discharged without ventilation both in the HC and non- HC group - Apparently the HC + AZ group did better than the other two groups.
This is supported by the observation that the adjusted HR for ventilation is 0.43 (0.16 - 1.12) - It was better than the control arm with regard to disease progression and no different than the control for death.
So in patients that were sicker at baseline, HC + AZ appears to have had a better outcome - than the other two groups - with regard to being discharged without requiring an ICU admission.
(3) Please provide a better justification to exclude the 17 women Please go back and perform the analysis including the 17 women.
(4) What were the doses of azithromycin and hydroxychloroquine administered? How are the different doses and dose regimens adjusted in the analysis? Not everyone in the HQ and HQ + AZ groups were dosed in the same fashion. Is there a minimum number of doses that you used to include them in the treatment groups?
(5) If the control group had less severe illness at presentation, it stands to reason that the mortality rate would be lowest in the control group.
(6) Was there a sub analysis looking at impact of secondary bacterial pneumonia - which occurs in 5-15% of moderate to severe COVID-19 patients? Were the antibiotics utilized the same over the 3 cohorts or were they different?
(7) How many patients were on ace inhibitors and/or angiotensin receptor blockers? Were these medications balanced in the 3 arms? What about corticosteroid use in the 3 cohorts? Was corticosteroid use balanced?
(8) Please go back and re-run the analysis with an additional 14 days of COVID-19 data (using April 25th cut -off) as your sample size will undoubtedly be greater and we would expect that the HQ + AZ group will now have a p value < 0.05. for discharge without ventilation.
(9) Please include length of stay in your analysis as well
(10) Please include readmission rates to the hospital in your analysis
On 2020-04-23 17:45:06, user Pedro de Almeida wrote:
How come that in this "paper" Reference 2 is claimed to be an example of studies showing poor results of the use of hydroxychloroquine, but when we go and look into it (https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.03.22.20040758v3.full.pdf)"), it (itself) claims the exact opposite?????
On 2020-04-22 10:39:55, user Niall Toibin wrote:
***First Point***
Obviously the state of the patient and their progression may have influenced the decision to prescribe HC. To quote the paper
QUOTE<br /> baseline characteristics corresponding to clinical severity varied across the three groups of patients and could have influenced the non-randomized utilization of hydroxychloroquine and azithromycin<br /> UNQUOTE
This is the context in which the following has to be taken
QUOTE<br /> A total of 368 patients were evaluated. Rates of death in the HC, HC+AZ, and no HC groups were 27.8%, 22.1%, 11.4%, respectively.<br /> UNQUOTE
No media outlet should report the second quote without the first.
***Second Point***
The authors attempt to account for this obvious bias - the patient's state influencing the decision to use HC.
They compute propensity scores (for different clinical outcomes) for HC use and HC+AZ use based on all baseline characteristics.<br /> i.e. they attempt to look at people who are equally sick in each cohort and see if HC made a difference.
There is a problem with their attempt to account for these baseline characteristics (Age, BMI, pulse, breaths per minute, heart rate, blood pressure, blood count etc.)
Clearly we need to know patient's baseline characteristics at the start of treatment.<br /> (We don't know the dates on which the decisions were made to start HC treatments. We only know the dates of admission.)
If we don't know their medical states on the date of that decision we can't discount that HC was more likely to be tried on the desperate cases. This is the main issue the authors identify and are trying to overcome. Without which the study is meaningless.
But (page 21)<br /> QUOTE<br /> Patient demographic and clinical characteristics, including those associated with the Covid-19 disease severity, were evaluated ***at date of admission,***<br /> UNQUOTE
How the patients illnesses had progressed and what state they were in when it was decided to start them on HC neither we nor the authors have any idea.
On 2020-03-25 14:25:55, user Sinai Immunol Review Project wrote:
Summary<br /> The use of heat inactivation to neutralize pathogens in serum samples collected from suspected COVID-19 patients reduces the sensitivity of a fluorescent immunochromatographic assay to detect anti-SARS-CoV-2 IgM and IgG.
Major findings<br /> Coronaviruses can be killed by heat inactivation, and this is an important safety precaution in laboratory manipulation of clinical samples. However, the effect of this step on downstream SARS-CoV-2-specific serum antibody assays has not been examined. The authors tested the effect of heat inactivation (56 deg C for 30 minutes) versus no heat inactivation on a fluorescence immunochromatography assay. Heat inactivation reduced all IgM measurements by an average of 54% and most IgG measurements (22/36 samples, average reduction of 50%), consistent with the lower thermal stability of IgM than that of IgG. Heat inactivation caused a subset of IgM but not IgG readings to fall below a specified positivity threshold.
Limitations<br /> Limitations included the use of only one type of assay for testing heat inactivated vs non-inactivated sera, and the use of the same baseline for heat inactivated and non-inactivated sera. The results indicate that heat inactivation affects the quantification of SARS-CoV-2-antibody response, specially IgM, but still allows to distinguish positive specific IgG. Therefore, the effect of heat inactivation should be studied when designing assays that quantitatively associate immunoglobulin levels (especially IgM) to immune state.
Review by Andrew M. Leader as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.
On 2022-01-09 12:17:20, user Christopher Hickie wrote:
What? No comments allowed? I am a pediatrician with a PhD in neuroscience. My comment earlier was valid.
On 2020-06-30 06:03:47, user Verde wrote:
This model suffers from an incomplete data problem resulting in an order of magnitude error. Easily resolved by either using all cause death data, or seeding the time series upfront with a large number and adding a constant to each day’s new deaths.<br /> https://drive.google.com/fi...<br /> https://drive.google.com/fi...
On 2021-06-22 15:51:27, user Amit Kumar wrote:
The paper has been published in a peer-reviewed journal Aerosol and Air Quality Research with the following DOI number.<br /> https://aaqr.org/articles/a...
On 2021-02-11 15:28:29, user Robert Olinski wrote:
You are speaking about Ct values that are not part of clinical diagnostics. What were clinical symptoms of people post-vaccination with decreased viral load? Does it mean that the vaccination did not prevent infection?
On 2020-05-26 19:59:48, user Boonton wrote:
So 318 outbreaks were identified. At least 3 people are required to make an outbreak. The outbreaks were grouped into categories. So if a husband, wife, and grandparents all got the disease that would be categorized as 'home'. Fair enough.
Now this study is being cited as evidence the virus does not transmit outdoors. Is that consistent with the details of this study? If no one spent much time outside in China because it was bitterly cold, that doesn't seem to have tested outdoor transmission then. People gathered together at the beach betting that sun and warm air will keep them safe should not count on this study as evidence supporting that, or should they?
On 2020-11-17 21:24:47, user George wrote:
The two leading comorbidities associated with COVID-19 mortality, SCD and kidney disease, are mechanistic causes of selenium deficiency. Selenium deficiency is associated with hemolysis in SCD and has been strongly associated with mortality and other outcomes in 4 COVID-19 studies so far. High-dose sodium selenite infusion is safe and well-tolerated in dialysis patients.<br /> Vitamin D and dexamethasone both alter selenoprotein expression, and thus may be ineffective if selenium is deficient.
On 2021-08-11 01:19:08, user dustinst22 wrote:
Delta is infecting those who are not vaccinated at a much higher rate -- this tends to be those less at risk (i.e. younger). The older/vulnerable populations are vaccinated at higher rates and hence are infected with Delta in lower numbers and also experience less severe infection. This is why the CFR is lower -- younger population infected + less severity if infected with vaccination.
On 2020-08-26 18:09:14, user Ger Rijkers wrote:
Our manuscript has now been published by Journal of Infectious Disease. Please use that for future reference.<br /> Rijkers G, Murk JL, Wintermans B, et al. Differences in antibody kinetics and functionality between severe and mild SARS-CoV-2 infections [published online ahead of print, 2020 Jul 29]. J Infect Dis. 2020;jiaa463. doi:10.1093/infdis/jiaa463
On 2024-12-19 23:00:26, user pietje puk wrote:
This paper is based on a corrupted vaccine registration dataset.<br /> Details about this can be found in the following link:
https://virusvaria.nl/en/is-dit-de-smoking-gun/
In summary: vaccinees were registered in CIMS, but there often was a time lag of a few weeks between vaccination and registration in CIMS. Because of a ministerial decree from 19 November 2020, the following happened:<br /> If vaccinees died after vaccination, but before registration, they were not registered as vaccinated. In addition, for a period of time, registered vaccinees that died were actively removed from CIMS.<br /> This makes the data in CIMS not suitable for analyses.<br /> The authors should retract this paper.
On 2020-04-01 08:31:20, user Bob O'Hara wrote:
The paper refers to Supplementary Material which gives details about the model fitting. Could this be uploaded too, please?
On 2020-08-28 09:33:01, user Dr Aniruddha Malpani, MD wrote:
Shouldn't the Conclusion be exactly the opposite ?
Conclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines thelow risk of acquiring COVID-19 from surface and air contamination in managing COVID-19.
Conclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from surface and air contamination in managing COVID-19, and the need for effective use of PPE, social distancing, and hand/surface hygiene.
On 2020-02-14 00:59:07, user acm_ian wrote:
Doesn’t the accuracy of the modelling depend on the input data. Identifying an unknown infectious agent in routine practice is not simple. It is feasible that the virus has been around longer without being recognized and the spread coincides with the usual winter influenza and other respiratory virus spread.
On 2020-10-27 03:12:46, user Critical Dissection 2 wrote:
Dear authors,
First I just want to say I think that it was great you pursued such an important topic. There were a lot of good things about your article like your clear abstract that very well laid out the different parts of the paper and the main summary of each section. I also like that you laid out the limitations of your study and how they should be solved for in further investigation of this topic. However, there is still some room for improvement in this paper. I thought that the introduction could use more background to contextualize the issue and put some scope into it to explain why people should expand upon your results and see if the data is helpful in the future. I also think that the figures need more explanation in the results section, unless a highly experienced physician is reading it, it is a little hard to tell what we are supposed to be looking at and drawing from the figure that supports your hypothesis. There was also an emphasis drawn between the two patients whose ablation was done with a little more targeting of certain factors compared to patients who underwent standard ablations that was only mentioned in the discussion but is a great point that I think should be brought up earlier maybe somewhere in the results section. I think with these changes you will have a good paper.
On 2020-10-27 02:05:30, user Critical Dissection wrote:
Dear author,
I enjoyed reading the article and I liked how the abstract was divided and broken down to introduction, methods, conclusion and results. I think that really helped me get an idea of what I will be reading. The methods section was detailed which was good. However, I had some difficulty and confusion when reading the paper. I thought the figures could be explained better because I had confusion dissecting them. Some issues with the methods were the reduced sample of the study and the lack of long-term follow up for atrial flutter relapse.
On 2021-06-25 18:19:47, user Xolo wrote:
Very good point about the masks. And why weren't all the previous outbreaks contained this way - shutting down everything? Probably the prior viruses weren't as contagious? Interesting to see how climate change, economic system reset all intersecting at the same time.
On 2021-06-01 23:55:02, user Justin Lovern wrote:
Could your refer to any good research purporting to show that masking reduces transmission rates? The burden of proof is on the maskers if they want to impose masking, not on those who don't want to wear masks to prove they don't work.
On 2021-05-27 04:38:06, user rusbowden wrote:
The study does not show what you say it does, and from your response, my fear that people will use it to think that unmasking is as safe as masking, means we have study here that puts people in peril.
On 2025-10-15 04:54:32, user CDSL JHSPH wrote:
Dear Author, This work presents an interesting analysis of the connection between the development of islet autoimmunity in early childhood and bile acid metabolism controlled by the gut microbiota. Understanding how early-life gut microbial and change in metabolism may affect type 1 diabetes risk is made possible by the long-term research design and the use of multi-omics methods, which combine metabolomics and metagenomics. Could gut microbiota regulation, such as with probiotics, prebiotics, or bile acid-targeted therapies, help normalize bile acid profiles and lower the risk of type 1 diabetes progression in children at risk, considering the noted changes in bile acid metabolism that occur before islet autoimmunity?
On 2021-01-14 00:01:25, user Emmanuel Aluko wrote:
Will such a study be done on older cohorts to show the age-dependent efficacy of Ivermectin on reducing mortality, for mortality is seen mainly in older patients with associated co-morbidities? Days to negative tests on such cohorts would also provide a better basis for accepting Ivermectin as a worthy COVID-19 therapeutic.
On 2021-09-22 19:56:45, user Steve E wrote:
Unfortunately, even your high-vaccination-hesitancy-level scenario, which leads to national vaccination coverage saturation at 70%, now seems too optimistic. Today's CDC vaccination data shows we may not even reach 60% by year's end (unless the Biden mandates change the situation dramatically). How do your projections change in a 60% scenario?
On 2020-06-08 12:47:44, user OxImmuno Literature Initiative wrote:
On 2020-04-18 16:08:42, user Tom wrote:
All Swedes have not died, but we predict that most soon will ? Or just the 96000 ? Actual at this point on April 17 is about 1511. Seems like a long way to go.
On 2021-06-20 08:07:15, user Stephen Smith wrote:
note bottom-left panel in Fig1 needs replacing with the correct scatterplot; have tweeted the corrected sub-panel and will update PDF here shortly.
On 2021-12-15 06:28:56, user Robert Clark wrote:
From the article:
We used two-month periods as our basic time interval for defining the sub-cohorts, but combined months 12 to18 for the Recovered cohort and omitted months 8 to 10 for the Vaccinated and the hybrid cohorts due to the small number of individuals.
And also:
Typically, infection rates among recovered or vaccinated individuals are compared to the infection rate among unvaccinated-not-previously-infected persons. However, due to the high vaccination rate in Israel, the latter cohort is small and unrepresentative of the overall population; furthermore, the MoH database does not include complete information on such individuals. Therefore, we did not include unvaccinated-not-previously-infected individuals in the analysis.
Frankly, I don’t think the researchers are being completely open about the real reason they aren’t including the unvaccinated/uninfected in their study. The vaccination rate in Israel is about 80%. At a population of 9 million, that would mean 1.8 million unvaccinated. Obviously they could get high statistical significance with that many people.
I think the real reason is they would find the same as what was seen in the UK and in Sweden, post 6 months the vaccine effectiveness is worse than being unvaccinated to begin with.
Stunning after this length of time so many countries are refusing to present this data. They’ll collect the data up to 6 months and find the vaccine has waned to having no effectiveness in comparison to the unvaccinated. But except for Sweden and the UK, they refuse to go beyond that point.
Robert Clark
On 2020-04-22 03:57:44, user IanM wrote:
Hi,<br /> Could you explain how you performed your quantitative RT-PCR?<br /> Also, could you comment on whether a recombinant or plaque purified version of each virus carrying a mutation of interest may increase the strength of these in vitro observations? Cheers!
On 2020-04-21 08:07:22, user Chao Jiang wrote:
The detailed number of mutations that are in the allele-frequency form should be indicated in the figure 1 of the manuscript.
On 2020-08-20 18:06:49, user PB wrote:
For campus opening in the US at present, the analysis in this manuscript is irrelevant. This analysis assumes a single starting infection in a completely susceptible (i.e. uninfected) population. A rough but reasonable order-of-magnitude estimate (but see https://mggg.github.io/esti... ) suggests that in a population of 10K student-age people in California, we would expect O(100) students who would test positive on day 1 if tests were available. That is, the outbreak is already there! It arrives with the students. The idea that it can be controlled with a single round of entrance testing is also wrong. Assuming 85% sensitivity (the value used by these authors, which is very optimistic but not crazy high), the test would catch 85 of those 100 positives, but miss 15. Again, the outbreak is already there. The analysis in this manuscript may be reasonable, but for managing campus infection in the US at the moment, it simply addresses the wrong question.
On 2020-06-04 00:48:58, user James Van Zandt wrote:
Vitamin C is a common supplement. I suggest you track whether patients had taken vitamin C (and how much) before or in the early stages of their illness. If it is helpful, then we would like to know when it is most helpful.
On 2020-03-23 20:09:08, user halrhp wrote:
wha is the mechnism that changes at 2 m? Gravity or particle mass or both. my guess is the combination. ahow large are COV19 virus droplets. Y. Li is very smart.
On 2020-04-04 14:57:41, user Alexandros Heraclides wrote:
Maybe better to refer to "differing Relative Risks for dying", rather than "differing mortality impacts"? The latter points to absolute risk difference, while you are referring to relative risks. Great paper though!
On 2020-03-30 21:04:58, user Sinai Immunol Review Project wrote:
Keywords<br /> death biomarkers, cardiac damage, Troponin, Blood type, respiratory failure, hypertension
Summary<br /> This is a retrospective study involving 101 death cases with COVID-19 in Wuhan Jinyintan Hospital. The aim was to describe clinical, epidemiological and laboratory features of fatal cases in order to identify the possible primary mortality causes related to COVID-19.
Among 101 death cases, 56.44% were confirmed by RT-PCR and 43.6% by clinical diagnostics. Males dominated the number of deaths and the average age was 65.46 years. All patients died of respiratory failure and multiple organs failure, except one (acute coronary syndrome). The predominant comorbidities were hypertension (42.57%) and diabetes (22.77%). 25.74% of the patients presented more than two underlying diseases. 82% of patients presented myocardial enzymes abnormalities at admission and further increase in myocardial damage indicators with disease progression: patients with elevated Troponin I progressed faster to death. Alterations in coagulation were also detected. Indicators of liver and kidney damage increased 48 hours before death. The authors studied the deceased patients’ blood type and presented the following results: type A (44.44%), type B (29.29%), type AB (8.08%) and type O (18.19%), which is inconsistent with the distribution in Han population in Wuhan.
Clinical analysis showed that the most common symptom was fever (91.9%), followed by cough and dyspnea. The medium time from onset of symptoms to acute respiratory distress syndrome (ARDS) development was 12 days. Unlike SARS, only 2 patients with COVID-19 had diarrhea. 98% presented abnormal lung imaging at admission and most had double-lung abnormalities. Related to the laboratorial findings some inflammatory indicators gradually increased during the disease progression, such as IL-6 secretion in the circulation, procalcitonin (PCT) and C-reactive protein (CRP), while platelets numbers decreased. The authors also reported an initial lymphopenia that was followed by an increase in the lymphocytes numbers. Neutrophil count increased with disease progression.
The patients received different treatments such as antiviral drugs (60.40%), glucocorticoids, thymosin and immunoglobulins. All patients received antibiotic treatment and some received antifungal drugs. All patients received oxygen therapy (invasive or non-invasive ones).
Limitations<br /> This study involves just fatal patients, lacking comparisons with other groups of patients e.g. patients that recovered from COVID-19. The authors didn’t discuss the different approaches used for treatments and how these may affect the several parameters measured. The possible relationship between the increase of inflammatory indicators and morbidities of COVID-19 are not discussed.
Relevance<br /> This study has the largest cohort of fatal cases reported so far. The authors show that COVID-19 causes fatal respiratory distress syndrome and multiple organ failure. This study highlights prevalent myocardial damage and indicates that cardiac function of COVID-19 patients should be carefully monitored. The data suggest that Troponin I should be further investigated as an early indicator of patients with high risk of accelerated health deterioration. Secondary bacterial and fungal infections were frequent in critically ill patients and these need to be carefully monitored in severe COVID-19 patients. Differences in blood type distribution were observed, suggesting that type A is detrimental while type O is protective – but further studies are needed to confirm these findings and elucidate if blood type influences infection or disease severity. Several inflammatory indicators (neutrophils, PCT, CRP and IL-6, D-dimer) increased according to disease severity and should be assessed as biomarkers and to better understand the biology of progression to severe disease.<br /> Reviewed as part of a project by students, postdoctoral fellows and faculty at the Immunology Institute of the Icahn School of Medicine at Mount Sinai
On 2020-10-25 19:08:24, user Daniel Haake wrote:
Dear study team,
Thank you for your study, which shows that the risk of COVID-19 death increases significantly with age. To improve the quality of the study I have some comments regarding the statistical analysis of the study. In the following I would like to go into it.
You write that antibodies are formed in 95% of people after 17-19 days. In contrast, 95% of deaths are reported after 41 days. That is a difference of 22-24 days. Nevertheless, you take the number of deaths 28 days after the midpoint of the study. Why do you take a later point in time than you yourselve have determined? Even with this approach, you are 4 - 6 days too late and overestimate the number of deaths. Why even this would be too late, I will explain in more detail below.
The 41 days were given for the USA. But what is the situation in other countries? In Germany, for example, there is a legal requirement that the death must be reported after 3 working days at the latest. Of course there can also be unrecognized deaths in Germany, where it takes longer to report. But this should be the minority. If we transfer however this fact of the USA to other countries, in which the risk of the long reporting time does not exist in such a way, you take up too many deaths into the counter of the quotient with. This leads to a too high IFR.
Counting the deaths 28 days after the study midpoint is also problematic because in the meantime, further deaths may appear in the statistics that were not infected until after the infected persons identified in the study became infected. This is because not all deaths take as long to report. These are then deaths that are not related to the study. You yourself write that the average value of the report of a dead person lasts 7 days with an IQR of 2 - 19 days. These figures speak in the statistical sense for a right-skewed distribution in the reporting of death figures. This in turn means that the majority of the deceased have a rather shorter reporting time. The procedure leads to a too high number of deaths. This is a problem especially with still existing infection waves, even with already declining infection waves.
You write: “The mean time interval from symptom onset to death is 15 days for ages 18–64 and 12 days for ages 65+, with interquartile ranges of 9–24 days and 7–19 days.”<br /> If we assume the 3 days reporting time for Germany, we receive 18 days for the age 18-64 and 15 days for 65+. In contrast, 95% of the antibodies are formed after 17-19 days, which is about the same or later than the time when the dead appear in the statistics. For other countries this may be different and would therefore need to be investigated. In any case, a blanket assumption from the USA is not possible for studies outside the USA.
Since the mean time interval from onset of symptoms to death is 15 days for the age 18-64 with the interquartile range of 9-24 days, but the midpoint of the range would be 16.5 days, this suggests a right-skewed distribution in the values. The same applies to the mean time interval from the onset of symptoms of 12 days with interquartile range of 7-19 days for the age 65+, where the midpoint of this range is 13 days. This also speaks for a right-skewed distribution of the values. This would mean that the majority of the values would be below the mean value in each case, making shorter times more likely. This also shifts the time too far back. Therefore it would be better to assume the median value, because it is less prone to outliers.
Your example infection wave from figure 1 also shows the problem with this procedure. As you say, antibodies are formed in 95% of people after 17 - 19 days. Now you have an example study with the median 14 days after the start of infection. At that time, only a few of the infected persons have formed antibodies at all, since just 14 days before the infection wave starts with low numbers and then increases. Only 4 days before is the peak of the infection wave. This means that the time period, which is very strongly represented, cannot have developed any antibodies at all. This leads to the fact that only very few infected persons are recognized as infected. In your example, 95% of the deceased are now infected, but only very few of the infected. This leads to a clear overinterpretation of the IFR.
Due to the problems mentioned, the number of deaths should therefore be taken at the median time of the study. Of course, it would be best if the studies took place immediately after the end of a wave of infection, where the death rates are stable and the expression of antibodies is complete.
You write: "A potential concern about measuring IFR based on seroprevalence is that antibody titers may diminish over time, leading to underestimation of true prevalence and corresponding overestimation of IFR, especially for locations where the seroprevalence study was conducted several months after the outbreak had been contained.“
You have made many assumptions about the death figures and adjusted the death figures (upwards) accordingly. Here you find that the antibodies disappear over time and that this can lead to an underestimation of the number of infected persons. However, you do not adjust the number of infected persons upwards, unlike your approach to adjusting the death figures. For example, a study by the RKI found that 39.9% of those who tested positive for PCR before did not develop antibodies (https://www.rki.de/DE/Conte... "https://www.rki.de/DE/Content/Gesundheitsmonitoring/Studien/cml-studie/Factsheet_Bad_Feilnbach.html)"). From this, we could conclude that the antibody study only detected around 60% of those previously infected and that the number of infected persons would have to be adjusted accordingly. But you have not done that. I can understand that you did not do that. I wouldn't have done it either, because we don't know how this is transferable to other studies. But in adapting the dead, you have transferred such assumptions to other studies. This should therefore also be avoided. There, too, we do not know how transferable it is. If you only make an adjustment in the dead, but not justifiably in the infected, this leads to an overestimated IFR.
You write in your appendix D: "By contrast, a seroprevalence study of Iceland indicates that its tracing program was effective in identifying a high proportion of SARS-CoV-2 infections“.
In my opinion this is a wrong conclusion. In my opinion, it is not the success of the tracing program, but the number of tests and thus fewer unreported cases. To date, Iceland has performed almost as many tests as there are inhabitants in Iceland. Therefore they could keep the number of unreported cases lower. Other countries did not test as much. Therefore the results are not easily transferable to other countries. The PCR tests only show the present, but not the past and not the untested.<br /> You write it yourself: „(…) hence we make corresponding adjustments for other countries with comprehensive tracing programs, and we identify these estimates as subject to an elevated risk of bias.“<br /> Nevertheless, you leave these studies in meta-analysis, although for the reasons mentioned above this leads to severe problems. The figures for countries with tracing programs should therefore not have been included. The estimated number of unreported cases is not known and cannot be taken over by Iceland.
You sort out some seroprevelence studies. These include Australia [63], Blaine County, Idaho, USA [67], Caldari Ortona, Italy [72], Chelsea, Massachusetts, USA [73], Czech Republic [75], Gangelt, Germany [79], Ischgl, Austria [81], Riverside County, California, USA [98] , Slovenia [101] and Santa Clara, California, USA [116]. For the most part, these studies are sorted out because there is no age specification for seroprevelence. Since this is the study's investigation, this is of course understandable. However, these studies in particular have shown calculated IFR values between 0.1% and 0.5%. At the same time, you leave the numbers of PCR tests from countries with tracing programs in the meta-analysis. As already mentioned, this is not correct due to the unknown dark figure and the transfer from Iceland is also not possible, as described before. This leads to the fact that studies with low values are sorted out, but at the same time uncertain numbers with high values are left in the study. This shifts the calculated IFR value upwards in purely mathematical terms.
It is precisely the outliers upwards that cause problems in the calculation. Since the numbers are rather small (in a mathematical sense), there can be no deviation as strong downwards as upwards. This means that there may be studies that deviate perhaps 0.2 percentage points downwards, but other studies that deviate upwards by 1.2 percentage points. This is a problem for the regression, because the regression then leads to too high values. Therefore, outlier detection should be performed upstream and the outliers should be excluded. You can also make it easier by taking the median value, since it is less susceptible to outliers. But then you would have only one value.
You write: “The validity of that assumption is evident in Figure 3: Nearly all of the observations fall within the 95% prediction interval of the metaregression, and the remainder are moderate outliers.”<br /> You can see it in figure 3, but due to the logarithmic scale it is difficult to estimate the ratios. Better suited is Figure 4, which would be desirable for the different age groups to be able to make a better estimation there. Figure 4 shows that many studies are outside the confidence interval, often to a considerable extent and to a greater extent also towards the high IFR values. Looking at the values and the confidence interval, these studies must have significant z-scores, which would show that these are clearly outliers that should not be considered. This leads to the fact that the regression will be brought further in the direction of high values, which results in too high IFR values.
In Appendix Q you write: "In the absence of accurate COVID-19 death counts, excess mortality can be computed by comparing the number of deaths for a given time period in 2020 to the average number of deaths over the comparable time period in prior calendar years, e.g., 2015 to 2019. This approach has been used to conduct systematic analysis of excess mortality in European countries.[159] For example, the Belgian study used in our metaregression computed age-specific IFRs using seroprevalence findings in conjunction with data on excess mortality in Belgium“
I understand why you want to do this. But there are some dangers involved. The above statement may be true for Belgium, but it cannot be transferred to other countries in a general way. Especially since you cannot say in general terms that every dead person above average is a COVID 19 dead person. Mathematically, this would mean that there have been COVID-19 deaths in some of the last few years, because there have been periods with more deaths than the average. This makes the average straight. Especially since, as I said, you can't simply say that every death above the average is a COVID-19 death. The majority will be it, but not necessarily everyone. Thus, even cancer operations that did not take place or untreated heart attacks due to the circumstances and unnoticed visits to the doctor may have contributed a share. Whether this is the case, we do not know without a study. A blanket assumption that every death above the mean value is a COVID-19 death is not correct. From the statement "For example, the Belgian study used in our metaregression computed age-specific IFRs using seroprevalence findings in conjunction with data on excess mortality in Belgium", we could also conclude that the number of reported COVID-19 deaths is correct and can therefore be used as the numerator of the quotient for calculating the IFR. <br /> If you take this as a blanket assumption, how do you deal with those countries that do not have excess mortality but have several thousand COVID-19 deaths in the official statistics? Would you then correct the number of COVID-19 deaths downwards, perhaps even to 0? Certainly not.
You write: "We specifically consider the hypothesis that the observed variation in IFR across locations may primarily reflect the age specificity of COVID-19 infections and fatalities.“
It is also possible that the variation in the calculated IFRs occurs due to still different dark figures. If, for example, the PCR tests are taken in countries with a tracing app, but an IFR based on Iceland is calculated there, this can lead to incorrect and too high IFR values. Also the adjustments of the death rates themselves or the late time of the death rate determination 4 weeks after the study center can lead to this high variance.
In Table 1 you write that on July 15 there were 8 million inhabitants with a projected 1.6 million infections. According to my research there are 8.4 million inhabitants. You calculate the 1.6 million infected on the basis of the 22.7% infected in the study. However, the blood samples were taken between April 19 and 28, so the infections occurred before or until the beginning/middle of April. So you now take the number of infected persons from the beginning/mid-April or from April 24 (study midpoint) and insert them for July 15, i.e. just under 3 months later! In the meantime, however, not only people have died, but have also become infected and formed antibodies. They thus increase the numerator of the quotient, but leave the denominator unchanged, although the denominator would also be higher. So you shift the IFR upwards here as well.
The study on Gangelt, which was not taken into account, shows a similar picture. You write that at the end of June there were 12 deaths and therefore the IFR rises to 0.6%. That is 8 weeks (!) after the study center. This does not take into account that in Germany the deaths must be reported after 3 days. If you have proceeded in this way when calculating the other IFRs from other studies, this suggests that the IFR values are too high.
You calculate the IFR of influenza based on the CDC figures for the 2018/2019 influenza season and indicate the IFR as 0.05%. Firstly, it should be said that statistically it is never good to look at just one value. The average of a time series should be considered. You calculate the value by looking at the estimated deaths and looking at how many were estimated to be symptomatically infected with influenza. You use a study according to which about 43.4% of cases are asymptomatic or subclinical (95% CI 25.4%-61.8%). You then take the mean value from the confidence interval with the value 43.6% and use this figure to calculate how many people were probably infected with influenza. Statistically it is not correct to take the average value of 43.6%. The value of 43.4% must be taken. Due to the small difference, this does not make much difference, but it shows the statistically imprecise consideration that runs through the study and generally leads to an IFR that is too high or, in the case of influenza, too low.
Now a statement on the selection of the 2018/2019 flu season, the CDC writes: "These estimates are subject to several limitations. (...) Second, national rates of influenza-associated hospitalizations and in-hospital death were adjusted for the frequency of influenza testing and the sensitivity of influenza diagnostic assays, using a multiplier approach3. However, data on testing practices during the 2018-2019 season were not available at the time of estimation. We adjusted rates using the most conservative multiplier from any season between 2010-2011 and 2016-2017, Burden estimates from the 2018-2019 season will be updated at a later date when data on contemporary testing practices become available. (...) Fourth, our estimate of influenza-associated deaths relies on information about location of death from death certificates. However, death certificate data during the 2018-2019 season were not available at the time of estimation. We have used death certification data from all influenza seasons between 2010-2011 and 2016-2017 where these data were available from the National Center for Health Statistics. (…)
The CDC writes the same for the 2017/2018 season, so the values, which were always only estimated anyway, were estimated even more due to missing data. Therefore we should have considered the figures for the seasons 2010/2011 to 2017/2017. If we calculate the IFR of influenza in this way and also use the confidence interval to calculate the number of people potentially infected per season, we get an IFR of influenza of 0.077%, ranging from 0.036% to 0.164%. Every single year prior to the 2018/2019 season was above the 0.05% and the average of 0.077% is also 54% above your reported value. This means that influenza is still not as lethal as COVID-19 has been so far, but the factor is not as high as suggested by your study.
It should also be noted that it is not possible to compare an IFR calculation that is equally distributed over age with an IFR of influenza that is not equally distributed over age. You do not do it directly, but by naming these numerical values, this has been taken up by the media. The IFR just indicates the mortality per actually infected person. Therefore the IFR of the actually infected persons of COVID-19 must be compared with the IFR of influenza. You can of course calculate a hypothetical IFR assuming that every age is equally likely to be infected. In this case, however, the calculation must be performed not only for COVID-19, but also for influenza.
I hope I can help you to improve the study in terms of statistical issues. I remain with kind regards.
On 2020-07-19 22:45:26, user George wrote:
" Lettuce consumption increased COVID-19 mortality."<br /> If you lack the commonsense to see how ridiculous that is, at least put it in non-causal language next time.
On 2021-12-22 19:07:52, user Pedro Abdalla wrote:
The complete paper was published on: https://rdcu.be/cDtQS or https://doi.org/10.1186/s13...
On 2020-08-01 22:03:19, user Pablo Richly wrote:
Could you please provide further details of the randomization of the control group?
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 2021-09-18 04:47:08, user Jack Albanese wrote:
big congrats to the authors on the publication in NEJM , september 15 <br /> as well as the press on Nature News
https://www.nejm.org/doi/10...<br /> https://www.nature.com/arti...
On 2020-04-30 20:54:07, user Frank Conijn wrote:
I don't have any objections against retrospective cohort studies, because they are sometimes all one can do, and they can give valuable insight. But the compared groups must have equal baseline disease severity. And am I overlooking something, or is that information missing?
Furthermore, the Brief Summary on page 2 says (emphasis by me): "The use of antiviral drugs (chloroquine, oseltamivir, arbidol, and lopinavir/ritonavir) did not shorten viral RNA clearance, especially in non-serious cases." But the text and figures show that that still concerned patients with pneumonia or worse. I don't find that non-serious cases. Those are moderate cases on a scale from light - mild - moderate - severe - critical.
On 2020-05-04 18:24:02, user Dan Kammen wrote:
The full data sets and code can also be accessed here:
On 2021-05-25 18:52:14, user Stephen J. Collings wrote:
Looks like an error in the extraction of data from Niaee 2020 has led to an incorrect mortality conclusion. Please correct as a matter of urgency.
On 2021-05-25 20:24:16, user Green Ranger wrote:
The results and conclusions of this study are wrong. The authors mistook the ivermectin and control arms of one of the RCTs that they included. Look at figure 2. The results from Niaee 2020 are dramatically misreported. The actual results for that study are as follows:
Control groups: 11 deaths out of 60 patients.<br /> Ivermectin groups: 4 deaths out of 90 patients.
When this is corrected, the results of this meta-analysis confirm what other meta-analyses have found. Ivermectin use is associated with approximately 66% reduction in Covid fatalities. And this result is statistically significant.
On 2021-05-25 15:23:14, user Dinofelis wrote:
The results for Niaee are inverted between control group and ivermectin group. Control group 11 death, IVM group: 4 death. See https://www.researchsquare....
Didn't check others.
On 2020-04-26 14:32:05, user Retelska wrote:
It would be useful to have a clear reference to each publication listed in table 2. Scientists would mostly use this table. What specificity an antibody should have to be used? I see studies that 2%, 3%, 6% of a population have been infected with covid, I would say it needs > 99% specificity, as an anitbody with 98% specificity would give 2% false positives.
On 2020-05-08 16:24:34, user Rich Dzikowski wrote:
If this virus can survive in the air for so long and still remain infectious, you don't need to be an expert to imagine that it will stick to clothing, skin, hair and other objects we carry with us. So what good is wearing masks, washing our hands and using a tracking app if we inevitably end up reaching for the virus-contaminated areas and infecting ourselves at home?
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 2021-01-12 17:22:08, user Ann Marie Miller wrote:
Can any one tell me if the Herpes 6/7 loads could cause a false positive Hep. C test, during a flare up? Thank you!
On 2021-08-27 17:01:38, user Peter Novák wrote:
The selection may be more forceful in some countries applying apartheid rules - i.e. if unvaccinated must perform tests in order to get access to some settings where vaccinated are granted access without test.<br /> So the question on cohort selection is legitimate.
On 2022-01-10 09:16:10, user Roger Helgesen wrote:
How accurate are self-reported measures in determining actual illness?
Those who know that they have had a positive Covid test might report symptoms more often than those that know they have not had a positive Covid test due to confirmation bias.
On 2021-04-15 03:32:28, user Mark Czeisler wrote:
Note from the authors:
This paper was published in BMC Public Health on 15 March 2021 following peer review. Below is a link to the article, along with the PubMed citation.
https://bmcpublichealth.bio...
Czeisler MÉ, Howard ME, Robbins R, Barger LK, Facer-Childs ER, Rajaratnam SMW, Czeisler CA. Early public adherence with and support for stay-at-home COVID-19 mitigation strategies despite adverse life impact: a transnational cross-sectional survey study in the United States and Australia. BMC Public Health. 2021 Mar 15;21(1):503. doi: 10.1186/s12889-021-10410-x. PMID: 33722226; PMCID: PMC7957462.
On 2020-08-13 16:38:14, user SonOfAnOnion wrote:
If you look at just look HIGH RISK patients in this study, you will see that the medications cut the risk of a bad outcome in half.
So despite the given conclusion, when you drill into the ACTUAL DATA, this study supports the use of the medications.
On 2022-01-14 14:59:21, user mike tomlin wrote:
Those ending up in hospital are more likely to be elderly who are more likely to be vaccinated, so it's not surprising more are in hospital. Presumably not dying as often as the elderly non vaccinated.
On 2021-10-19 01:20:29, user Sir Henry wrote:
From the study: "median age was 51 years"
On 2021-08-14 06:20:55, user john vegan wrote:
No, it does not say that they died "as s result".
However, it says that "Between 8 December 2020 and 11 June 2021, a total of 5,522 people died within 28 days of receiving a COVID-19 vaccine in Scotland".
All of them died "as a result" ? How can we be sure ?<br /> None of them died "as a result"? How can we be sure ?<br /> Some of them died "as a result"?, And, if yes, how many ?
On 2021-08-09 05:35:12, user Tim Freeman wrote:
You picked that row out of 26 other rows, so you have to correct for multiple analyses. To a first approximation that multiplies the odds of seeing something like that by accident by 26, so not significant.
On 2021-06-07 11:30:24, user Bridget Lockyer wrote:
Please be advised that this article has now been peer-reviewed and published in Health Expectations. <br /> https://onlinelibrary.wiley...
On 2022-08-25 21:34:09, user Ariana wrote:
What software did you use for the figures portraying the genetic correltion between the PDs? very cool, thank you!
On 2021-08-20 00:25:35, user troRx wrote:
So where are the "eMethods" they refer to? If this was driven entirely through Facebook, I would have some validity issues with applying it to the general population.
On 2021-08-13 10:16:11, user Earth Med Research wrote:
Please break down the PhD's according to what their field is, if it has not been done already. It would be very interesting if the sciences had stronger hesitancy, for example.
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 2021-11-15 06:45:38, user A Pharma person wrote:
Clearly you don’t have a clue about how FDA approval works if you are blaming the fda for this delay. There are regulations in place that govern vaccine approvals and they require a full 6 month safety followup for the patients on trial. Smh.
On 2020-04-22 05:08:10, user G.O.B. wrote:
Well, this changes everything...
“County of Santa Clara Identifies Three Additional Early COVID-19 Deaths”
First US death was in California over 3 weeks earlier than the previously reported first US death in Seattle on Feb 29.
On 2020-04-19 20:45:06, user John Smith wrote:
people who thought they have been exposed to covid-19 would want to get a free test. Others who thought they don't have the virus and have been in lockdown for a month would not go out of the house for the free test. This means you're selecting only the people who have been exposed and invalidates the study.
On 2020-04-21 21:10:27, user Bruno Vuan wrote:
Article says, page 7,
"This study had several limitations. First, our sampling strategy selected for members of Santa Clara County with access to Facebook and a car to attend drive-through testing sites. This resulted in an overrepresentation of white women between the ages of 19 and 64, and an under-representation of Hispanic and Asian populations, relative to our community. Those imbalances were partly addressed by weighting our sample population by zip code, race, and sex to match the county. We did not account for age imbalance in our sample, and could not ascertain representativeness of SARS-CoV-2 antibodies in homeless populations. Other biases, such as bias favoring individuals in good health capable of attending our testing sites, or bias favoring those with prior COVID-like illnesses seeking antibody confirmation are also possible. The overall effect of such biases is hard to ascertain."
In summary sample has
Overrepresentation white woman 19-64<br /> Age imbalance not accounted <br /> Partial weighting by zip code, race and sex<br /> Biased favoring good health individuals and those seeking antibody confirmation
Conclusion: "overall effect of such biases is hard to ascertain"
Additionally
There is no discusion on sampling effect by facebook ads, as answering rates, impact of facebook ads algorithm which is optimized to get maximum amount of answers. It is well known that this convenience samples are non probabiistical, so this has to be included in error range evaluation, (1)
On 2020-04-27 06:03:14, user think wrote:
The Stanford study, as well as the USC study, demonstrated that the case fatality rate is much, much lower than was projected and is currently being reported. The CDC is currently showing a cfr of 5.6%. The NYC survey also corroborates the conclusion that the cfr is orders of magnitude lower than what was projected and used to inform our policy decisions and drive our panic and mass hysteria. Obviously, different geographical regions with different demographics are going to have disparate outcomes in cfr. A finding of .36% most certainly does vindicate a finding of .2% when the figure being challenged by these tests is 5.6%
On 2020-04-23 18:52:50, user Deplorable Kev wrote:
So they tested for IgG antibodies in the population and found a high number of persons already had been exposed and likely immune. Interesting, but that depends on if their assay cross reacts with US Coronavirus and if so, then this is not correct. If their test is specific for COVID-19 antibodies then he is correct. PS: I believe it was here much earlier and we saw cases as early as mid November. Most of this early antibody testing has not been evaluated for cross reactivity yet.
On 2020-04-18 15:41:56, user Zev Waldman MD wrote:
I agree with other commenters that people who suspected prior Covid infection (or exposure) are more likely to seek antibody testing than those who did not. While participants were asked about prior symptoms, it is not clear what if anything was done with this information. It would also have been nice to ask about prior exposure concerns/risks, and report/use that information.
My other concern that has gone less discussed is their calculation of the case fatality rate. While they recognize that reported case numbers as of April 1 are an underestimate, it seems that they forget this skepticism when looking at reported deaths. They seem to take it as a given that 50 people died of Covid in the county as of April 10 as reported, and used this to project to deaths by April 22; however, like case counts, there are multiple reasons to suspect this number of deaths might be higher:
Reporting of deaths is well-known to be delayed - i.e., date of reporting does not equal date of death
People who actually died of Covid may never have been tested, and thus may not be included as cases or deaths
The doubling time of deaths used to project to April 22 is also based on reported deaths; if reporting of deaths is delayed, the doubling time may appear slower than it actually was.
If their death estimate due to illness before April 1 is too low, their corresponding CFR would be an underestimate as well. (This would be exacerbated if their case estimate is too high due to self-selection into the study, as seems possible.) At the very least, some sort of uncertainty around the death estimate should be provider, which in turn would increase the uncertainty around the final CFR.
I know CFR wasn't the main focus on the article, but worry that, because these results support their prior beliefs, some readers may take the results at face value and push them to policymakers before they have been more widely vetted by the scientific community.
On 2020-04-18 21:58:35, user dakbio wrote:
What cross reactivity validation have you done against other Corona viruses and what did you use for an antigen (s)? Did you run a BLAST against all other known Corona virus sequences?
On 2025-03-15 20:37:50, user Ali Molaverdi wrote:
The authors appear to conflate correlation with causation when attributing excess mortality to political populism, an interpretative leap that undermines the scientific validity of their conclusions and calls for a more cautious analysis.
On 2020-07-02 15:39:58, user Kamran Kadkhoda wrote:
The reported prevalence is very high suggesting high false positivity rate. The actual sero-prevalence for that county is estimated to be around 6% as of today (if we assume only 20% of cases are tested by for RNA). It would have definitely been much lower back in April. Another reason serology should not be used given it's high rate of false positivity mostly due to common CoVs like OC43 and HKU1.
On 2021-08-18 17:39:03, user John Baron wrote:
The first sentence states that this is a study of individuals who underwent SARS-CoV-2 testing, presumably (though not stated) during the study period. Unless there was population testing surveillance in place, there must have been selection factors (e.g. COVID exposure or symptoms) prompting testing. This would bias estimates of infection rates upward, though the bias is unlikely to differ between vaccine groups and may not differ much between vaccinated and unvaccinated groups. Tthe relative infection measures might be OK.
In the "final" paper, it would be important to account for the usual cohort issues: censoring, movement out of the health care system, etc. Also, the first paragraph of the Methods section should include unvaccinated individuals.
On 2021-08-15 00:21:45, user Covid Hospitalist wrote:
This abstract of this pre-publication is highly irresponsible. There is no clear delineation between 'infection' and 'illness'. This is going to be taken out of context as 'vaccine failure' by multiple groups and news media sources. The drop in prevention of 'infection' ei detectable virus on PCR is important. AND without the data showing that it is still exceptionally effective at preventing hospitalization, is reckless. The authors need to fill in the rest of the blank... they quote the ability of the vaccine to decrease illness/hospitalization from the wild-type "wuhan" strain EUAs in the intro, but then completely leave it out of the results portion of the abstract??? How many antivaxxers/news media are actually scrolling down to table 7 to see that the rate of covid death for pfizer was 0/38,000(n rounded) and moderna 1/36000(n rounded). Seriously irresponsible headline grabbing abstract.
On 2020-04-15 18:51:04, user Greg Lambert wrote:
The study seems to incorrectly use a UVAB meter instead of a UVC meter to measure the exposure from their UVC lamp, consequently their UV exposure readings are wrong(low).
From the study:
"Ultraviolet light. Plates with fabric and steel discs were placed under an LED high power UV germicidal lamp (effective UV wavelength 260-285nm) without the titanium mesh plate (LEDi2, Houston, Tx) 50 cm from the UV source. At 50 cm the UVAB power was measured at 5 u W/cm2 using a General UVAB digital light meter (General Tools and Instruments New York, NY)."
Their lamp emits effective UV wavelength of 260-285nm but a General UVAB meter only measures from 280 to 400 nm with a calibration point of 365nm.
A
On 2021-04-29 12:05:31, user Anders Julton wrote:
Correct me if I'm wrong, but the actual air rate change efficiency of the HVAC alone in the hospital room seems quite low. My calculations yielded about 30% using eq. 2.1.6 in a book (linked below, linking is weird) on air rate change measurements. The control room had roughly 100% efficiency.
I used C(t = 10)/C(0) ~ 0.5 for the hospital room and C(t=40)/C(0) ~ 0.2 for the control room.
On 2020-08-14 09:13:14, user Alexandre Júlio wrote:
Louis Pasteur taught us the importance of Pasteurization. Against air-borne epidemics, we are needing a safe indoor place to drink & eat. Up-down HEPA laminar flow is a speciality that I knew only in semiconductor clean rooms. Against Covid-19, my faith is going to UHT treated air, cooled to lower than 50ºC by heat-exchange with aspirated air. Further cooling may be provided by humidification with sterilized water and/or heat pump. What are UHT-air parameters used in Japan?
On 2020-08-12 11:44:27, user My Opinion wrote:
In my opinion...this supports the explanation why certain facilities (e.g. nursing homes, prisons, cruise ships, church gatherings) experience large numbers of individuals who become infected....I have never believed that the primary mode of transmission was a cough or sneeze....in some prison facilities....we have seen 80% of the population inside the facility become infected, including prison guards....the virus spreads too efficiently to blame it on a cough or sneeze....for example, we know that small pox can be spread through exhaled respiration...this research appears to be the first published study to definitively prove COVID-10 can float in the air and infect people quite distant from the infectious source (17-feet)....this explains how large numbers of people can become infected quickly...it is in the air...Thomas Pliura, M.D., Le Roy, IL
On 2021-09-04 14:10:39, user criticalscientist wrote:
It is likely that emergence of the Delta variant, which has 10 mutations in the spike protein, is due to leakiness of the mRNA vaccines based on the single spike antigen. I more worry about the Mu variant that appears to be completely resistant to monoclonal antibodies. Time to develop new vaccines using multiple viral proteins or develop an "attenuated" virus mimicking natural immunity.
On 2021-09-16 22:57:34, user Chadwick wrote:
Having read the paper, Ben, I can say with certainty that the very first experimental condition numbers showed that, by matching variables like sex, they created a vaccinated group that literally had 2.5x more immunocompromised people than the natural immunity cohort which they then assessed for reinfection. Go do the math. As for socioeconomics, the mean rank decreases from Exp. 1-3, while the +/- valences show being poor and being immune compromised make you less likely to be infected. (You have to look at more than p-values, and that includes the sign.) As for your reflexive condescension, I'll give your opinion the weight which it so clearly deserves.
On 2021-12-24 18:05:32, user Michael Ruimerman wrote:
Natural Immunity > Vaccinated Immunity!!! Governments should be considering EVERY unvaccinated person that has survived a COVID infection as "immunized", since they have a stronger, more robust resistance to another COVID infection.
On 2021-11-19 03:50:43, user VolComp wrote:
I recently found this thread as a critique leveled at the paper in question.
https://twitter.com/DGBassa...
Just wondered what your thoughts were
On 2021-09-08 13:40:29, user Kim Zuber wrote:
You seem to miss the point. Anyone who hasn't gotten COVID SHOULD get the vaccine, but anyone who has antibodies due to a past infection, should not or at least should be able to opt out. I tested exceedingly high after almost a year, even higher than someone who had the vaccine. Vaccine wanes after 6 months. It is pointless, even dangerous to get vaccinated if you have already had it.
On 2021-09-24 06:28:51, user gzuckier wrote:
I admit to not having perused either the comments here nor the blog sites listed above, however there is a viable alternative model for these results, other than the one posed in the conclusions.<br /> Firstly, from the small number of infections in either the vaccinated or the previously infected group, it's clear that we are looking at outliers here; not a general waning of immunity over the time period, but rather a minority of individuals in either group whose immune system did not respond fully for one reason or another.<br /> This highlights a rather striking conceptual difference between the two groups, however; a large proportion of any individuals with defective immune systems have already been eliminated from the previously infected group, by the initial Covid infection having killed them. This group as now tested would absolutely be expected to have fewer immune failures than the Covid-naive individuals making up the vaccinated, not previously infected group, and thus fewer infections and fewer hospitalizations.<br /> This interpretation, of course, also perfectly explains the reduction in infections and hospitalizations seen in the previously infected and vaccinated group, relative to those previously infected and not vaccinated.<br /> Unless some way can be found to eliminate the effect of this unintended selection bias, i.e. filtering out previously infected group members with persistent severe immune incompetence, this study can tell us nothing regarding the relative protections of vaccine vs "natural" immunity.
On 2021-11-21 23:23:25, user Alberto wrote:
Vaccines are not injections of antibodies, they are injections of antigens. Please, no speculations, and specially of this kind. To try to explain a bad speculation with a worse one is not doing us any good.
On 2021-09-12 18:42:41, user Matt Jolley wrote:
Over 90 percent of Covid patients in hospital today in Oregon are not fully vaccinated. anti vaccination efforts are killing not only infected but others who are denied hospital services.
On 2021-09-02 10:58:33, user yuk wrote:
You make a great point. The reason this is an issue, some people already had Covid and do not wish to see what the long term effects may be taking the vaccine for something they have very good immunity against.
I would say survivability from covid is now known. If you look at anyone under 18 that is healthy they are very rare. Obesity is the big one that is easy to deal with. Lose weight and you take out about over 70% of the victims.
Here is the Swedish government dashboard for Covid. Gives a great breakdown from a country many consider the control group that had a big covid party. Over 80 were old and the rest generally were fat. Big number is cases, medium number is hospitalization and small number is death.
On 2021-10-28 12:44:20, user n0b0dy0fn0te wrote:
No. Peer-review is still a real thing, and critical. In fact, peer-review can take some time, especially when it concerns something like SARS-CoV-2, simply because of the sheer volume of papers being released on this topic.
It's also not that the study is merely 'not perfect', it is in fact deeply, deeply flawed, suffering from a severe shoddiness in experimental design. I explain why here:
https://www.medrxiv.org/con...
This is not a trivial problem. Meanwhile, the paper's conclusion if flatly contradicted by data from elsewhere, such as the UK Office of National Statistics.
More importantly, what's clear from the data overall is that ALL immunity wanes. This is not unusual, which is why, for example, if you've ever had a puncture injury, you probably got a tetanus booster if you hadn't already had one in the preceding few years.
What is clear is that acquired immunity doesn't give humoral immunity, while conferred immunity does.
On 2021-12-31 13:45:45, user Allan Lorentzen wrote:
What does negative effectiveness mean?
On 2021-12-29 02:31:04, user Jason wrote:
Have you seen results of other similar studies that align or contribute to further understanding?
If prior research on CoV already identified ADE as a potential issue.. and all vaccine makers are aware of the concept.. why aren’t there more studies looking for it and tracking larger data sets? Of all the things counted and measured isn’t vax effectiveness high on the list?
On 2021-08-27 06:09:19, user William Brooks wrote:
This is interesting paper showing that the first three states of emergency (SoE) and GoTo campaign didn't have much effect on the fluctuation on K. However, rather than conclude that the medical system is at no risk of collapse and the government should copy Texas and Florida and eliminate all business restrictions, the author calls for stricter border measures, lockdown, and more tests for healthy people despite it being clear for over a year that "Rapid border closures, full lockdowns, and wide-spread testing were not associated with COVID-19 mortality per million people" [1].
Since Covid's case fatality rate in Japan is now close to 0.1%, it's hard to see the point of spending even more time, money, and effort copying testing strategies that have been ineffective even in advanced countries like Germany [2] and Denmark [3].
[1] https://doi.org/10.1016/j.e...<br /> [2] https://www.ncbi.nlm.nih.go...<br /> [3] https://doi.org/10.1101/202...
On 2021-09-30 10:12:58, user Alberto wrote:
There seems to be something wrong with the numbers. Among the vaccinated, there are 82 infections, but 168 hospitalizations. Many undetected infections? False negatives? In contrast, among those with natural infection there are 28 infections and 19 hospitalizations.
Leaving the infections aside, among the vaccinated, the rate of hospitalizations was 0.45%, while among those with natural infection it was 0.34%. That's an overall better protection against hospitalization from natural infection than from vaccination, something completely missing in the abstract. It's true that among those above 65 of age, hospitalizations are a bit higher in the natural infection group (0.52% vs. 0.43%), a less significant difference especially given the low numbers (13 hospitalized in the 65+ natural infection group).
As for deaths, the numbers are too low in all groups to draw any conclusions. The total deaths in each group are 2 (0.036%) in the natural infection group and 8 (0.021%) in the vaccinated group. Among the 65+ groups, is 2 in the natural infection group (0.08%) vs. 5 in the vaccinated group (0.03%). But then in the <65 group it's 0 deaths in the natural infection group (0%) vs 3 deaths in the vaccinated group (0.02%).
The numbers and conclusions shown in the abstract are completely at odds with these real numbers shown in the paper itself. Not to mention the strange pattern shown in the numbers where most infections/hospitalizations in the natural infection group happened in June (with very low numbers in July and August), while in the vaccinated group most infections/hospitalizations happened in July and August, with very low numbers in June. So an analysis of June alone would agree with the abstract, but an analysis of July and August alone would be the complete opposite.
On 2021-07-03 12:53:03, user The Truth wrote:
It's a five month study!! What about a year from now?? In able to continue immunity do they need to become reinfected??
On 2020-06-19 06:18:19, user Kato Peterson K wrote:
This is an interesting study Nicholas, looking forward to you coming up with a harmonised country specific manual to guide nutrition education and counselling for T1DM in Uganda
On 2020-09-13 07:18:07, user OxImmuno Literature Initiative wrote:
On 2021-06-22 12:20:41, user PattonWasRight wrote:
Is this a concern now? The CDC is now recommending a lower PCR threshold for testing vaccinated patients. Are vaccinated patients showing less or more severe symptoms than unvaccinated patients with VOC infections? If vaccinated patients are showing less symptoms, then there’s risk here as the entire world is operating under the idea that vaccinated persons can travel freely (air travel) while while unvaccinated cannot.
I’m seeing a scenario where vaccinated are more likely to be spreading more virulent strains - especially if the vaccine keeps them from developing more severe symptoms and the new lower CDC threshold is missing positive cases -although I think the lower threshold should have been used this entire time because of false positives (false negatives are probably more rare).
On 2020-06-13 08:10:15, user KAMAL KUMAR MALUKANI wrote:
Hi,<br /> There is an issue in figure S3. All text are inverted, Please update.<br /> Thanks
On 2020-08-04 12:21:27, user Mike Dammann wrote:
Here are some of the important parts from the revised version of the NYP study highlighting the important of the Rh negative blood group: https://www.rhesusnegative....
On 2020-05-06 15:17:26, user Sinai Immunol Review Project wrote:
Summary: Based on peripheral blood samples from 117 COVID-19 inpatients and convalescent patients, the authors demonstrate that all patients sampled became seropositive with neutralizing antibodies within 20 days since onset of symptoms and stayed seropositive until day 41-53. Seropositivity of neutralizing antibodies was defined as a geometric mean titer (GMT) of 1:4 higher and the titer was calculated using a modified in vitro cytopathogenic assay where the dilution number of 50% protective condition from cytopathic effect of the virus represented the titer. The GMT of neutralizing antibodies (average: 1:271.2) was the highest at 31-40 days since onset and multivariate Generalized Estimating Equations (GEE) model controlling for clinical variables (i.e. gender, age, clinical severity, etc.) showed that antibody titer at 31-40 days was significantly higher than 10-20 days past onset. In addition, their multivariate GEE analysis showed that age- and clinical severity-dependent rise in antibody titers with the youngest (age 16-30) and patients with mild or asymptomatic conditions having a lower antibody tier than its elderly and moderately-sick counterparts.
Limitations: Several shortcomings limit the impact of this study. While it has been the intent of the authors to sample PBMCs from patients at various time points in order to establish a robust profile of antibody response against SARS-CoV2, in reality, sampling has been limited and inconsistent across different time points. For instance, PBMCs of only 12 out of 117 patients have been collected three or more times and it is not clear from the data whether samples from patients whose blood has been collected only once (n = 37) are evenly distributed across the time frames under analysis. Furthermore, the authors have tried to show differences in kinetics of antibody response between patients with mild and moderate conditions by sampling their blood at four different time points. However, not only do two of eight patients sampled in this study have only two data points, but also the authors have found that the antibody response varies considerably across individuals—further underscoring the need to have PBMCs sampled from each patient at multiple time points and normalizing their response before comparing the titers across individuals. In addition, due to the fact that patients were enrolled using convenient sampling instead of random sampling methods, it’s evident that the authors could not control for disease severity as they only had four patients in severe condition. Beyond the sampling issues, the modified cytopathic assay used to calculate the neutralizing antibody titers may be less sensitive and specific than ELISA-based assays that use purified antigens from the virus.
Significance of the finding: Limited. While it is informative to have descriptive studies like this one showing the dynamics of the antibody response against COVID-19, the failure of the study to collect samples in controlled manner prevents the reader from using the data to answer key questions regarding the humoral immune response against COVID-19: do differences in clinical severity manifest in different kinetics of antibody response? When controlled for age, is higher antibody titer predictive of their clinical severity and prognosis? Future studies may address those questions with more controlled experimental setup.
Review by Chang Moon as part of a project by students, postdocs and faculty at the<br /> Immunology Institute of the Icahn school of medicine, Mount Sinai.
On 2020-05-18 14:25:25, user OxImmuno Literature Initiative wrote:
On 2020-02-14 11:34:58, user Igor Nesteruk wrote:
Dear friends,
On February 13 I have found tree different values of the cumulative number of confirmed cases (number of victims Vin my paper) on the official site Chinese National Health<br /> Commission:
46551; 59805 ; 59493
and the communications that they have changed the principle of cases
registration:
1) As of 12 February 2020, numbers
include clinically diagnosed
people not previously included in official counts. The definition of a
confirmed case changed to include clinically diagnosed people who had not yet
been tested for SARS-CoV-2.
2) Starting from February 12th, confirmed cases are now considered by officials as both tested confirmed cases as well as clinically diagnosed cases. All
percentage values that have this note tag, are calculated using the confirmed
cases values which are the sum of both the tested and clinically diagnosed
values. Thus any very large percentage value changes seen from the marked
percentage when compared to previous percentage values are caused by this.
I have put the new points (crosses) on the plot see attached file. I
think further statistical analysis is impossible. Please let me now, if you
have some recommendations.
Best regards,<br /> Igor
PS. Unfortunately, I cannot put any plots here. You can fint it on Research gate
On 2020-07-18 10:11:08, user Richard Harrison wrote:
Much to discuss in this paper, including whether the hypothesis of a uniform prevalence across the country is consistent with more plentiful new confirmed cases data at LTLA level (showing significant variations in rates even in May), confidence limits on estimated R, possible self selection bias (related to low response rate), apparent differential response rates, and implications of high false negative swab rates for Test, Trace and Isolate strategy. Useful confirmation of a number of things, including high pre-/asymptomatic %, significant % of children infected and high infection rate of care home workers, with important recommendation for continuance of 'social distancing' measures.
On 2020-04-16 23:38:02, user Edwin wang wrote:
April 16, 2020
Just a short note, China has changed the quarantine period for imported travellers (14-day for a centralized quarantine plus another 14-day home quarantine).
just read the news here: http://news.creaders.net/ch...
It is in Chinese, the key sentence is '14?????????+14?????????'. you could enter it into Goole translator
On 2020-05-26 02:41:41, user Frank Bentrem wrote:
Nice study. I have also looked into the COVID-19 connection to genetic ancestry. See the preprint <br /> https://www.researchgate.ne...
On 2020-05-21 20:00:27, user Babak Javid wrote:
Please note that the Supplementary File refers to the earlier version of this manuscript and is no longer current. Unfortunately, it cannot be removed for clarity!
On 2021-01-07 08:06:09, user Giuseppe novelli wrote:
Congratulations, the work confirms and extends the considerations reported by us in two published papers, which happy if the authors could quote in an updated version of the manuscript Thanks
Giuseppe Novelli
Novelli A, et al., Analysis of ACE2 genetic variants in 131 Italian SARS-CoV-2-positive patients. Hum Genomics. 2020 Sep 11; 14 (1): 29. doi: 10.1186 / s40246-020-00279-z. PMID: 32917283
Latini A, et al., COVID-19 and Genetic Variants of Protein Involved in the SARS-CoV-2 Entry into the Host Cells. Genes (Basel). 2020 Aug 27; 11 (9): E1010. doi: 10.3390 / genes11091010. PMID: 32867305
On 2024-12-11 16:30:22, user Andrew Hagen wrote:
This preprint has now been published in its final form as:
Hagen AC, Tracy BL and Stephens JA (2024)<br /> Altered neural recruitment during single and<br /> dual tasks in athletes with repeat concussion.<br /> Front. Hum. Neurosci. 18:1515514.<br /> doi: 10.3389/fnhum.2024.1515514
On 2025-08-21 13:47:47, user J.C. Rome wrote:
The acronym makes no sense. STARD, meaning Standard, Accuracy, Reporting, Diagnostic? It should be SRDA, I understand that you're trying to make it into a word, but it doesn't work.
On 2022-12-22 13:04:03, user Howard Waitzkin wrote:
Here is another peer-reviewed article from the same project. Constructive comments welcome. Thanks.
Howard Waitzkin
Fassler E, Larkin A, Rajasekharan Nayar K, Waitzkin H. Using absolute risk reduction to guide the equitable distribution of COVID-19 vaccines. BMJ Evid Based Med 2022. doi:10.1136/bmjebm-2021-111789. [Epub ahead of print: 07 Mar 2022
On 2025-07-22 09:31:17, user Abdullah Jinah Ali wrote:
Boys under 18 and men over 65 can also be mistaken for combatants, and your data does show evidence ifvthus. Therefore it would be useful to have a gender breakdown among child and senior citizen fatality estimates.
In addition, I would like to emphasize that your survey missed the most vulnerable group - large households that did not leave Rafah and the north. They are more likely to be targeted by AI due to having more combat aged men, and more likely to suffer nonviolent deaths due to the poorer conditions in these governorates and the disproportionate number of children and the elderly in these larger households.
On 2020-08-08 18:25:58, user DFreddy wrote:
Overall, valuable study that gains some more insight into Belgian's seroprevalence. From the charts, I see clear waning of seroprevalence for those 80 and older.
I have problems with one statement in the conclusion: "... The latter (i.e. the response to future waves) is still a challenge as the low reported seroprevalences (2.9-6.9%) are far from required herd immunity levels. "
What are required herd imm. levels to avoid deaths? This is a debatable number, since most covid-19 deaths come from seriously unhealthy people who likely die not so much from covid, but from their frail health and /or old age. As far as I know, dying is a reality of living a life.
On 2021-08-12 16:39:53, user Mika Inki wrote:
I have several questions about the normalization. How precisely are the ages matched? You only mention that the participants’ ages were over 18. There is no normalization on whether the people belong to a risk group? Of course, that latter information may not be easily available. I would assume that older people and people in risk groups (including the immunocompromised) have vaccinated themselves at a much higher rate than younger and presumably healthier people, or at least people that believe themselves to be healthier. And lately there have been more infections in these younger groups, which would bias the probabilities. A young person in a risk group (even after vaccination) may have a much higher risk of severe illness than a typical person of the same age. Therefore, the overall effectiveness of both vaccines would likely seem significantly lower than what their true effectiveness is. Therefore, I would assume that the comparison between the vaccines may very well be valid, but the comparison between the vaccinated and unvaccinated may be significantly distorted.
On 2021-12-06 20:48:32, user Nicholas Morrish wrote:
Is this research team aware of the Ratpenats bat monitoring groups sampling of both bats and surrounding sewage/drainage canals? We can see from their own website https://rius.ratpenats.org/... they have hundreds of samples that are geocached and dated. Due to strict EU laws protecting these animals, sampling bats for disease can be very difficult and requires direct permission from local governments. The Ratpenats also have over 30 bat boxes located across the river from the same WWTP2 facility this research paper used to find the early outbreak; is the research team aware of this or asked permission to sample such boxes? https://pbs.twimg.com/media...
On 2021-01-30 20:33:49, user Michael Höhle wrote:
Dear authors,
thanks for posting this very relevant manuscript! Please consider having a look at the statistical methodology of the two following papers, which are very closely related to your work, as they also use an imputation + nowcast + backprojection approach:
Nowcasting the COVID-19 pandemic in Bavaria:<br /> https://onlinelibrary.wiley...
Analysis of the early Covid-19 epidemic curve in Germany by<br /> regression models with change points:<br /> https://www.medrxiv.org/con...
Best regards,<br /> Michael Höhle
On 2021-08-13 07:20:28, user Technicus AcityOne wrote:
Where exactly did the first discovery occur? And when ?
On 2020-04-07 16:19:25, user pigah wrote:
This is a really neat paper and a neat use of aggregate data. I wonder if you might be confounding two things in this analysis, though. Obviously, the increase in cases in a country is a result of both import and endogenous growth (and to a lesser extent export). Import will tend to lead to higher apparent initial growth while socioeconomic status and climate would tend to effect the endogenous growth. One potential way to get around this would be to model time to X number of infections and include countries that don't have infections in the database. This would isolate, to a certain extent, the effects of import versus endogenous growth.
On 2020-04-04 21:18:14, user Lucy Branum wrote:
Does this data reflect the lack of PPE for healthcare providers, and other essential workers? I appreciate that is a difficult factor to quantify, but if your healthcare providers are becoming infected and therefore spreading the virus to others, and becoming ill themselves, I would have to assume that would have a massive impact on the trajectory of the curve.
On 2022-12-01 02:06:01, user Kenneth Alfano wrote:
NOTE: A revised version of this article has been published in the Journal of Clinical Trials (open access), after peer review and edits/corrections. Citation: Tarasev M, Chakraborty S, Alfano K, Muchnik M, Gao X, Davenport R. (2022) Use of Packed Red Blood Cell Mechanical Fragility to Indicate Transfusion Outcomes. J Clin Trials. S19:001. DOI: 10.35248/2167-0870.22.S19.001
On 2021-10-10 04:55:53, user kdrl nakle wrote:
One of many recent papers indicating that Moderna is ahead of Pfizer.
On 2020-10-30 07:38:22, user Rajeev A wrote:
Dear Sir,<br /> Thanks for the answer to a question I was waiting for.<br /> In America COVID has surpassed the road accident death stats already.<br /> Thanking You<br /> Yours sincerely<br /> Rajeev
On 2020-11-07 15:33:38, user Mark Schagane wrote:
Did your study consider the effect from carbon monoxide? I have some more information on this if you’re interested.
On 2020-05-17 07:03:23, user Marc Pelletier wrote:
There is a large Phase IIb, but without Zinc. https://clinicaltrials.gov/...
On 2020-05-19 16:28:02, user Bubba Shmertz wrote:
“This article is a preprint and has not been peer-reviewed. It reports new medical research that has yet to be evaluated and so should not be used to guide clinical practice.“
On 2024-06-05 18:47:47, user Zhaolong Adrian Li wrote:
This study is now published as: Li ZA, Ray MK, Gu Y, Barch DM, Hershey T. Weight Indices, Cognition, and Mental Health From Childhood to Early Adolescence. JAMA Pediatr. Published online June 03, 2024. doi:10.1001/jamapediatrics.2024.1379
On 2021-09-17 21:12:27, user Jeff wrote:
The denominator for the number of vaccinations seems wrong. The paper says "we recorded all vaccinations given in the Ottawa area between 1st June and 31st July 2021," and "there were 15,997 doses of Moderna vaccine, and 16,382 doses of Pfizer vaccine administered over the study period, for a total of 32,379 doses". But this seems like a gross undercount. On the Ottawa public health vaccine dashboard (https://www.ottawapublichea... "https://www.ottawapublichealth.ca/en/reports-research-and-statistics/COVID-19_Vaccination_Dashboard.aspx)"), the chart showing doses administered per week <br /> suggests that over 800,000 mRNA vaccine doses were administered in this time period. Were other criteria applied to reduce the denominator, or is this an error?
On 2021-09-17 17:39:54, user Gabriel Lee wrote:
I do not understand the numbers for vaccine doses administered during the June 1 to July 31 period. These seem far too low for a city with a population of almost 1 million and the two-month period during which some of the fastest vaccination rates in Ontario and Canada were recorded. I downloaded the CSV file from open.ottawa.ca and tallied the Moderna and Pfizer-BioNTech doses in this period, and found 352,687 and 481,264 doses, respectively. Could you please explain?
On 2020-06-25 11:29:20, user MAGB wrote:
Your basic reproductive number of 2.68 based on early Chinese data is at odds with the effective reproduction number of less than one in all Australian states by Easter, as tweeted by James McCaw. His data indicate that voluntary controls and border closures had the epidemic well under control before lock-downs had any effect.
On 2021-08-02 01:30:33, user Paul wrote:
Hasn't the CDC indicated that data says people who get the vaccine are just as likely to get the virus and be just as contagious as those who don't take the virus? That means that to 70% of the population getting the virus, or even 100% will not lead the herd immunity since the vaccines fight the virus in the blood after it has reproduced in the throat. What is the point in a vaccine with 50% effectiveness it still makes you five times as likely to develop serious symptoms compared to one with 90% effectiveness. Being vaccinated does nothing for the common good, only for the individual good of reducing symptoms. At the rate the Philippines is spreading the virus it will take 100 years to burn itself out with or without the vaccines. It seems to me the best way to deal with it is to vaccinate as many as possible to prevent symptoms and then open the economy and remove the mask mandates so the virus will burn itself out quicker. It is a tough decision to make but I have known many more who died do to being unemployed and not able to afford their maintenance medicines during the lockdowns than who have died from COVID
On 2021-03-29 21:57:10, user Carl Steinbeisser wrote:
Great work. Really minor comment: In the acknowledgement only the Grant Agreement number is mentioned not the name of the project. Many IMI projects (and H2020 projects) do mention the name of the project too. Suggest to add the project name EHDEN.
On 2020-07-30 08:11:24, user Partho Sen wrote:
Now published in Biological Psychiatry<br /> https://www.sciencedirect.c...
On 2023-04-12 16:21:36, user Lizeth Perales wrote:
Hello, I would love to get access to the data. Please let me know if that is possible! thank you in advance
On 2020-12-01 17:48:06, user Pandora wrote:
Interesting. Did you take into account regular medication taken for each group. Studies have been done on Ace2 inhibitors and blockers. There's also studies on proton-pump inhibitors and Sarscov2. They seem inconclusive too, but it may skew your results. Interestingly, a lot of these drugs and a diabetes drug are under recall for NDMA contamination at present.
On 2021-11-30 12:53:37, user BSTL wrote:
This represents the first publication of data to support the use of closed system device components in pharmacy technical services such as dose banding in the UK where compliance with the NHS yellow cover document (YCD) guidelines is required.
On 2020-04-04 09:25:59, user Oleg Gasul wrote:
The main proof will be if you found statistic of dead by reason of virus with BCG and without.
On 2020-05-03 18:38:20, user nomad monkey wrote:
In this chart, the circle colors indicate the BCG vaccine policy for different countries. The circle locations are plotted according to the the median age and % urbanization of the population. The circle size is proportional to covid-19 deaths per capita. As we have all noticed, the older and more urbanized populations tend to have greater per capita covid deaths (i.e. bigger circles). But notice how huge the circles are for the countries that don't do universal BCG (green & pink circles), as compared to similarly aged & urbanized northern Asian countries that have universal BCG policies (blue circles).
Ecuador strongly supports the the BCG theory, as Ecuador has comparatively high per capita covid deaths for a relatively young and less urbanized population. And Ecuador (pink circle) doesn't do universal BCG vaccination like the rest of South Amercia (blue circles).
On 2021-04-09 01:13:34, user Patrick Palmieri wrote:
Did a large number of COVID-19 infected people seeking health services at EsSalud know they were not receiving evidence-based interventions? In addition, were EsSalud leaders aware there were five different 'treatment groups' as well as a ‘control’ group? Thanks to the researchers of this manuscript, we can now ask for more details to address previous 'rumors' about poor clinical management of people with COVID-19. This point is especially important as the EsSalud hospitals have been sealed to family and visitors. As such, patients are left in these hospitals without advocates for their safety and wellbeing.
Although the current study presents an ‘observational cohort’ design, we need to ask about the circumstances, structures and processes, that resulted in the original data reported in this manuscript. The researchers clearly stated they ‘compare[d] five treatment groups to the standard of care treatment regimen, as a control group....’ To this point we need to ask how could these six groups (with significant variations from the care standard) be organically organized from the existing patient population at EsSalud? In this regard, I am not claiming the researchers did anything wrong, but we need to understand how EsSalud ignored or facilitated the reported situation. In fact, we should thank the researchers for exposing some potential issues only believed to be unsubstantiated rumors in the previous months. Again, we need to recognize the vulnerability of patients as there were no family members present to be advocates in the EsSalud hospitals.
In explaining the possibility for five treatment groups and one control group at EsSalud, the researchers added ‘...decision to administer one of these treatment groups depended on clinician’s own criteria guided by the Ministry of Health recommendations, which was changing over time according to updated evidence-based reviews...’ Given the fact that EsSalud is the social security system for workers in Peru with separate clinical practice guidelines claimed to be based on evidence, this statement indicates the quality of care and standards of medical practice were not monitored by EsSalud. Again, this is concerning as the patients were vulnerable, without family advocates, and there is no statement in the manuscript about the normal informed consent process for clinical management.
The current observational study indicates there was a single ‘standard treatment’ authorized yet five alternative protocols were implemented as seemingly 'n of 1' studies. How were physicians in EsSalud able to clinically manage patients with unapproved protocols that significantly varied from the evidence? Were EsSalud leaders aware of the deficiencies in clinical management that probably contributed to the deaths of many patients? As a matter of normal clinical management did each patient consent to the treatment they received understanding there were more effective treatments available?
From the information reported in this manuscript, the clinical management of patients with COVID-19 at EsSalud needs to be reviewed by external authorities to determine the possibility of clinical negligence, medical malpractice, unethical conduct, unauthorized research, and/or illegal activities. Again, I want to emphasize these comments are in no way focused on the researchers. I sincerely appreciate the researchers exposing several serious ethical concerns that have only been rumors for months.
On 2020-10-06 22:07:43, user Jema Rushe wrote:
A suite of interventions, including graded exercise and cognitive behavioural therapy, are<br /> needed to manage CFS and may be relevant to post infectious fatigue (57-59).<br /> Would you please consider revising the above sentence. The data from the original PACE trial (your reference 58) has been reanalysed and found not to support either graded exercise therapy (GET) or cognitive behavioural therapy (CBT) as an effective treatment for CFS. There is a general consensus among CFS physicians, researchers, and patients that both GET and CBT are often detrimental to CFS sufferers and there is a growing body of literature that supports this. The US CDC have dropped both from their list of recommended treatments for CFS. The UK are currently reviewing their NICE guidelines for CFS (update due April 2021) but they did release a statement July 2020 specifically advising people not to assume the current guidelines for GET as a treatment for mild-moderate CFS applies to post-COVID19 fatigue ( https://www.nice.org.uk/gui... "https://www.nice.org.uk/guidance/gid-ng10091/documents/statement)"). The HSE guidelines are currently under review and will presumably be published after April 2021. Thanks for your consideration.
On 2020-06-06 15:24:15, user wbgrant wrote:
The analysis of data from European and perhaps other countries is problematic for a couple of reasons. One is that the 25OHD concentrations used may not be appropriate for those who develop COVID-19 due to age mismatch or not being for winter.<br /> Another is that life expectancy, an index for the fraction of the population that is elderly, has a stronger influence on COVID-19 rates than does 25OHD. See this preprint<br /> Kumar V, Srivastaa A. Spurious Correlation? A review of the relationship between Vitamin D and Covid-19 infection and mortality<br /> https://www.medrxiv.org/con...<br /> I verified their findings by using more recent case and death rate data.
On 2021-10-10 04:14:01, user kdrl nakle wrote:
Nice. So you are 20 times more likely to have COVID induced myocarditis than vaccine induced one. And on the top of that the COVID induced one will last longer.
On 2020-04-11 00:52:38, user Moi wrote:
Reality:<br /> In Austria, recently an excess of 2.35 has been measured (8,500 officially infected vs. 28,500 immunized overall for A), see SORA-Prävalenzstudie.
vs.
Model:<br /> Here, we have to assume that 75,000 officially infected (JHU) are the tip of a 33 million iceberg of immunized ...That would be a factor of 440 instead of 2.35. Hard to believe, for me at least.
We need actual and reliable tests, and more tests, worldwide.
On 2020-07-24 07:38:54, user Sally Anderson wrote:
What volume of buffer/medium do you add to your swabs? Do you test the whole of the sample by RTPCR or do you only process a proportion of it? When you plot no. of copies / ml is this per ml of buffer/medium you initially place the swab in? Do you think you are just targetting virus particles or is there a contribution from infected cells?
On 2022-02-02 19:32:41, user Eric D wrote:
This is on Sky News as<br /> BA.2 "More likely to infect vaccinated people"!
SSI report is ambiguous<br /> https://en.ssi.dk/news/news...
The headline<br /> "BA.2 is more transmissible than BA.1 but vaccinated persons are less likely to be infected and to pass on infection"<br /> contradicts a sentence that looks badly-written or edited<br /> "In addition, comparing the risk of household members being infected in BA.2 relative to BA.1 infected households, was higher in vaccinated and booster vaccinated than in unvaccinated, which suggests immune evasive properties of the BA.2 variant."
On 2021-12-14 19:48:50, user nitin wrote:
how you have made this frequency graph
On 2021-10-28 14:15:15, user orzoro123 wrote:
In the long run, yes it is. It will create a stronger, more durable society. People who are vulnerable, like yourself, should get the vaccine, the rest of us should deal with this disease the way humans have since the dawn of time, by relying on our natural defences.
On 2021-11-02 23:21:24, user CptNerd wrote:
It's as bad as he Spanish flu to those that died from Covid...
On 2024-05-01 23:32:45, user ppgardne wrote:
This is an excellent paper, showing a clear association between variation in RNU4-2 and NDD phenotypes. The enrichment of variation in the gene between undiagnosed NDD and population cohorts was remarkable.
I thought there were a few areas where the manuscript could be improved slightly.
* Figure 1: Clearly define the measures “genotype quality”, “allele balance” and “total coverage”. We can infer what these mean, but definitions of each in the method section would be helpful.
* Table 1: I spent some time gathering the population sizes for each of the count columns. Please add an extra row or two, giving the number of individuals in GEL NDD, Non-GEL NDD and the population cohort.
* The statement “Humans have multiple genes that encode the U4 snRNA, although only two of these, RNU4-2 and RNU4-1, are highly expressed in the human brain” is slightly inaccurate. The HGNC database and reference (https://doi.org/10.15252/em... "https://doi.org/10.15252/embj.2019103777)") list just those two functional copies of U4 in the human genome. There are ~100 annotated pseudogenes however.
* You state that there is “97.2% homology” between RNU4-1 & RNU4-2 – this is a wrong (but common) use of the term homology. You should have stated “similarity” instead.
* Figure 3: I understand that the BrainVar RNAseq data are from samples of human dorsolateral prefrontal cortex. This should be stated in the caption.
* Figure 3: you state that “expression of RNU4-1 & 2 is tightly correlated”. Looking at the figure it appears the tissues with higher expression are also the ones were more samples were taken. Was the potential confounding of sample depth and/leverage accounted for in the analysis?
* Figure 4: it is unclear what this heatmap is showing. Is it really normalised on a per-gene basis, or is the null for SNV densities derived from the 1,000 random intergenic sequences mentioned in the methods? That would seem to be a more useful measure of variant enrichment or paucity. The ordering of the sequences is odd too, why are the paralogous genes U4/U4ATAC, U1/U11, U2/U12, U5 etc not next to each other? Surely the paralogs are more comparable. What is the justification for an 18bp window? –Other than that is the size of the variable region in RNU4-2.
* The recurrence of n.64_65insT is fascinating. And speculation on the mechanism is very worthwhile. You mention early in the manuscript the possibility of slippage in homopolymer regions, but this is not mentioned again in the appropriate section. You mention local secondary structure as a possible driver, but there seems to be very little evidence to support this based on free energy modelling.
On 2020-04-10 08:00:58, user jingbrit wrote:
"No evidence"?
On 2021-12-28 10:05:23, user Elyta Siregar wrote:
i want to ask, how to get survival probability?
On 2021-05-04 16:08:58, user Don Lotter wrote:
Could much of COVID vaccination resistance be due to the person having had the virus & belief that they are immune?<br /> What portion of that vaccine hesitant 30% are people who have had the coronavirus and, quite justifiably ( https://www.nature.com/arti... "https://www.nature.com/articles/s41590-021-00923-3)"), feel that they are already immune and don't need the shot? Why isn't this question being asked? Because it is an important one in the herd immunity calculation.<br /> And yes, I realize that there are no hard stats on previous infections, but an estimation could at least be made based on studies. It might pull the 30% down to, say, 20%, that 20% being those who were<br /> likely never infected and yet are hesitant. It would push the total immune percentage up in the herd immunity quest.
On 2024-05-28 13:49:19, user Jenn Ferris wrote:
This preprint is now published: https://www.neurology.org/d...
On 2021-09-17 11:53:19, user LE wrote:
So many questions about these methods. <br /> - Was there any approach to minimize the possibility of including duplicate entries from VAERS?<br /> - Application of the case definition for “probable” myocarditis requires that no other alternative explanation is found - how did they reduce the inclusion of myocarditis which could have been caused by COVID-19 or other etiologies? <br /> - Outcomes should be reported as likelihood of having a report to VAERS for probable myocarditis as compared to hospitalization during a 120-day period.<br /> - They apply quite a lot of assumptions related to incidental covid-19 and presence of comorbidities.<br /> - Hospitalization for covid may be expected to be of longer duration, severity, and potentially with longer term outcomes or mortality. <br /> - No inclusion of MISC hospitalizations.
On 2021-03-17 10:01:33, user Bernhard Brodowicz wrote:
As the aim of this study was to determine the prevalence of SARS-CoV-2 it is essential to define the parameters when a test is rated as SARS-CoV-2 positive or negative (as a qualitative analytical test result, ct-value cutoff, handling of different results for viral targets…). Neither the paper itself, nor in the supplementary data, gives an evidence about how positive and negative test results were delimited. Especially as different analytical setups were used, validation data of the different RT-qPCR setups should be reported, discussed and comparability should be shown. When reporting quantitative analytical results (viral loads in children and adults via ct-values), the method should be validated for its quantitative purpose (including standardization). Especially when using different analytical setups this is crucial to assure the reporting of valid and comparable results. Looking on ct-values given in Supplementary Figure 2b the results from Graz (first round), which used a FDA authorized and (also for pool samples qualitative) validated diagnostic kit, showed comparable results for both targeted genes (E and ORF1a/b) and suggests robust positive results for 9 samples in pupils. For other assays the human housekeeping gene RPP30 (RP2) was used as sample control. In clinical diagnosis it might be useful (specially to reduce the risk of false negatives) to also report test results as positive when only one viral target is detected or RPP30 (RP2) was absent, but only when covered by method validation results. However, in a study, where different analytical setups were used, it should be further investigated and discussed, when viral target N2 and ORF1b were report as positive results also with high ct-values (> 40) and in the absence of RPP30 (as it is suggested by Supplementary Figure 2b). This could question the validity of the analytical setup used in this study and is calling for the presentation of the validation parameters of the different RT-qPCR setups to interpret comparable results.
On 2020-12-04 20:45:12, user Sam Wheeler wrote:
Is the full text possible to read as HTML somewhere? I did find the PDF.
On 2021-11-12 17:30:08, user Andre Bafica wrote:
Thanks for sharing this very interesting data! Have you guys seen our eLIFE paper with a similar IFN/IL6/CEBP signature in human TB? https://elifesciences.org/a...
On 2020-02-13 16:13:57, user Xiaolin Zhu wrote:
This is an important work to estimate this risk at early stage of outbreak
On 2025-07-05 14:12:08, user Kamila Premji wrote:
This article was published in BMJ Open in December 2023: Premji K, Green ME, Glazier RH, et al. Characteristics of patients attached to near-retirement family physicians: a population-based serial cross-sectional study in Ontario, Canada. BMJ Open 2023;13:e074120. doi: 10.1136/bmjopen-2023-074120, https://bmjopen.bmj.com/content/13/12/e074120
On 2020-05-11 08:52:50, user Prof. Janusz Jankowski wrote:
Sounds like most people find this tool useful to date.
On 2021-09-18 22:00:44, user Peter Sonners wrote:
Here is a couple... https://www.pharmaceutical-...
On 2025-02-24 23:42:40, user Stephen Goldstein wrote:
Manuscript summary
The authors report a small study comparing patients with “post-vaccination syndrome” or “PVS” with vaccinated, healthy controls. They used a variety of immunological techniques and report they have identified potential immune signatures in PVS patients, which may reflect an underlying mechanism of this condition.
Personal disclaimer
This manuscript has received considerable attention and attracted much commentary, including critical commentary from myself on twitter (@stgoldst). I was immediately skeptical of these findings given the attention to it, small study size, and amplification by anti-vaccine activists. However, the potential for vaccine injury is a serious matter, so a rigorous review of this manuscript is a critical need. I attempt here to account for my biases, and to check for these I used a Google AI model to conduct an orthogonal review. That is posted separately.
Review
Overview
This study described by this manuscript is methodologically flawed to a degree that undermines the authors’ stated goal to identify biomarkers for post-vaccination syndrome (PVS). These flaws are systematic, ingrained into the study design, and compounded by analytic flaws throughout the manuscript. As is, this study provides weakly informative data at best towards understanding chronic illness following vaccination. The methodological flaws are listed below and subsequently expanded upon.
The study provides no evidence for a causal link
PVS and control cohorts are very small, and even smaller when stratified by infection status.
The PVS cohort comprised only 44 patients originally, and was reduced to 39 due to pharmacological inhibition in 2 patients. The authors acknowledge that due to the small size of the study and its exploratory nature they did not conduct a power analysis. They acknowledge the difficulty in producing robust results due to the sample size. Despite acknowledging these problems, the authors repeatedly invoke the statistical significance of various analyses and in some cases rely on extremely involved statistical testing to identify weak signals. This presents an impression that the authors understand the inability, baked in from the start, of the study to be informative yet press ahead anyway.
he authors stratify the cohorts by infection status, with the primary determination based on serological status of anti-nucleocapsid (N) antibodies. The study participants were recruited in December 2022 at the earliest, nearly 3 years after the first SARS-CoV-2 infections were identified in the United States. Given the expected decline in serum antibody titers over time, it’s likely that people infected in the first year of the pandemic (and possibly even later into the pandemic) would test seronegative. Therefore, the -I cohorts likely include individuals who were in fact infected with SARS-CoV-2 at some point. This is a critical issue. The number of individuals without infection history is likely even smaller than presented, reducing the utility of stratification. In addition, this may actually confound the ability to disentangle the effects of vaccination vs infection in the development of chronic illness. It would be difficult to methodologically correct for this without a prospective longitudinal study. However, larger sample sizes might allow researchers to mitigate its impact. Given these sample sizes and the inability to reliable sort by prior infection status, the issue precludes making robust inferences from the data.
The authors describe the health of study participants based on GH VAS scores and note that PVS participants were in worse health than the control participants. In the Discussion, the authors expand on this, noting that PVS participants also had worse health than the U.S. general population. Given the real potential for other disease processes to impact every one of the biomarkers tested, the lack of unvaccinated, chronically ill participants (reporting the same syndromic profile as PVS patients) confounds any correlates between these biomarkers and vaccination. The study analyses are uninterpretable with respect to the impact of vaccination on health, as a result.
PVS was previously described by some of the same authors based on self-reported chronic sequelae following vaccination. This definition is then relied upon in this study. However, many of these symptoms are non-specific and certainly there is no evidence, given the lack of complete overlap, that they represent a single syndrome. There does not appear to be any clinical assessment to verify any of them. This is a repeated issue with descriptive studies of long covid (PACS) and now PVS, and I acknowledge the inherent challenges in establishing other criteria. Nevertheless, it represents a major problem in trying to describe a unified syndrome downstream of vaccination.
Throughout the manuscript the authors describe differences between PVS and patient cohorts solely through the p-value returned by statistical testing. Looking at the figures themselves the effect sizes turn out to be extremely small in virtually every case. Small effect sizes don’t mean there is no biological significance, but the authors in this study expend no effort to offer context or even a coherent hypothesis for why these effect sizes are significant. Expecting the reader to favorably interpret the data, or indeed interpret it all, based purely on p-values is…disconcerting. It’s not clear in the writing that the authors even consider effect sizes to be relevant, or if getting a sufficiently small p-value is good enough to report and believe a major finding. I’m not confident that the authors really interpreted the data to any depth themselves.
There is simply no causality evident in the data or really presented by the authors. Given the generally poor health of the PVS participants, all of the elevated inflammatory biomarkers and the elevated EBV reactivity could all be due to varied other disease processes, infectious or not. One clear example of this is Figure 4K where the authors correlate EBVgp42 reactivity with the percentage of CD8+ T cells producing TNF?. The Correlation R value is 0.47, indicating a weak to moderate link. Because EBV reactivation is tightly linked to general stress, the weakness of this correlation is highly suggestive of other disease processes making a significant contribution, or the PVS link being artifactual. The authors make no effort to account for this.
Specific Points
References 16 and 18 need to be corrected
“interaction with full-length S, its subunits (S1, S2), and/or <br /> peptide fragments with host molecules may result in <br /> prolonged symptoms in certain individuals16.”<br /> -Ref16 is a study describing circulating spike and S1 <br /> following vaccination, but does not mention anything about<br /> prolonged symptoms.
“Recently, a subset of non-classical monocytes has been shown to harbor S protein in patients with PVS18.” <br /> -Ref18 is a study on PACS (post-acute covid-19 sequelae) <br /> and does not mention vaccination or post-vaccination <br /> syndrome<br /> -Ctrl+F for “vaccine” “vaccination” “PVS” returns no results in <br /> this manuscript
Figure 3 on the kinetics of serological findings is generally confusing<br /> -For Control and PVS+I groups the authors report no decline <br /> in anti-spike antibodies over the course of months to year. <br /> -This runs counter to basic immunological principles and <br /> robust, repeatable findings with respect to anti-SARS-CoV-2<br /> spike antibodies in particular<br /> -One explanation for this is subsequent mild infections that <br /> boost antibody levels, but there are no spikes evident, but <br /> rather a steady maintenance.<br /> -The exception to this is PVS-I antibodies which decline at <br /> what is to the naked eye a normal rate. <br /> -This suggests an issue with the control or PVS+I cohorts, or <br /> a disturbing indication that they are not representative of the <br /> immunological state in their respective populations. Due to <br /> the small sample size, this seems likely<br /> -The authors should explain that because the PVS-I <br /> participants weren’t infected, their “days since post-<br /> exposure/vaccination” data are identical. Absent that, it’s <br /> confusing to notice that the PVS-I data in rows B and C are <br /> identical and raises concern about duplication in figures
The authors don’t describe the rationale for the EBV coinfection analysis displayed in Figure 4, and so there’s no way for the reader to interpret what (if any) significance to ascribe to it.<br /> -Figure 4D shows a small but statistically significant <br /> increase in IgG against EBVgp42 for PVS cohort relative to <br /> controls – however...<br /> -When the PVS cohort is stratified by prior infection status <br /> there is no statistically significant difference<br /> -This make it really difficult to interpret the difference when<br /> the PVS group remains together<br /> -It raises the question for me of whether the statistical <br /> significance is just sensitive to the number of data points,<br /> which for me makes it not robust<br /> -Again – as throughout the paper no biological context is<br /> given
Even the correlation between EBVgp42 in serum and EBVgp42 antibody reactivity is low<br /> -Again very difficult data to interpret and unclear what the <br /> biological significance would be<br /> -Problems with the correlation analysis in Figure 4K were <br /> discussed above<br /> Figure S4C is discussed in the text, but briefly and important data is ignored<br /> -It appears true that PVS participants have elevated<br /> autoantibodies of IgM and IgA isotypes, but their IgG <br /> autoantibodies are actually similar to controls<br /> -Not clear if there might be a class switching defect that <br /> could be related to a pathogenic process, or other<br /> explanation – the authors don’t address<br /> -The authors just say PVS patients just have autoantibodies,<br /> which obfuscates their own data that it’s isotype specific<br /> The interpretation of Figure 5C is also strange – most PVS patients have no circulating anti-S1 antibodies and the statistically significant difference is driven by a minority who do<br /> -The authors state there’s a difference without any effort to<br /> interpret it<br /> -This suggests that PVS, which the authors are trying to<br /> characterize as one syndrome, is either not one thing, or the<br /> presence or absence of anti-spike antibodies is ancillary<br /> -Unfortunately the authors gloss over any nuance in the data<br /> The data on specific biomarkers in Figure 5H is based on such small sample sizes I question whether it was even appropriate to do this analysis at all<br /> -To be clear, the issue isn’t whether the question is worth<br /> asking, it is. The issue is that one should not do an analysis<br /> that is so underpowered it will be definitionally <br /> uninterpretable<br /> -The fact that the authors had to jump through statistical<br /> hoops to find a statistically significant effect is concerning <br /> -the fact this includes a sub-group of only three patients is <br /> just methodologically inappropriate.<br /> Given the authors’ use of machine learning failed to reveal any coherent set of biomarkers further argues against the contention that PVS is a definable syndrome<br /> -Or, that this study is so small it lacks value in defining the <br /> syndrome
Final summary
Ultimately this study adds little value, at best, towards understanding post-vaccination sequelae experience and reported by some individuals. At worst, it injects claims and interpretations into the field and discourse that are unfounded, and will ultimately slow efforts to help patients. These results have already been used to advance anti-vaccine narratives in online discourse. If the data were robust, no one could complain. Because the data are not, it is tragic. Ultimately, there is no compelling evidence in this paper for an immunological signature associated with chronic illness following vaccination. Perhaps reflecting this, the authors provide almost no biological context for any of their findings, often reporting data merely as a p-value with no comment on the effect size (whether large or small). This leaves it unclear to a reader whether the authors are even aware of flaws in their work. Given the methodological flaws of this study, it is a questionable investment for researchers to follow up on it in a targeted way. Rather, well-powered, controlled, and methodologically sound studies should be conducted at scale to enable actionable findings to be made.
On 2022-09-13 08:47:26, user Gabriel Costa wrote:
Here is Gabriel, the first author. Some updates:
1 - There is an error in the prisma flowchart diagram (Fig 1), it is missing one observational study that was excluded. We had 81 RCTs, 7 phase one trials and 1 observational study (this last is missing in the figure). It was excluded due to the replication not having the same PICO components.
2 - We are conducting the direct comparison meta-analyses via R and the results with simple coding are almost the same, using MH method for all. Network and IPD meta-analyses it was not possible for this to be done.
3 - Since (2) = TRUE, we are excluding the highly cited article of the meta-analysis, making the meta-analysis a complete independent replication. We do not observe substantial differences in the effects, suggesting that the 50% cutoff was adequate and only if the trial weights 90% of the meta-analysis this dependency becomes a problem.
4 - Since (2) and (3) = TRUE, we are calculating prediction intervals as well.
5 - There is a typo in the abstract in the Methods section. "We... and potential predictors or replicability". The correct is predictors OF replicability.
An overview of the project, datasets and analysis code can be found at https://osf.io/a8zug/. As well as these updates.
Thanks for the interest,<br /> Gabriel Costa on behalf of all authors
On 2024-07-04 09:09:56, user Rohit Satyam wrote:
I was wondering if you can also provide the major/minor sublineage assignment the authors obtained for the case studies included in the paper as a supplementary file.
On 2020-07-03 14:59:40, user Uncle Long Hair wrote:
A similar statistical analysis received many formal requests to be retracted "based on easily falsifiable claims and methodological design flaws":
https://metrics.stanford.ed...
There are many relevant variables, not only masks but population density, age of population, other precautions that were taken, etc. Correlation is not causation. Most studies have struggled to show statistically significant benefit to wearing masks.
On 2021-02-20 03:01:31, user kdrl nakle wrote:
Believable but samples too small and Fisher Exact is not a reliable test.
On 2021-03-09 09:36:30, user Raph Ohuru wrote:
Published here https://doi.org/10.1093/cli...
On 2021-09-09 02:21:14, user Jonathan Laxton wrote:
I'm sorry, but in Spring/Summer 2021, did they 1) NOT give dexamethasone or low molecular weight heparin to hypoxic patient with COVID-19 as is the standard of practice? I am unsure how this got ethics approval for this. They also had 2 patients who did not want their treatment who died - but nowhere does it say they received standard of care instead. There is also referral bias as patient were referred from like-minded physicians to this study. I would be surprised if this makes it past peer review. It is especially shocking for someone with a research background such as Peter McCullough to undertake such a trial.
On 2021-01-20 21:42:40, user Cort Johnson wrote:
Such close similarity to ME/CFS with some expected differences - such as shortness of breath, early fever, loss of taste and smell.Unrefreshing sleep and IBS-like symptoms are very common in ME/CFS and it might be useful to track those in future studies. Low body temperature has been reported for ME/CFS and it would be interesting to see if that shows up over time.
Very early studies of the ME/CFS or rather ME outbreaks described a heterogenous melange of symptoms specific to each outbreak which resolved into a familiar pattern of fatigue, cognitive problems, etc. The groundbreaking Dubbo studies demonstrated that a wide variety of infectious triggers can produce the same long term symptom set. The results, then, are not surprising but it is still startling to see them.
Congratulations Body Politic for getting this and the other study together. We are in your debt.
On 2020-08-02 18:17:55, user iVX Engineering wrote:
Why on earth they used such dangerously high dosing (800mg per day is the daily max according to FDA, with most other studies using 400) 2400mg dose in the first day? This is a toxic dose. What is these doctors trying to do, intentionally poison patients? Who reviewed the ethics of doing this and why was this allowed? And why are there no comments in the paper justifying this extremely high dosing? In addition to this there needs to be a portion of the discussion addressing how potential toxicity from high doses may have influenced the results.
On 2021-03-27 21:56:08, user Jesse Knight wrote:
Please note the following correction to the posted article (including numbers in the abstract):
In the previous version of this work, the parameters theta = [alpha, beta] were calculated incorrectly because the Kullback-Leibler divergence was defined in the wrong direction. The impact on generation time parameters and statistics is as follows (original -> fixed): shape (alpha): 1.813 -> 1.633, scale (beta): 2.199 -> 2.498, mean: 3.99 -> 4.08, SD: 2.96 -> 3.19. The qualitative interpretation of results is unchanged, and the corrected version should appear soon on the Infectious Disease Modelling journal site. We are unable to edit the version posted here. We sincerely apologize for this error.
The error correction is shown here: https://github.com/mishra-l...
On 2019-10-04 08:05:29, user Guyguy wrote:
EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS AT 02 OCTOBER 2019 <br /> Thursday, October 03, 2019 <br /> Since the beginning of the epidemic, the cumulative number of cases is 3,198, of which 3,084 are confirmed and 114 are probable. In total, there were 2,137 deaths (2023 confirmed and 114 probable) and 995 people healed. <br /> 427 suspected cases under investigation; <br /> 1 new case confirmed in Ituri in Mandima; <br /> 1 new confirmed case;1 person cured out of the CTE in North Kivu in The cumulative number of confirmed / probable cases among health workers is 161 (5% of all confirmed / probable cases), including 41 deaths. <br /> 17th day without response activities in the Lwemba Health Area in Mandima, Ituri.<br /> LEXICON <br /> • A community death is any death that occurs outside a Ebola Treatment Center. <br /> • A probable case is a death for which it was not possible to obtain biological samples for confirmation in the laboratory but where the investigations revealed an epidemiological link with a confirmed or probable case.<br /> NEWS<br /> Prime Minister ready to implement the commitments of the Head of State through the ST / CMRE <br /> - Prime Minister, Sylvester Ilunga Ilukamba, considers that the commitments of the Head of State, President Félix-Antoine Tshisekedi Tshilombo, recalled from the top of the UN platform, are relayed in the field by the effectiveness of leadership and the Coordination of the Government of the Democratic Republic of the Congo through the Technical Secretariat of the Multisectoral Ebola <br /> - He said it during a meeting he chaired this Thursday, October 03, 2019 with the ST / CMRE delegation led by his Technical Secretary Prof. Jean-Jacques Muyembe Tamfum who was accompanied by Dr. Kebela and Prof. Michel Kaswa; <br /> - From this meeting, we note that as early as next week, the Prime Minister will bring together the ministers of Health, Budget and Finance to support the interventions of the response; <br /> - To this end, he stressed that the multisectoral vision of the response is, at the same time, to end the Ebola Virus Disease and to respond to the security and socio-economic needs of the populations affected by this epidemic ; <br /> - He promised that his government will support the approach of the Technical Secretariat of the CMRE to work for the Strengthening of the whole health system of the DRC; <br /> - Since July 20, 2019, the Head of State, the President of the Republic Félix-Antoine Tshisekedi Tshilombo, is coordinating the response to the epidemic to the Ebola virus disease and has decided to entrust the responsibility of the Technical Secretariat of the Multisectoral Committee to a team of experts under the direction of Professor Jean-Jasques Muyembe Tamfum; <br /> - The mission of the technical secretariat is to put in place all innovative measures that are urgent and indispensable for the rapid control of the epidemic.<br /> VACCINATION<br /> - Preparation of the Vitamin A Polio Immunization Campaign and Mebendazole Deworming in the 17 health zones of the Butembo Antenna, an area affected by Ebola Virus Disease; <br /> - 17 days already without opening rings around 5 confirmed cases in the Lwemba health area in Mandima in Ituri due to interethnic conflicts and insecurities. <br /> - An expanded vaccination ring was opened around the confirmed case of September 30, 2019 in Biakatp health area in Mandima in Ituri after dialogues and sensitizations carried out by the communication and psycho-social subcommittees; <br /> - Since vaccination began on 8 August 2018, 232,160 people have been vaccinated; <br /> The only vaccine to be used in this outbreak is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, following approval by the Ethics Committee in its decision of 20 May 2018.<br /> MONITORING AT ENTRY POINTS- A FONER Komanda checkpoint provider (PoC) was abducted on Wednesday 02 October 2019 by unidentified men who released him 75 km from the PoC. This provider of surveillance at the Control Points has already resumed its daily services; <br /> - Since the beginning of the epidemic, the cumulative number of travelers checked (temperature measurement ) at the sanitary control points is 101,714,685 ; <br /> - To date, a total of 111 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.<br /> As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows: <br /> 1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes; <br /> 2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number; <br /> 3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days; <br /> 4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination. <br /> 5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding). <br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
On 2021-12-30 12:54:21, user madmathemagician wrote:
Lots of double tongue wording to imply evidence of fraud without even claiming evidence of fraud.
On 2020-06-07 05:04:06, user Marm Kilpatrick wrote:
This is a very interesting study. There are several important details that aren't clear from the methods and data presented that would help in understanding the patterns:<br /> 1) When were household individuals tested for viral RNA? After the first person in the household became asymptomatic? If children are less likely to be symptomatic, they could be the primary case but not tested until after they infect their household member and that person develops symptoms which could be quite a long time after the child was infected (at the least, an average of 5.5 days after being infected). Given that sensitivity by swab decreases quickly with days since symptom onset, this by itself could lead to an underestimate of infection in children.<br /> 2) Are there data on viral loads? This would help greatly in supporting or refuting the potential infectiousness of children.<br /> Thank you,<br /> marm
On 2020-03-21 19:57:31, user KnowItAll wrote:
I am struggling to understand the labeling of the individual sequences in the tree. For France there are sequences such as hCoV-19/France/IDF0372/2020 and hCoV-19/France/IDF0372-isl/2020. IDF refers to Isle De France and I assume 0372 refers to a patient or sample number, so what does the -isl refer to, are these two sequences from the same sample? Same with hCoV-19/France/IDF0386-islP1/2020 and hCoV-19/France/IDF0386-islP3/2020
On 2020-04-02 23:06:44, user Zvi Herzig wrote:
The intro to this paper states:<br /> "through a follow-up survey, we found that none of our 80 SLE patients who took long-term oral HCQ had been confirmed to have SARS-CoV-2 infection or appeared to have related symptoms."
On the other hand, COVID-19 Global Rheumatology Alliance reports that "25% of patients who developed a COVID-19 were on HCQ at the time of diagnosis".
On 2020-08-22 21:15:39, user Joserra wrote:
There is something I do not understand completely. It has to do with numbers. The autors state that they exclude deaths in nursign homes but estimated taht outside these institutions there have ben 19,228 laboratory-confirmed COVID-19 deaths and 24,778 excess all-cause<br /> deaths. But in reality the number of deceased in nursing home was 68% of the total laboratory confirmed deaths, that is 19,699, that leave the number confirmed deaths ourside nursing home in less than 9000 thousands and not 19.699. Consecuently IFR is about 0.3% similar to seriours flu outbreaks. The total death excess should no be appointed at the virus but to the lockdown and other measures taken by Spanish Goverment.
On 2020-07-05 00:22:50, user Philip De Groot wrote:
1.5 gray is the equivalent of 1,500 mSv. 100 mSv is the top of the low dose bracket. Are the authors claiming that an exposure of 1,500 mSv is safe, that it constitutes a low dose?
On 2021-01-22 03:19:14, user Roberto wrote:
Ok but, whats is this? More than 200 scientifics and academic articles saying HDC is effective for COVID-19? https://c19study.com
On 2020-04-06 01:21:26, user iggy wrote:
TLDR; Does Coronavirus lower the testosterone of those who survived it, long term? <br /> What percentage of men who had Covid-19 is affected by lowered testosterone? <br /> How much is it lowered?
On 2021-09-16 10:11:35, user kdrl nakle wrote:
The paper claims significant increase of virulence yet many epidemiologists in the US would claim that is not the case with any of the variants. Which is true?
On 2024-11-08 19:59:59, user Andre Boca Ribas Freitas wrote:
Important Observations on Underreported Chikungunya Mortality in Light of Global Burden Analysis
Dear Authors,
I thoroughly appreciated your recent preprint on the global burden of chikungunya and the potential benefits of vaccination. Your work provides critical insights into the widespread impact of this disease and emphasizes the significant potential of vaccine interventions.
However, I wanted to highlight a critical issue that our research and that of others in the field have identified: the substantial underreporting of chikungunya-related mortality across many regions. While chikungunya is often categorized as a non-fatal disease, a growing body of evidence reveals severe and sometimes fatal cases that frequently go unrecorded by epidemiological systems. Our recent studies in Brazil documented excess mortality rates from chikungunya far surpassing those officially reported, with mortality rates up to 60 times higher than recorded by standard surveillance systems?Freitas et al., 2024?. Additionally, studies like those by Mavalankar et al. (2008) in India and Beesoon et al. (2008) in Mauritius underscore the elevated mortality associated with chikungunya during epidemic outbreaks, further reinforcing this critical gap in mortality surveillance.<br /> This growing evidence highlights the critical need for increased investment in molecular diagnostics, integrated surveillance, and more comprehensive mortality tracking for chikungunya. These measures are essential for aligning public health responses with the true impact of the disease and ensuring the full scope of chikungunya’s burden is addressed.
Thank you for advancing this essential conversation. Through improved surveillance and research collaboration, we can work toward effective strategies to mitigate the severe impact of chikungunya globally.
Best regards,
Dr. André Ricardo Ribas Freitas<br /> Faculty of Medicine, São Leopoldo Mandic, Campinas-SP, Brasil
Freitas ARR, et al. Excess Mortality Associated with the 2023 Chikungunya Epidemic in Minas Gerais, Brazil. Front Trop Dis. 2024. doi: 10.3389/fitd.2024.1466207.
Mavalankar D, Shastri P, Bandyopadhyay T, Parmar J, Ramani KV. Increased mortality rate associated with chikungunya epidemic, Ahmedabad, India. Emerg Infect Dis. 2008 Mar;14(3):412-5. doi: 10.3201/eid1403.070720. PMID: 18325255; PMCID: PMC2570824.
Beesoon S, Funkhouser E, Kotea N, Spielman A, Robich RM. Chikungunya fever, Mauritius, 2006. Emerg Infect Dis. 2008 Feb;14(2):337-8. doi: 10.3201/eid1402.071024. PMID: 18258136; PMCID: PMC2630048.
Manimunda SP, Mavalankar D, Bandyopadhyay T, Sugunan AP. Chikungunya epidemic-related mortality. Epidemiol Infect. 2011 Sep;139(9):1410-2. doi: 10.1017/S0950268810002542. Epub 2010 Nov 15. PMID: 21073766.
Freitas ARR, Donalisio MR, Alarcón-Elbal PM. Excess Mortality and Causes Associated with Chikungunya, Puerto Rico, 2014-2015. Emerg Infect Dis. 2018 Dec;24(12):2352-2355. doi: 10.3201/eid2412.170639. Epub 2018 Dec 17. PMID: 30277456; PMCID: PMC6256393.
Freitas ARR, Gérardin P, Kassar L, Donalisio MR. Excess deaths associated with the 2014 chikungunya epidemic in Jamaica. Pathog Glob Health. 2019 Feb;113(1):27-31. doi: 10.1080/20477724.2019.1574111. Epub 2019 Feb 4. PMID: 30714498; PMCID: PMC6427614.
On 2020-04-29 14:04:12, user JWL36 wrote:
Would someone mind posting the link to the GIthub source code when they find it?
On 2021-07-27 22:07:18, user ReviewNinja wrote:
Some remarks:<br /> - confidence intervals would be necessary when interpolating data from such small numbers<br /> - 90 days is a long period after a positive test for an acute event…<br /> - if you want to compare these numbers to vaccine-caused myo/pericarditis, you need to use the same method (same criteria and same codes) to determine these
On 2021-01-19 14:10:34, user Curbina wrote:
It is very sobering to finally be presented with a study that gives statistical dimensions to what has been already suspected: that surviving COVID-19 is not the end of it, and that the sequelae can be life altering, and even lethal. May this open the eyes to those that insist that there’s no justification to strict measures, but above all, that those who oppose the measures finally realize that this is not a game and that the personal responsibility to stay healthy is also for the sake of keeping your personal sphere of relations healthy.
On 2021-01-24 19:40:01, user Han-Kwang Nienhuys wrote:
I have further analyzed the data in fig. 2; the odds ratios (frequency ratio B.1.1.7 / other) grow exponentially with daily growth factors between 1.06 and 1.09 between 6 weeks and 1 week before the of the data (only considering the UK regions where the error bars in Fig. 4 were reasonably small: EE, EMid, London, NEE, SEE, SWE, WMid). For this I need to assume that a fraction of the SGTF cases are 'false positive', since most regions show a constant SGTF rate in October, before taking off with exponential growth.
Also notable, genomic analysis in UK SEE, Denmark, Netherlands, and Portugal show consistently growth rates between 7 %/d and 9.4 %/d with only Denmark showing a slowdown (from 12 %/d to 7 %/d).
Also, one would expect the odds ratio to grow exponentially over time if there are just two competing variants, each with their own transmissibility or reproduction number. However, the other strains that make up everything else than B.1.1.7 are likely to have slightly different transmissibilities. Over time, one would expect the transmissibility to drift to higher values, also among those other strains. The fact that the odds ratio growth rate is decreasing does not necessarily mean that the B.1.1.7 is getting less infectious; rather, the mixture of other strains could be getting more infectious over time, just because the contributions of the less infectious ones in the mix gradually decreases.
Summarizing: I believe that 6 %/d is an estimate that is significantly too low.
For graphs of my analysis, please see https://twitter.com/hk_nien... .
On 2021-10-10 05:19:33, user kdrl nakle wrote:
I don't know about UK but in case of US this is useless since the mitigation measures are so heavily politicized in the US to the point of absurd actions. For example Alabama, does not even report outbreaks in schools any more and does not quarantine nor test exposed students.
On 2020-07-08 19:13:51, user Will Jones wrote:
Many countries have ramped up testing in recent weeks. Does this not make case data largely useless as an indicator of infection levels? More generally do the constant changes in testing regimes not undermine the usefulness of case data?
When I have analysed death data in various countries I have usually found a brief period of exponential growth, for a week or so. For example there is a brief period of exponential growth between 17-23 March in the death data for London hospitals (by date of death rather than report, using 7 day rolling average). How does this fit into the Gompertz function model - is it too short to 'count'?
On 2021-09-02 16:09:30, user MS Simon wrote:
How do you explain the results in India and Zimbabwe, or are these stories false or misleading:
On 2020-07-28 19:39:18, user Dude Dujmovic wrote:
People that are vaccinated are usually more careful about their health so direct causality here is non-existent.
On 2019-10-17 08:06:25, user Øystein Haaland wrote:
Paper published in PLOS ONE.
On 2021-07-13 02:15:24, user ?0 wrote:
Will note that absolute risk reduction between vaccinated and unvaccinated is 0.32%. The authors’ claims that the vaccines prevented death are not supported by the evidence provided, which is correlated in nature, not causative.
A good paper on this issue: https://www.ncbi.nlm.nih.go...
On 2022-09-30 04:17:40, user Thamir Alandijany wrote:
On 2022-07-29 13:52:30, user Stuart MacGowan wrote:
This is great work! A few years ago I worked on something similar - mapping missense variants to Pfams and defining constrained positions https://doi.org/10.1101/127050 . We also saw enrichment of pathogenic variants at constrained positions. Great to see this area moving forward!
On 2022-06-07 10:39:46, user M. M. Welling wrote:
For the 2 patients, both were vaccinated before the PET scans. The control patents were from 2019 thus uninfected and not vaccinated for COVID-19. Neuroinflammation can be initiated by the vaccination after liposomal transfer of the mRNA through the BBB. This needs to be discussed as well.issue
On 2022-01-22 07:07:07, user JanLotvall wrote:
Vaccine equity is certainly important, but does this data really support the conclusion that vaccination rates explain difference in COVID mortality?. If you use the January 2021 rCFR numbers as a baseline, it was 1.83 (95% CI: 1.24-2.43) in highly vaccinated countries, and in rest of the world it was 2.32 (95% CI: 1.86-2.79). This suggests that other factors than vaccination may explain presumed differences in mortality between the different countries, presumably quality of health care, and perhaps other public health variables in the different countries. Tobacco smoking is potentially one factor that could explains differences in trends between countries.
On 2020-07-20 15:23:57, user Gerald Williams wrote:
I have followed your work since April NBC story.
You said you followed the Zelenko protocol (HCQ+Zn+Azithromycin) and just added Ivermectin in order to get the great results.
In a prior version of your article I recall reading that you had HCQ & Azithromycin in a large % of the Ivermectin group as well as in some of the "usual care" group. You never mention critical Zinc in your article. If I was reviewing, I would want to know the details even if they may make the study more "messy". Your results are already very significant in that adding Ivermectin to the Zelenko cocktail extends efficacy to many days later.
There is no need at this time to kill placebo group by having a DB RCT.<br /> What is needed is to repeat protocol where the the control group exactly follows the Zelenko protocol (now in preprint or via Yale's Dr. Risch review in Am. J. Epidemiology) vs the Zelenko protocol + Ivermectin. You should reference.
It is not just you, but every article on treatment should document # of days since first symptoms and # of days since hospitalization (if former isn't known).
The current hypothesis is that Zelenko protocol works great up to 5 days of symptoms and sometimes up to 7 days. Your addition of Ivermectin extends that window into many days after hospital admission, It is no coincidence that in the less severe group you did not find significance because the Zelenko protocol was adequate (except some patients didn't receive full Zelenko protocol.
Your protocol makes Remdesivir obsolete, dangerous and too expensive.
You can follow me and others on Twitter to continue discussion,.<br /> It is shameful that Big Pharma is suppressing this very important finding.
Ideally the Zelenko protocol should be followed at first signs of symptoms and if fails, around day 6, can add Ivermectin to cocktail and repeat (in outpatient or hospital setting).<br /> As you know, Doxycycline (doesn't interact w/HCQ) can be substituted for Azithromycin, so no need for outpatient cardiac monitoring. Perhaps even use the 4 drug cocktail in outpatient setting from start, if continues to prove safe.
On 2021-08-11 09:43:54, user Sebastian wrote:
Could you please add some other vaccine that "reprogram" the immune-system like BCG or MMR (compare Netea et al. 2020)? You present (indirect) the reprogramming as a new effect of mRNA vaccines, what isn't exact enought in my opinion.
On 2020-10-14 05:33:25, user Gennadi Glinsky wrote:
Different interpretation of these analyses suggest that preexisting T cells cross-reactive against SARS-CoV-2 are more likely to affect diseases severity because high levels of pre-existing immunity in uninfected individuals appears associated with lower mortality (https://www.bmj.com/content... ). The significant direct impact on the innate herd immunity against COVID-19 and effect on populations’ susceptibility to the infection seems less likely because no association was observed between levels of preexisting immunity and prevalence of the infection.