6,062 Matching Annotations
  1. May 2026
    1. 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.

    1. On 2021-09-17 20:03:09, user USA Bottom Line wrote:

      Would it be possible to include a fourth "No Immunity" group in this study? It would be extremely interesting to see how a group with no prior infection or vaccination would perform under the same conditions. The authors would be able to compute the efficacy of both vaccination and natural immunity during the study's time period. Perhaps they didn't do this because the population of "no immunity" people has become too small in Israel?

    2. On 2021-09-09 22:31:22, user Sam wrote:

      I'd be very interested to see a similar study conducted in Sweden, given the philosophy they took in the early days of the pandemic and letting it spread. I wonder how many of their population will have longer and stronger immune systems due to this tactic

    1. On 2021-09-19 18:43:06, user 4qmmt wrote:

      While interesting and adding to our knowledgebase, and thus possibly important for something in the future, these quantitative titer studies don't seem to be useful for predicting outcomes or comparing protection. Many are now using these titer comparisons in vaccine studies in what appears to be the hope of predicting future immune response for both vaccine and or previous infection.

      But the authors of the study Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection in Science point out the obvious and logical problem with these studies. pg 6:

      While immune memory is the source of long-term protective immunity, direct conclusions about protective immunity cannot be made on the basis of quantifying SARS-CoV-2 circulating antibodies, memory B cells, CD8+ T cells, and CD4+ T cells, because mechanisms of protective immunity against SARS-CoV-2 or COVID-19 are not defined in humans.

      pg 2:

      A thorough understanding of immune memory to SARS-CoV-2 requires evaluation of its various components, including B cells, CD8+ T cells, and CD4+ T cells, as these different cell types may have immune memory kinetics relatively independent of each other.

      https://doi.org/10.1126/sci...

    1. On 2021-10-03 07:50:36, user kdrl nakle wrote:

      Interesting, there is another paper here on medrxiv that states that, in case of Delta VOC, there is no difference in viral load.

    1. On 2021-10-07 08:31:47, user Emmanuel André wrote:

      1) Considering that the risk of myocarditis exists during natural infection and after vaccination (altough the risk after vaccination is lower)

      2) Considering that it has been shown by others (https://www.nejm.org/doi/fu... ) that the risk of myocarditis was higher after the second dose of vaccine than after the first dosis, suggesting a "boosting effect" for that risk

      3) Considering that immunization among younger populations can be achieved after a combination of natural infection followed by one only dose of vaccine

      -> Did you observe a higher risk of post-vaccination myocarditis among previously infected individuals?

      Best regards,

    1. On 2021-10-21 23:30:23, user hiennessey wrote:

      Dr. Adragao et al,

      It was with great interest that I reviewed your article, and I thank you for taking the effort to report on an important subject. Atypical atrial flutter is more difficult to treat as compared to typical atrial flutter, and precise mechanism generating atypical ECG flutter patterns can only be determined by mapping and pacing EP studies. Your finding of low-density and prolonged LAT-Valley in a heterogeneous low-voltage area being an important triad to determine areas for successful ablation seems very relevant. I also appreciated the technical description of the mapping techniques as well as the diagrams which showed conduction through normal and scarred cardiac tissue, which would be helpful to other researchers who would like to duplicate your work. <br /> I share your opinions regarding the limitations of your study, the main ones being the reduced sample size, the "survival bias" and the unicentric retrospective aspect. As a clinician and a public health student, I would have liked to see more clinical information regarding the 9 patients chosen for the study: were they on any medications that may have affected their success rates, did they have prior cardiac surgeries. This information, if available, would have been helpful for me to determine whether my patients are similar to your study population. Furthermore, you also indicated that 2 of them had a previous typical AFL ablation and 1 patient had a previous mitral AFL ablation, and 3 patients had never performed a catheter ablation procedure. Therefore, it seems that your study is of a mixed patient population consisting of those who once had typical and now atypical atrial flutter, those who have had prior ablation which failed, and those who are undergoing ablation for the first time. It is interesting to see that all types of patients were successfully converted using your triad technique. However as you noted, there is no information regarding whether these patients successfully remained in sinus rhythm, or whether they converted back to atrial flutter. Furthermore, having such a heterogenous population may make stratification of results more complicated.<br /> Thank you again for a wonderful paper. I look forward to seeing future studies on this important subject.

    1. On 2021-09-02 22:45:41, user Roger Marble wrote:

      I understand the increase in hospitalization but doesn't that also mean a significant increase just in the number / % infected? CDC wants people who test as infected to be under quarantine for 2 weeks. How does that work when we soon will have a majority of the population infected even if they are not sick? I thought the vaccines prevented serious illness and some deaths but did not prevent infection.

    1. On 2021-09-03 13:01:15, user David B wrote:

      Wouldn't Figure 4 and Suppl. Table 2 suggest that for Biontech/Pfizer 19-29 days between doses should be preferred over 30-44 days for people aged 50-64 years, if longer periods between doses aren't possible? Seems to me that there is something in the data, some confounding variable that leads to lower VE in that group. If this is not due to some uncontrolled underlying factor, then 3 weeks between doses should be the preferred option over 6 weeks, if you want to stay within the range suggested by the manufacturer.

    1. On 2021-09-06 17:33:34, user Shannon Marie BScPharm (she/he wrote:

      Very curious to know what this means for patients who suffer from mast cell disease prior to COVID19 infection. Limited research to date shows that if MC disease is well controlled, outcomes are no more severe than in general population. But as the mother of a child with yet uncontrolled mast cell disease, I shudder at the thought of her system ramping up even more during a SARS-CoV-2 infection. Hoping for more details on this as the science rolls in, regardless of her vaccinated status. Thanks to these researchers for their efforts to identify cellular mechanisms of COVID19 sequelae.

    1. 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.

    1. On 2021-09-09 19:16:04, user Piotr Radzinski wrote:

      This paper is already published under titled "Factors Associated With Psychological Disturbances During the COVID-19 Pandemic: Multicountry Online Study" in JMIN mental health

    1. On 2020-05-04 01:53:51, user TinHo Mak wrote:

      This explained why Singapore has low infection rate among local residence but high infection rate among external workers. They are living within a tiny island and they do not have significant DNA or mask wearing difference. Their key difference is their jab history.

    2. On 2020-03-30 11:19:13, user zetevar wrote:

      It can be an explanation of fewer cases in childhood. The effect of the vaccination decreases in elder aged. Comparing of the number of children patients in the mentioned countries is necessary to get closer to the solution.

    3. On 2020-04-16 17:25:11, user forevertheuni wrote:

      This is tricky:

      Can you do a graph with "tests per capita" as a variable in this? I think that it would abate some differences.

      I think that on how robust the testing has been plays a bigger role in this, because it reduces the % per million inhabitants. Which is usually a correlative on how resources are put into healthcare in general, and where vaccines are probably well implemented.

      Then you have another big and totally opposite confounder, if you don't to tests...you don't have reported cases, and you will go down the graph (and that in some cases correlates with low income places, that will have the BCG because tuberculosis is very prevalent).

      Well, I still appreciated the article, but there are many variables to be explored.

    1. On 2020-04-23 17:09:17, user Sinai Immunol Review Project wrote:

      Key findings:

      Using clinical data of patients and statistical analysis, the authors wanted to explore the potential role of vitamin D in decreased cytokine levels which may lead to the reduction of disease severity in COVID-19 cases. The study shows that Spain and Italy present a severe vitamin D deficiency. Concerning COVID-19, the two countries have a higher mortality rate and a higher case fatality rate (CFR) for patients age >= 70. The authors have also shown that subjects with a severe deficiency of Vitamin D have 1.4 times higher risk for production of high C reactive protein (CRP), used to indirectly evaluate cytokine storm. They hypothesized that Vitamin D deficiency increases the risk for cytokine storm in COVID-19 patients and they estimated a 15% reduction in the number of severe COVID-19 cases after vitamin D deficiency elimination.

      Potential limitations:

      The results of this study remain hypothetical. Future studies should assess vitamin D and pro-inflammatory cytokine levels in COVID-19 patients upon hospital admission and the role of vitamin D in decreasing cytokine levels should be evaluated. In addition, the authors did not take into account the medical history of COVID-19 patients in their analysis, as previous studies have shown that patients who have comorbidities (cardiovascular disease, hypertension, chronic pulmonary disease, hepatic disease, diabetes and other chronic disease) are more likely to develop a severe COVID-19 https://www.medrxiv.org/con... <br /> https://www.medrxiv.org/con...<br /> Furthermore, age has been reported to be a major factor that determines outcome for COVID-19 and it has been demonstrated that aging affects the formation of the active form of vitamin D.https://www.ncbi.nlm.nih.gov/pmc/ar... https://www.ncbi.nlm.nih.go... It would be interesting to see if vitamin D deficiency in the elderly associates with severe forms of COVID-19. Finally, the authors have chosen to use CRP to evaluate the intensity of cytokine storm which is an indirect approach. However, even if CRP has been regarded as a pro-inflammatory molecule, it has some anti-inflammatory functions (recruitment of complement inhibitors and release of anti-inflammatory cytokines such as IL-10 and IL-1ra) and could be replaced by more specific inflammatory markers. https://www.fasebj.org/doi/... https://www.sciencedirect.c...

      Overall relevance for the field:

      The study results did not demonstrate a direct and obvious link between vitamin D deficiency and severe cases of COVID-19 that feature cytokine storm. The study is based only on analysis of the reported clinical data from multiple studies, the results are therefore hypothetical and require a more direct demonstration to see if vitamin D supplementation could really suppress cytokine storm in COVID-19 patients.

      Review by Meriem Belabed as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

    2. On 2020-05-10 16:29:06, user Puddin'Head wrote:

      Given A) the prevalence of conspiracy theories running through the <br /> internet regarding the cause and treatment of covid-19, and B) the <br /> abundance of quacks selling vitamin mega-dosing as a reliable cure, I <br /> hope the authors plan to make clear just how tenuous their conclusions <br /> are in this manuscript. Drawing cum hoc conclusions based on the <br /> possible correlation of a correlation (that's not a typo) with a <br /> surrogate marker that requires rough estimation of nearly every <br /> pertinent variable (e.g. case numbers, mortality rate, vitamin D status)<br /> doesn't yield a particularly compelling result.

      It might be better to look at aggregate US states which more closely approximate the<br /> land area and population density of the UK, for instance, if <br /> comparisons are going to be made. The mortality rate in New York, New, <br /> Jersey, and Massachusetts are all higher than that in the UK, so how <br /> does that affect the observed trends? The overall mortality rate in the <br /> US as a whole is lower on account of a variety of circumstances, likely <br /> including the low population density through much of the mid west and <br /> northern mountain west states. Sun exposure, and thus presumably vitamin<br /> D status, is also not as uniform across the US as it can more <br /> reasonably be expected to be across the UK. Lots of variables.

      It's also worth pointing out that the observations of low vitamin D <br /> correlating with high CRP and high CRP correlating with severity of <br /> covid-19 do not allow for the conclusion that low vitamin D causes (or <br /> even correlates in a meaningful way with) more severe covid-19 cases or <br /> worse outcomes, despite the ability of equation 4 to generate a number. <br /> In other words, the observations that:

      A) consuming 4 drinks correlates with my having a headache and <br /> B) my having a headache correlates with getting kicked in the head by a mule

      ...don't lead us to the conclusion that:

      C) consuming 4 drinks is going to cause me to get kicked in the head by a mule.

    1. On 2020-06-27 15:28:25, user Tim Pollington wrote:

      I'm finding Fig.1 helpful. But to avoid confusion would be good to use different symbols for fwd/backward/intrinsic distribs e.g. currently \tau_{i1} for Fig.1A,B & C.

    1. On 2021-12-24 07:09:23, user ZedMcG wrote:

      The UK has a seropositivity rate of 95%? Why the panic there? Do they not trust South African data or do they have a valid reason to believe that Omicron will have a different impact there?

    1. On 2020-07-03 09:19:51, user Lolo229 wrote:

      Do these findings suggest that people with severe food allergies are more at risk for ventilation if they were to contract COVID-19?

    1. On 2021-09-22 14:02:47, user Carolina Faria Ferreira wrote:

      I didn't understand very well... The conclusion was that the delta variant is more dangerous in the vaccinated group than in those who received placebo? Or was it the opposite? I got confuse

    1. On 2020-08-15 01:08:32, user leon islas wrote:

      Great work. I have only one question, how representative are the serum cytokines measured, of the cytokines in the lungs and other organs? Are they truly representative? If not, this would be another limitation of your study.

    1. On 2020-07-13 22:42:36, user Lutz Froenicke wrote:

      Hello authors, could you help?<br /> The primer sequences seem to be missing? The methods supplement states<br /> "The sequences for primers used are shown in Supple Table 1."<br /> The only available table lists samples, but not primer.<br /> Thanks in advance!

    1. On 2021-06-22 16:51:16, user Timothy Dennis wrote:

      Humanigens lenzilumab is much further along that this one. $HGEN is a much better bet at this time with EUA for US & UK submitted.

    1. On 2021-02-13 09:18:54, user Matt Huber wrote:

      Ct values are an inferior and controversial proxy for infectiousness and there are quite a lot of confounding factors here. Studyalso shows that difference is quite small comparing vaccinated and control group.

    2. On 2021-02-13 17:05:48, user Harold Erickson wrote:

      The question raised below seems much more important than the Qt estimate of viral load. What fraction of people tested positive after vaccination? What were their symptoms? Hopefully this will come soon

    3. On 2021-02-25 11:19:58, user Manuel Riegner wrote:

      Have the participants of the control group been symptomatic or asymptomatic ?<br /> What kind of symptoms did they include in their study ? ( The Oxford study with Astra Zeneca did not include gastrointestinal symptoms neither fatigue, muscle pain, headache or any psychological symptoms ).

    1. On 2019-08-28 13:55:51, user Larry Parnell wrote:

      Among 237 variants in miRNAs, we found rs2291418 in the miR-1229 precursor to be significantly associated with Alzheimer disease. Our in silico analysis and in vitro miRNA expression experiments demonstrated that the mutant G allele enhances the production of miR-1229-3p {Ghanbari Ikram 2016 Sci Rep 6:28387, PMID 27328823}

    1. On 2020-04-18 08:24:25, user YishaiK wrote:

      I am sorry to say that the basic assumptions and math of this research are wrong.<br /> The researchers quoted the kit performance provided by the manufactures, but quoted the wrong information, twice.<br /> The correct information appears here: http://en.biotests.com.cn/n...

      A short introduction: Estimating the real prevalence from the sampled one depends on the accuracy of the serology kit used. The lower the sensitivity of the kit, the higher the estimate should be in comparison to the survey results. On the other hand, the lower the specificity of the kit, the lower the estimate should be.

      Now we're ready to explain how the researchers got it all wrong:

      Since the decision of being positive for covid-19 was taken by IgG *or* IgM,<br /> then the kit sensitivity should be taken as the unity of both IgG and IgM, which leads to 100% sensitivity, and not 91.8% as taken in the research (the writers "chose" to quote the lower sensitivity relating to IgM only, which is just wrong).

      It gets worse - when quoting the specificity of the kit, the writers quoted the higher level of 99.5% which fits this time to IgG only (how convenient). But again, since the criteria in the survey was IgG *or* IgM, the specifity is actually lower - at maximun 99.1%, and possibly even 98.65% (if the false-positives in the manufactures validation were for different people between IgG and IgM).

      Taking both these mistakes into account (i.e. assuming 100% sensitivity and 98.65% specificity) - the estimated prevalence (before correcting by sex age etc., which I find to be irrelevant) is a mere 0.15%. A lot less exciting.

      I find it surprising that the writers quoted different (let alone wrong) values for sensitivity and specificity (once IgM and once IgG), in a way that miraculously led to higher prevalence estimations.

      I allow myself to ignore the writers self validation of the kit, since specificty was tested accross an outrageously small sample size of 30 non- Covid19 samples. As the writers acknowledged themselves, that is only enough to ensure the kit has a specificity above 90%. Nothing to write home about.

      To sum it up, the research found 50 positives out of 3,330. This can teach us more about he false-positive of the serology kit used, than about the real prevalence of Covid-19 in the population surveyed.

      There are lies, and then there are statistics...

    2. On 2020-04-22 16:02:11, user Stacy Johnson wrote:

      How I wish Dr I were right! From March 1 to today, we have had 45k Covid-19 deaths. That is 30% more than during the entire 2018-2019 influenza season (12 months). My data suggest that before this is over (by mid-August, i.e., in 5.5 since the outbreak), we shall have about 245k Covid-19 deaths. That's the same number as the sum of influenza deaths between 2013 and 2019 (6 seasons, each of 12 months). How can anyone trust Dr I? If I'm wrong, I'll be celebrating, and I'll know by April 25, 2020, when my data suggest we'll exceed the 60k mark predicted by the "Chris Murray model" for the final death toll. Just wait 4 more days, please! Dr I is talking about Santa Clara (not nationwide), about infections detected (not deaths), and about discovering infection rates that are 85x worse than anticipated. Sadly, from that terrifying observation, he infers that mortality rates must be 85x better than anticipated, and, therefore, he finds them approximately equal to those from the flu. The maximum mortality from the flu for the period of 2010 to 2019 was 0.18%. In my Covid-19 model, I assume a mortality rate of 2.3%, i.e., 13x worse than the flu. Sadly, countries like Italy and Spain have reported Covid-19 mortality rates up to 10%, i.e., 4x worse than my assumption. Doubly sadly, my model has consistently predicted the death toll in the USA since 4/4/20; furthermore, it shows that by 4/24, our toll will exceed 60k, which the "Chris Murray model" provides as our ultimate count in early August. Trust me, I do not want to be right. This is not an argument I desire to win. It tears me up to think that I am the bearer of bad news. I want to rejoice in the Resurrection, not to wallow in death statistics. I check my numbers every day, and the same conclusion has been coming out for 17 days now. I shed tears over this, I pray to God to please let me be wrong. I don't want to be Jonah, preaching to the Ninevites: Another three days and the city will be destroyed. I want to flee to Tarshish! But the coffins are there, for everyone to see. If our mortality rate were equal to that of the flu, we should have had only 1400 deaths (=825k x .17%). But we actually have 40k coffins. Our actual mortality rate is 5.4% (=45k/825k). My assumption is barely half of that, Sorry, Dr I, I do not trust your inference, as much as I have high respect for your data and methods. The quality of data is always a problem. Analysis must include data from a variety of sources to minimize bias. On the other hand, coffins are coffins: 45k of them in the USA, and counting. No one can wish that figure away.

    3. On 2020-04-18 20:30:49, user John Stevens wrote:

      Many posts here have missed critical point - samples maybe biased (off by 50-75%) but if these data are even partially correct means COVID-19 can be managed down to zero. Many comments here about NYC infection rate are not correct.

      NYC data has a near zero new case rate today (0.7%/day) if true that actual infected rate is 50X over reported we are at 70% of population (about 6 million) infected in NYC - explains actual drops in mortality rate and new cases to near zero in NYC and must be herd immunity

      Many posts here are just not accurate and not aware of real data. have summarized www.rubee.io/nyc - see NYC posted data today look at graphs at bottom.

      https://en.wikipedia.org/wi...

      John K. Stevens Ph.D.

    4. On 2020-04-19 05:25:09, user Kaliahk wrote:

      Meanwhile in Alaska, 97% of all persons tested (those who are symptomatic or have had contact with a Covid patient) test negative. One would think if there are 80 times as many people who have it and don't know, that they would be catching a bunch of asymptomatic people in those tests.<br /> This study adjusts from a true rate of 1.5 % up to 2.8% or 4.2%? what adjustment do you make for finding your voluntary participants through Facebook ads? <br /> This study will not survive peer review, but it is not meant to. It is meant to be a talking point.

    5. On 2020-04-19 06:51:19, user DFreddy wrote:

      reference 2 -> link not correct

      Report 12 - The global impact of COVID-19 and strategies for mitigation and suppression [Internet]. Imperial College London. [accessed 2020 Apr 7];Available from: http://www.imperial.ac.uk/m... epidemiology/mrc-global-infectious-disease-analysis/covid-19/report-12-global-impact-covid-19/

    6. On 2020-04-19 19:15:06, user Michael A. Kohn, MD, MPP wrote:

      From the 3439 people who showed up for testing, they were able to obtain 3330 valid specimens on which to perform the Premier Biotech serology test. Of these, 50 were positive. That’s 50/3330 = 1.5% . They tried to adjust for the fact that the people who actually showed up were not representative of the county population’s sex, race, and zip code distribution. But the main potential source of error is the accuracy of the test. At a low sero-prevalence like this, a small proportion of false positives can result in a large overestimate. They ran the Premier Biotech test on 30 serum specimens drawn prior to the pandemic and it was negative on all 30. If the error rate on truly uninfected individuals is 0.5%, and the test properly identifies 91.8% of previously infected individuals, then the true sero-prevalence is 1.1%. As the authors say, “Additional validation of the assays used could improve our estimates and those of ongoing serosurveys.” Having reviewed the test accuracy studies of this and other lateral flow immunoassays (http://covid-19-assay.net/ ), I believe we will end up with a true sero-prevalence of about 1% in Santa Clara County. But the authors made a reasonable estimate and did a great job of collecting this data and reporting their results and assumptions.

    7. 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.

    8. On 2020-04-23 14:43:46, user Jason Bayer wrote:

      My question is this, in his interview he concluded that mortality rates in relation to this data (suggesting significantly more coronavirus cases then that being documented) is significantly lower, being in relation to this new higher estimate of cases....but how is he accounting for untested, undocumented coronavirus deaths? I do not see how one can claim anything on mortality in relation to undocumented cases but only count survivor data....am I missing something?

    9. On 2020-04-17 18:22:03, user Anon wrote:

      The authors state: "We used Facebook to quickly reach a large number of county residents and because it allows for granular targeting by zip code and sociodemographic characteristics." This gives an inaccurate impression of how participants were recruited. I participated in the study, but don't have a facebook account. In truth, anyone with a link could have registered to participated in the study. So the author's claims here are dubious on the evenness of recruitment.

      In this survey we were only allowed to have one adult get test. Naturally, we selected the person with the most relevant symptoms (me). So there's an element of self selection going on here as well.

    10. On 2020-04-18 02:04:32, user Ngallendou Dièye wrote:

      This study applies to a single county. Such studies must be conducted in representative communities across a nation or nations, before it can be said to have general relevance.

    11. On 2020-04-18 04:24:55, user Vasyl Zhabotynsky wrote:

      The conclusion seems to heavily rely on the fact that specificity is really 99.5%<br /> If specificity is 98.5% (which is still in the confidence interval for the estimate of specificity), one would expect to get 50 positive tests from 3330 tests (as stated in second paragraph of page 7) in a completely disease free population.

    12. On 2020-04-18 10:07:24, user Dean Karlen wrote:

      Ignore this pre-print. They have insufficient evidence due to a weak measurement of the false positive rate. Consider that they saw 50/3330 in the test, and use the manufacturer false positive measurement of 2/371. I estimate the p-value (probability for seeing something as anomalous or more anomalous under the null hypothesis) to be about 0.08. There is weak evidence that even one of the 50 had COVID-19. And they are using that data to make an extraordinary claim?

      It appears that none of the 26 comments below pick up on this point...

      If you need help thinking about this problem, under the null hypothesis, ask yourself

      Is it anomalous to see 50 or more positive tests in a sample of 3330 (all negative) when there was also an independent measurement of 2 positive tests in a sample of 371 (all negative)? Easiest to estimate by taking the first datum as a measure of false positive rate (50/3330) and the expected number of positives in the sample of 371 is therefore 5.6. Seeing 2 or fewer is not unlikely: p=0.08.

      In fact the experiment was flawed in its design. With a poor false positive measurement they would have no chance to measure the expected small fraction of individuals with COVID antibodies. Why did they even embark on the study, when it was doomed to fail?

      I hope this pre-print can be retracted somehow, and the community informed to not take this result seriously!

    1. 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.

    1. On 2021-10-20 13:35:41, user kdrl nakle wrote:

      LOL. Posted on Oct 18 "predicting" drop in mid-October, and on Oct 20 Ukraine posted the highest number of new infections during the delta wave, 18912.

    1. On 2021-03-13 07:48:06, user Dr Gareth Davies (Gruff) wrote:

      I noticed that you had previously reported finding a strong correlation but withdrew it. I've studied these data myself and the quality of country data is very poor and inconsistent, so naively applying a regression to it isn't very meaningful.

      It's criticially important to use only high quality data sources that as much as is possible represent the nationwide prevelance of deficiency over winter. They should be recent and seasonally adjusted if taken outside winter. Furthermore, the data set should be representative of the entire country.

      Each country data set should then be appropriately weighted according to some objective criteria for quality otherwise the regression is meaningless.

      Also, very small countries will end up with distorted per million figures, and some effort must be made to ensure that reported figures for deaths and recoveries are accurate since many countries use very different policies. Data for Estonia, Turkey, Czech Repulbic and Boznia and Herzegovina have been added to the regression with apparently equal weighting to other reporting countries despite huge differences in reporting, population densities and data quality for deficiency, mortality and recovery rates.

      The data for Czech Republic, for example, is based on Mayer et al. 2012, which cites a study from 2008 of just 560 people which reports 24.4% deficiency. This study was performed over 2–3 months during autumn 2008 - just after summer - and therefore in no way reflects the general population prevelance of vitamin D status in winter in 2020. Indeed, it is wholly inconsistent with other studies which suggest that in Czech Repulic, vitamin D deficiency in winter is widespead (e.g. Bischofova et al. 2018).

      It's not surprising that the original clear trend has been wiped out by adding this "dirty data".

      The conclusions stated are not merited by the analysis.

    1. On 2020-03-18 21:57:53, user NymRod wrote:

      "Estimating the cure rate and case fatality rate of the ongoing epidemic COVID-19 "it is inferred that the cure rate of this epidemic is about 93% and the case fatality rate is about 7%."

      Totally false. The ONLY accurate method to calculate the cure rate and the fatality rate of an ongoing pandemic is to use the number of closed cases, anything else is irrelevant.

      Currently as of 3-18-2020 at 5pm CT in the US there are 256 closed cases, 106 recoveries and 150 deaths. That calculates to a 41% recovery rate and a 59% fatality rate.<br /> https://www.worldometers.in...

    1. On 2021-03-23 17:22:24, user Nuno Sepúlveda wrote:

      We are currently extending our analysis regarding the impact of misclassification on the detection of a putative association with ME/CFS.

    1. On 2020-03-24 04:17:55, user Sinai Immunol Review Project wrote:

      Main findings<br /> The authors performed single-cell RNA sequencing (scRNAseq) of peripheral blood mononuclear cells isolated from whole blood samples of COVID-19 patients (n=10). Data was compared to scRNAseq of samples collected from patients with influenza A (n=1), acute pharyngitis (n=1), and cerebral infarction (n=1), as well as, three healthy controls. COVID-19 patients were categorized into those with moderate (n=6), severe (n=1), critical (n=1), and cured (n=2) disease. Analysis across all COVID-19 disease levels revealed 56 different cellular subtypes, among 17 immune cell types; comparisons between each category to the normal controls revealed increased proportions of CD1c+ dendritic cells, CD8+ CTLs, and plasmacytoid dendritic cells and a decrease in proportions of B cells and CD4+ T cells.

      TCR sequencing revealed that greater clonality is associated with milder COVID-19 disease; BCR sequencing revealed that COVID-19 patients have circulating antibodies against known viral antigens, including EBV, HIV, influenza A, and other RNA viruses. This may suggest that the immune response to SARS-CoV-2 infection elicits production of antibodies against known RNA viruses.

      Excluding enriched pathways shared by COVID-19 patients and patients with other conditions (influenza A, acute pharyngitis, and cerebral infarction), the authors identified the interferon-MAPK signaling pathway as a major response to SARS-CoV-2 infection. The authors performed quantitative real-time reverse transcriptase polymerase chain reaction (RT-PCR) for interferon-MAPK signaling genes: IRF27, BST2, and FOS. These samples were collected from a separate cohort of COVID-19 patients (critical, n=3; severe, n=3; moderate, n=19; mild, n=3; and cured, n=10; and healthy controls, n=5). Notably, consistent with the original scRNAseq data, FOS showed up-regulation in COVID-19 patients and down-regulation in cured patients. The authors propose that FOS may be a candidate marker gene for curative COVID-19 disease.

      Limitations<br /> The sample size of this study is limited. To further delineate differences in the immune profile of peripheral blood of COVID-19 patients, a greater sample size is needed, and longitudinal samples are needed, as well. A better understanding of the immunological interactions in cured patients, for example, would require a profile before and after improvement.

      Moreover, the conclusions drawn from this scRNAseq study point to potential autoimmunity and immune deficiency to distinguish different severities of COVID-19 disease. However, this requires an expanded number of samples and a more robust organization of specific immune cell subtypes that can be compared across different patients. Importantly, this criterion is likely needed to ensure greater specificity in identifying markers for COVID-19 infection and subsequent immune response.

      Relevance<br /> At the single-cell level, COVID-19 disease has been characterized in the lung, but a greater understanding of systemic immunological responses is furthered in this study. Type I interferon is an important signaling molecule for the anti-viral response. The identification of the interferon-MAPK signaling pathway and the differential expression of MAPK regulators between patients of differing COVID-19 severity and compared to cured patients may underscore the importance of either immune deficiency or autoimmunity in COVID-19 disease.

    1. On 2020-05-17 21:43:32, user Ricky wrote:

      For those who are skeptical, just visit Quota (a website) and read all the anectdotal evidence, in the form of testimonials direct from Covid survivors, about the after-effects of the disease.

    1. On 2020-05-22 16:02:44, user Ivan Berlin wrote:

      There are more health care workers among those who tested positive for SARS-CoV2 than among those who tested negative (31.14 vs 14.27 %). Health care workers are more likely to be non smokers or former smokers. Did you look at the association of current smoking prevalence and being health care worker? Being health care worker may confound the observed lower current smoking prevalence among SARS-CoV2 positive individuals.

    1. On 2020-05-26 07:22:53, user Yashawant Gothankar wrote:

      The important thing to consider here is the author of the paper does NOT say that Ivermectin should be ruled out.

      Positive outcome of this research would have been identifying what concentration/dose can work to counter covid19 infection in humans.

      Ivermectin is one of the most promising candidate currently under investigation world over.More data is expected from human trials being conducted.

      Some of the most interesting results and progress is coming form human trials conducted in Bangladesh using combination of Ivermectin and Doxycycline . It looks like BD doctors have figured out the Ivermectin doses need to be administered for human trials.

      Refer following links:

      https://www.ibtimes.sg/mira...

      https://www.trialsitenews.c...

      https://asia.nikkei.com/Spo...

    1. On 2020-05-26 13:58:13, user Sinai Immunol Review Project wrote:

      Main findings<br /> While the growing scientific literature on the immune responses to SARS-CoV-2 infection has highlighted several immunological markers for COVID-19, molecular or cellular determinants of disease severity have not yet been well-described. In this report, Sánchez-Cerrillo et al. profiled myeloid and T cell subsets across mild (G1, n=19; whole blood), severe (G2, n=21; whole blood), and critical COVID-19 cases (G3, n=23; whole blood and paired bronchoscopy samples), and healthy controls (n=22). Clinical parameters, including serum IL-6, procalcitonin (PCT), C-reactive protein (CRP), D-dimer levels, and serum LDH, increased with worsening disease severity.<br /> Using high-dimensional flow cytometry, the authors assessed changes in classical monocytes (C Mo; CD14+CD16-), transitional monocytes (T Mo; CD14+CD16+), and non-classical monocytes (NC Mo; CD14loCD16+), CD14-CD16hiHLA-DR- granulocytes, CD141+ dendritic cells (cDC1), CD1c+ dendritic cells (cDC2), and CD123hi dendritic cells (pDC) in blood and bronchoscopy samples. While almost all myeloid subsets in COVID-19 patients were significantly reduced in the blood circulation compared to healthy controls (with the exception of T Mo), no statistically significant correlations between these myeloid subsets and disease severity were observed. Of note, the overall sparsity of C and T Mo subsets corresponded to high levels of serum IL-6; otherwise, there were no remarkable correlations between the frequencies of the aforementioned subsets and inflammatory markers. Importantly, in the bronchoscopy samples, an unpaired analysis identified an enrichment of granulocytes and inflammatory T and NC Mo. Importantly, a paired analysis of blood and lung samples demonstrated that T, NC, and CD1c+ DCs are significantly enriched in the lung. Collectively, these results reflect a notable recruitment of monocytes to the lung. The authors used CD40 expression as a marker of myeloid activation. While CD40 expression decreased with increasing disease severity, this trend was not significant, and expression was comparable to the cells isolated from healthy controls. Lastly, a survey of markers associated with compromised effector function of T cells isolated from blood and bronchoscopy samples of G3 patients showed that CD38+CXCR5+ T cells are significantly more prevalent in the lungs than in the blood, and differences to healthy controls were significant.

      Limitations<br /> Technical<br /> One notable limitation are superinfections as a confounding variable; their effects need to be accounted for with careful multi-variate analysis and should be replicated in larger, multicenter studies. Moreover, flow cytometry markers used in the present study can present a biased view of cell populations, so future studies using higher-dimensional, unbiased techniques may provide a more inclusive view of myeloid heterogeneity in COVID-19 patients with differing severities of disease.

      Biological<br /> It is important to note that almost all patients across the different groups had been receiving concurrent therapy, including antivirals, antibiotics, steroids, and immuno-modulators (anti-IL-6); it is unclear when these treatments were administered, relative to the collection of samples. Furthermore, the DC subsets defined in this report comprised significantly small proportions (< 5%) of total CD45+ immune cells isolated from blood and bronchoscopy samples of COVID-19 patients. Lastly, while T cell exhaustion was evaluated based on expression of CD38 and CXCR5, the expression of other, more prominent co-inhibitory receptors, including PD-1 or Tim-3, was not evaluated. Therefore, this report would benefit from a better study of myeloid activation and T cell exhaustion using additional markers that define activation of the myeloid subsets, including an analysis of cytokine production, and markers for T cell exhaustion.

      Significance<br /> In summary, this report offers some insight into the profiling of different circulating cell subpopulations across various degrees of COVID-19 severity. However, interpretations of the results should be approached with caution, given the lack of statistical significance and of detailed analyses of important cell groups, including better-defined exhausted T cells. However, thus far, the findings outlined in this report support the notion that monocyte dysfunction, involving a pro-inflammatory state and an overall recruitment from the peripheral blood to disease-afflicted tissues like the lung, characterizes the immune response to COVID-19.

      This review was undertaken by Matthew D. Park as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

    1. On 2020-05-28 14:07:28, user Masakazu ASAHARA, PhD(????) wrote:

      I am afraid that Miller et al. had been posted faster than that blog. Furthermore, clinical studies had begun before such ecological studies.<br /> I would be grateful if the author explains why the author ignored the most important argument that has been criticized by many researchers. That is, the effect of timing of propagation in which probabilistic events would have been involved. If the probabilistic timing is important (as my study had suggested), only Fig S21 might be the effects worth considering.

    1. On 2020-05-28 14:09:42, user steve mike wrote:

      I'd like to clarify a few major misconceptions people seem to have taken away with regards to this study.

      All of the people observed in the study were positive for Corvid-19, the disease caused by the novel corona virus, ergo, a lot of people with type O blood can and do contract Corvid-19

      The study says it got the patient test results from three hospitals, but it does not say if the patents in question were hospitalized.This is huge, because, for example, lets say all the people who are A blood type in the study had a mild case of the illness, and all the type O patients were on ventilators.In that case, yes, type O is statistically less likely to get sick from corvid-19, but more likely to get life threateningly ill.

    1. On 2020-05-30 18:48:21, user Craig Travis wrote:

      The endocannabinoid system plays a fundamental role in the immune system to reduce and resolve inflammation. A recent preprint showed that CBD reduced the expression of ACE2 and TMPRSS2, both of which are required by CoV2 to enter cells. The authors also stated that cannabis did the same. More research needs to be done to determine the risk or benefit of this class of drugs especially now during this pandemic without a prohibition-minded bias clouding the picture. Research has established the antioxidant properties of the inhaled substances. Not true for tobacco.

    1. On 2020-05-30 19:03:45, user Harry Powell wrote:

      Might this be used in conjunction with the "Naväge" device I recently saw on television that is used to clean your nasal passages? It seems to pump fluid through your nostrils. There is a demo of it by a "reviewer" on YouTube.

    1. On 2020-05-31 13:42:32, user Pete Jones wrote:

      Really nice work.

      "the prescribed minimum dose of 42 hours across 6 weeks" -- dear lord, that's an hour of tetris every day! That's bordering on "guantanamo bay" level torture (and that's speaking as someone who used to run experiments on Perceptual Learning)!

      In all seriousness, the fact that that over half of children managed to complete more than half of that is amazing, and really quite encouraging IMHO. Participants were also better and more honest at judging adherence than I would have expected. I'd say this on balance this is Good News (and maybe just a 'lower bound' on what could be achieved if the task was something more addictive, like Fortnite etc.)

      It would be nice to see session durations as histograms (maybe even for individual children), and maybe medians/IQR for Table 1. It would also be great to see adherence as a function of time graphically. Finally, I probably just missed it, but do you discuss how adherence compares to standard patching in the literature?

    1. On 2020-06-01 14:11:35, user Anne Thomas wrote:

      It's a shame there was no differentiation between UVA and UVB. UVB is blocked by the atmosphere so is more abundant at higher altitudes and of course it's UVB which is responsible for vitamin D synthesis, supporting the vitamin D hypotheses, which is of course supported by 7 preprints. https://www.bmj.com/content... and what we were predicting based on the known role of vitamin D in immunity and reducing inflammation. It appears that vitamin D is particularly important in Covid-19 in preventing a cytokine storm.

    1. On 2020-06-04 00:20:18, user wbgrant wrote:

      It would be useful now or in further studies, what baseline and achieved 25OHD concentrations were. <br /> It is not clear in the text how many were given DMB prior to April 6, how many after April 6. Those given DMB prior to April 6 would be the less severe cases, thereby biasing the study.

    1. On 2020-06-04 17:08:10, user Rob wrote:

      Three points :

      1) There's a strong prior that vitamin D against month should peak somewhere around August, as 3 of your 4 curves do. Can you explain why the curve for non-white females peaks in April/May? This looks wrong... so wrong that I think it needs to be investigated / explained before relying on this data.

      2) The covid-19 infection dates are in March - May. Extrapolation of vitamin D levels measured later in the year to this period will in reality be subject to any individual differences in half-life. For example, your White curves appear to imply that men have a shorter half-life than women. This suggests that sex based adjustments might be a good idea. Presumably individual variation in half-life can also be expected. How much uncertainty would this imply for your adjusted values?

      3) Your vitamin D levels are 10-14 years old. That is plenty of time for people to start taking supplements, move house, move city, change jobs, or even get old. Perhaps this is worth mentioning in your limitations section.

    1. On 2020-06-04 20:57:33, user Marm Kilpatrick wrote:

      It didn't appear that heterogeneity in transmission was a key part of the calculations. For SARS-COV-2 many estimates suggest that dispersion parameter assuming negative binomial distribution for spreading is ~0.2 which means lots of cases spread to 0, some spread to many. That'd extend the tail of # of cases that might occur after a super spreading event (e.g. a house party or several) before detection. Given concurrent large social events (e.g. holidays, welcome week), this seems like a high probability.

    1. On 2020-06-05 13:25:21, user Arnar Palsson wrote:

      Legend to figure 1. mentions "MZ represents monozygotic; DZ dizygotic twins." but nothing in the figure indicates the two types of twins. The legend is very short also.

    1. On 2020-06-27 14:00:46, user Kevork Hopayian wrote:

      Study duration too short, 3 weeks definitely not long enough to pick up a cluster when the background risk is running low

    1. On 2020-06-09 15:10:48, user Steve Hayes wrote:

      Counter argument<br /> Madrid is quite high 650m, 2000ft. The typical UV index in march and April is 5-6, one tans easily. And yet Madrid as we all know suffered terribly

    1. On 2020-06-11 14:13:14, user John Mallinckrodt wrote:

      The thesis of this manuscript is palpable nonsense. The seven-day cycle is very clearly a reporting anomaly. To suggest that incubation periods, times to death, or any other time associated with the development of the disease could be sharply enough defined to support an explanation like this is simply absurd.

    1. On 2020-06-13 21:16:27, user John Liang wrote:

      I think the theory of urns estimate is highly inaccurate for a lot of reasons, it is highly unscientific and a lot of guess work. I will list a few points here

      1. Cremation service requirement in Wuhan is always around 28-2900 every six months this is before the addition of CVOID related death and can be found .

      2. You did your guest work about urns number base on urns order by '1' cremation service company and there were no observation at other 7 cremation service companies. There is no confirmation of the other 7 actually order that many urns, how frequent the urns were ordered during the period? Was the urns ordered was a preparation for the future 6 months or 3 months?

      3. Each cremation service companies have different service capability it is unscientific to assume all 8 would be order the same number of urns.

      4.The time take for a complete cremation service is around 3-4 hours not 2 hours.

    1. On 2020-06-14 18:28:21, user Dana Mulvany wrote:

      The featured snorkel mask looks like it could also be used to provide a view of the wearer’s mouth for speechreading purposes. That can be extremely important for the high numbers of professionals and patients with significant hearing loss, who can be enormously incapacitated by not being able to understand most people due to not being able to lipread them.

      Could attention be paid to how to minimize fogging?

    1. On 2020-06-15 10:19:40, user Rosemary TATE wrote:

      An interesting article, but so many different models and variables for only 50 observations. <br /> Looks suspiciously like overfitting, but I would be glad to be convinced otherwise.

    1. On 2020-06-15 15:45:17, user Schwebe Pan wrote:

      Some previous studies have suggested that smoking might reduce the risk of infection with Covid-19, but I am unaware of studies claiming that smoking might reduce the severity of the disease. On the contrary, the current state of the art is that smoking is a risk factor for more severe outcomes. Why, then, is this study trying to check a claim for which there is no evidence but not the actual question of interest?

    1. On 2020-05-20 00:57:13, user David Philpott wrote:

      For the discussion: If you wish to make a comparison with influenza, please give a citation for this "a (0.1%, 0.2% in a bad year)". I have not found a reference for fatality risk for influenza using serologies that is in the 0.1-0.2% range. Typically, those numbers are for doicmented symptomatic cases which is not what is being addressed in this manuscript. Rather, the available evidence is much lower for influenza, perhaps in the range of 0.01%. See here for example: https://www.ncbi.nlm.nih.go...

    1. On 2020-05-20 18:56:53, user Sander Greenland wrote:

      Here are two papers that deal with the general causality theory of collider bias and related phenomena:<br /> Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology 1999;10:37–48.<br /> Greenland S. Quantifying biases in causal models: classical confounding versus collider-stratification bias. Epidemiology 2003;14: 300-306. <br /> See also Ch. 12 of Rothman Greenland Lash, Modern Epidemiology 3rd ed. 2008.

    1. On 2020-05-21 13:02:18, user Fred Douthwaite wrote:

      A vaccine will not protect us from each successive mutated or novel virus. Correcting the underlying zinc deficiency that is the common denominator in the Covid-19 comorbidities is the answer.

      The federal government should be stockpiling supplemental zinc for distribution to vulnerable groups.

      Zinc deficiency is estimated to contribute to over 800,000 deaths per year - primarily in third world countries. This time, zinc deficiency has impacted the whole world. Correcting this problem is long overdue.

    1. On 2020-05-21 19:46:25, user TS Francis wrote:

      There are a lot of problems with this study making me embarrassed to have graduated from Columbia. The report repeats the obvious, that forced social distancing reduces the infection rate, and the report does this with impressive mathematical models but in total the research is misleading in a number of areas.<br /> The report states "a substantial number of cases and deaths could have been averted". This may be true in the measurement period, likely the cases and deaths occur after the measured period. In other words, you prove what we all know that the "control measures" slow down the virus but don't stop it. Even the data shows "control measures" don't stop the cases and deaths.<br /> Assumptions - You are only looking at a snapshot in time. Of course, social distancing slows the virus. Absent a magic cure or herd immunity, the virus will pick back up again after "control measures" are removed. There is an implied assumption that a person saved by "control measures" won't die from the virus soon after your measurement period.<br /> You are assuming Death is a good measure for public policy. Everyone will die, it is a given. Loss of life is what should be measured and this can be estimated based on Covid morbidity by age and life expectancy tables. At the same time you should estimate how much life was taken by your "control measures". Using data from Sweden and my state, I have done this and the loss of years of life from "control measures" far exceeds the loss of years of life saved. <br /> Obviously the objective of the research is to promote a certain public policy to save lives. But it does the analysis without looking at the costs which can be weighed using years of life. Overall, very impressive modeling but not useful except for promoting a biased agenda.

    1. On 2020-04-19 00:43:04, user JK wrote:

      Model is surely under estimating cumulative deaths by Aug 4th - trajectory suggests 80k - 90k...believe this was one of the earlier IHME projections

    1. On 2020-03-25 00:18:42, user Sinai Immunol Review Project wrote:

      Summary of Findings: <br /> - Retrospective study of 59 patients assayed key function indicators of the kidney–including urine protein, blood urea nitrogen (BUN), plasma creatinine (Cre), and renal CT scan data. <br /> - Found that 34% of patients developed massive albuminuria on the first day of admission, and 63% developed proteinuria during their stay in hospital; and 19% of patients had high plasma creatinine, especially the terminal cases. <br /> -CT analyses of 27 patients showed all patients to have abnormal kidney damage; indicate that inflammation and edema of the renal parenchyma very common.

      Limitations: <br /> -No analysis of immunity-dependent damage and cytokines in blood/plasma/urine. Will be worth correlating disease progression with cytokine production, immune activity and kidney function. <br /> -Extrapolating to earlier SARS-CoV studies provides the only rationale for viral-damage in kidney and resultant pathologic immune response (understandable for this clinical study).

      Importance/Relevance: <br /> -Multiple lines of evidence along this study’s finding point to the idea that renal impairment/injury is a key risk factor in 2019-nCoV patients similar to what has been reported for SARS-CoV [1]; this may be one of the major causes of virally-induced damage and contribute to multi-organ failure. <br /> -ACE2 expression in kidney proximal tubule epithelia and bladder epithelia (https://doi.org/10.1101/2020.02.08.939892) support these clinical findings. <br /> -Study argues for closely monitoring kidney function, and applying potential interventions including continuous renal replacement therapies (CRRT) for protecting kidney functions as early as possible, particularly for those with rising plasma creatinine.

      References:

      1. Chu, K. H. et al. Kidney Int. (2005) 67, 698-705, <br /> doi:https://doi.org/10.1111/j.1...

      Review by Samarth Hegde as part of a project by students, postdocs and faculty at the <br /> Immunology Institute of the Icahn school of medicine, Mount Sinai.

    1. On 2020-04-20 17:09:11, user Michele Faucci Giannelli wrote:

      Could you add the fraction of asymptomatic in Table 2. I.e. provide it broken down by age? This can really help in modelling the infection beyond Vo'. Thanks!

    1. On 2020-04-20 17:25:20, user Dylan Skola wrote:

      Can anyone see where they're presented the MAF of the mutations? How many were fixed in the isolate and how many represented intra-host quasispecies at low abundance?

    1. On 2020-04-21 09:37:53, user Walter Langel wrote:

      The article describes the calculation of the time-dependent reproduction number Rt for the present Coronavirus pandemic. These calculations recently resulted in values below 1 and had an enormous impact on political decisions in Germany. <br /> As a physical chemist I have major concerns on the validity of these results:<br /> (1) The calculations are based on a kinetic model with originally eight compartment, which has later been refined by them to as much as 14 compartments. This affords a huge number of parameters, which are known with limited precision. The authors try to circumvent this problem by using various combinations of values for these parameters. <br /> Unfortunately the most important fit parameter R1, which describes the feedback from infected individuals to non-infected, was not quoted. I have fitted the total confirmed infection data for Germany, China and Italy in https://www.medrxiv.org/con... by a simple logistic function with very few parameters. For Germany the effect of the lock down is clearly manifested around March 21st: The fits of the data before and after lock down predict final values of 340 000 and 180 000 infected individuals, respectively (see supplement to my paper). In the paper by Meyer-Hermann et al. the lock down should be seen as a sudden decrease in R1, if not buried in statistic scatter. The missing values of R1 are thus crucial for the validation of their compartment models.<br /> (2) The values of Rt , which are the fundamental result of their calculation, are superimposed by an oscillation with significant amplitude beyond noise (Figure 2(B)). I suspect that this is an artifact of their approach to evaluate the reproduction factor in time windows of seven days. This should be checked by repeating the calculation with variable time windows. As small differences in the asymptotic value of Rt (say 1.2 or 0.8) already have a huge influence on political decisions in Germany, it is urgently important to verify, if the final value is independent of such artifacts.

    1. 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.

    1. On 2020-06-28 20:38:28, user itellu3times wrote:

      OK I'll say it, I find this entirely opaque, I cannot tell what you are even proposing, much less whether you found it or proved it.

    1. On 2020-06-29 02:58:37, user David F. Priest wrote:

      Study has not been peer reviewed and was funded by Suez which has a joint venture in China with the state-controlled China Everbright International Limited.

    1. On 2020-06-30 16:44:31, user Kamran Kadkhoda wrote:

      Mathematically-speaking there is no such thing as 100% specificity!<br /> Also why authors like Abbott itself did not include a large number of sera from known cases of common CoVs?

    1. On 2020-07-08 14:37:20, user rede2fly wrote:

      Association does not indicate causation. The study has no control for the Covid-Quarrantine-Frustration factor. The author began the project with the intent to show causation and failed. The research was funded by anti-firearm organizations with the same goal.

      Why is no one talking about WHO is doing the shooting and WHO is getting shot?

    1. On 2021-05-29 20:55:44, user Robert Clark wrote:

      Seriously, it’s like some researchers opposed to the concept of EARLY treatment of COVID will go to any lengths to provide evidence against it, even if it crosses the line of scientific ethics.

      Sorry, to have to say this but the authors no longer have any credibility on this issue.

      Extremely important to recognize the importance of this: to provide evidence against IVM researchers have had to change data to fit their conclusion.

      What does that tell you about the effectiveness of IVM?

      Robert Clark

    1. On 2021-01-26 19:45:53, user ingokeck wrote:

      The title is misleading, as the data is not about viral load, but instead only on the density of the E gene in the samples. Also, it seems the samples were not normalized to the amount of human dna in the sample. Without that the Ct values can vary by 10 and more just because of differences in the collection, which renders the analysis done in the article useless, unfortunately. Please see Dadouh et al at https://pubmed.ncbi.nlm.nih...

      It would be great if the authors could find out if maybe some samples have been normalized and then restrict their analysis to them. The result would still be interesting.

    1. On 2021-01-28 09:56:34, user Georges Borgès Da Silva wrote:

      Despite some methodological reservations, the effect on the reduction in hospitalizations seems possible.<br /> Statistical significance is limited and does not take into account the multiplicity of comparative tests (absence of Bonferroni correction). The composite outcome is questionable.<br /> It will be necessary to take into account an increased risk of pulmonary embolism in the context of a problematic association (difficult to prescribe colchicine + anticoagulants).<br /> Note a slightly favorable level of comorbidity in the treated group compared to the placebo group.<br /> Our doubts might have been dispelled if the trial had not been interrupted before its end.

    1. On 2021-01-31 19:53:18, user Lisa Brosseau wrote:

      This is not how one should test filter efficiency. This instrument is designed to test the fit of a respirator. It samples at a relatively low flow rate and compares the concentrations of particles inside and outside of the facepiece to arrive at a fit factor. Filter testing requires a completely different set of test conditions, such as those used by NIOSH for evaluating performance of respirator filters. If I were reviewing this paper for a journal I would reject it outright for failing to use this instrument correctly and for the correct purpose. If you were to perform a more thorough literature review you would find that the filters of surgical masks, face covering materials and respirators have been correctly tested using NIOSH-type methods. You would also find that the filters of surgical masks and face covering materials would not perform to the high level of performance you report here.

    1. On 2021-02-01 01:14:00, user gogettem wrote:

      Sam Moore is quoted in Telegraph as trying to justify extending lockdown post vaccination saying “The vaccines are not going be 100 per cent effective at stopping serious disease. So if you manage to get, say, 85 per cent of people to take it and it turns out to be 90 per cent effective, that's still 25 per cent of people who could die from it, which is a lot of people," But if the Case Fatality Rate for Covid is 1% overall and the most vulnerable will be vaccinated first, then the CFR for the 25% will be much less - let’s be pessimistic and say 0.1%. So only 0.025% could die from it. A lower rate than annual flu. Even at 1% the impact will only be 0.25%, still tiny. No way this can justify the carnage lockdown is inflicting on jobs, lives, people’s futures in the devastated private sector. How can we have any confidence in the response to Covid when we hear this kind of exaggerated fearmongering from those driving it?

    1. On 2021-02-03 22:16:44, user Eric Goodyer wrote:

      Whilst I can see how the authors support their claim that the efficacy is good after 21 days, I cannot see any data to support their later claim that it would continue to be effective for a further 9 weeks. I am not saying they are wrong but I cannot see any data here to support that claim

    1. On 2021-02-08 21:37:49, user Raymond Lam wrote:

      Note that another study on this topic, using the same database, was published recently: Rhee SJ, Lee H, Ahn YM. Serum Vitamin D Concentrations Are Associated With Depressive Symptoms in Men: The Sixth Korea National Health and Nutrition Examination Survey 2014. Front Psychiatry. 2020 Jul 30;11:756. PMID: 32848932

      Although our analysis methods were slightly different, we came to the same conclusions, so we will not be submitting this to a journal, but wanted to have it available to other researchers.

    1. On 2021-02-10 09:58:44, user ad4 wrote:

      Thank you for this thorough consideration of Knock 2020. I hope this can be influential in shifting our policy away from harmful lockdowns. I wonder if you had also considered that a) reported COVID-19 deaths are likely also to be an overestimate and b) that the IFR used in Knock (2020) is likely to be exaggerated? https://www.who.int/bulleti...

    1. On 2021-02-13 13:49:05, user Matt wrote:

      Very interesting paper. On the topic of Figure 3 in the paper and the relationship between "Relative predictive performance with UK (reported in figure 2) compared to PC distance with UK", have you looked at the performance using other measures than PC distance? The outgroup f3 statistic is often proposed as an alternative measure to Fst that is less sensitive to recent drift (for example as in - https://www.nature.com/arti... "https://www.nature.com/articles/srep42187.pdf)") and more sensitive to overall population divergence time, and Fst has a relationship to PC distance as you've established.

      For an example, I've quickly compared the stated relative predictive performance from your paper to an outgroup f3 set I had to hand: https://imgur.com/a/Vz5KLDG. Potentially there could be a closer and more linear relationship with the outgroup f3 statistic than PC distance in some ranges (the range of Han Chinese->European populations). Restricting to this range, R^2 is 0.96 against R^2 for PC Distance of 0.89. However the predictive performance in West African ancestry populations would seem to be outlying the prediction from an outgroup f3 statistic (with the inclusion of the West African population, R^2 drops for f3 statistic to 0.84, against improvement to 0.93 for PC distance). Alternatively the distribution suggests a potential exponential or power relationship between outgroup f3 statistic and relative predictive power. Of course this is just a proxy (from a different dataset!) and a comparison using your datasets directly might be more informative.

      It might be interesting to quantify if another measure that better reflected real population divergence times among present day people might be even more predictive of relative performance. Discrepancies between Fst and divergence time might be important for a naive baseline sense of portability of scores to Indigenous American populations in particular, where Fst seems to be particularly high relatively to estimated divergence times from European populations (e.g. divergence time from European populations is probably lower than Han Chinese, which is reflected by the outgroup f3 statistic, while Fst and PC distance is quite a bit higher, reflecting a strong population bottleneck).

    1. On 2021-02-14 13:10:19, user Rafael Green wrote:

      Hi,<br /> In the article it's written:<br /> "Summing up the excess mortality estimates across all countries in our dataset gives 2.1 million excess deaths. In contrast, summing up the official COVID-19 death counts gives only 1.3 million deaths, corresponding to the global undercount of 1.6 million deaths."<br /> but 2.1 - 1.3 = 0.8 (and not 1.6)<br /> am I missing something?<br /> Thanks,

    2. On 2021-03-17 14:25:05, user John Smith wrote:

      Hi<br /> I look forward to seeing what you come up with for Turkmenistan who have 'officially' recorded no cases of Covid but more than 1000 deaths have been unofficially noted including that of one senior government official and hundreds of medical staff.<br /> The country also has a rule implemented that all grave markers must be flat so as not to be seen from the air which indicates high mortality trying to be hidden.

    1. On 2021-02-15 22:13:34, user Robert van Loo wrote:

      in one of the figures the thetas for old and voc seem to be interchanged, theta voc put at 25 % and old at 22 %, should be reverse?

    1. On 2021-02-20 19:30:05, user Valerie DeLaune, LAc, CNMT wrote:

      There is an assumption in the paper: "Third, the likelihood of exposure to SARS-CoV-2 may be dependent on vaccination status to a greater extent in the real world than it is in the context of a randomized trial. That is, vaccinated individuals may feel more comfortable participating in social situations that pose a higher risk for infection, whereas this bias did not exist by definition in the context of the observer-blinded clinical trials." I would think the opposite would be true. Those who are most concerned about getting the vaccine as quickly as possible would be those already taking the most precautions both before and after vaccination i.e. wearing a mask, physical distancing, so also would be less likely to contract COVID in the first place.

    2. On 2021-02-21 01:30:58, user Offer wrote:

      Were the vaccinated tested at the same rate as the unvaccinated after enrollment date?<br /> As the tested unvaccinated population is larger than the tested vaccinated population following enrollment date is larger - we also don't know how frequently the unvaccinated were tested after enrollment date compared to the vaccinated population. (We only know they were tested 1 or more times, but not the actual test rate).

    1. On 2021-02-21 19:05:04, user hugh_osmond wrote:

      The assumptions regarding vaccine administration are already out of date, with the program well ahead of that assumed; this makes a very significant difference to outcomes. Take up rates amongst most vulnerable groups are also ahead of those assumed. Data suggest that just one dose reduces likelihood of hospitalisation by close to 100% (after two weeks) so assumption that 40% of deaths will still occur amongst those vaccinated appears ludicrously pessimistic. The study seems to take no account of those who are resistant/immune following infection (c. 15 million in UK), which will combine with number immune through vaccination to significantly reduce R. The fatality rate assumed amongst those infected in subsequent waves appears significantly higher than currently being experienced amongst the relevant age groups and certainly appears to take no account of improvements in treatments and newly available drugs. We now have better data on the effectiveness of the vaccines at preventing transmission, so the lower estimates in the study can be disregarded. The study also takes no account of the known seasonal effects, as seen last year after lockdown was released.

      The combination of all the above suggests that the likely outcomes will be approximately 1/10-1/20 of those calculated, making the conclusions of limited relevance.

    1. On 2021-02-25 15:12:43, user jubel wrote:

      "It was estimated that 80% (95% CI 65-92) of the patients that were infected with SARS-CoV-2 developed one or more long-term symptoms." – do these 80% refer only to infected people who were hospitalized, or are mild cases (no hospitalization) included? That would be very important to know.

    1. On 2021-02-27 22:24:10, user ABO FAN wrote:

      The latest version, version 3, seems to have the wrong reference number. For example, in the text, No. 11 is supposed to be a GTTC document that started in July 2020, but the corresponding MHLW page is from April 2020.<br /> Also, I do not think it is appropriate to use regression analysis to examine the effect of Emergency status. This is because even if an Emergency status is declared "after" the mobility and R(t) start to decrease, it will still be statistically significant. Figure 4 suggests that this is the case.

    1. On 2021-03-01 19:34:43, user Alexander Buell wrote:

      Dear authors,

      my students and I have studied your paper in our course at DTU (Quantitive analysis and modelling in protein science). We have reproduced some of your analysis and we have noticed something that we wanted to hear your views on. In the methods section you mention that "For the MAAP measurements, varying fractions of human plasma samples were added to a solution of the antigen of concentrations varying between 10 nM and 150 nM..." At the same time, if we look at the red and yellow binding curves in Figure 2 a) they cannot have been measured at a RBD concentration above 100 pM. Indeed, we were unable to fit the data with a concentration of RBD higher than 100 pM or so.<br /> This would mean that you have added 99% serum and 1% labeled protein at 10 nM. Is this the case? Is the instrument really sensitive enough to get good signal at sub-nM concentration?<br /> Thanks in advance for your clarification!<br /> Alexander Buell<br /> Professor of Protein Biophysics at DTU

    1. On 2021-03-02 13:11:50, user Syarranur Zaim wrote:

      Hello, I’ve read the article and your article on vaccination was very enlightening. If possible, can you provide the code for your mathematical model and also the source of datasets for my reference.

    1. On 2021-03-03 00:47:52, user Bin Jiang wrote:

      Dear readers,

      I am the corresponding author of this article. Please kindly notice this article has been published in the Environment International Journal. Please check out the article at the following two webpages:

      1. https://www.sciencedirect.c...

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

      Bin JIANG<br /> Ph.D., UIUC, USA<br /> Co-Chair, Research and Methods Track, Council of Educators in Landscape Architecture (USA)<br /> Founding Director, Virtual Reality Lab of Urban Environments and Human Health<br /> Associate Professor, Division of Landscape Architecture, Faculty of Architecture<br /> The University of Hong Kong, Hong Kong

    1. On 2021-03-07 14:54:48, user Dr Ish Midha wrote:

      Changing strains of SARS CoV -2 are pose big epidemiological and therapeutic challenge. Retinol has great impact on immunity and there is possible role of differences in retinol metabolism behind immune dysregulation that hallmarks severe Covid-19. During infections there is decreased mobilization of retinol stores as well as decreased conversion to active form ATRA.

      Since there exist correlation between low circulating retinol level and severity of infections especially measles and supplementation of under 5 children with retinol is associated with decreased infection related mortality and morbidity.<br /> Thus, it may be interesting to assess serum retinol levels in patients with severe Covid-19 and study the impact retinol supplementation on outcome.<br /> If found favourable, supplementation at community level may augment circulating retinol level in population aborting the peak of on going peak of pandemic.<br /> Retinol supplementation being rapid acting and easy intervention may be of use during peak of pandemic.

      https://onlinelibrary.wiley...

    1. On 2021-03-09 21:18:20, user Antonio Beltrão Schütz wrote:

      In this meta-analisis, the I2 is very rise to be accept and CI to recovery time is, also, very big to be accept. Therefore, the results of this meta-analisis are not confiable. Is possible that personal interpretation of Grade parameters has contribute to increase I2

    1. On 2021-03-15 13:44:09, user Daniel Mølager Christensen wrote:

      Congrats on an important and well-written paper. I'm particularly interested in your eTable 4d. It's an important analysis as it in my opinion seems unreasonable to compare hospitalized patients to a matched general population when investigating clinical sequalae. Seems like there would be conditioning on the future if COVID-19 hospitalization status was determined after time zero of follow-up. If that was not the case; how did you in that analysis handle patients that were hospitalized with COVID-19 after start of follow-up?

    1. On 2021-03-23 14:35:54, user Malcolm Semple wrote:

      Hi Folk, Your search strategy missed "Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study. BMJ 2020; 369 doi: https://doi.org/10.1136/bmj... (Published 22 May 2020). This paper describes predictors of mortality, and describes length of stay. Other papers missed include all global ISARIC reports in MedRxiv. These alone would give you an additional samples size of 300,000 cases.

    1. On 2021-03-24 00:01:30, user Elle wrote:

      I'm surprised the paper doesn't discuss weight as a factor. If you look at the last figure, you'll see that nearly all categories are overweight (both controls and long haulers), with many with a BMI >30 (obese).

    1. On 2021-03-24 05:27:18, user Eik Dybboe Bjerre wrote:

      Dear Authors, Please find a comment to your paper here. On page 31, line 608-609. You write:" None of the 78 published articles from the 31 trials were free from incomplete, 609 inconsistent, or selectively reported outcomes". As I can see in your review of trials, the trial I were responsible for (The FC Prostate Community trial) only fail on 1 criteria, the ability to present a "statistical analysis plan (SAP) ". As per ICH-GCP guidelines and the detailed guideline from Cochrane on assessing bias then a SAP should be in place before unblinded data was accessed. In the FC Prostate Community trial. The data collection was fully done by a web-based system and all data was keept logged and confined. As stated in publications of the results);the trial randomised 1.participant in June (15th)2015 an the SAP was published in Nov 2015 on clinicaltrials.gov. No participants had at that time complete the full 6 month intervention period. The data had not accessed from the database I only have detailed knowledge on the trial I was responsible and I acknowledge that it is difficult to evaluate if the SAP was in place in a timely matter. It is of course an important aspect but as it is not possible to assess without contacting authors/trialist I suggest the author group to consider the judge on this domain. Also if you look at the COMPare project, this do not evaluate the SAP. <br /> Best regards<br /> Eik Dybboe Bjerre

    1. On 2021-04-02 11:10:37, user Rudy Faelens wrote:

      It's obvious why, in the first wave, GP's didn't get higher infection rates. The physical exam was replaced by a pure telephonic anamnesis, diagnosis, and therapy. Pure horror. Unethical. <br /> Definitely safe for the GP, but at a severe sometimes lethal cost of false diagnoses and wrong therapies.

    1. On 2021-04-09 15:15:15, user Martin Bleichner wrote:

      We read this preprint in our journal club and have collected some comments I would like to share. <br /> Overall, we liked the approach and the straightforward message of the paper. <br /> Comments regarding the paradigm<br /> • Do you control somehow for word length? In the given example, “swift” is shorter than “swrfeq”. <br /> • Are word combinations repeated? I.e., do participants see ‘swift horse’ as well as ‘swrfeg horse’? In that case, participants may remember that they saw a similar item before. Hence, memory could play a role<br /> Controls and Patients<br /> • The ACE-R scores overlap between the two groups (range controls 83 – 100, range MCIR (64-99). Isn’t it then surprising that the results in figure 8 show such a good separation?<br /> Signal Analysis<br /> • The ERP subtraction was only done for the cap. Based on those results, it was concluded that it does not make a difference, and hence this approach was not used for the cEEGrid data. Since the segmentation of the ERP components depends on the data quality that differs between the two devices, this transfer might not be valid.<br /> • It is stated that the lexical retrieval effect is absent in the MCI-group, but in figure 3, the alpha rebound, for example, seems to be present in both groups to some degree. Furthermore, in figure 4, the main difference between the conditions (bottom TRF) is between 600 and 800 msec), i.e., exactly before the alpha rebound kicks in (around 800 msec figure 3). <br /> Comparison Cap cEEGrid<br /> For Figures 7 and 8, individual electrodes were used. It would be interesting to know how variable that was across subjects and how often the different electrodes were chosen. Furthermore, given that the results of the individualized electrodes and standard electrodes are comparable, it would be interesting to see the spectra of all channels. <br /> • The electrodes used for referencing and re-referencing are not completely clear to us. Unfortunately, different people use different names for the electrodes A layout-plot of the cEEGrids with indications of gnd, ref, etc. would be helpful.

      Figures<br /> • The figures are difficult to compare to each other (different units [% signal change for the cap, but t-values for the cEEGrid] in same-colored color bar, different time axis, etc.) E.g., in figure 6 Top TRF x-axis is from 0 to 1.4, Bottom TRFs from 0 to 1. Figures are differently scaled along the x-axis.<br /> • Please indicate in the figures the important time points (word off-set, onset, etc.)<br /> • Explain the ROC-curve in detail. What data goes in exactly? Should be added to the method section.

      On page 20 the is a space missing between “The” and “current”.

    1. On 2021-04-13 09:35:53, user Economy Decoded wrote:

      There are also claims that there have been cases of vaccine wastage and shortage in production funds. Massive exports have also been cited as one of the reasons for vaccine scarcity. India has exported 64 million doses of vaccines to 85 countries in the form of “gifts,” commercial agreements signed between the vaccine makers and the recipient nations, and under the Covax scheme, led by the World Health Organisation (WHO). The experts have pointed out that vaccine shortages have become a problem in some parts of India due to supply bottlenecks. They claimed that vaccine makers had oversold their capacities while taking orders from all over the world. The steadily rising cases of COVID-19 and the issues related to “vaccine scarcity” are significant challenges to making India free from COVID-19. There is an urgent need to plan and prioritize providing vaccinations to achieve the target of inoculating 400 million vaccine doses by July, as stated by the Ministry of Health and Family Affairs. This piece has also taken a look at the current shortage of vaccines India is facing: We Analysed Whether The COVID-19 Vaccine Shortfall Is Due To Exports Or High Domestic Consumption https://edtimes.in/we-analy...

    1. On 2021-04-23 10:30:29, user Paul-Olivier Dehaye wrote:

      Following criticism that the results announced do not match the data (for instance at<br /> https://pdehaye.medium.com/...<br /> or<br /> https://lasec.epfl.ch/peopl... )

      the main author seems to now (2021.04.21) acknowledge problems.

      From https://www.youtube.com/wat... :

      “What I also need to mention here is... We do see this time advantage [but] this is also early[?] days. We have not been able to dissect each and every secondary survey. This is still ongoing, but what is kind of confusing is that about 8 of those 43 specifically mentioned that they received the exposure notification before they were called up by manual contact tracing. So there is an accumulation of those people in those groups but then you still have a few [sic, given it’s 35/43!?] where manual contact tracing was first and then exposure notification was second. So the picture is still a bit blurry, but overall I think we are getting closer and we are doing additional analysis”.

      Note that, in accordance to the "Data/Code" section presented on medrxiv ("We are open to sharing individual participant data that underlie the results reported in this article, after de-identification upon reasonable requests to the corresponding author. Data requestors will need to sign a data access agreement."), the author of this comment has requested access to the data, but his request has not been acknowledged.

    1. On 2021-05-02 07:00:13, user Peter McIntyre wrote:

      This is an interesting and detailed analysis. One metric not provided is whether any identified infections arose from persons who had no travel history outside Australia. Such persons from NZ have not been required to quarantine on arrival in Australia so comparison of this metric would be helpful for policy assessment. A long and extensive list of potential interventions and/or policy changes to reduce or eliminate infections in MIQ is provided but their relative cost/ time to implement or difficulty not discussed. Vaccination is listed as only of value if transmission is eliminated - while this is the case if the target is no instances of infection in the context of a population unprotected by Immunization, once vulnerable populations are protected and risk of adverse outcomes from infection greatly reduced, this will have a major impact on the cost-effectiveness of a continued zero infection target and therefore on cost-effectiveness of listed interventions. <br /> Although this retrospective review is valuable, forward thinking is now needed to estimate future cost effectiveness

    1. 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.

    2. On 2021-07-19 03:08:20, user Miles Babbage wrote:

      OK, authors, there are problems here. You don't have the sample numbers to make the claims you do, and your data do not bear it.

      Interferon alpha effect you report here seems, from your own graphs, to be an artifact of changes between the first and the second dose, not between vaccine and lack of vaccine. I.e. you have a very minor shift up at t2, which makes the drop at t3 significant - but there is no actual effect between t1 and t3.

      For TNFa data, the R848 seems to be based on one single patient who had a strong increase at t2 that declined in t3. The only significant observation that holds is the one with candida, which then brings up the problem of sampling (test enough things, and you'll get a result somewhere). You need to state your statistics much more clearly.

      There is an possible trend here, but that trend a) needs to also be interpreted in the light of known post-viral effects on the innate immune system (such as e.g. those seen with post-influenza effects on bacterial resistance), and b) needs to stated as trend, not as a definite finding.

    1. On 2021-08-18 13:24:26, user Justin -O'Sullivan wrote:

      We have a povidone iodine product 0.58% (zero surfactant) which showed in vitro inactivation of SarsCoV2 published with the awareness of public health England. Can't understand why this strategy not used more. Would be interested to know what final volume of irrigation fluid was and was it standardised in protocol?

    1. On 2021-08-22 13:37:42, user ingokeck wrote:

      Dear authors,

      Thanks for publicizing this research. I notice that the main point of your article, figure 1C, is purely based on a model you derived, however I was not able to find the data that went into it, i.e. positive and negative cell cultures plotted against the Ct values for the samples in each group.

      I also notice that the probability for culture positivity you present in your model is vastly different from the source you quote for it (19. van Kampen et al. Duration and key determinants of infectious virus shedding in hospitalized patients with coronavirus disease-2019 (COVID-19)). To be more specific, the form of the curve you present for the non-vaccinated sample is vastly different from the one you cite, and your patients are vastly more infectious, with 50% positive cell culture probability already at 10^5.1 copies/ml, while van Kampen et al. writes 10^8.5 copies/ml. This corresponds to a difference in Ct-Values of 11! I cannot think of any sensible explanation for this difference.

      To me, it looks like there must be errors in your model. It would help a lot if you revise your model and publish a scatter plot of positive/negative cell cultures per Ct/Values for both groups, as well as positivity against days of sample taken after symptom onset.

    1. On 2021-07-07 10:03:14, user ateamrdr wrote:

      This is very interesting. What about the issue of transmission? If you do come into contact with the virus after having been previously infected, you might recover faster, but are you more likely to transmit if you have natural immunity vs vaccine immunity?

    2. On 2021-08-04 07:40:19, user Philippe Meisburger wrote:

      Question : should this finding be proven true, would it imply that someone who's got vaccinated (2 doses) before he/she ever got Covid 19 will benefit from the same level of protection convalescents have once they'll successfully fight a potential breakthrough infection ?

    1. On 2021-07-10 20:28:13, user Michel Prémont wrote:

      Interesting study but I have two questions.

      1. The study indicates "honey (1 gm/Kg/day) and Nigella sativa (80 mg/Kg/day)". The quantity of honey: is it 1 milligram/kg or 1 gram/kg ? 1 g/kg is a huge quantity (70 g/day for a 70 kg person.
      2. Under what form was the nigella ? Seed, oil, ground seeds....

      Thank you.

    1. On 2021-07-13 14:03:05, user Olga Mazlova wrote:

      “Patients admitted to hospital were eligible for the trial if they had clinically suspected or laboratory confirmed SARS-CoV-2 infection and no medical history that might, in the opinion of the attending clinician, put the patient at significant risk if they were to participate in the trial… Patients with known hypersensitivity to aspirin, a recent history of major bleeding, or currently receiving aspirin or another antiplatelet treatment were excluded.”<br /> So, after having excluded patients with initially extreme blood viscosity values, you left the wide middle part of the normal (Gaussian) curve of blood viscosity value distribution. It means that the trial participants probably had normal or somewhat low or, on the contrary, somewhat elevated – but underdiagnosed - blood viscosity. Why did you prescribe aspirin to the whole range (except extremes and control, of course) – and not only to those predisposed to elevated viscosity of blood?.. It is logical that the dose of aspirin should be increased proportionally to the excess of the blood viscosity values. Patients with initially normal blood viscosity may need only minimal (preventive) doses of aspirin or need none. Patients with low blood viscosity can be at risk of bleeding, so the substance should not be prescribed in such cases. There should be a personalized approach to the patients, with analyzing their blood tests and even tiny individual symptoms.

    1. On 2021-07-14 18:07:08, user Alexander Domnich wrote:

      It seems that this SRMA lacks of data elaboration/transformation procedure at all. For instance, in our paper (icluded in this review) it has been clearly stated that no false positive results were detected. It is therefore obvious that the specificity is 100%. The overall specificity with 95% CIs was reported "The overall sensitivity and specificity were therefore 78.7% (95% CI: 73.2%–83.3%) and 100% (95% CI: 94.7%–100%), respectively". The authors instead stated that the specificity estimate was not reported. I agree that we had not reported the 100% specificity for each test analyzed in order to save the space. To us, it was clear.<br /> You only needed to calculate the 95% CI from the available raw data.

    1. On 2021-07-23 12:04:42, user Harry Matthews wrote:

      Very fascinating work. I read it with great interest. I think some Supplementary Tables are missing, though. In the supplementary pdf I see up to Supplementary Table 3. But in the caption of main figure 5 there is reference to a Supplementary Table 6.

    1. On 2021-07-26 20:40:57, user Double_Up wrote:

      So far, so good. No infectious agents added to the SARS-2 shot, that's a plus, and if Phase 3 goes as well this Medicago-GSK shot may be safe enough for many I know to take, with so many possibly having SARS-2 already but no way to prove it since tests are garbage and antibody tests are about 50% accurate at best. Safety over hype. People I work with cannot take any SARS-2 shots due to medical conditions they have but they're being treated like cattle, horrible.

    1. On 2021-07-30 02:26:52, user tobydelamo wrote:

      Unfortunately, publishing this is going to increase vaccine hesitancy among those with prior covid illness. Not sure this is something that should have been studied...

    1. On 2021-07-30 14:06:06, user Luiz wrote:

      Even though, the vaccine protects the people from the very dangerous infection of covid -19, 15 people died in the vaccinated group and 14 died in th unvaccinated group?

    2. On 2021-08-01 16:33:46, user RationalSkeptic wrote:

      "Most participants who initially received placebo have now been immunized with BNT162b2, ending the placebo-controlled part of the study. "

      Um, doesn't the defeat the purpose here? Isn't this trial suppose to be ongoing until Feb 2023?

    3. On 2021-08-01 20:08:35, user Billium wrote:

      After six months monitoring >45,000 patients, there were 14 deaths in the placebo group and 15 deaths in the vaccine group. This not only demonstrates lack of efficacy in the most important endpoint, but highlights the extremely low fatality rate of Covid-19 in most people.

    4. On 2021-08-04 12:24:22, user Will Helm wrote:

      regarding the 29 deaths, this number seems under-rated to me. <br /> Guess what, here's a novelty, PEOPLE DO DIE, with or without Pfizer shot. <br /> 29 dead over a six months period for 44'000 people is certainly consistent with the US demographics which actually would yield 230 deaths for all causes. Something's not right here.<br /> Check mortality table

    5. On 2021-08-07 08:58:47, user john vegan wrote:

      • In the treatment group (N=21,926), 1 covid death
      • In the placebo group (N=21,921), 2 covid deaths

      So, one reading is that the treatment reduces 50% the deaths.<br /> Another reading is that the covid death rate in the placebo group is 0.00009 (2 / 21,921 = 0.00009), which is double than the treatment group, but Influenza and pneumonia deaths (15.2 / 100.000 = 0.000152 (1)) are 68,8% higher (0.000152 / 0.00009 = 1.688) than the covid deaths.

      So, should we have this treatment in our arsenal ? <br /> Yes.

      Should it be mandatory for everyone ?<br /> Considering the fact that the treatment for influenza is not mandatory, then this treatment should also not be mandatory.

      However, this is just my opinion which may be wrong, and if it is wrong I would like to hear why it is wrong

      1. https://www.cdc.gov/nchs/fa...
    1. On 2021-08-07 01:35:26, user MamaDoc2012 wrote:

      Older people are probably more likely to have symptomatic infections than younger people and therefore would get tested more frequently. Or am I misunderstanding known facts about COVID?

    1. On 2021-08-08 08:39:21, user Armand Sarkizians wrote:

      Hello,

      I am trying to replicate the code for some parts of this interesting paper.

      please can you let me know:

      Figure 'c', page 18m, found in the supplementary material. has the Y-scale been scaled, or that is simply plotting the Err.

    1. On 2021-11-30 09:20:59, user Glenn LGG wrote:

      Crucially the study also misses clear criteria for testing (including symptoms - if any) and the number of people subjected to PCR testing (under which regimen?) in each cohort.<br /> Arbitrary PCR testing does not imply any true event.

    1. On 2021-12-03 03:11:46, user virtualcappy wrote:

      How many of the reinfections in November were associated with omicron and what fraction of the total infections in November were from omicron vs other variants? Without that information the data does not seem to support the authors' conclusions that omicron is responsible for the increased risk of reinfection.

      It stands to reason, risk of reinfection will eventually increase with time, as naturally acquired immunity wanes. Further, it is to be expected that the risk of reinfection will increase with a variant that has variation in sequence. The question is how much does each of these factors contribute to an overall reinfection rate and this paper doesn't seem to do much to answer that.

      The authors also should suggest some plausible mechanism for the decrease in reinfection rate with time through the end of wave 3. Why would a mutated virus be less likely to reinfect? Seems more likely due to the dynamics of the immune response over time than genetic variation. Why then would the authors not consider that mechanism for the increase in reinfection rate in wave 4?

      Anyway, the New York Times picked up on this preprint already and the headline suggests that "prior infection is little defense". This is a far cry from "substantial and ongoing increase in the risk of reinfection" which could be from multiple factors and 2-3x increase in risk of reinfection is not "little defense". The authors should provide better context to head off alarmism.

    1. On 2020-04-23 06:40:04, user Sergey Morozov wrote:

      Very actual study that brings new information to fill in the gap on the histological features of lungs in those who died of COVID-19. The paper seems methodologically correct and based on multicentre (2 hospitals) study with histological assessment performed by 2 pathologists blinded to the results of each other. For better compliance GPP, I would suggest to add the information of immediate cause of death to the description of the study population; the results of analysis of concordance of the results of tissue evaluation performed by 2 pathologists, who were involved to the study. As pre-existing chronic obstructive pulmonary disorders are described in 3 patients, could these cases influence the results of pulmonary fibrosis assessment? It would be nice also, if the past-malignancies localizations also were described. The statements are logical and are based on the described results. This study is rather explorative by nature and larger studies are necessary to make an association between different aspects that characterize the disease flow (including co-morbid pathologies, medications used, laboratory deviations, etc) with histological features<br /> observed in pulmonary tissues much clear.<br /> I have no conflict of interests in the regard to this review.

      https://publons.com/review/...

    1. On 2021-12-06 18:46:16, user Griefer HD wrote:

      The authors claim at the beginning of the abstract AND at the beginning of the introduction: "Vaccines are the most powerful pharmaceutical tool to combat the COVID-19 pandemic." This appears to be a foregone conclusion.

      I am no a mathematician and thus cannot evaluate the elaborate models used in the study. But it is obvious that the models hinge on the R value and are very sensitive to even small changes in R. And the assumptions going into the R values used in the study are flawed: the authors estimate that 67-76% of the R value is caused by the non-vaccinated population. This is in stark contrast to the incidence in the vaccinated and non-vaccinated population ad reported by the RKI on a weekly basis. And even the RKI states that these reported incidence value probably under-report the incidence among the vaccinated population. Consequently, the authors report that "In order to obtain breakthrough infection rates in adolescents on the order of observed symptomatic breakthrough cases we assume a vaccine efficacy of s = 92% for adolescents." Which again is in stark contrast of RKI's own estimate for efficacy of 67% (weeks 42 to 44). And while the authors cite two studies showing that infected vaccinated individuals are equally like to transmit the disease that non-vaccinated (and only citing one study showing lower transmission), they state that "Considering these results, we assume a conservative transmission reduction of r = 10% for breakthrough infections" (for vaccinated individuals) - a baseless claim, to say the least. In addition, the authors also resort to slander: "As individuals that are not opposed to vaccination typically adhere to protection measures more consistently..." This goes against my own observation in my direct vicinity, where non-vaccinated are very aware of the risks they are taking and are often more careful. While vaccinated individuals seem to take greater risks, as for example shown by the large number of infections of vaccinated at '2G' Halloween parties. A more risk-aware behavior of the non-vaccinated would also explain the seemingly increased infection rate among vaccinated individuals in the UK (for example in the age bracket 40-49, as reported by UKHSA) compared to non-vaccinated peers.

      And finally, I would like to point out that the state of Berlin introduced 'selective NPIs' (read: 'lockdown' of the non-vaccinated) and the Mayor of Berlin advocates introducing such measures on a nationwide basis. At the same time, the state of Berlin is the main funding body for the host University (Humboldt University) of the majority of authors. The authors fail to acknowledge this conflict of interest.

    1. On 2021-12-12 13:59:45, user Andrew Hayward wrote:

      “This research is part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref MC_PC_20029)”

    1. On 2021-12-14 14:10:03, user Ergellegre wrote:

      We would all benefit for this proposition to be widely considered for replacing the current 'crude' model adopted in the UK to assess risk level. The fundamentals are well established, irrefutably so.

    1. On 2021-12-16 04:37:21, user Jordan Atchison wrote:

      A little concerned about the comparison to the NNT for ASA of 333. That value of 333 is calculated over a span of 6.6 years, but it's unclear over what time period the author's NNE applies to. Is it 1 prevented transmission per day, 1 prevented transmission per average length of incubation period, or some other time period?