On 2020-04-16 20:15:01, user alg3257 wrote:

Will an N-95 mask be safe if it is used once and then left to sit for a week then used again?

On 2020-04-16 23:50:15, user Brian T wrote:

MISLEADING UV DOSE! DO NOT FOLLOW THIS 5 uW/cm2 dosing!

The UVA/B meter used in this study “General UVAB 137 digital light meter (General Tools and Instruments New York, NY)” (look up General Tools UV513AB Digital UVA/UVB Meter on Amazon) advertises measurement from 280-400nm, barely overlapping the wavelengths used in this study, 260 – 285 nm. Furthermore, this meter does not report on the energy at a given wavelength. Its possible that this study is grossly underreporting the dose of UV needed because their meter doesn’t read many of the wavelegths used.

Previous research on SARS and MERS used 254 nm and noted much, much higher energy needed to kill these corona viruses vs this study's reported 5 uW/cm2):

• MERS is inactivated at 90 uW/cm2 x 60 mins[1], dose of 0.324 J/cm2<br /> • SARS is inactivated at 4016 uW/cm2 x 6 min[2] , dose of 1.446 J/cm2

[1] http://www.diniesturkiye.co...

[2] https://www.sciencedirect.c...

On 2020-04-17 20:25:02, user Mortal Wombat wrote:

Hold on, they made no adjustment for self-selection of symptomatic people in their study?

Researchers, at least please tell us the number of people shown the Facebook ad so we can have some sense of the potential for self-selection -- i.e. how many saw it but chose not to participate.

This seems problematic given the population demographic adjustments that were necessary. The researchers say that white women were heavily oversampled while hispanics and Asians were heavily undersampled, and that population adjustments led them to adjust the observed prevalence of 1.5% up to a population-weighted prevalence of 2.81%.

It would appear highly likely that the relatively affluent population (white women) would have the interest and capacity to be a roughly random sampling -- people with no prior symptoms just interested in knowing. Whereas for lower-income populations, there may be less ability to simply participate out of interest, and may have been a higher self-selection drive of the previously symptomatic to get themselves tested.

Thus I find the upward adjustment in the numbers quite suspect. To come up with an overall result that's _higher_ than the raw outcome of the study when you know that people who've been sick will be the most motivated to get themselves tested just seems perverse.

Did the study not even ask people whether they had been sick over the past couple months? Why not? That at least could've given some sense of whether self-selection was biasing results in the samples.

On 2020-04-18 02:01:37, user mendel wrote:

First, he picked the county that had the earliest cases in California and had the outbreak the first, ensuring that the population would be undertested. This means that it's likely that every other county in California has fewer unregistered infections than Santa Clara.

Second, study participants were people who responded to a facebook ad. This is a self-selected sample, and this property completely kills the usefulness of the study all by itself. This is a beginner's error! People who think they had Covid-19 and didn't get tested or know someone who did are much more likely to respond to such an ad than people who did not. (By comparison, the Gangelt study contacted 600 carefully chosen households per mail, and 400 responded. Still somewhat self-selected, but not as badly.)

Third, age is the one most common predictor of mortality. He did not weigh the results by age, and old people are underrepresented in the study. Anything he says about mortality is completely useless if we don't know how prevalent the infection was in the older population. (In Germany, cases show that the prevalence among tested older people was low initially and took a few weeks to rise.)

Fourth, instead he weighs prevalence by zip code--why? This exacerbates statistical variations, since there were only 50 positive results, and Santa Clara has ~60 zip codes. If you have a positive result fall on a populous zip code by chance where only a few participants participated, then the numbers are skewed up. They must have seen this happen because their estimated prevalence is almost twice as high as the raw prevalence.

Fifth, the specificity of the test is "99.5% (95 CI 98.3-99.9%)". This means that theoretically, if the specificity was 98.5%, all of the 50 positive results could be false positives, and nobody in the sample would have had any Covid-19. This means the result is not statistically significant even if the sample had been well chosen (which it wasn't). (It's not even significant at the 90% level.)

Sixth, they used a notoriously inaccurate "lateral flow assay" instead of an ELISA test and did not validate their positive samples (only 50) with a more sensitive test -- why not?

Seventh, The Covid-19-antibody test can create false positives if it cross-reacts with other human coronavirus antibodies, i.e. if you test the samples of people who had a cold, your speficity will suffer. Therefore, a manufacturer could a) test blood donor samples, they not allowed to give blood if they have been sick shortly before; b) test samples taken in the summer when people are less likely to have colds than in March.

To state the previous three points this in another way, a large number of positive results (a third if the specificy is actually 99.5%, but probably more than that) are fake, and depending on which zip codes they randomly fall in, they could considerably skew the results.

On 2020-04-18 06:35:20, user DomesticEnemy wrote:

About 5 weeks ago based on a study of the Diamond Princess cruise ship I estimated the death rate to be around 0.23% or so. Welcome.

On 2020-04-18 18:39:18, user jj wrote:

Where is the discussion of selection bias? You invite folks to get tested by advertising on Facebook... I think there will be an over-representation of folks who fear they have COVID-19 based on their recent interactions in places with or around COVID-19 cases.

Without randomization to eliminate self-selection bias, the authors should not be making any far-reaching conclusions that are now being picked up and reported by the media without providing proper interpretation.

I think this publication should be rejected for not doing this study properly.. and then seeking publicity!

On 2020-05-21 01:21:24, user Morat Gurgeh wrote:

This whole affair has been entirely unedifying. I do not know the truth of the allegations published in Buzzfeed, but then neither does anyone else commenting here, on Twitter and elsewhere. There are a lot of people, including senior academics, who should be ashamed of their behaviour.

Turning to the central controversy, it is entirely possible and indeed likely for different populations to have different IFRs. The fact that the IFR in NYC appears to be significantly higher than reported here does not “debunk” this work and indeed is not even inconsistent with these results.

NYC was hit early by the virus, when protocols for managing infected patients were still developing and mistakes were made. In addition, those most susceptible to COVID-19 (e.g. the old) were much less likely to be voluntarily shielding. Catastrophic errors have been made in many countries in care homes. So the likelihood of those over 80 being infected was likely much higher than in this study. Given the incredibly steep fatality gradient with age, this alone could explain the IFR differences.

I think the main take home message of this paper is this: the lives of healthy, working age people should return largely to normal while those groups identified at elevated risk should continue to shield. Amongst the young, we should treat infection by SARS-CoV-2 as more akin to measles than Ebola.

We need most of the healthy, young population to develop what immunity they can to this virus so that we can properly protect those most susceptible.

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?

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.

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.

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.

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!

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.

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.

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/

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.

On 2020-04-19 20:45:06, user John Smith wrote:

people who thought they have been exposed to covid-19 would want to get a free test. Others who thought they don't have the virus and have been in lockdown for a month would not go out of the house for the free test. This means you're selecting only the people who have been exposed and invalidates the study.

On 2020-05-20 17:22:05, user Omari wrote:

Anybody know the false positive rate of the test used?

On 2020-04-23 15:59:44, user gmshedd wrote:

If we take the observed fatalities (by residence) in the Bronx (2258 as of 4-22) and Queens (3432), and apply the suggested infection fatality rates of 0.12% to 0.20%, we can infer that between 80% and 133% of Bronx residents have already been infected, and that between 76% and 127% of Queens residents have also been infected. Therefore, Bronx and Queens residents have achieved herd immunity, so they can re-open everything immediately. This is such great news! Oh, but you say, these populations aren't similar. OK, so I'll use Nassau County (Long Island)--median income $111k vs $116k in Santa Clara County. 1431 Nassau County residents have died, from which we would infer that between 53% and 88% of the 1,356,924 county residents have been infected. My point is that the suggested infection fatality rates don't pass the eye test, and, since they are derived from the infection rates that are at the center of the controversy, it would seem that the publication's Santa Clara County infection rates are higher than seems reasonable for the NYC area--unless California COVID-19 has a significantly lower infection fatality rate than New York COVID-19.

On 2020-04-24 05:58:27, user JM V wrote:

With 80 (1.7%) people dead in Castiglione d'Adda (Caveats: Old/Smoking/Unlucky/Collapse of Health care system/Some would have died anyway) this was already extremely unlikely. Now, with NYC 0.22% excess deaths and 21.2% of shoppers having antibodies, an IFR of 0.8% - 1.2% appears plausible.

On 2020-04-18 12:30:31, user Jack Prior wrote:

Is it possible that people are seeking treatment for flu-like symptoms at a higher rate than normal due to anxiety over consequences of severe Covid-19 infections?

On 2021-12-01 14:08:31, user watcher wrote:

The authors mention that vaccination efficacy might be affected by underreporting of mild symptomps. This raises another issue, which was not accounted for in the model. Asymptomatic infected individuals may still transmit the virus, but due to the lack of symptoms will not alter their behavior. It should be assumed that the more prominent symptoms are, the more individuals will reduce their contacts, to protect themselves and others. As unvaccinated infections generally result in more symptoms, these individuals will likely reduce their contacts naturally. Symptoms alter the behavior of individuals and thus transmissions, which is not accounted for in the model, but could change the outcome quite a bit.

On 2021-12-07 17:04:15, user LizzyJ wrote:

''Pandemic modeling'' is quickly becoming the astrology of mathematics & physics.

Want to get a lot of media attention and citations? Simply code up a little model and write up a paper about your unscientific predictions. As in all models, the chosen values for the parameters in this simulation are naive assumptions. There is no sufficiently high-quality or complete data on vaccine status and mode of infection (e.g. infected by a vaccinated or unvaccinated person) currently being reported by hospitals or health departments.

Pandemic modeling is an abstract and theoretical mathematical exercise with very high bias and uncertainty based on the underlying assumptions, factors included or excluded, incomplete input data, very poor data quality with big non-random gaps in the data, etc. It should not guide public policy. Only clinical studies and real-world medical data should guide policy.

On 2023-03-08 12:30:58, user Carlos Oliveira wrote:

This study has been published on Frontiers in Public Health: <br /> Routine saliva testing for SARS-CoV-2 in children: Methods for partnering with community childcare centers<br /> Frontiers in Public Health, 11, 1003158 - February 2023<br /> https://doi.org/10.3389/fpu...

On 2022-01-12 17:15:59, user Rick Sheridan wrote:

This was a laudable effort and I congratulate all of the authors and the study’s primary driver for pushing this through. Am in agreement that insight from results is likely limited by the maximum dosage as stipulated by the NNHPD guidelines. I enclose here daily quantitative PCR results from a high-quality PCR vendor during my own Jan '22 experience with high-dose hesperidin in context of a documented SC2 infection during the omicron wave.

https://emskephyto.medium.c...

As can be seen in the data log made available, subject (100 kg male) was taking multiple grams at a dose, often successively within hours of each other. Critically for DDI, no other pharmaceutical drugs were taken concurrently. For independent auditing purposes, will be happy to disclose the PCR vendor, the collector, and the hesperidin nutritional supplement brand to any relevant reachout.

With what little one has to go on from the posted results, I would offer a model that during an active infection, the minimum possible Ct value is correlated to the sustained serum hesperetin glucuronide level during the 0.5 - 2 days prior to the nasopharyngeal sampling from which Ct is determined.

Addressing the standing issue that viral load has often peaked prior to trial enrollment, this challenge remains tolerable in context of a clinical trial because one can still show an accelerated viral load reduction in the experimental group as compared with control, sufficient to demonstrate the mechanism.

Rick Sheridan<br /> EMSKE Phytochem<br /> 11-Jan 2022

On 2022-01-12 20:21:08, user Mike B wrote:

Do we know it's VOC? Dogs detect Parkinson's using same technique, perhaps from minor expression of misfolded protein. Discrimination and scent memory by dogs is much more complex than we know. <br /> Implies we could build a molecular filter to mimic dog's nose. That however, is elusive.<br /> Fantastic study. Much ??? to working dogs, especially Belgian Malinois!

On 2022-01-13 12:47:09, user kdrl nakle wrote:

On Christmass Eve Dec 24, 2021 there was 948 in ICUs in California. Yesterday CA DoH posted 1903 in ICUs. What happened? Most of that in Southern California, imagine. This flies in the face of this paper. This paper lacks multivariate analysis as most infected by Omicron were double vaccinated and most infected by Delta were unvaccinated. That way these people make sensational paper without serious research. <br /> We had 2730 deaths yesterday in the US (NYT), now the 7-day-avg is approaching the height of Delta wave, currently 1825. The peak of Delta wave was 2087 on Sep 20. I actually expect Omicron will crash this Delta record shorty as it will crash ICU overall record in no more than two days from today. Currently 24,711 in ICUs on January 11, 2022.<br /> This sad attempt to make Omicron look mild will actually cost many lives for all the people that took masks off and are believing this "mild Omicron" propaganda. Wait until you see.

On 2022-01-14 20:11:27, user Boback wrote:

Why are the event number exactly the same between vaccine arms in Table 1 in multiple places. Calculated point estimates and CI also the same for those. Did you have a coding error with events? I wouldn't expect so many exact event counts to overlap.

On 2022-01-16 16:21:39, user Titan28 wrote:

I don't see ivermectin on the list of suspect drugs. Was it not tested?

On 2022-01-18 13:58:07, user William Henry Talbot Walker wrote:

Were all of the vaccinated participants studied for pre-existing cellular response or globulin levels before the vaccines were administered?

On 2022-01-21 05:16:30, user Victor Schoenbach wrote:

I found this article both valuable and important, but I do not understand the last two sentences ("Despite the small numbers of individuals included in this study, the findings are uniquely valuable because of the early detection of Omicron infection in frequent workplace Covid-19 testing to prevent spread. In real-world antigen testing, the limit of detection was substantially lower than manufacturers have reported to the FDA based on laboratory validation.")

The first of these sentences is confusingly worded; copy editing would help.<br /> The second sentence refers to a lower limit of detection. Perhaps I do not understand the technical meaning of that term, but I would have thought that meant that the real-world sensitivity of the antigen test was higher (virus detected at a lower level), whereas the article suggests the opposite.

On 2022-01-21 14:18:29, user Rosanna wrote:

It will be interesting to know which organs are mostly affected by these autoantibodies. No mention about it is done in the manuscript. Do the authors have this information? Are lungs more affected in patients that suffered a critical COVID-19? <br /> Also, do the authors have data in male patients?

Rosanna Paciucci, Ph.D. Faculty Attending, Vall d'Hebron University Hospital, Barcelona, Spain

On 2022-01-21 21:47:18, user Brian Roberts wrote:

Thanks for this most up-to-date study. The only "sensitivity" number that matters to the clinician is how the BinaxNOW compared to the PCR for the entire group. That number was not provided. Or how they compare in symptomatic vs. asymptomatic groups, since that information is known to the clinician. How they compare in groups defined by the PCR Ct counts is all but useless, since it is unknown information at the bedside.

On 2022-01-22 13:19:19, user Torsten Selle wrote:

Is it possible to extend the measurement series to smaller aerosols (5-2µm). I am asking in regards to aerosols that cannot be stopped by ffp2 or n95 masks.

On 2022-01-24 01:15:03, user Fergal Daly wrote:

The paper uses linear regression on a non-linear variable (cases/100k). Does it apply directly to cases/100k or is it against log(cases) which should be somewhat linear?

On 2022-01-28 20:32:03, user Ranya Srour wrote:

The article is a good baseline for future studies involving suicidal ideation and bar graphs are very clear and easy to interpret. By extension, the study addresses the question: is it necessary to wait for sobriety before defining a patient as suicidal?

Maybe discuss this question directly in the discussion more; since it is the main question it may be valuable to expand on the in discussion.

On 2022-01-28 20:32:54, user Dhuha Al-Rasool wrote:

Very interesting article! It would be interesting to see the impact of THC on the SI results considering that it does not wear off as quickly as alcohol.

• D and T

On 2021-10-15 21:36:23, user Mary V wrote:

Please add results for the unvaccinated individuals who recovered from Covid prior to Feb 28, 2021. How does their risk of symptomatic infection during the delta uptick compare with the individuals who were fully vaccinated by Feb 28, 2021 and didn't have a positive Covid test before 6/1/21? Did one shot of the Pfizer vaccine improve the immunity of that group during the delta uptick? Other studies have shown a very strong immunity for those who have recovered from Covid symptoms. The data in this paper only supports a vaccine recommendation for people who've had asymptomatic cases of Covid. Hence the data for those who've had symptomatic cases needs to be added or your conclusion about the vaccine recommendation should be qualified.

On 2022-03-22 12:17:46, user panos101 wrote:

What's the status of this article. Has it been published?

On 2022-01-29 14:26:08, user Alberto wrote:

Thank you for this study. It's important to have this kind of study in a country like Greece where mortality has been very high in 2021 (compared to other European and worldwide countries and compared to itself in 2020) because we can appreciate the difference between the reality observed and the projected modeling based on the data that is available about vaccination status. The resulting model, which is incompatible with the reality observed worldwide, is a good measurement of the quality of the data available. I hope this can be looked at in more <br /> detail by more people thanks to this study.

On 2022-01-30 23:47:58, user HereHere wrote:

I'm a registered massage therapist in Ontario. We received no clear advice from our regulatory college about ventilation. I was waiting and waiting and waiting. They were very slow with recommending N95s and KN95s, and to my knowledge, still have not acknowledged that surface transmission is exceedingly rare. I don't know how they coordinate with Public Health Ontario, but you would think that, given we work in close contact with patients in typically small, poorly ventilated rooms, ventilation would have been given greater consideration.

On 2022-02-01 14:15:41, user Richard Reynolds wrote:

There is another reason, the most likely one, why immune cell aggregates were not found in the meninges in this study, which is due to the nature of the tissue used. This study used formalin fixed paraffin embedded tissues which are suboptimal for studying the delicate meningeal compartment. Cells are lost from the meninges at every stage of the embedding, cutting and section mounting stages, when compared to using snap frozen blocks. When you float FFPE sections on to a water bath before mounting them on slides you can actually see with the naked eye parts of the meninges floating away from the sections. Presumably this would also results in losing various components of the meninges, including immune cells. We dont find nearly as many immune cell aggregates in the meninges when we use FFPE sections and in order to see them in the FFPE sections we needed to change out protocols substantially to much milder procedures in order to better preserve the cellular components of the meninges.

On 2022-02-01 21:24:21, user Dylan wrote:

Would have been nice to see loss of taste and smell included given their pediatric prevalence per https://link.springer.com/a...

On 2021-10-18 16:39:30, user Kim Noble wrote:

Well-thought, provoking article. Congratulations to Dr. Gõni and her team.

On 2022-02-02 07:40:56, user Gerald Zavorsky, PhD, FACSM wrote:

I have no idea why studies like these are coming out all of a sudden. Politics should not be a part of science and I think these diversity studies and social injustice studies actually do a disservice to the minority groups in the U.S. There are several studies that show racial differences in lung function. I, for one, have published one of these studies (10.1186/s12890-021-01591-7) that demonstrate differences lung function after correcting for several factors. In order for these authors to truly test the hypothesis that reference equations should not be adjusted for race (i.e., no adjustment if you are African American, Hispanic, or Asians), then you would need to perform a Kappa Statistic and ROC analysis in both whites and the other ethnic groups. In addition, in each race/ethnic group, you would need a substantial proportion of individuals with CONFIRMED DISEASE. That is, confirmed disease via CT imaging or via strict criteria (i.e., GOLD criteria). Using a subjective assessment of breathlessness is not right, in my opinion. The authors have not used the objective criteria of FEV1/FVC ratio for definitive obstruction; they only used FEV1 and FVC separately, and according to ATS/ERS guidelines, it is strictly the FEV1/FVC ratio that confirms obstruction. Then, using the LLN criteria for FEV1/FVC, then one should assess the sensitivity, specificity, positive predictive value, etc., comparing reference equations for different races in those with confirmed disease and those without the disease OR at least confirmed obstructive pattern (FEV1/FVC < LLN). That is, what is the sensitivity in detecting lung disease (or confirmed obstruction via the FEV1/FVC ratio) in blacks when using the GLI reference equation for blacks? What are the false negatives in blacks when using the black reference equation? THEN compare these results against the same group using the prediction equations for whites. This is really the only way. Subjective scores of breathlessness do not confirm the disease. All that their tables and figures show that if you use a white reference equation in blacks you can falsely over-diagnose lung disease in blacks. In this case, 870-890 blacks had falsely low FEV1 or FVC when using the white reference equation. The authors data actually go against their conclusions. First, 9% of whites were below the LLN for FEV1 when the white equation was used. Similarly, 9% of blacks were < LLN for FEV1 when the black equation was used. To me, this shows that the equations for blacks correctly identify low FEV1 values in blacks, and the reference equations in whites correctly identify low FEV1 in whites. The proportions are the same! As well, their Figure 1b, the mortality for Blacks that had an FEV1 that was normal when using black reference equation (orange line) was the SAME as when using the white reference equation for whites (blue line). This shows that the reference equations for whites used on whites are just as appropriate as the reference equation for blacks used on blacks. Indeed, if anything, Figure 1b demonstrates that using the white reference equation in blacks underscored mortality in blacks by 5% (i.e., 5% of the deaths are missed in blacks when using white reference equations). Thus, in conclusion, the authors have this all wrong and have misinterpreted the data. We should be correcting for race.

On 2021-10-18 18:05:16, user Francis Bascelli wrote:

How can I access this data on UK Biobank? The data/code section says the data can be accessed though UK Biobank, but I am having trouble finding it.

On 2022-02-02 11:42:04, user Philip Ashton wrote:

Hello,

Thanks for posting this really fascinating paper, so much food for thought!

We looked at this in our journal club today, and one practical issue that came up is that we would like to know over what period and what season sampling was done at each site and how this relates to typhoid season at each site. Because Typhi is often seasonal and this could influence the results.

Thanks again!

Phil

On 2022-02-03 03:23:31, user Chris wrote:

Do we know why the vaccines cause myocarditis yet?

On 2022-02-03 08:04:13, user dgatwood wrote:

Any chance a future update to this article could include the VE data against hospitalization *prior* to the third dose of mRNA-1273 (for comparison purposes)? Even a citation would help.

On 2022-02-03 21:59:47, user Suzy Huijghebaert wrote:

Line 148: "susceptibility of potential secondary cases was highest among the unvaccinated"<br /> Yet, some % in Table 1 striked me, and Table 9 does not really confirm that in the OR values. So I checked a few numbers, as when it comes to transmission of the omicron it is not so much sex or age that will matter, but - in real life - rather the total number of people you are in contact with, in view of the speed of transmission and the fact kids are affected by this mutants as well. Neither does it matter - economic-wise- whether the secondary case is vaccinated or not (yet, I agree an interesting aspect to study).So, how did you define the "potential cases"? The potential cases (per group of index cases) were apparently much lower in the vaccinated group than in the unvaccinated, and just proportionally correcting for that parameter, suggests that the rough highest attack rate -t as would be in real life - would have occurred with both BA1 and BA2 among the fully vaccinated (62-63%), provided they would have been in contact with as many potential cases in their households as the unvaccinated. Please clarify what induces the divergence/where the divergence with the outcomes arise from. Another question: what was the proportion of omicron cases among the people having already received vaccine, yet not considered fully vaccinated and now counted among the unvaccinated in your unvaccinated sample? Already thank you for clarifying.

On 2021-10-20 16:54:26, user helgarhein wrote:

You reported a surprising result: in the group of 51health care workers who were replete (above 75 nmol/l) only 2 took supplements. I would not have expected so many people (49) to have replete 25(OH)D levels in indoor workers in Birmingham (52ºN) in May, without supplements. However we had in the UK an unusually sunny and pleasant spring in 2020. I guess many people used their free time to go outdoors, because it was so sunny and dry, cinemas, restaurants etc were closed and socialising happened mostly outdoors. I presume that the unusual finding of so many people with excellent 25(OH)D levels could be explained by having acquired those levels in the week or two before May 2020. But maybe a longer timespan with good vitamin D supply is needed to make really all immune actions work optimally?? Could this have skewed the curve to make it look U-shaped?<br /> But, as pointed out by Dr. Gareth Davies, the most important observation was missing: the out comes of those infected, the ICU admissions and the mortality rate.<br /> Helga Rhein, ?retired GP, Edinburgh

On 2022-02-07 21:44:51, user Isaac Tian wrote:

Hello, we're the authors of citation #20. We had a few suggestions after reading your work.

1. A practical deployment of the network would ideally use some other silhouette imaging method such as a CT scan or an RGB photo like you suggested in the Study Limitations section. The sentences that referenced our work didn't mention our attempt to estimate total and regional body fat using a 2D RGB camera image.<br /> Our study data, composed of 2D coronal and sagittal images coupled with 2-fold DXA composition measurements, may be relevant to validating your method on non-MRI inputs. Additionally, I believe our parallel effort in estimating body composition from 2D silhouettes should be cited and compared against. We also did estimate compartmental body fat as arm, leg, and visceral fat. Our initial model was not very flexible in pose due to the smaller training set available at the time, but we have since corrected for this.

2. I recomputed our errors as MAEs to directly compare against your results, and assuming an adipose tissue density of 900 g / L, this came out to 121 g and 151 g for males and females, respectively. This is about 3-4x less than the magnitude of error reported in your draft. An analysis on the RMSE may be appropriate as large scale data may inflate R2.

A collaboration may be appropriate once this draft has passed peer review in which your network is used as the pre-trained initialization to fine-tune on silhouettes segmented from another imaging source, such as our Shape Up! dataset which was stratified by age and BMI. This also addresses the bias concern you mentioned as your MRI dataset has an average age of 65.

Thanks for sharing your work!

On 2022-02-15 09:24:31, user Shelly L Miller wrote:

This paper has been peer-reviewed and published here: https://www.nature.com/arti...

On 2022-02-25 06:26:07, user Abhishek Mallela wrote:

As of February 24, 2022, Figure 5 in the published version of this manuscript is missing axis labels. Please refer to the preprint version of Figure 4 for the axis labels.

On 2022-04-10 09:05:20, user dyctiostelium wrote:

The manuscript describes an analysis made from a database of suspected and confirmed COVID cases, with information about whether they had received any COVID vaccine at least 14 days prior and in the case they were, which one of 7 different vaccines.

It is stated that "vaccination status, date and specific vaccine<br /> product was collected from evaluated persons as part of epidemiological follow-up of suspected COVID-19 cases" and table S1 includes a row titled "Follow-up - person days", but the design does not seem to involve any clinical follow-up, given that the persons had either been vaccinated or not at the time their data was included in the database.

Could the authors clarify what is the source of the numbers in the row "follow-up" of figure S1?<br /> Of note, when the person-day is divided by the n of each column it gives a number of 114 days for the unvaccinated and around 200 days for the 7 different vaccines.

On 2022-04-15 17:26:22, user Young Juhn wrote:

A version of this article has been accepted for publication in the Journal of the American Medical Informatics Association (JAMIA) published by Oxford University Press. A link will be forthcoming.

On 2021-11-06 19:24:57, user Eleutherodactylus Sciagraphus wrote:

: This preprint includes data from human subjects that are under ethical scrutiny. The<br /> majority of patients enrolled were not informed nor agreed on participating in the study. The Brazilian National Comission forResearch Ethics (CONEP) has been bypassed, documents have been tampered, and the situation is now under investigation.

References supporting this statement (both in English and in Portuguese):<br /> https://brazilian.report/li...<br /> https://www.emergency-live....<br /> https://www.dire.it/14-10-2...<br /> https://www.matinaljornalis...<br /> https://g1.globo.com/rs/rio...

On 2022-06-07 10:39:46, user M. M. Welling wrote:

For the 2 patients, both were vaccinated before the PET scans. The control patents were from 2019 thus uninfected and not vaccinated for COVID-19. Neuroinflammation can be initiated by the vaccination after liposomal transfer of the mRNA through the BBB. This needs to be discussed as well.issue

On 2022-07-11 07:19:17, user Thijs Blok wrote:

Question: <br /> - A PCR can stay positive for weeks after infection, is that taking in account?<br /> - Is a throat swap executed with the selftests ?

On 2021-11-23 20:58:11, user jackbutler5555 wrote:

Did the study include all samples from the formerly infected or just those hardy and viable enough for the study?

On 2020-05-26 06:00:43, user Hooman Noorchashm wrote:

Cyclosporine could b the critical pharmacological block for arresting progression of COVID-19 disease to critical illness. Congratulations to our Spanish colleagues. https://www.drugwatch.com/n...

On 2020-05-26 06:02:08, user Hooman Noorchashm wrote:

An open Letter to the editor-in-chief at JAMA on the potential efficacy of Cyclosporine in COVID-19 disease: https://medium.com/@noorcha...

On 2020-05-26 20:56:26, user Sinai Immunol Review Project wrote:

Main Findings<br /> In this study, Bouadma and authors longitudinally profiled multiple immune parameters of a fatal case of Covid-19 that quickly developed multiorgan failure. An 80-year old male patient presented with fever and diarrhea that developed into multiorgan failure and hemoptysis over the course of 24 days that resulted in death. During this time, he was treated with broad-spectrum antibacterial agents, Remdesivir, and interferon beta-1a. Peripheral naive CD4+ and CD8+ T cells remained stable throughout, but effector memory T cells continually increased. Exhausted and senescent CD4+ and CD8+ T cells, and gamma delta T cells increased following day 14. Activated and exhausted B cells peaked on day 20. After day 16, NK cells and monocytes generally declined possibly due to lung trafficking. These fluctuations in immune populations were accompanied by induction of pro-inflammatory cytokines and Th1/Th2 factors that increased on day 14. Although some cytokines decreased following day 14, cytokines associated with T cell activation, exhaustion, and apoptosis continued to increase.

Limitations<br /> It is difficult to draw broad conclusions from one patient and this longitudinal study did not start at the onset of infection and symptoms. Furthermore, these observations were done on the peripheral blood without complementary analysis of the lung where they suspect NK cells and monocytes have trafficked to.

Significance<br /> This shows that immune cells proportion, functional state, and soluble factors fluctuate throughout disease progression. This is a broad overview of potential blood biomarkers that can be used to assess progression and severity.

Credit<br /> Reviewed by Dan Fu Ruan, Evan Cody and Venu Pothula as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai

On 2020-05-27 00:52:35, user Jayanti Prasad wrote:

Please post your comments, suggestions & feeback.

On 2020-05-28 15:04:54, user Judith Levine wrote:

Small point — probably just a typo, but the use of “country” in the abstract should be corrected to “county”.

On 2020-05-28 16:49:42, user Ruth Cleary wrote:

Wondering whether Metformin Instant Release and Extended release readings were different from each other and, if so, by how much.

On 2020-05-28 22:26:40, user Andrew Cohen wrote:

NOTE: On the Supplementary Material page, the file "Supplemental Material" goes with the currently posted preprint. (The file "Supplementary Appendix" belongs to an earlier version of the preprint that was posted on medRxiv.)

On 2020-06-02 05:55:45, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-06-29 19:44:04, user Justin wrote:

30/10/2020 in figure 2 appears to be a typo. It should read 30/10/2019

On 2020-06-10 09:40:43, user CC wrote:

are there any subtle clotting differences between A and O blood groups that could be relevant to the coagulopathy seen in CoVID19?

On 2020-06-04 18:17:08, user Fahd Al-Mulla wrote:

The Illumina arrays they used do not contain The ACE2 nor FURIN genes we have identified as the most possible cause of why Europeans are most affected by COVID-19. Our study was not mentioned!? <br /> https://www.biorxiv.org/con...

On 2020-06-09 17:42:33, user Rintaro N. wrote:

Do this study think about the possible different? blood donation behaviors depending on blood type?<br /> cf.?<br /> ?Blood Type and Blood Donation Behaviors: An Empirical Test of Pure Altruism Theory<br /> https://papers.ssrn.com/sol...

On 2020-05-03 14:25:00, user Geoff Turner wrote:

Comparing diagnostic tests like this is a classic signal detection problem. What is your d'? What's the d' for the nasopharyngeal test? What's the bias of each? This is the only way to know which test is most sensitive AND simultaneously least biased.

On 2020-05-14 05:11:14, user Matthew Ward wrote:

Hi Anne, brilliant study - Well done to all the team.

Could a saliva sample prove sensitive enough for Sars-Cov-2 to be detected on a lateral flow test?

Or would the sample almost always require amplification via PCR to increase sensitivity?

Many thanks<br /> Matt

On 2020-05-06 17:35:54, user Research Explained wrote:

Our group made a general public friendly summary of this study and its strengths and weaknesses. Check it out at: https://www.researchexplain...

On 2020-05-07 00:13:23, user mpeaton wrote:

Layman question: What were the aerosols made of, and did they evaporate? I have yet to find a paper demonstrating that ANY virus is viable after being exhaled in a droplet containing NaCl, proteins etc. and then dehydrating. Though there are some that claim otherwise, such as this one: https://dx.doi.org/10.1098%...

On 2020-03-18 08:25:22, user Alberto wrote:

Althought It could survive for some period of time, its title (concentration) maybe is constantaly descending as a negative exponential function. That means that in a shorter period of time the efective probability of transmisión is lower. I have studied bacteriophages, but I suppose that dynamics of inhabilitation shows the same dinamics.

On 2020-05-07 15:07:58, user Thomas Meunier wrote:

KEY TAKEAWAYS FROM STUDY:

1. The research, which has not yet undergone standard peer review evaluation, does not question the efficiency of social distancing.

2. The research looks specifically at the impact of police-enforced home containment policies in some European countries.

3. The work suggests that social distancing may be just as effective as home containment.

4.The results show that the epidemic was already in decline (that is, the number of cases was growing less and less rapidly for 2 to 3 weeks before the lockdown and kept declining at the same rate afterwards) before the full lockdown, possibly thanks to social distancing measures already in place.

On 2020-05-07 20:20:44, user Gregory Kreiss wrote:

6% positive cases for 5-19 years old versus 8.5% for 20-49 years does not look like similar but an increase of 40% for the middle ages adults!<br /> Also one of the major limitation of this study is that we do not know wether the infected children are part of household where one of the adult has been also infected (cluster effect) and if it is the case who has infected who. To be conclusive the children should have been selected randomly within the population and not part of the household members.

On 2020-05-07 20:47:41, user Dan T.A. Eisenberg wrote:

We are thinking of implementing this in my lab for research purposes and hopefully to expand testing capacity. Have you or anyone else you know of tested the stability of saliva samples for longer periods of time, adding in preservative, and/or keeping more of a cold chain (e.g. stored at +4 or -20 for some time)?

On 2020-05-08 04:39:19, user Robin H wrote:

This study is weird.

First of all : why a daily dose of 600mg of HCQ?<br /> Raoult and his team use a dose of 200mg per day to treat Covid-19. The general dose for the treatment of rheumatoid arthritis or lupus is 200 to 400mg, max 600mg if there is no response.<br /> There is a high debate about the potential cardiac toxicity of HCQ... But with this dose, we can understand that "Eight patients receiving HCQ (9.5%) experienced electrocardiogram modifications requiring HCQ discontinuation." Of course...<br /> Did the authors intend to favor the cardiac toxicity of HCQ to invalidate this treatment?... I Wonder.

Second point: when you display the characteristics of the observed populations in the first table, you should indicate the p-value concerning the comparisons. If I'm correct, the HCQ group tends to have a more severe condition BEFORE treatment, at admission. 14 HCQ-treated patients (21.9%) vs 8 control patients (12.1%) had >50% of lung affected in CT scan... There is a trend to a significant difference with a p-value of 0.08...

Then, you can't be conclusive with such bias...

On 2020-05-08 05:56:07, user Masfin Otta wrote:

Obviously, the conclusion of the paper was dead wrong: the covid-19 outbreak in Okinawa had already been completely suppressed without stringent stay-home measures by 28 April and we are not seeing 20,000 deaths but only 5 so far. Perhaps, the total number of the cases was not on the exponential line, particularly after the middle of April, 2-3 weeks after the start of the outbreak as Professor Michael Levitt of Stanford observed from the outbreaks of China, Italy and Iran. Another discussion may be that Rt might have been much lower than assumed and the outbreak died out without many new cases imported from the mainland or perhaps Europe and North America where the epidemic is much much severe than the mainland.

On 2020-05-09 23:51:12, user Sinai Immunol Review Project wrote:

Main findings<br /> The humoral response to SARS-CoV-2 infection has been largely studied in the context of antibody distribution in the peripheral blood of COVID-19 patients. However, little has been explored that evaluates immunophenotyping of B cells in patients with different clinical courses of COVID-19. Here, Woodruff et al. investigated B cell populations by spectral flow cytometry to understand the protective and non-protective humoral responses using PBMCs from 9 critically ill and 8 mild patients with COVID-19.

Comparing CD45+ hematopoietic cells from 22 healthy controls and 17 COVID-19 patients, the authors found an expansion of CD19+ B cells in COVID-19 patients with a significant increase in CD138+ antibody-secreting cells (ASCs) among other B cell subpopulations: transitional, naive, double-negative, and memory. Interestingly, a greater abundance of these mature, CD138+ ASCs, which are often associated with protection during a vaccine-induced response, was found in COVID-19 patients with worse outcomes. Previously, this group described, in flaring systemic lupus erythematosus (SLE), an activated IgD-CD27-double negative B cell population that they characterized as part of an extra-follicular (EF) response. The comparison of the PBMCs across COVID-19 samples revealed two clusters: one that strongly upregulated the EF response pathway (EF-CoV), and one with a low EF response but a high transitional B cell signal (Tr-CoV).

Within the EF-CoV cluster, ASC expansion correlated with enriched ASC maturation and an increase in the active naïve (IgD+CD11c+) and a subset of the double-negative (DN2: IgMlo IgD- CD11c+ CD21-) cell compartments. The composition of the double-negative component with skewing to the ASC-associated DN2 group in the EF-CoV cluster appeared identical to the B cell landscape of patients with active SLE. Similar to the increase in IL-6 and IP-10 during active SLE, association with upregulated IL-6 and IP-10 and poor prognosis for COVID-19 was also found in this study. Higher serum IL-6 and IP-10, a CXCR3 ligand, and expression of CXCR3 by B cell subpopulations belonging to the EF-CoV cluster, supports the notion that peripheral homing of B cells to inflamed tissue sites, as described in both the lung and kidneys, takes place in COVID-19 patients.

A minor subset of B cells in the EF-CoV cluster were CD21lo transitional B cells. These cells were enriched in the Tr-CoV cluster and associated with mild disease. They shared several B cell immaturity markers, such as high levels of CD10 and CD38, and expressed high levels of surface IgM and muted surface IgD, which indicate extrafollicular homing. A longitudinal comparison of two ICU patients in each cluster (two EF-CoV patients and two Tr-CoV patients) revealed that the paucity of CD21lo transitional B cells in the EF-CoV was associated with higher severity of disease and a decrease in PaO2/FiO2 ratio (a measure of gas exchange efficiency). EF-CoV patients had higher levels of CRP, which correlated with a low frequency of transitional B cells, a high number of DN2 B cells, and elevated serum IL-6. Importantly, these patients faced poorer outcomes.

Limitations<br /> Aside from the small sample size in the primary study and in the longitudinal follow-up, this report relies on surface markers to assess B cell heterogeneity in COVID-19 patients and characterize potential autoimmune subpopulations that are also present in SLE patients. However, there are limitations to the scope of coverage that flow cytometric analyses can provide. Single-cell RNA sequencing (scRNAseq) can provide a broader expression profile to distinguish subsets based on transcriptomic expression, as opposed to relying on existing, classical categorizations as done in this study. Therefore, higher granularity in the evaluation of cell-type heterogeneity may yield more precise assessments of cell-type similarities and differences between COVID-19 and autoimmune disease.

Importantly, the characterizations of B cell subpopulations in this study have largely been correlative. Trends with clinical outcome or existing prognostic markers are insufficient to define the roles that these cell types may play in the pathogenesis of COVID-19. Without additional studies (and accounting for the general lymphopenia already reported in COVID-19 patients), it is unclear whether these phenotypes are by-products of abnormal or absent T cell help or actual reactions to/consequences of SARS-CoV-2 infection.

Lastly, since this report identified similar B cell subsets in both critically ill COVID-19 patients and SLE patients, additional serological studies exploring any evidence of autoreactivity in EF-CoV patients with high IL-6 are warranted.

Significance<br /> Using a specialized flow cytometry panel for B cell analysis, the authors provide a description of the B cell landscape in COVID-19 patients. Understanding the role of potentially pathogenic B cell modules could be crucial for designing immuno-modulatory therapies that target pro-inflammatory or potentially autoimmune phenotypes seen with SARS-CoV-2 infections.

Reviewed by Matthew D. Park and Miyo Ota as part of a project by students, postdocs, and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

On 2020-05-11 11:49:19, user Medicos Lk wrote:

According to the data four SARS-CoV2 virus strains are circulating among Sri Lankan Covid-19 infected patients.

Source:https://drnews.lk/four-type...

On 2020-05-11 12:28:29, user Sinai Immunol Review Project wrote:

The main finding of the article: <br /> This study analyzed the effects of the arterial hypertension and of the use of renin-angiotensin-aldosterone system (RAAS) inhibitors on mortality and recovery in patients with Covid-19. Through medical records, the authors performed a multicenter retrospective study of 3017 COVID-19 patients hospitalized within the Hackensack Meridian Health network in New Jersey. Among these patients, 52.5% presented a diagnosis of hypertension. The authors showed a significantly increase (2.7 times) of the mortality in patients with hypertension compared to Covid-19 patients without hypertension. However, when adjusted for age, the effect of hypertension in mortality decreased, as the incidence of hypertension was higher in older populations. In addition, when other clinical or demographic conditions were taken into account, no effect of hypertension on mortality was found. <br /> In relation to the RAAS inhibitors, angiotensin converting enzyme 1 (ACE1) inhibitors and angiotensin-receptor blockers (ARBs) were used in 22.8% and 18% of hypertensive patients. The use of ACE1 inhibitors and ARBs were found not to have detrimental effects and perhaps offer some protection to hypertensive patients in comparison with other anti-hypertensive agents. Hospital discharge rates were 9% higher for hypertensive patients prescribed RAAS inhibitors compared to other anti-hypertensive agents.

Critical analysis of the study: <br /> The manuscript needs a better scientific writing, especially more in-depth details on the description of the patient population, clinical parameters, treatments used, other co-morbidities. The implications for COVID-19 disease of the upregulated cascade of vasoactive peptides belonging to RAAS on hypertensive patients, the relationship between the use of RAAS inhibitors on cytokine storm, plasma angiotensin II and ACE2 activity, could be better discussed. There is no information on which ARBs or other anti-hypertensive agents were used, despite being an important information given the different pharmacological characteristics of each one.

The importance and implications for the current epidemics: <br /> While there is still uncertainty on the effect of RAAS inhibitors on Covid-19 severity in hypertensive patients, this manuscript demonstrates that ACE1 inhibitors and ARBs therapy are not detrimental, and can even be protective in hypertensive individuals. These results thus support the recommendations of the guidelines for maintaining therapy with these classes of drugs in hypertensive SARS-CoV-19 patients.

Reviewed by Bruna Gazzi de Lima Seolin.

On 2020-05-12 21:48:21, user Clive Bates wrote:

I think the conclusions are radically overstated given the method. The authors summarise:

? Current e-cigarette use is positively associated with COVID-19 infections.<br /> ? Current e-cigarette use is positively associated with COVID-19 deaths.<br /> ? This study emphasizes the importance of studying the susceptibility of current e-cigarette users to COVID-19 infection and death.

It would be more accurate to say "statewide prevalence of vaping is correlated with COVID-19 infections and deaths". The study did not discover if e-cigarette use is associated with COVID-19 because it did not actually measure this: "we did not have data on what proportion of those who actually contracted COVID-19 or died from COVID-19 were vapers".

It is a "helicopter view" of the situation using variables covering millions of people in gigantic aggregations, and looking at the progression of the epidemic at different stages as it moves unevenly through the different states over time. There are so many factors that determine the progression of the epidemic, it is hard to imagine how any vaping signal could be detected among the roaring cacophony of confounders and noise.

Luckily, we can also assess the usefulness of the method in the investigation of new associations that have not so far been established (e.g. vaping) by seeing how well it discovers associations that have been already well-established by other research. For example obesity and male sex have been found to be risk factors for COVID-19. But the big finding in this study (see Figure 1) is that obesity and, especially, being male appear to be protective, thus overturning the broad consensus. That would be the big news and should feature heavily in the conclusions if the authors were confident in the method. The trouble is that it could equally lead observers to dismiss the method used as self-evidently flawed. No such objection can be raised about vaping, however, because there is little other data available and therefore no reality-check is possible. So to act with integrity, the authors have a choice: stand by the method and challenge the consensus on male sex and obesity risk factors or accept that if the method does not reveal well-established associations then it should not be used to look for novel ones.

Other than pure chance, the second most likely explanation for the result is that vaping is a marker for some larger scale confounding phenomenon (poverty, hospitality trade, housing density, urbanisation, cosmopolitan, early spread of the virus etc) that is contributory to COVID-19 susceptibility but that cannot be fully adjusted for by the variables available to the authors. It would require heroic assumptions to draw any conclusions about vaping from an analysis like this.

On 2020-05-13 10:08:28, user Benjamin Hartley wrote:

Hi, Can you clarify the meaning of the theta "infection" parameter in equation 1 (years 2015-2019) which multiplies the death rate? Is this a typo, or set to 1?

On 2020-05-13 13:19:20, user T Christopher Bond wrote:

Looking for an explanation of the total numbers and the comparator group in Table 4 (ICI vs ?).

On 2020-05-13 18:50:12, user John wrote:

Lack of Vitamin D has been implicated, paler skin produces more, and there are potential genetic factors too, watching this may help https://youtu.be/Ja-jhcXMGj0

On 2020-05-14 12:31:40, user Riccardo Pecori wrote:

Very nice work. A couple of questions for the authors: <br /> 1 - in the self-collected samples (Triton experiment) what is the volume of PBS in which the swabs are rinsed? <br /> 2 - would it be possible to get the written instructions for self-sampling? It would be beneficial for the standardization of the sampling.

On 2020-05-14 17:42:03, user MiCo BioMed wrote:

This paper makes a false claim, because the authors didn't follow MiCo Biomed's PCR test instruction.. The authors used an RNA extraction kit manufactured by Invitrogen, which is incompatible with MiCo BioMed's PCR kit. MiCo BioMed's PCR kit instruction clearly tells users to use only MiCo Biomed's RNA extraction kit.

On 2020-05-15 16:11:42, user David Simons wrote:

Please in future versions of this article consider reporting a descriptive analysis by your outcomes of interest. Saying current smokers had a 5 times greater risk of ITU admission or 10 times greater risk of death is not helpful when you are not reporting the absolute numbers.

On 2020-05-15 16:48:46, user Will Wiegman wrote:

A combo of severe Thiocyanate and Iodine Deficiencies shuts down the pitting function of the spleen making it impossible for the body to eliminate the viruses trapped inside of mature red blood cells with no nucleus for the virus to use to replicate.

https://www.ncbi.nlm.nih.go...

https://pubmed.ncbi.nlm.nih...

Paragraph 4:<br /> https://pubmed.ncbi.nlm.nih...

On 2020-05-15 22:28:30, user Sally Elghamrawy wrote:

Any one need the dataset ,,,just send to me.

On 2020-05-18 02:17:06, user welko welko wrote:

There were 33 positive IgG among 1,000 serum samples 33 per 1000<br /> so...<br /> 330 per 10,000<br /> 3300 per 100,000<br /> 4950 per 150,000<br /> From your data I calculated; its 3.3% not 33%:Give Kobe a break

On 2020-05-19 16:06:56, user Jared Roach wrote:

The main point of this article is really good. The more variance there is in the infectiousness of individuals, the greater the probability of complete elimination. Indeed, if there is zero variance, then elimination will never occur. This assumes a fairly simple model (e.g., SIS compartmental model). The article would benefit from a few references to classical epidemiological models that the assumptions are based on. The point about animal reservoirs in the last paragraph should be more strongly emphasized. We know this virus originally came from bats, and we know that dogs and felines can be infected. So it seems very likely that there will be animal reservoirs. This point should be emphasized, with references.

On 2020-05-19 20:06:13, user Achint Chaudhary wrote:

I have gone through this and similar articles recently published.I found some issues on which I am bit skeptical about approach followed by the authors:

1. Data Augmentation (using SMOTE) is done before splitting the data into Train-Test sets, which will leak information from train set to test set.

2. Data balance is achieved on Test set also, I agree that class balanced data set will led to a better classifier, but reporting metric values on a test set with different class ratio from real world testing is an issue to be raised

3. XGBoost is shown to be the best known algorithm in this article, but XGBoost algorithm is already known to handle class imbalance, so why do we need SMOTE at first place. Would not it be right if experiments without data augmentation would be also shown

On 2020-05-20 06:25:22, user Bob wrote:

How do the authors reconcile a lower bound of 0.02 IFR with the fact that 0.026% of Americans have already died from SARS-CoV-2? Kind of difficult to have an IFR lower than that.

On 2020-05-23 11:05:15, user John Jacobson wrote:

It would be great to see more of a discussion on how many of the recorded deaths from COVID-19 are directly attributable to Sars-CoV-2. Is the current ~96K US deaths the true death toll of direct deaths from Covid-19 or does this number include significant number of deaths from other causes, but list Covid-19 in death certificate (e.g. a patient with renal failure or end-stage liver cancer or head trauma picks up nosocomial infection and is recorded as part of covid burden)? This would surely reduce IFR estimates if accurately known. Is testing for Covid-19 more widespread than for routine influenza A or B infections? Seems likely in the current climate in the midst of a pandemic, which might be another caveat when comparing. Important to factor in both of these points to get a greater appreciation and context of the current pandemic?

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...

On 2020-05-20 06:50:14, user Chris Valle-Riestra wrote:

Thank you, I can see that this is a very important finding for understanding the development of the epidemic in any nation, region, or city. That heterogeneity in susceptibility would have this effect can be understood intuitively, as soon as one really starts to think about it. Determining an average R nought for an entire nation, and making projections based on that alone, plainly doesn't tell the whole story.

A simple thought experiment will demonstrate this. If an entire population is split into two sub-populations of equal size, and the individuals in one of the sub-populations all have low susceptibility, effective R just for that sub-population can be well below 1.0, in spite of a generally high virulence of the virus. Very few in this sub-population will ever become infected. The other half of the full population will be highly susceptible, and a substantial majority of that sub-population would be expected to become infected over time. Adding it all up, something well under 50% of the total population will ultimately become infected, and herd immunity will have been achieved.

Recent small serological studies around the U.S. have typically indicated a middle-of-the-road level of infection, ranging between perhaps 6 and 30 percent from place to place, many weeks into the epidemic. This has struck me as perplexing. Based on the usual naive model of the development of an epidemic, one would have thought it likely to find either (1) a very low level of infection, such as under 5 percent, implying great success in suppression efforts, or (2) infection levels moving steadily past 50 percent, implying a high R nought that suppression efforts were inadequate to suppress. Basically, either suppression would work or it wouldn't. It would be surprising to find that that the virus had enough power to infect a major fraction of the population, carrying a big head of steam going forward, and yet be able to be halted that late in the game.

Your finding points to a likely explanation for this phenomenon. It suggests to me a likelihood that the epidemic in the U.S. has been working its way through the most susceptible sub-populations, not successfully checked, but that it has made little progress in infecting less susceptible sub-populations.

I think it should be recognized that to the degree that an individual's susceptibility is based on his social conditions, that may change over time. An individual living far out in the country may have little connectivity, and therefore little susceptibility. If he moves into the heart of a city, that may change. This implies that herd immunity is likely to "erode" over time. COVID-19 is likely to remain endemic and to continue to cause a low level of disease, serious and otherwise, for a long time to come.

Be that as it may, there's a strong likelihood that public health officials and political leaders have been seriously misinterpreting the progress of epidemic. This has major implications for public policy choices. Further research is urgently needed, and decision makers need to develop a more nuanced understanding. They are currently making weighty decisions based upon a probably badly flawed model.

On 2020-05-21 01:11:37, user Brian Richard Allen wrote:

Be interesting to see how New Zealand:- whose authoritarian government has effectively prevented its subjects from acquiring herd immunity;- will fare when its borders are opened and tourists -- and the virus -- flow in.

On 2020-05-21 20:00:27, user Babak Javid wrote:

Please note that the Supplementary File refers to the earlier version of this manuscript and is no longer current. Unfortunately, it cannot be removed for clarity!

On 2020-05-22 23:43:28, user Malcolm Semple wrote:

Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study<br /> BMJ 2020; 369 doi: https://doi.org/10.1136/bmj... (Published 22 May 2020)<br /> Cite this as: BMJ 2020;369:m1985

On 2020-05-23 09:25:08, user Francesca Simonato wrote:

What hydrogen peroxide concentration was used?

On 2020-05-24 09:26:14, user Count Iblis wrote:

The natural vitamin D levels are way higher than the average levels found in populations living in the civilized world. Biologically normal vitamin D levels are between 120 nmol/l and 250 nmol/l. Levels below 100 nmol/l are from a natural biological point of view extremely low, but such levels are the norm in the civilized World, even in the tropics as people there too spend most of the day indoors.

Studies like this that look into the correlation between vitamin D levels naturally found in society and harmful effects of COVID-19 effect are interesting, but they cannot detect all of the effects of the severe vitamin D deprivation of the western population. It's similar to a study into the effects of exercise on heart disease if you're studying a population of couch potatoes. You may detect a difference between those couch potatoes that don't sit all day long on the couch and those that hardly get up at all during the day. But the large effects on heart health that kick in when you run for more than half an hour a day, cannot be extracted from such a study.

On 2020-06-12 05:44:01, user Alex Backer wrote:

Good control. Yet a deeper look reveals that vitamin D deficiency is indeed linked to COVID-19 fatality rates outside of life expectancy: https://papers.ssrn.com/sol... .

On 2020-05-25 11:34:54, user Rogelio Macías-Ordóñez wrote:

Even after countless revisions prior tu submission to medRxiv we found a minor mistake in the sentence (lines 375-378):

"A group of five countries (Brazil Fig 4, México, India, Peru and Russia) with IFR values below 1% and an already high death toll (above 1,000) may experience a high number of casualties if, as our estimates suggest, they experience 68-82% more deaths in the next 23 days."

It should say "...64-82% more deaths in the next 23 days." since 64% is the lowest value (for Brazil) among those countries in Table 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...

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.

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.

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.

On 2020-06-14 15:12:53, user Francesco Zagami wrote:

In mar 22, 2020, I posted on Researchgate a correlation between ABO blood group and SARS-CoV#1/2 susceptibility, see you https://www.researchgate.ne...

On 2020-04-03 14:51:49, user Jack Debrueil wrote:

What is the biological plausibility of this association. The ABO-type is related to red blood cells. IT is important to know associaitons with leukocytes and HLA-types. Before concluding anything these reulst must be stratified by HLA-types.

On 2020-03-23 16:59:18, user Rintaro N. wrote:

?“The distribution of ABO blood groups in blood donors... (ref.7)” might not be a good control. It can be different from “true” distribution.?

?cf.?<br /> ?Blood Type and Blood Donation Behaviors: An Empirical Test of Pure Altruism Theory<br /> https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3171957

On 2020-04-02 20:21:31, user drumfucius wrote:

I am an O positive nonsecretor. Wondering if there is any info regarding susceptibility in regards to secreter status.

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.

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.

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?

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.

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.

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.

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.

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.

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

On 2020-06-08 12:31:29, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-06-08 12:47:44, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

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

On 2020-06-13 08:10:15, user KAMAL KUMAR MALUKANI wrote:

Hi,<br /> There is an issue in figure S3. All text are inverted, Please update.<br /> Thanks

On 2020-06-09 20:23:05, user Nicolo de Groot wrote:

Was there any difference in the treatment with LMW Heparin between the 2 groups ?

On 2020-07-03 11:10:45, user Stuart Shaw wrote:

A CRITICAL LOOK AT A PREPRINT INFERRING THE COVID-19 INFECTION FATALITY RATE

http://hildabastian.net/ind...

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.

On 2020-06-12 20:05:26, user Bob wrote:

public interest has followed ecological studies

I think they meant epidemiological

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.

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?

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.

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?

On 2020-06-16 05:17:57, user Sukhyun Ryu wrote:

This manuscript was published, please follow the link below<br /> https://wwwnc.cdc.gov/eid/a...

On 2020-06-20 22:42:57, user many wrote:

Within the boundary of assumptions, the paper is mathematically accurate. I have a list of technical questions, which if addressed could make the paper noteworthy. See here: https://publons.com/publon/...

On 2020-05-19 22:56:46, user Jared Roach wrote:

In response to some of the tweets: <br /> This report out of Snohomish County, WA suggests a case in December 2019. https://www.seattletimes.co...

On 2020-05-20 17:21:18, user Christopher Leffler wrote:

Update: now 14 people have died on Diamond Princess. Updated mortality from Diamond Princess as well as coronavirus mortality for New York MTA (transit) workers are in this paper:<br /> https://www.medrxiv.org/con...

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.

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.

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.

On 2020-05-22 18:43:01, user Madhurjya P Bora wrote:

This paper has lifted many portions of texts in several places from the paper https://hal.archives-ouvert... by Eber Dantas, Michel Tosin, Americo Cunha Jr, especially in the Trust-Region-Reflective section.

On 2020-11-18 22:09:57, user Owen Parry wrote:

10 days in hospital without the intervention.

Is this a fair trial?

On 2020-04-04 01:25:42, user GLB wrote:

The data from Wuhan are used to characterize the influence of social distancing. From the paper "To be specific, the generalizable information from Wuhan was the impact that social distancing had on maximum death rate and time to reach the inflection point.". Many sources have raised doubts about the veracity of the Wuhan data. Does this render the characterization of the efficacy of social distancing methods in the model suspect? Can the model be tested by using a different location (say, Italy) as the training data set to see how the analysis changes?

On 2020-04-02 04:55:23, user Sola Grantham wrote:

I would like to see an explanation of why states with lower current rates of growth are projected to have later peaks. This makes sense to me only in the case of herd immunity being the cause of the peak. Then the area under the graph would remain the same. Thus, to reach the critical percentage of population with immunity, a slower rate of infection would lead to a later peak. But if the cause of the peak is the assumed perfect adherence to social distancing, then wouldn't the date of the peak be more related to the date of practical enactment of the social distancing measures?

On 2020-04-02 16:55:14, user VWFeature wrote:

What happens if instead of "assuming full social distancing through May 2020" we see what's actually happening? (Deaths go way up.)<br /> What's the assumption of death rates when hospitals and ICUs exceed capacity?

When no beds are available, a reasonable assumption would be that 80% of people needing hospital, and 100% of those needing ICU would die.

This study keeps getting cited as "best possible outcome." It's intellectually dishonest to present a "best possible" without a "most likely" and "worst case" projection.

This study is already inducing a false sense of security. This is the BEST POSSIBLE outcome. The most likely is far worse.

On 2020-04-02 22:52:25, user Qi Ying wrote:

The error function used in the study can be derived from the assumption that the daily death follows a normal distribution. Our experience in China shows that it is not the case. The tail in the daily death rate distribution is much longer. The predicted deaths are likely underestimated. Also, the error function fitting leads to significant under-predictions when the inflection point in the death rate has not arrived, which is likely the case for many US states. Thus, I believe these estimations presented in the paper as well as on their website are going to be significantly biased low. The actual situation could be much much worse.

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