On 2021-07-16 22:51:17, user Leo G. wrote:

Another way to slow down immune escape might be prophylaxis (IVM or HCQ + Zinc), starting about a week before the first jab and ending (or calculated to end) when the person acquires maximum immunity.

On 2021-07-21 20:13:20, user Dean Karlen wrote:

The first claim in the title (vaccines dampen diversity) is not substantiated.

Linear entropy is a measure of how evenly spread are the lineages. As stated, if for one month all sequences are the same lineage (no diversity), then the measure is zero… on the other hand if the sequences are evenly distributed amongst all 1296 lineages, the value is about 7. When a VOC appears, it is labelled a VOC precisely because a large number of cases are seen of that lineage! So, of course, when VOCs begin to dominate the linear entropy decreases.

Figure 1 shows that starting in January 2021, just as alpha becomes dominant, linear entropy begins to decline as expected. January 2021 happens to be the time that mass vaccination gets underway - so it is not clear how much (or if any) of the reduced diversity is due to vaccination.

The second claim in the title (regarding breakthrough infections) is questionable. The sample size is small and no statistical test is presented that supports the claim of greater diversity in the unvaccinated compared to vaccinated.

On 2021-07-22 10:33:41, user David Simons wrote:

Hi,

Thanks for sharing this data, it may be interesting to include descriptives for those that were test negative for COVID-19.

Also in table 1 your numbers for smoking status do not appear to be accurate. You seem to be missing 2,610 smoking statuses for your COVID-19 positive sample.

On 2021-07-23 11:43:39, user andyround wrote:

Fascinating, thanks. What I can’t square between studies like yours and the one on cynomolgus monkeys on the one hand, and the range of estimates of sizes of droplets in respiratory exhalations on the other (as recently reviewed by Lydia Bourouiba for example), is how the quantity of viral RNA apparently scales with the *number* of droplets rather than their volume. It implies that the concentration of viruses per droplet is hugely dependent on the size of the droplet and I can’t think of a mechanism to achieve this - are viruses massively self-repelling? (over micron-scale distances?) Are they highly surface active? I wonder if you have thought about this.

On 2021-07-27 01:29:32, user Vaskor Basak wrote:

Thank you very much for sharing this paper. I am interested to know what lag time was assumed between infections and deaths when calculating or inferring the CFR figures. Was it possible to use a distribution of variable lag times, based on real life data, for more sophisticated modelling? Also, has the lag time increased and/or CFR decreased as new medical interventions have slowed down the rapid onset of Covid-19 for severe cases requiring hospitalisation, since the beginning of the pandemic?

On 2021-07-29 07:39:07, user ncn wrote:

Will this preprint be submitted to a peer-review journal? Wish the research team all the best.

On 2021-07-29 08:08:57, user Salvatore Chirumbolo wrote:

The evidence that acetaminophen (paracetamol) worsened the rate of hospitalization in COVID-19 patients respect to NSAIDs such as indomethacin, is the major highlight of this contribution. The Italian Ministry of Health (Nov 30th 2020) recommended only paracetamol and a watchful attitude during the early onset of COVID-19, to simply dampen fever and related pain (see https://www.bmj.com/content... "https://www.bmj.com/content/368/bmj.m1086/rr-18)"). So, NSAIDs are not dangerous for early and mild COVID-19 as initially believed, whereas paracetamol is. In this perspective, in Italy a wide community of citizens, professionals, physicians, caregivers, practitioners, attorneys and so on, joined together to promote the best COVID-19 home therapy at the earliest (within first three days from positive swab or symptoms), to reduce hospitalization (and mortality), as paracetamol exacerbates COVID-19 immuno-thrombosis, probably leading even to death (see Pandolfi S, Simonetti V, Ricevuti G, Chirumbolo S. Paracetamol in the <br /> home treatment of early COVID-19 symptoms: A possible foe rather than a <br /> friend for elderly patients? J Med Virol. 2021 Jun 25. doi: <br /> 10.1002/jmv.27158). Hoping that politics may take into account the evidence reported by the current medical science.

On 2021-07-29 12:52:04, user Erik Widen wrote:

Please note that the published version contains both corrected and more expansive information and supplants this preprint for all purposes. It is available through the link above or use https://doi.org/10.3390/gen....

On 2021-07-29 16:28:46, user aep wrote:

link to competing interest statement broken

On 2021-07-31 09:03:45, user Eli Baied wrote:

Per abstract, vaccine efficacy at 6-months 97% for severe disease. It seems logical to me to vaccinate the unvaccinated globally in hope to better control this pandemic.

On 2021-08-01 00:45:40, user Michael Kowalik wrote:

18 deaths in the study group and 16 in the control group, so (at best) zero lives saved from Covid.

On 2021-08-06 22:27:57, user Ewin Barnett wrote:

Am I the only person wondering where the discussion of the autopsy results was published? In fact VAERS is now easily over 5,000 vaccine-related deaths yet zero autopsies.

On 2021-09-02 13:44:56, user ABO FAN wrote:

I do not understand the meaning of this paper at all.<br /> 1. There is no statistically significant difference between one and two corona deaths. This means that the vaccine is not effective.<br /> 2. The test period is until March 13, 2021, therefore the current mainstream delta strains were not tested.

On 2021-07-30 00:51:33, user Les Campbell wrote:

Your method seems insecure.<br /> Firstly there were no vaccinations during your “wild type “ period. There were during your alpha and delta periods. How have you controlled for this? Have you considered findings in USA and Israel that vaccine adverse reactions have influenced your results?<br /> Secondly just because you have defined periods when variants were of concern it does not follow that the women in your study had that variant without sequencing for each subject. <br /> Thirdly why do you recommend that pregnant women should get the vaccine? Women of child bearing age are at very small risk of death or severe complications. <br /> What were the cohort numbers? Especially for the delta variant? <br /> Why were no pregnant women treated with therapeutics or given simple vitamins and minerals or antibiotics (pneumonia) as they would be in many other countries?

On 2021-08-01 17:03:27, user imogen88 wrote:

Why didn’t they breakdown the vaccinated by specific vaccine formulation? It would be very helpful to see if there’s a significant difference in viral load by vaccines among different age and risk groups.

On 2021-08-03 21:39:28, user Fruit Gal 522 wrote:

Judging from how long ago this was published...June 1, and it's now Aug. 3, it's taking excessively long for this to be peer reviewed. Sources from a google search say it should take 3-4 weeks to review.

On 2021-08-03 23:09:01, user Ankita Bag wrote:

Hi,<br /> Can I get an access to this code.<br /> Thanks in advance.

On 2021-08-04 08:41:37, user ingokeck wrote:

Dear authors,

Thanks for this fascinating study! I am really surprised that you only found 6+3=9 cases in your sample.

Your sample corresponds to 60/330=18% of the population of the US. If we assume that it is representative, this means in the whole US there were only ca. 50 cases of myocarditis in 12-17y old in total. Using the estimation of the CDC that 37% of all kids were infected (see https://www.cdc.gov/coronav... Table 2), this is a rate of just 50 in 30,000,000*37%, thus 4.5 cases per million infected.

So, the risk of myocarditis from Covid-19 is 4.5 cases per million infected and factor 100 smaller than what you report in your result, probably because your cases in the dataset are mostly hospitalized which make only a very small subgroup (ca. 1%) of all cases in this age group.

Compared to the risk from the mRNA vaccines of 10 per million (your source) or rather 333 per million based on the results from Israel (1:3000), the risk from the vaccination is 2 to 100 times greater than the risk from a Covid-19 infection.

I would appreciate it very much if you could correct your calculation to the correct base infection rate as given by the CDC, and also discuss why you assume a smaller vaccine risk than the data from Israel suggests.

On 2021-08-04 15:55:17, user Hassan Rostam wrote:

The article has been publishet @ New Microbes and New Infections

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

On 2021-08-09 15:16:23, user VT wrote:

Please do not post docx-files. Some researchers will be unable to download those files due to security concerns.<br /> Thanks!

On 2021-09-12 05:30:50, user kdrl nakle wrote:

William Brooks talks nonsense. The increase in mg household secondary infections could easily be the result of prior infections (before lockdowns) as a source of infection. It could have easily happen, perhaps even worse without lockdown. And indeed the hospitals in NYC, Brkln and Qns were overflowing with patients. But when you have political slant it is hard to think, isn't it?

On 2021-09-12 13:38:28, user synchromystic wrote:

"As no vaccine is 100% effective".<br /> Really?<br /> I'd love to see the breakthrough cases of polio, small pox and many other viruses.

On 2021-09-12 21:59:19, user Swapnil Hiremath wrote:

<reposting with="" minor="" edits="" as="" disqus="" thought="" last="" one="" was="" spam="" for="" some="" reason=""><br /> The authors have undertaken an ambitious project: briefly, taking numerators from the VAERS database, denominators from vaccine numbers from elsewhere. They then perform a ‘harm-benefit’ analysis looking at COVID hospitalization as the only harm. The whole analysis is restricted to the 12-17 age group in whom the concern of myocarditis is admittedly higher.<br /> They report a risk which was anywhere from 1.5 to 6.1 times higher for vaccine associated myocarditis vs COVID causing hospitalization. Vaccines must be bad, surely.

However, several problems are quickly apparent.<br /> 1. The rate of myocarditis is much higher than the ones reported in Ontario: 160/million for 12-15 males compared to 72.5/million from Ontario (which includes Moderna as well - which has higher rates of myocarditis than the Pfizer/BioNTech). Why would this be so? There are many possible reasons, including the overestimation from VAERS being probable cause. On a perusal of the supplement, there are many which are other viral diseases which could be the reason; additionally many descriptions are quite vague (‘the doctor told us troponin was elevated’). It is very easy to submit cases to VAERS, so the numbers reported could be an overestimate - proper case ascertainment with source documents is necessary to be sure of the cases. Needless to say, simple arithmetic to derive 'rates' is also problematic. The VAERS website specifically suggests the numbers should *not* be used for estimating rates.

1. It was not clear why the authors chose Jan 1, when vaccines EUA for 16-17 started in March, and for 12-15 in May. In their database, there seems to be one case in March and most of the VAERS reports from May or later.

3.Next, the authors make many assumptions when it comes to who had comorbidities and who did not among the children, and multiply numbers to come up with some crude estimates. It would be useful for a pediatric diseases researcher to assess these assumptions. The 40% assumption of children hospitalized 'with COVID' and not due to COVID is a very crude untruth that the authors and others have needlessly perpetuated on social media with little foundation.

1. Most importantly, the authors assume that hospitalization is the only bad thing for children who develop COVID. Some 12-17 years olds have died due to COVID, and some may have had a 1 day hospital stay - their analysis treats these equally and incorrectly. Some teens developed MIS-C. Some developed longer term sequelae. To group them under ‘hospitalization’ seems overly simplistic. Similarly, from perusing some of the vaccine-myocarditis, many seem to have recovered with symptomatic care alone. The authors seem to be minimizing COVID and maximizing vaccine associated adverse events.

2. It should be noted that the involvement of children in the first two waves seems to be different than the one we have seen in the last 2 months with delta (for whatever reason - perhaps with lower immunization numbers in these).

3. Lastly, the pandemic is not yet done. Many more children are going to get COVID in the next few months and years. We are going to have many more hospitalization, morbidity and sadly many more deaths. There will be long term morbidity and sequalae. We do need better data to assess the risks and benefits. This study is not it.

Unfortunately this study has been picked up uncritically by media and will worsen vaccine hesitancy. This seems unwise in the face of an ongoing pandemic.

On 2021-09-13 21:04:36, user Thomas Mohr wrote:

If I review a paper, I start with the methods section. So let us do this:

Quote: "We searched the Vaccine Adverse Event Reporting System (VAERS) data for <br /> females and males ages 12-17 in reports processed from 1/1/2021 through <br /> 6/18/2021 with diagnoses of “myocarditis,” “pericarditis,” <br /> “myopericarditis” or “chest pain” in the symptom notes and required the <br /> term “troponin” in the laboratory data. We defined a CAE using the CDC <br /> working case definition for a probable case."

As others, and VAERs, have pointed out, the VAERs database can not be used to do this.

Quote: "Cases and hospitalizations with an unknown dose number were assigned to dose 1 or dose 2 in the same proportion as the known doses: 15% occurred following dose 1 and 85% occurred following dose 2."

Nope. That is not how it works. Incomplete data have to be excluded.

Where is the proper prior probability of myocarditis?

At this point latest I would stop reviewing and return the verdict: reject without encouragement to resubmit.

On 2021-09-10 19:50:39, user Roger Seheult wrote:

Were you able to exclude the subjects from VAERS based on SARS-CoV-2 positivity? Was it even known if they had tested positive for SARS-CoV-2?

On 2021-09-13 09:36:17, user Michal wrote:

Excellent work! The link to direct-diabetes as in:

The IMI-DIRECT data access policy is available from www.direct-diabetes.org

does not seem to work. Also, the metabolite names (both on the plots and in the supplementary information) seem to be malformatted by R, it would be wonderful if you could restore clean names (with correct punctuation instead of dots) for the final publication.

On 2021-09-14 17:21:09, user Auriel Willette wrote:

Hi everyone, I am the Corresponding Author. Please note that Sara A. Willette's author affiliation was incorrect in the pre-print version of this article, and a correction has been filed with the journal. Specifically, her correct affiliation was and is IAC Tracker Inc, Ames, IA, USA. I apologize for any inconvenience. Thank you.

On 2021-09-18 17:00:44, user Ruben wrote:

Would love to see events stratified by age. There are almost 700 more people age > 65 in the biontech group as compared to moderna. With total hospitalizations being only 43 and icu only 7 patients for pfizer vaccine, it makes you wonder how much of this is skewed by age.

On 2021-09-03 18:17:49, user Sam Wheeler wrote:

What if someone takes 30mcg Pfizer Comirnaty on left arm, and immediately goes to other vaccination site and takes 30mcg Pfizer also on right arm? 60mcg total.<br /> Could this be more efficient? Even better than 100mcg in the same arm?<br /> Or would it be even better to get 60mcg in the same arm, but separated by a small distance? Would the latter vaccinator notice it and refuse to vaccinate if she/he sees the arm?

Moderna has 100mcg of mRNA.

30mcg is ridiculously small amount, as I believe they are testing exactly this 30mcg Pfizer dose even for children who are 6 months or older.

On 2021-09-19 09:24:45, user Cengiz Kiliç wrote:

Dear Dr Swedo et al,

We read with enthusiasm your consensus paper. We are looking forward to its publication, since it is very timely and much needed. We believe such a consensus, reached at by an international panel of experts, and using rigorous criteria, will be very helpful to set the main principles for advancing research, in an area where little is known. Such a clinical guideline will limit the circulation of several existing diagnostic criteria sets that have little relevance with the clinical presentation of the disorder. We especially appreciate your (strongly) emphasizing the fact that misophonia is a sound-sensitivity disorder, and not a disturbance of any sensory input.

At our Stress Assessment and Research Center (STAR) of Hacettepe University, Ankara, we have been conducting research on misophonia (as well as other stress disorders) since 2015. Our first study*, which was just published last month, presented prevalence rates on a random population sample, using our own proposed diagnostic criteria (it is a pity that our study did not appear in time to be included in your literature search). Our second study was a treatment study comparing the effects of psychoeducation, filtered music and exposure in 60 misophonic outpatients, which we are preparing for publication. Our follow-up study (of the population-study sample) is still ongoing. We touched upon the limitations of the existing proposed diagnostic criteria sets in our BJPsych paper’s supplement, and would be happy to share our views in more detail (if requested).

Sincerely,

Cengiz Kiliç, Professor of psychiatry<br /> Gökhan Öz, psychiatrist <br /> Burcu Avanoglu, psychiatrist<br /> Songül Aksoy, Professor of audiology<br /> Misophonia Research Group, Stress Assessment and Research Centre (STAR)<br /> Hacettepe University, Ankara<br /> Email: star@hacettepe.edu.tr<br /> Phone: +90-312-3051874

* Kiliç C, Öz G, Avanoglu KB, Aksoy S. The prevalence and characteristics of misophonia in Ankara, Turkey: population-based study. BJPsych Open. 2021 Aug 6;7(5):e144. doi: 10.1192/bjo.2021.978. PMID: 34353403; PMCID: PMC8358974

•

On 2021-09-21 21:41:54, user tooearly wrote:

Did the authors mean to say 10 cases per 100,000?

On 2021-09-24 06:28:51, user gzuckier wrote:

I admit to not having perused either the comments here nor the blog sites listed above, however there is a viable alternative model for these results, other than the one posed in the conclusions.<br /> Firstly, from the small number of infections in either the vaccinated or the previously infected group, it's clear that we are looking at outliers here; not a general waning of immunity over the time period, but rather a minority of individuals in either group whose immune system did not respond fully for one reason or another.<br /> This highlights a rather striking conceptual difference between the two groups, however; a large proportion of any individuals with defective immune systems have already been eliminated from the previously infected group, by the initial Covid infection having killed them. This group as now tested would absolutely be expected to have fewer immune failures than the Covid-naive individuals making up the vaccinated, not previously infected group, and thus fewer infections and fewer hospitalizations.<br /> This interpretation, of course, also perfectly explains the reduction in infections and hospitalizations seen in the previously infected and vaccinated group, relative to those previously infected and not vaccinated.<br /> Unless some way can be found to eliminate the effect of this unintended selection bias, i.e. filtering out previously infected group members with persistent severe immune incompetence, this study can tell us nothing regarding the relative protections of vaccine vs "natural" immunity.

On 2021-09-01 06:52:27, user Jeffrey Bachant wrote:

The key model rests on ~260 total cases out of ~65,000 individuals between the cohorts. <br /> Cohort studies are biased towards statistical artifacts if the outcome of interest is rare. Thus, the main finding should be that delta infections in both cohorts is infrequent. The confidence intervals for the way cases bin between acquired immunity primed by viral infection vs vaxx is statistically only applicable to their cohort group. Whether it has predictive value in a larger setting is unexplored. It's clearly written in way that, just from what I've bumped into, has allowed it do be heavily talisman linked by the anti-vaxxer crowd. Its unfortunate.

On 2021-09-08 03:11:32, user TBonePickenz wrote:

Too much discussion for something that has ***not been peer reviewed*** <br /> Also bear in mind this is a retrospective study.<br /> The gist is vaccines < natural immunity < natural immunity + vaccine. It is unclear if the 3rd option is statistically significant in my read.

In the first part of the discussion, they say "Individuals who were previously infected with SARS-CoV-2 seem to gain additional protection from a subsequent single-dose vaccine regimen. Though this finding corresponds to previous reports, we could not demonstrate significance in our cohort." However, in the final paragraph of the discussion they say, "Notably, individuals who were previously infected with SARS-CoV-2 and given a single dose of the BNT162b2 vaccine gained additional protection against the Delta variant." <br /> This needs to be clarified in the peer review process.

What it does do (in the US which is swimming in unused vaccines) is open up the question of the necessity of vaccine mandates in those who have already had a natural infection.

• a vaccinated doctor

On 2021-09-08 20:11:56, user David H wrote:

This is an interesting and potentially important study that seems to be playing into a political narrative - 'natural infection' vs vaccination. But before going there we need to acknowledge that it has not been fully analysed and the potential sources of bias have not been fully explored. In observational studies time is a critical factor and it is not clear how it has been handled in the analysis. There are no Kalpan Meier curves. Potential baseline confounding is partly addressed, but not time varying confounding. I cant find information on person time at risk in the three cohorts and that is critical, because it determines the periods during which individuals are at risk of reinfection or breakthrough infection. Is there an immortal time bias in the naturally infected cohort? Additionally, I cant see information on testing rates in the 3 cohorts - most infections will have been mild so this is critical. If testing rates (hypothetically) are lower after natural infection then that could introduce a major ascertainment bias. The study doesn't seem to have measures (or proxies) of health protecting behaviors, which may also be different between the groups. I am not saying that the results are wrong, rather that I think the authors need to complete more detailed analyses to address confounding, temporal selection and ascertainment biases.

On 2021-08-26 19:01:49, user zlmark wrote:

Do your results change significantly, if you analyze different age groups separately?<br /> In other words, what happens if instead of using the age as a covariate, you limit your analysis to a particular age group?

On 2021-09-26 20:12:47, user kdrl nakle wrote:

Poorly written. Better paper:<br /> https://www.medrxiv.org/con...

On 2021-09-29 07:40:06, user Richard Stone wrote:

How many people are in the study? Human chemistry is very complex. Some markers work well with some people and not so well with others.

On 2021-10-03 07:08:57, user kdrl nakle wrote:

Midwestern state? Write South Dakota, what is the point of obfuscation? A lot of stuff in this paper, nothing usable.

On 2021-10-14 09:10:21, user J Hung Tran wrote:

They just measured virus load/Ct at one timepoint, I wonder how the Ct value progress over the course of infection between groups?

On 2021-11-14 14:59:17, user Martin Jezierski wrote:

If they can't isolate/purify it, how can they determine 'viral load', or show any spread (especially in the asymptomatic)?

On 2021-09-03 11:19:28, user Sam Wheeler wrote:

I noticed the main author Naaber worked at Synlab Eesti. <br /> Do you still use the same IgG spike test for customers of Synlab Eesti and Synlab Finland? It is difficult for customers to interpret as the only information Synlab tells that the reference value is below 50 AU/mL. I was tested at Synlab and got about 6000 AU in June 2021 and about 4000 AU in August after two vaccinations and no covid-19 disease. Perhaps I should take a third dose already in September for maximum protection? I got my second dose in June.

On 2021-09-06 06:07:04, user William Brooks wrote:

The author estimates that Tokyo contains 6 infectious people for each person who tests PCR positive, which is probably an underestimate since the test positivity rate was over 20% during most of August. But even if the ratio is 6:1, this means Tokyo's infection fatality ratio outside of healthcare settings is below 0.01%, which hardly justifies the author's desire to search the city for health people who might have enough SARS-Cov2 DNA in their nose to return a positive result on a 40-cycle PCR test.

Also, the author seemingly doesn't know that the first three states of emergency (SoE) started after infections were already falling [1], making all three not just ineffective but also unnecessary. Overestimating the effects of non-pharmaceutical interventions, he calls for the government to copy policies that have failed throughout the world based on a superficial understanding of a few cherry-picked examples: Taiwan has has a 5.2% case fatality rate, so there are obviously a lot of infected people who don't get tested, while Australia and New Zealand are back in lockdown again after failing to "control the virus." Since there are more cost-effective pharmaceutical interventions for actual Covid patients, they should be prioritized.

[1] https://doi.org/10.1101/202...

On 2021-09-08 03:23:50, user Tim Hinchliff wrote:

This is such a great natural experiment to test lockdown efficacy. One wonders why it hasn't been published yet?

On 2021-09-10 07:18:16, user Michael Gardner wrote:

JimmyJoe6000, I have the same question. If you, or anyone else, has the answer please post it.

On 2021-09-10 18:07:48, user dm wrote:

After watching the delta varient spread effectively between vaccinated individuals it would be irresponsible to not cite the following from the study:

"This study has several limitations that must be considered. First, the study cohort size is small, thus making it hard to draw firm quantitative conclusions. Second, our study cohort is biased towards breakthrough infections that were detected in our on-campus screening programs (saliva-based RTqPCR at UIUC, nasal swab-based LAMP assay at NU). Finally, enrollment in this study concluded before the arrival of the Delta variant at either study site. It remains unclear how well the effects of vaccination on viral infection dynamics that we describe apply to Delta variant breakthrough infections, given the unique features12 and enhanced transmissibility13 of this variant relative to the viruses we captured here."

On 2021-11-30 12:53:37, user BSTL wrote:

This represents the first publication of data to support the use of closed system device components in pharmacy technical services such as dose banding in the UK where compliance with the NHS yellow cover document (YCD) guidelines is required.

On 2021-11-30 20:32:11, user Toa_Greening wrote:

Based upon MoH data 30/11/2021 Unvaccinated are 3.5x (8.21/2.35) likely to be hospitalised from a case of Covid not 25x.

No doses received prior to being reported as a case <br /> Cases 3518 Hospitalised 289 Hospitalisation rate 8.21%

Fully vaccinated at least 7 days before reported as a case <br /> Cases 1194 Hospitalised 28 Hospitalisation rate 2.35%

On 2021-12-01 11:23:59, user Drago Varsas wrote:

Common sense will do. "No matter the causes of death ( like being killed by a truck driving over you ) if covid ( a test run at 35-45 cycles never meant to be used for diagnostics ) it counts as covid" The test does not exclude other corona viruses or influenza and most people had serious chronic illnesses and died from them after a very questionable covid positive test. People will other illnesses were also falsely labeled as covid and not treated for their actual illness and often consequently died. It's a crime against humanity and no hair splitting talk will change this.

On 2021-12-01 21:39:22, user Heinrich Schweizer wrote:

The Title of this paper is very misleading. It is evident, and the authors explicitly state it in the discussion section that: (citation: "The analyses performed here represent model-based estimations that are limited by data quality..."). So I think the title would rather have to be formulated as a question than as the firm statement (lending itself to propagandistic abuse).

On 2021-12-01 22:33:29, user Volker wrote:

I estimate (from the text) that vaccinated individuals are involved in 5-6 out of 10 new infections. Which is more than half of all new infections. Seems not to be minor.<br /> Why is it not mentioned in the text ?!? Any reason, why this is not an important information, taken from the same graphics as the estimation about unvaccinated individuals ?!

The model used does not look like the real world in October and November. 2G, 3G, (no) testing have significantly different impact on the contact behaviour of vaccinated individuals, and even more on unvaccinated individuals. The contact model seems not to be realistic at all.

Is there any verification for the (contact) model with actual real data, instead of using static parameter for getting "some" results out of the model? How good (or bad) does the model match with the real world ? Do the results match anyway with increasing number of infected vaccinated individuals, and vaccinated individuals in hospitals ?

Looks like the model does not consider the absence of testing for vaccinated individuals, but this should have an impact on the number of known infections.

On 2021-12-02 12:45:22, user drummy_b wrote:

The Study worked with a effectiveness of the vaccines against <br /> transmission between 70% and 40%. Do we have any evidence for these <br /> assumptions? Furthermore, the effectiveness against transmission and <br /> against disease is not clearly separated in the text. The study should <br /> also provide a modelling with 0% effectiveness against transmission, <br /> then we could see, what matches best with the numbers we have in the real<br /> world.

On 2021-12-03 00:07:08, user Nils S wrote:

If it was true that unvaccinated were responsible for 90% of the infections, the incidences in Denmark would not be possible. Denmark’s share of unvaccinated in the population is roughly 1/3 lower compared to Germany. Following the results of the paper, the growth-rate in the exponential function for spreading the virus would be much smaller in DK, which would theoretically lead to much lower infections in DK compared to DE – if the analysis was correct.<br /> However, the incidences (7-day incidence per 100.000) in DE and DK started October 20th at the same level, close to 90. On November 28th Germany peaked with 482 while DK reached 505 and continued to increase. The Danish numbers do not fit to the results of this paper. The incidences should be much smaller due to higher vaccination rates in DK. Thus, the share of unvaccinated does not explain the growth of infections. It rather looks like, vaccinated and unvaccinated spread the virus similarly.<br /> I strongly recommend to test your hypothesis with data from other countries. And furthermore, I have strong concerns with respect to the RKI data used as input for your analysis. Due to the German regulations vaccinated do not test at all – except for a few exemptions. The entire data is heavily biased. UK for example provided much more reliable data. <br /> Best regards<br /> By the way, have a look at this Nature articel: <br /> https://www.nature.com/arti...

On 2021-12-04 10:54:27, user Martin Backhaus wrote:

Competing Interest Statement

The authors have declared no competing interest.

3 random checks on the authors and 3 times it is RKI staff. And no one identifies themselves.

• Benjamin Maier (Representative P4) - Employee at the RKI
• Marc Wiedermann - PostDoc / Data Scientist at RKI
• Mirjam Jenny - senior scientist of the science communication project group at the RKI

On 2022-03-20 18:28:48, user SB wrote:

First, in Table I there is an error for the RKI report of elderly. RKI printed 60.9 %!

On 2021-12-02 13:07:03, user Max Daigle wrote:

Is this Peer Reviewed?

On 2021-12-03 04:03:08, user Srinivasa Kakkilaya wrote:

This is completely misleading. There is no proof about the so called re-infections as being due to omicron variant, yet the authors seem to blame and speculate further that omicron is causing and can cause re-infections.

On 2021-12-21 00:17:11, user lbaustin wrote:

Were the "reinfections" in people who were symptomatic both times, or is it possible that one or both of the tests were false positives? It could well be that people are being tested more now, or that the cycle threshold has been raised, either of which results in more false positives.

On 2021-12-05 07:06:49, user Robert Thompson wrote:

This is another paper with undisclosed PCR Ct values. Diagnostic confidence is thus limited. There should also be some discussion of the kinetics of the serology tests- Nucleocapsid antibodies decay over time. There were a high number (12 of 20) apparently asymptomatic infections, but due to the assays used it would be difficult to identify those who had previous infection and recovery. As well, there is a time function, both seasonal and position in the epidemic wave, which complicates the analysis. A similar geographic uncertainty exists, as the timing of wave peaks varied through 2020 from region to region.

On 2021-12-05 14:19:41, user J Sato wrote:

Where can I see the hospitalization result by vaccination status for "Hospitalisation for COVID-19 was unchanged."?

On 2021-12-06 09:49:29, user Johan Auwerx wrote:

Interesting that you pull out ZIC1/4 as potential candidate genes for MSA. In our analysis focused on mouse aging, ZIC family members, in particular ZIC1, was also a top candidate involved in mediating age-associated changes in gene expression - see PMID 32997995 - Maroun Bou Sleiman & Johan Auwerx

On 2021-12-12 23:59:14, user tobydelamo wrote:

78% of cases were in males. As a trangender man, I own and recognize my responsibility to correct historical health inequities that favored men. I'm proud to be vaxxed and boosted, and I urge all men to do the same. Myocarditis is extremely rare. Folks, let's not use these few cases as an excuse to not get vaxxed and keep current with boosters.

On 2021-12-14 16:32:02, user Ole K. Fostad wrote:

What is the rationale for excluding 154 patients vaccinated with one dose <21 days before positive test? If they are hospitalized they shoud be counted as they have to be dealt with in the hospital-system too. There is no discussion nor justification for the exclution, it looks like this omission would result in survivorship bias.

On 2021-12-16 21:14:55, user Spaceman3 wrote:

What's with the P-value of 0.41 in Table 7? Isn't that the key data upon which the conclusions are drawn?

On 2021-12-17 11:01:11, user Ctina wrote:

Considering that those with natural immunity have antibodies against other proteins in the virus than just the spike, wouldn’t making conclusions on effectiveness of natural immunity on Omicron based on a pseudotype containing only the highly mutated spike protein be problematic?

On 2021-12-18 12:52:58, user DeshanPerera wrote:

Published version:<br /> https://journals.plos.org/p...

On 2021-12-24 17:48:21, user BernardP wrote:

Looking at the graphs, I see both vaccines lose all effectiveness at 90 days, but worse, actually drop into strong negative effectiveness after that time.

This would mean that these vaccines *increase* one's chances of infection after the initial 90 days "honeymoon" period.

Am I getting this right?

If so, why are governments pushing third doses as Omicron is becoming dominant?

On 2021-12-25 21:50:03, user Daniel Song wrote:

This is just a poor quality paper

On 2022-04-28 18:16:46, user Darwin's Monkey wrote:

The conclusion seems to be contradictory with the "agreed scientific consensus". If the Negative Efficacy in the vaccinated group is because of behavior differences, leading to acquiring and spreading the disease, then how can more vaccines be the answer? It's illogical and contradictory. Surely someone in the research group recognised this!

For example, the vaccinated are getting tested more (which is logical since they are more likely to be concerned about covid than the unvaccinated). However, the prevailing narrative suggests that vaccines reduce symptoms. Therefore the behaviour of vaccinated (with reduced or no symptoms) would logically lead to more risky behaviour and more spreading of the disease. So efficacy of the vaccine is great because it reduces symptoms, according the authors. Then they say. behavior is the reason there is low efficacy. But logically it's also the reason they are catching and spreading it more, so the vaccine isn't so great is it?

You can't have your cake and eat it if you are going to claim behaviors is a significant variable but only for the variables that show the vaccine is great, otherwise we completely ignore behaviors.

These behavior claims appear to always err on the side of the vaccinated (as with the UK Vaccine Surveillance Report which makes the same claims without evidence).

Negative efficacy rates appear to be increasing for the vaccinated week by week (as confirmed by the UK Vaccine Surveillance Report over the last few months). So for these "behavior" claims to be true, you would have to be able to prove that the behavior of the vaccinated has changed dramtically, but the unvaccinated behavior has changed very little. Why would this be? A previous study showed that levels of concern and consequential behaviors were quite different for the two cohorts with the vaccinated being more concerned. But why has this changed so dramatically? Are the vaccinated getting more concerned OR are the unvaccinated getting less concerned?

This flawed and unexplained analysis is driving the conclusion for more vaccine rollout, without considering the possibility that this is NOT the full story and it could more plausibly be some kind of loss of immunity in the vaccinated or another reason

On 2021-12-29 00:28:31, user madmathemagician wrote:

Why were Liechtenstein and Iceland excluded from the article? They're part of the ECDC dataset studied in the article. Such study should not exclude samples without appropriate motivation!

On 2021-12-30 12:02:04, user madmathemagician wrote:

Small whole numbers, like "daily new cases and deaths", can not even be expected to obey Benford's distribution.

Zeroes can even not occur in a Benford's distribution, but are numerous in the source data set.

On 2022-01-02 23:31:07, user Florian Binder wrote:

Very interesting hypotheses and analyses.<br /> Did you also check for an effect of absolute humidity?<br /> As the air is quickly warned up when breathed in, this might influence the amount of virus material breathed out.

On 2022-01-04 18:56:26, user Thomas Barlow wrote:

Did you account for pre-existing T-Cell immunity (pre-2020) which was known in 30 to 50% of the population in year 2020?<br /> Did you account for the fact that most people have had covid (most without even noticing) and will have developed T-Cell immunity naturally? How did you control for that? It's not a static measure and is more expressed and less expressed at different times of month, year.

Studies on INFECTION FATALITY RATE (IFR) - peer-reviewed studies [Studies conducted long before any vaccine] :

(notice the one confirmed for publication by the W.H.O. in September 2020 [published Oct. 2020] - A 0.23% IFR...about the same as flu).

MARCH 2021<br /> “the available evidence suggests average global IFR of ~0.15%”<br /> https://onlinelibrary.wiley...

FEB. 2021<br /> “The infection fatality rate for both the Bureau of Prisons and U.S. was 0.7%. Among institutions that tested >=85% of inmates, the combined infection fatality rate was 0.8%”<br /> https://www.ncbi.nlm.nih.go...

JAN 2021<br /> “The overall non-institutionalized IFR was 0.26%.”<br /> - https://www.acpjournals.org...

DEC 2020<br /> “This rate varied from place to place, with a lower range of 0.17% and a highest estimate of 1.7%.”<br /> https://www.sciencedirect.c...

DEC. 2020<br /> “Results show a fatality ratio of about 0.9%, which is lower than previous findings.”<br /> https://www.mdpi.com/1660-4...

NOV. 2020<br /> “The overall infection fatality risk was 0.8%”<br /> https://www.bmj.com/content...

NOV 2020<br /> “In the United States, COVID-19 now kills about 0.6% of people infected with the virus, compared with around 0.9% early in the pandemic, IHME Director Dr. Christopher Murray told Reuters.”<br /> https://www.reuters.com/art...

NOV. 2020<br /> “The estimated IFR was 0.36% (95% CI:[0.29%; 0.45%]) for the community and 0.35% [0.28%; 0.45%] when age-standardized to the population of the community.”<br /> https://www.nature.com/arti...

OCT 2020<br /> “We know that antibody tests are not perfect, and there may be a considerable number of people who do not mount a detectable antibody response to SARS-CoV-2. However, even when this uncertainty is taken into account, we still find that COVID-19 has a high fatality rate - on the order of 1% for a typical high-income country.”<br /> https://www.imperial.ac.uk/...

SEPT 2020<br /> The W.H.O. posted a heavily peer-reviewed & critiqued study from May 2020, showing the deaths per cases are 0.23% overall, and going up to 0.5% in the worst hit cities. 0.05% for under 70s - The W.H.O. reviewed it again, then published it in September:<br /> - https://www.who.int/bulleti...<br /> - https://apps.who.int/iris/h...

AUG 2020<br /> The medical journal 'Nature' had an analysis and stated that:<br /> "This result was used to calculate an overall IFR for England of 0.9%”<br /> https://www.nature.com/arti...<br /> ________________

On 2022-01-13 14:10:25, user Hans wrote:

Why is only the infectivity of the virus in droplets 5-10µm mearured? Droplets > 10 µm can stay in the air for minutes and contain (much) more virus..

On 2022-01-27 00:09:31, user Autofan1 wrote:

Something seems to be fundamentally wrong with this study - because empirical observations tell exactly the opposite story: much more people have been infected in low relative humidity environments.

The best examples are...<br /> 1) ...the large Covid-19 outbreaks in meat producing companies in Germany (cold and dry air = very low RH).

2) ...the typical spread of the annual flu virus, where most people are infected in spaces with dry air (offices, public transport...) which is dominant indoors during European winter time.

And vice versa the rate of infections in high humidity environments has been negligible.

E.g. public swimming halls and SPAs are high RH and no mass infection events have been recorded in these areas at all, although people are not wearing masks and are regularly talking to each other with little distance.

So either the interpretation of the measurements are wrong - or there is a mechanism in human air intake that causes low RH environments to be more infectious.

Since this study has the potential to influence the behaviour of a large population (with potentially negative consequences) it would be a good idea of the authors to once again verify the interpretation of their findings.

On 2022-01-14 21:37:58, user Rich Condit wrote:

Authors: Please include controls in which negative NP samples are spiked with different amounts of virus and processed for quantification of FFU. In this way you can determine a limit of detection for the assay, and report negatives as "less than xxx FFU/ml"

LIsten to TWiV 854.

On 2022-04-15 03:46:27, user the paper link wrote:

P values from multiple comparison post ANOVA, should be adjusted

On 2021-10-16 13:49:08, user crippapy wrote:

The high viral load of Delta makes it already very pathological if an unvaccinated person catches it and has that initial viral load they will feel sick, thus most likely isolate, mass vaccination leads to people carrying groundbreaking viral loads and asymptomatically spreading it. The suggestions you’re making are based on a narrow research, which in itself already doesn’t adequately support the idea of universal vaccination.

On 2021-10-22 04:59:41, user Dave wrote:

The conclusions do not support the results. Excluding patients with CT > 35 was done post-hoc and this subgroup analysis should be secondary to the ITT population when analyzing the primary endpoint. Twice as many patients in the control group were excluded based on this criteria than in the IVM group which means there is no favorable outcome on viral negativity when you look at all the randomized patients.

If you look at how the clinical trial was registered, there were many exclusion criteria but the CT > 35 for the first 2 tests were not one of them. It seems suspicious to me that the authors would come up with this arbitrary threshold, which conveniently resulted in a positive result. This doesn't make sense either because the authors mentioned 3 definitions of covid negativity: a CT > 30, CT > 35, and CT > 40. You would think that the authors would exclude patients with CT levels 31-35 if they would later define those patients as being covid-negative, but they did not for unknown reasons.

Finally, the diagnostic test the investigators used defined a negative covid test as CT > 40. Not 30 or 35 but 40. You can see this from the EUA documents the company submitted to the FDA. It's also important to understand that the diagnostic test was validated as a limit test, meaning that it could only precisely and accurately tell whether a sample was covid-positive or negative. The test could not accurately and precisely distinguish between a CT of 35 and a CT of 36.

Hopefully the authors will address these points during peer review.

On 2021-10-26 03:31:17, user Y S wrote:

I don’t understand: do they follow people vaccinated in February for the month of March, April etc. and compared with unvaccinated. Or make correlations with Cox method including people vaccinated in each month. In the latter case for vaccinated in March, outcome for people in April somehow has to be translated to the month of March, as the first month after vaccination?

On 2021-10-29 19:58:57, user Taylor wrote:

This paper was peer-reviewed and published in PLOS Medicine on 8/31/21: https://journals.plos.org/p...

NIH press release: https://www.nih.gov/news-events/news-releases/common-genetic-disorder-blood-test-reveals-when-benign-tumors-turn-cancerous

WUSTL press release: https://medicine.wustl.edu/news/liquid-biopsies-may-aid-diagnosis-treatment-of-bladder-nerve-tumors/

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

On 2021-11-03 00:28:57, user Josh wrote:

65% chance of ending up with at least one symptom of long Covid, and still rising significantly 6 months out? That seems extremely high. Is there a reason why the risk of developing long Covid appears so much higher in your study than in others, many of which report numbers in the 30-35% range? Also, is there a reason why your study shows virtually no reduction in the chances of developing long Covid in breakthrough vs. non-breakthrough cases, whereas an earlier British study shows a roughly 50% reduction?

On 2021-11-05 19:59:20, user Sergio Kas wrote:

So does this study say,

"Fully vaccinated were more likely than unvaccinated persons to be infected by variants carrying mutations"? Thats not good.

On 2021-11-06 19:21:58, user Eleutherodactylus Sciagraphus wrote:

This preprintincludes data from human subjects that are under ethical scrutiny. The <br /> majority of patients enrolled were not informed nor agreed onparticipating 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):

https://brazilian.report/li...

https://www.emergency-live....

https://www.dire.it/14-10-2...

https://www.matinaljornalis...

https://g1.globo.com/rs/rio...

On 2021-11-15 08:01:21, user kdrl nakle wrote:

Interesting but your mothers in the sample are a bit older than usual "mother" population. How comes?

On 2021-11-15 21:17:25, user Mando Dido wrote:

This paper has been peer-reviewed and published now at;

https://www.mdpi.com/2076-3...

On 2021-11-18 19:38:56, user Fredrik Nyberg wrote:

Thank you for the comment!<br /> We state clearly already in the abstract that this is "...before the start of vaccinations" and specifically in 2020, which is before global and Swedish vaccinations started (some countries started in December 2020, Sweden had a handful of vaccinations in the last days of December 2020). So these obviously cannot be vaccine effects. Still, we can make it even more obvious in an extended updated version which we are working on.<br /> To be crystal clear - these rates are to show how common these conditions are WITHOUT vaccinations, so that in a world of vaccination it will become a little easier to evaluate if such conditions increase after vaccination or just occur as expected.

On 2021-11-23 11:21:00, user Zacharias Fögen wrote:

"Among fully vaccinated household contacts, the crude SAR was similar for fully vaccinated index cases compared to unvaccinated index cases (11% vs. 12%), but this was confounded by age of the index – both SAR and proportion of vaccinated index cases are higher in the oldest age groups (Supplementary Table S1)"

This is not counfounding. You are introducing a bias.<br /> Among age groups 50+, SAR between two vaccinated V-V (21.5%) is the same or less compared to U-V (22.22%, n.s.) or V-U (18.25%, n.s.) which clearly makes no sense if the vaccination had any benefit.<br /> There is an equal age distribution, about 10% over 75.<br /> (I used data from table S1).

Also, among age groups 30+, Vaccinated Index cases are significantly (p=0.01) less likely to infect an unvaccinated (12.55%) than a vaccinated person (18.15%) clearly indicating that a vaccinated person is more careful when interacting with an unvaccinated Person.

So, there is insufficient Control for confounding, as you did not control for risk avoiding behaviour. Risk Avoiding behaviour influences both likelihood of being vaccinated as well as compliance to social distancing and other infection-avoiding behaviour. So vaccinated people are less likely to be infected because they avoid it more through social distancing, which seems to decrease with age, as the analysis of age group 30+ and 50+ combined demonstrates.

On 2021-11-28 19:40:54, user Robert van Loo wrote:

44 relevant new variants up till now and on average some 2 per 10 million cases. Did we only see 220 million cases globally? I would think more with over 5 million deaths and an IFR of 0.6 %. I have papers and also WHO stating the reported 260 million cases is factors lower than the real number of infections. With over 5 million reported deaths and an IFR of 0.6 % the real number of infections would be over 800 million cases. The number of relevant new variants per 10 million cases would then be about 4 times lower. Of course if reported cases always underreport to the same extent the extrapolation of reported cases to new variants would not change. Still important to make the distinction as the underreporting factor is hugely variable.

On 2021-11-29 12:50:35, user Eleutherodactylus Sciagraphus wrote:

BMJ has further information regarding this case: https://www.bmj.com/content...

On 2021-11-29 13:55:01, user Valentin Klamka wrote:

I looked at "rem_burden_output.RDS" on your github. In the "S" column (which I think means suspectible?) there are zeros in some age groups for some countries. Is this intended? Looks like a bug. You should look into that, otherwise the plots in 3B are very misleading.

On 2021-05-25 20:30:08, user Marek J wrote:

You have wrong data in the mortality analysis for Niaee study. You swaped IVM for Control. According to the study, 4 deaths from 116 occured in IVM and 11/49 occured in Control.

Looking forward to your recalculation.

On 2020-04-23 14:14:16, user Pasquale Valente wrote:

How was the case of pneumonia defined?<br /> How was the causal link with SARS CoV-2 demonstrated?

On 2020-06-23 21:57:17, user Dr. D. Miyazawa MD wrote:

Please give me your opinion or point out any problems with this article.

On 2020-06-24 10:51:55, user Alev Kiziltas wrote:

Artificial intelligence has an important potential in healthcare. Not only the Dermatologist, all healthcare professionals can use TzancNet with less margin of error in the cytology of erosive-vesiculobullous diseases

On 2020-06-28 15:37:33, user Rakshanda Razi wrote:

TzanckNet seems like a promising tool for cost-effective diagnosis of erosive vesicobullous and granulomatous diseases.All the best to the researchers for their endeavours.

On 2020-06-26 08:49:57, user Christian Schultze-Florey wrote:

This article has been accepted for publication in EBioMedicine: https://info.thelancet.com/...

On 2020-06-27 10:57:04, user Filipe Pereira wrote:

Could be false positives. Some of the primers have similarities to the genome of other coronaviruses, which may be in circulation at the time.

http://covid.portugene.com/cgi-bin/COVid_primersandprobes_otherCoV.cgi

Could be contamination, it is crucial to sequence the amplicons to see which lineage was there (if any).

On 2021-12-22 16:54:13, user AndyUpNorth wrote:

Could there be a control population who were hospitalized for anything but COVID in order to rule out convalescence?

On 2020-06-28 15:15:20, user Norman wrote:

Thank you very much for your important article. There is an emerging “unseen “ epidemic of COVID-19 induced chronic fatigue syndrome. Do you have any information regarding this ?

On 2020-06-29 08:48:39, user Dr Mubarak Muhamed khan wrote:

RE: can creating new vaccine everytime is solution for new mutating viruses?

We published our view as e letter regarding old vaccine and it’s Possible use In present menace in science and C&E News<br /> Link:

https://science.sciencemag....

The e letter

(2 June 2020)<br /> Thank you very much for excellent update in new vaccines. We appreciate every efforts towards betterment of human life and fighting with new menace. Still Certain questions need to be asked while trying new vaccines everytime for Every new virus or any microbe mutation?<br /> Although we are Not immunologists, still certain questions haunts our mind. We hope that these queries and questions will ignite the minds of researchers and immunologists. With open minds we must ask these questions to ourselves in today’s tough time instead of getting rattled by situation<br /> 1. Does every new virus create specific antibodies? And for how long it works?<br /> 2. Is there any limit of immune response for any healthy Homo Sapien?<br /> 3. Whether body immune response of Homo Sapien get fatigued with every new challenges by new viruses?<br /> 4. Whether after multiple challenges by new viruses , body try saving Homo Sapien by cross immunity?<br /> 5. Although with new challenges by new virus, body may try responding by creating initial IgM .... And then IgG for certain time period, but whether memory is created for long time for such mutating viruses?<br /> 6. Why not to boost immunity with booster doses of existing vaccines and check cross immunity for fighting with new mutating viruses ?<br /> 7. When new vaccines are in development, why not to give a chance of revaccinations with existing proved vaccines (BCG, MMR, and many more) to masses??<br /> 8. Is there any harm in starting booster doses to children’s and adults of existing vaccines?<br /> 9. Till the new vaccines are developed for SARS Cov 2, good ample amount of time one will get to test boosting immunity with current vaccines and checking cross immunity for fighting corona?<br /> 10. We must continue searching new vaccines for every new virus. But what’s harm repurposing existing proved vaccine for strong cross immunity to neutralise many new menace?

Still Many more new mutated viruses will arrive and try to attack us in different ways in future. Why not to boost sustainable existing immunity with booster doses of existing well tested vaccines in vaccination programmes?

Sincere Regards<br /> Dr Mubarak khan<br /> Dr Sapna Parab<br /> Director & Consultant<br /> Sushrut ENT Hospital & Dr Khan’s Research Centre, Talegaon Danhade, Pune, India

On 2020-07-02 20:38:30, user Aiman Tulaimat wrote:

Another aspect of this study that I am still pondering is the decreasing response from high in ventilated patient, to moderate with some respiratory support, to none on no oxygen. Could the observed effect of dexa be limited to its ability to reduce ventilator induced lung injury (PMID: 27383928, PMID: 23451215, PMID: 24439582), especially that dexa is the least effective steroid in reversing the genetic activation in patients with viral pneumonia (https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.05.06.20076687v1.full.pdf)"). The study has not reported much of the ventilator data, particularly adherence with low tidal volume-low plateau pressure strategy.

Aiman Tulaimat<br /> Pulmonary, Critical Care, and Sleep Medicine<br /> Cook County Health<br /> Chicago, IL USA

On 2020-07-09 09:39:14, user ABCD2020 wrote:

Can Dexamethasone be used alongwith Remdesivir?

On 2020-04-12 12:44:15, user Clive Bates wrote:

Thank you for an extremely interesting and informative paper. I have a few suggestions about one aspect of the paper - tobacco use.

1. The paper alternates between use of 'smoking status' (the body) and 'tobacco use' (tables). It would be helpful to know which is appropriate and how the data on tobacco status was gathered. Tobacco use could include smokeless tobacco and, if FDA definitions are applied, it could include vaping.

2. The proportion of tobacco users assessed, hospitalised and developing critical conditions is substantially below the tobacco use prevalence for New York, even when age is considered. Is this worth mentioning?

3. The multivariate analysis shows an apparent protective effect against hospitalisation for current and former tobacco use as reported (OR = 0.71, 95% CI 0.57-0.87 p=0.001). This is a striking finding, but consistent with findings from CDC's summary of US data (MMWR (April 3, 2020 / 69(13);382–386)) and China (Farsalinos et al - pre-print) in which smoking appears to be underrepresented in the population with progression to more severe symptoms.

4. A weaker (non-significant) apparent protective effect of current or former tobacco use (OR = 0.89 95% CI 0.65-1.21, P=0.452) of was found in the progression from hospitalisation to critical condition. Hospitals generally impose smoking cessation and nicotine withdrawal at the point of hospitalisation.

5. Would it be possible for the authors to rerun the multivariate analysis with current tobacco use and former tobacco use as separate variables? It is possible that former use is masking a stronger effect from current use. Current and former tobacco use may have quite different effects on progression of the disease and former use can include people who quit smoking decades ago. The merging of current and former tobacco use may be obscuring valuable information in the data.

6. There are many possible explanations for an apparent protective effect. It is possible the tobacco use status has been underreported, or current and former users are overrepresented in the 'unknown' status. It is possible that patients fear disclosure of tobacco use will lead to discrimination in treatment or they may feel guilty about their 'contributory negligence'. However, it is also possible that there is a real protective effect from either smoking or nicotine use. This is not implausible: nicotine interacts with the same receptor that is responsible for development of the disease following exposure to the virus. This paper could yield useful supportive or falsifying insights into that hypothesis.

7. Even if such findings are disconcerting, we should be led by the data. It is not possible to rule out a protective effect at this point and this paper adds to the reasons to take the idea seriously. There could be significant implications for the population impact of COVID-19, implications for advice to tobacco users, and implications for practice in hospitals.

8. I have no conflicts of interest with respect to tobacco, nicotine or pharmaceutical industries.

On 2020-06-11 23:05:21, user hoipoloi wrote:

Why was the famotidine given in to the patients? If it was solely for treatment of acid/indigestion etc, have you ruled out the patients' physiological(digestive) makeup as the relevant factor ..... as opposed to the treatment of it?

On 2020-06-14 18:47:53, user Apratim Guha wrote:

k= p^{-1/2} is actually a very old and well known result. See Finucan (1964) or Samuels (1978).

On 2020-06-16 17:01:12, user Dr Jim Porter wrote:

Is there a fractal structure linking a "second wave" of spread with "second phase" of disease progression?

On 2020-06-17 16:13:35, user Naama Geva-Zatorsky wrote:

This paper is under review in Experimental Biology and Medicine

On 2020-04-16 01:21:40, user Bobenheimer wrote:

No more updates of the projections? Data tracking stops on 4/12.

On 2020-08-22 06:12:18, user Manal Said wrote:

It has been published in PLOS ONE:<br /> https://doi.org/10.1371/jou...

On 2020-08-25 16:15:15, user kagamiiii wrote:

Should have done leronlimab

On 2019-07-12 02:50:41, user Guyguy wrote:

EVOLUTION OF THE EBOLA EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI

Thursday, July 11, 2019

The epidemiological situation of the Ebola Virus Disease dated July 10, 2019:<br /> Since the beginning of the epidemic, the cumulative number of cases is 2,451, of which 2,357 are confirmed and 94 are probable. In total, there were 1,647 deaths (1,553 confirmed and 94 probable) and 683 people healed.<br /> 364 suspected cases under investigation;<br /> 14 new confirmed cases, including 9 in Beni, 3 in Katwa, 1 in Mabalako and 1 in Kalunguta;<br /> 2 new confirmed cases deaths:<br /> 2 community deaths in Beni;<br /> Data on deaths of confirmed cases in CTEs are not available this Thursday.<br /> NEWS<br /> OPERATIONS OF THE RESPONSE<br /> Adaptation of the vaccination strategy in areas affected by EVD<br /> Since June 13, 2019, the Ebola vaccination protocol has been adapted to better respond to the particular context of this tenth epidemic, especially the security context and the high rate of confirmed cases that are not listed on the contact lists.<br /> This new protocol contains three strategies that can be used depending on the environment in which confirmed cases are found. These three strategies are:<br /> Classic Ring: The classic strategy of vaccinating contacts of confirmed cases and contact contacts.<br /> Enlarged ring: It is also possible to vaccinate all inhabitants of houses within 5 meters around the outbreak of a confirmed case.<br /> Geographical Ring: In an area where team safety can not be guaranteed, they can vaccinate an entire village or neighborhood.<br /> In addition, following the international meeting on Ebola vaccination last month, the Minister of Health adopted a circular on Wednesday, July 10, 2019 concerning the use of other experimental Ebola vaccines in the context of this tenth epidemic. . Due to the lack of sufficient scientific evidence on the efficacy and safety of other vaccines as well as the risk of confusion among the population, it was decided that no clinical vaccine trials will be allowed throughout the country, the extent of the territory of the Democratic Republic of Congo during the ongoing Ebola outbreak.

HEALTH WORKERS<br /> 131 Contaminated health workers<br /> The cumulative number of confirmed / probable cases among health workers is 131 (5% of all confirmed / probable cases), including 41 deaths.

On 2019-07-16 13:28:54, user Guyguy wrote:

EVOLUTION OF THE EBOLA EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND

ITURI.

NEWS:

High-Level Meeting on Ebola in Geneva

On Monday, July 15, 2019, the Minister of Health, Dr. Oly Ilunga Kalenga, participated in the high-level meeting in Geneva to mobilize the international community to end the Ebola epidemic in the Democratic Republic of the Congo. His statement is available: Ladies and gentlemen.

Since August 1, 2018, the Democratic Republic of the Congo is facing the Ebola epidemic<br /> the most complex of its history and the history of public health.<br /> As you followed yesterday, July 14, a positive case from Butembo was declared in the city of Goma. This morning, the positive case, quickly identified and isolated, was<br /> repatriated to Butembo. Vaccination has been launched for all contacts. Since the beginning of this epidemic, we prepared with the WHO for the possibility of positive cases in Goma.<br /> The situation is therefore under control and is being managed, as we did a few weeks with the positive case reported in Uganda. By the way, as a reminder, Goma is not the first provincial capital to report a positive case. This was the case in Bunia there<br /> a few weeks and in Mbandaka during the ninth epidemic of Ebola Virus Disease<br /> occurring in the province of Ecuador from May 7 to July 24, 2018.<br /> The risk factors of the current epidemic remain:<br /> - The density of the population;<br /> - the high mobility of the population;<br /> - The geographical area concerned covering 23 health zones spread over 2 provinces;<br /> - Part of the response is deployed in areas of military operation where armed groups and community militias;<br /> - The instrumentalisation of the epidemic by certain political actors during the period<br /> election.<br /> The tenth Ebola outbreak is not a humanitarian crisis. It's a health crisis public service, which intervenes in an environment characterized by development and shortcomings of the health system. This crisis requires a technical public health response to break the chain of<br /> transmission of the virus by relying on the actors of the health system and its partners<br /> traditional.<br /> Several pillars are thus implemented to break the chain of transmission, whose<br /> vaccination. The Ministry of Health has invited the last 28 and 29 June in Kinshasa, the<br /> producers of the four most advanced vaccines to fight Ebola, as well as the experts<br /> national and international for a meeting of scientific exchanges on vaccination in<br /> part of the ongoing epidemic. It emerged from these exchanges that the vaccine produced by the Merck, currently used in this outbreak, is the only one that has demonstrated its<br /> efficacy for reactive vaccination in the case of the current response. The good news<br /> is that there are enough doses available of this vaccine. To avoid confusion and<br /> amalgams in the difficult context of this epidemic, the Ministry of Health decided that no other vaccine trial would be implemented in the DRC until the tenth epidemic<br /> will be in progress.<br /> To date, thanks to the commitment of all, sufficient funds have been mobilized for<br /> previous response plans. On behalf of the Congolese Government, I express my gratitude to all donors.<br /> In developing the third strategic response plan (SRP3), covering the period of from February to July 2019, a special effort was made to put in place information for monitoring activities and expenditures to increase accountability operational than the financial accountability of all actors.<br /> The process of developing the fourth strategic response plan (SRP4), which will cover the<br /> period from July to December 2019, ended this Friday, July 12, 2019 in Goma. The<br /> The process was participatory and inclusive, and took into account lessons learned on an ongoing basis.<br /> The methodology for budgeting - bottom up - is part of the unit costs and<br /> the volume of the different activities to be implemented in each zone of<br /> health; these were then aggregated by sub-coordination.<br /> The Government is grateful for the contribution of our various partners as well as<br /> donors. However, this support must be in the respect of the Government, and in<br /> partnership with institutions and not in parallel. Only the anchoring of the riposte in the<br /> health system and the strengthening of the actors of the Ministry of Health will<br /> to ensure the sustainability of all achievements of the response. All sectoral support plans for the response must be developed in the same spirit, in consultation with the ministries<br /> sector. Public health actors want to make SRP4 a "final push". To get there, we demand from all actors of discipline and accountability. In each pillar, in each sub-coordination, the Ministry of Health and the co-leaders accredit implementation agencies on the basis of five criteria to ensure accountability:<br /> - Have a demonstrated operational capacity with regard to the number and<br /> the expertise of human resources (not agencies in "learning curve", recruiting<br /> on Linkedin for North Kivu);<br /> - Rationalize geographical deployment and ensure an effective presence on the<br /> field (not just attending meetings);<br /> - Commit to implementing the activities according to the validated protocols for the response;<br /> - Make a commitment to transmit the data to the General Coordination of the response, in<br /> respecting the reporting tools that allow the monitoring of the indicators of<br /> performance and produce dashboards;<br /> - Commit to adopting the scales and the Manual of Procedures for the Management of<br /> human resources developed by the Ministry of Health and the World Bank, which<br /> that no other vaccine trial would be implemented in the DRC until the tenth epidemic<br /> will be in progress.<br /> To date, thanks to the commitment of all, sufficient funds have been mobilized for<br /> previous response plans. On behalf of the Congolese Government, I express my gratitude to all donors.<br /> In developing the third strategic response plan (SRP3), covering the period of<br /> from February to July 2019, a special effort was made to put in place information for monitoring activities and expenditures to increase accountability operational than the financial accountability of all actors.<br /> The process of developing the fourth strategic response plan (SRP4), which will cover the<br /> period from July to December 2019, ended this Friday, July 12, 2019 in Goma. The process was participatory and inclusive, and took into account lessons learned on an ongoing basis.<br /> The methodology for budgeting - bottom up - is part of the unit costs and the volume of the different activities to be implemented in each zone of health; these were then aggregated by sub-coordination. The Government is grateful for the contribution of our various partners as well as donors. However, this support must be in the respect of the Government, and in<br /> partnership with institutions and not in parallel. Only the anchoring of the riposte in the<br /> health system and the strengthening of the actors of the Ministry of Health will<br /> to ensure the sustainability of all achievements of the response. All sectoral support plans for the response must be developed in the same spirit, in consultation with the ministry<br /> sector. Public health actors want to make SRP4 a "final push". To get there, we<br /> demand from all actors of discipline and accountability.<br /> In each pillar, in each sub-coordination, the Ministry of Health and the co-leaders<br /> accredit implementation agencies on the basis of five criteria to ensure<br /> accountability:<br /> - Have a demonstrated operational capacity with regard to the number and<br /> the expertise of human resources (not agencies in "learning curve", recruiting<br /> on Linkedin for North Kivu);<br /> - Rationalize geographical deployment and ensure an effective presence on the<br /> field (not just attending meetings);<br /> - Commit to implementing the activities according to the validated protocols for the response;<br /> - Make a commitment to transmit the data to the General Coordination of the response, in<br /> respecting the reporting tools that allow the monitoring of the indicators of<br /> performance and produce dashboards;<br /> - Commit to adopting the scales and the Manual of Procedures for the Management of<br /> prepared by the Ministry of Health and the World Bank, whom I wish to thank in particular for its unfailing support for the Government since the beginning of this epidemic.<br /> Only discipline and accountability will allow us to put an end to this epidemic, which has<br /> that too long.<br /> Now is the time to think about the post-Ebola era and start developing with others<br /> sectors, ambitious development plans that alone will be able to resolve fundamental problems of the population.<br /> Thank you.<br /> Source: Ministry of Health press team on the state of the response to the Ebola epidemic in the Democratic Republic of Congo

On 2020-04-18 05:05:32, user Andy wrote:

Besides the self-selection, non-random bias, what's also troubling is that two of the authors argued that "fatality rate may be too high by orders of magnitude" in a WSJ commentary on March 24. Were they simply shooting for what they wrote in the WSJ commentary?

https://www.wsj.com/article...

In the WSJ commentary, they did not bother to cite data already available from the Diamond Princesses, which showed at most 2x cases (46.5% were asymptomatic at the time of testing, 17.9% of infected persons never developed symptoms).

https://www.cdc.gov/mmwr/vo...

In the WSJ commentary, they also discussed information from Vò. But they argued for 130x by applying cluster data to the whole province, when Dr. Crisanti who did the study said 50% to 70% were asymptomatic. That's maybe 2x to 3x, but nowhere near 130x.

https://www.wsj.com/article...

"Dr. Crisanti oversaw the testing of 95% of the residents of Vo’ in the days after the first infection was reported. He found 3% of the population had been infected and that just under half of those who tested positive were asymptomatic."

https://www.theguardian.com...

"Nonetheless, asymptomatic or quasi-symptomatic subjects represent a good 70% of all virus-infected people and, still worse, an unknown, yet impossible to ignore portion of them can transmit the virus to others."

When one incorrectly applies cluster data (3,300 people in Vò) to the entire province (population 955,000), one gets 130x, when you know for sure the factor should in fact be 2x to 3x. It seems the self-selection, non-random bias can be just as strong, and the 50x to 85x cannot be right.

On 2020-04-20 18:43:59, user A. J. wrote:

Advertisement for this study found on Reddit:<br /> "FREE COVID-19 Testing --needed to calculate prevalence of disease in Santa Clara County<br /> What? Get tested. FDA approved antibody test for Coronavirus. To test the prevalence of disease in our county, we need 2500 residents. The antibody test differs from the swab test that measures infectious patients actively carrying the COVID-19 RNA in their nasal passages. The serum antibody test determines whether your immune system has fought off the virus and created antibodies to protect you from future exposure. Since the first cases appeared in November/December in Wuhan, China until the U.S. banned flights from China, an estimated 15,000 residents of Wuhan would have traveled to the U.S. so the disease likely entered the California in December (based on 2019 travel data). <br /> Who? HEALTHY volunteer who lives in Santa Clara County. ONE PERSON per household. 2500 people to be tested.<br /> When? APRIL 3rd and 4th. THIS WEEK. 7AM-5PM. <br /> How? (link)<br /> Why get tested? (1) Knowledge - Peace of mind. You will know if you are immune. If you have antibodies against the virus, you are FREE from the danger of a) getting sick or b) spreading the virus. In China and U.K. they are asking for proof of immunity before returning to work. If you know any small business owners or employees that have been laid off, let them know-- they no longer need to quarantine and can return to work without fear. If you don't want to know the results, we don't need to send you the results. (2) Research - Contribute to knowledge of the prevalence of virus spread in Santa Clara County. This allows researchers to plan hospital bed needs and forecasting where to allocate public health resources. This will help your neighbors and family members."

On 2020-04-18 17:11:55, user Clint Cooper wrote:

Please allow me five responses to this study:<br /> 1. Yes, it's true that there are large numbers of people who have probably been exposed to Covid-19 and experienced no symptoms and have antibodies. The best country to look at with regard to testing mass numbers of people randomly is Iceland. Icelad has now tested over 11% of its entire population and a lot of testing was truly done randomly. The fatality rate there is around .6%, or one in about 167 people. In New Zealand, which has also engaged in massive testing, the fatality rate is about .8% or 1 in 125 people. HOWEVER (and this is a big however). the fatality rate in both countries has gone up steadily and is continuing to go up. Also, in those countries the average age of people infected is much lower than in other countries, like Italy, who have been hit much harder by this disease. This actually jives with the original fatality rate given by the World Health Organization, which was around .3 to .5% for younger people, and markedly higher for older people. <br /> Also, the average fatality rate for the common flu is about 1 in 2,000. So even if we can agree that the fatality rate of Covid-19 might be lower than originally thought, it's way more dangerous than any common flu. It is also exponentially more contagious, which is a huge part of the problem. <br /> 2. As testing becomes more and more widely available across the planet, the fatality rate has gone markedly up across the board, not down. For closed cases, the fatality rate has gone from 3-4% initially to now 21% and rising.<br /> 3. The fatality rate in this study assumes that no one ever died at home from Covid-19. As a matter of fact, NYC is now including people who died at home from Covid-19 but a lot of US states and other countries are not, which would artificially lower the fatality rate, when it could actually be much higher.<br /> 4. To say that somehow the fatality rate of this disease is no worse than just a really bad seasonal flu is looney tunes and doesn't pass the eyeball test since many, many perfectly healthy people with no pre-existing medical conditions are dying as a result of this disease left and right, including young and middle aged people. That simply doesn't happen with seasonal flu. The self-reported symptoms of Covid-19 are also far worse than most self-reported symptoms of the common flu. Recovery time also appears to be longer, and many doctors are reporting lung and heart tissue damage.<br /> 5. There are about 18,000 people in Santa Clara County who have tested for Covid-19 and about 10% of those tested positive. Is it possible that the same people who tested negative could have already had Covid-19 and it was already defeated by their immune system and they now have antibodies and are testing negative? Could it be that these very same people who already tested negative for the virus are now volunteering to test for antibodies? In this case, there is a massive level of redundancy and the study is useless and can't be extrapolated to the general public. This study is far too small and not random enough to provide any usefull information whatsoever. We would need truly random testing of a much bigger group of people to provide any insight at all into what the actual fatality rate is. We would also need to include a much wider age range.

On 2020-04-21 14:48:12, user Comfrey's Gone wrote:

I'd like to restate my comments in a manner that should be comprehensible to those who didn't dig into the statistical appendix, and in language that is less technical.

Many commenters have observed (correctly) that the uncertainty in false positives (cases in which the antibody test registers positive when no antibodies are present) are large enough to preclude exclusion of the possibility that nobody in the sampled population had Covid, i.e. that with 95% confidence, one cannot infer that more than 0% of the population had Covid. However, the results of the paper indicate that with 95% confidence, at least 2.49% of the population had Covid. This raises the question of what error or errors were made by the authors in computing their confidence levels.

Many of the comments have noted that the authors assume that errors are normally distributed when determining the final confidence interval (2.49% to 4.16% of Santa Clara county infected), and that this assumption is invalid, and will cause an understatement of the width of the lower bound of the confidence interval. This is absolutely correct, and is a significant problem, but is not large enough to explain the large discrepancy between the lower bound for infections of 2.49% and the observation that this lower bound should be zero.

The authors made a larger and more elementary error that is responsible for their overstatement of the lower confidence interval bound; they incorrectly scaled by the wrong 'sample number' in computing their errors. Here is an explanation:

Given a sample of finite size, e.g. the manufacturer has tested 371 pre-Covid blood samples with their antibody test, so N = 371, the uncertainty in the result (e.g. the number of false positives) is proportional to the reciprocal of the square root of N. This is, for instance, the 1/square root of N in the usual formula for standard deviation.

The authors of the paper compute the standard error of the infection rate by aggregating the errors due to the finite size of the sample of respondents (3,330 people were tested), the sample used to determine the number of false negatives given by the antibody test, and the sample used to determine the number of false positives (371 tests run by the manufacture, plus 30 additional tests run by the Stanford lab). When they perform this calculation, they do not rescale by 1/square root of N until the very end, when they divide the error by 1/square root of 3,330, the number of respondents.

This means that the authors have effectively rescaled the uncertainty due to the number of false positives by 1/sqrt(3,330), while they should have only rescaled it by 1/sqrt(371) (considering only the manufacturer's testing) or 1/sqrt(401) (adding on their 30 additional tests). They have therefore incorrectly reduced the contribution of the false positive error by sqrt(3,330)/sqrt(371), which is about a factor of 3. Since this is the main source of their error, they have understated the entire standard error by nearly a factor of 3, and likewise understated their confidence interval width by about that amount.

This is a basic, straightforward 'math' error, and should not be the subject of any controversy.

On 2020-04-23 02:02:17, user Joseph Cole wrote:

A couple of weeks ago I drew a scatterplot of confirmed cases (per million population) versus tests made (also per million population) using country-level data and found a pretty neat linear relationship (in log scales). Just extrapolating from the fitted model I estimated that 3.1% of the population would be found infected if everyone was tested. I know it's a wild extrapolation, but do you think it works as a "quick and dirty" method to obtain a reasonable ballpark figure?<br /> https://uploads.disquscdn.c...

On 2020-04-23 08:44:30, user Robert Ton wrote:

Confirmed cases of 1190 reduces the under-ascertainment to 40-68

On 2020-04-23 11:56:27, user Robert Ton wrote:

Thought I already commented but don't see it, maybe waiting to be moderated still...

I believe confirmed cases in Santa Clara as of 1 April should be 1190 (positives by sample collection date) vice 963 (positives by test result date)... By my back of a napkin calculation that drops the under-ascertainment rate to around 40-68x vice the 50-85x rate given herein.

On 2020-02-11 23:06:49, user Amir Aharon wrote:

This is the first comprehensive article based on scientific data and actual facts.<br /> Lacking vaccine this study is very useful to understand the characteristics of this virus and to learn about the treatment.<br /> To determine quarantine period it would be useful to add details on the incubation period (interquartile range, 95% percentile)<br /> For treatment it would be useful to add the results (success/failure) of certain medication treatment.<br /> It would be very useful to learn how the complications are statistically distributed in general and by geographical area (Province). Perhaps a larger sample would be required.<br /> Thank you Dr Zhong Nanshan and the entire team for this study

On 2020-02-13 16:13:57, user Xiaolin Zhu wrote:

This is an important work to estimate this risk at early stage of outbreak

On 2020-02-26 08:00:00, user bio.mehr wrote:

but something about Arak in this paper is wrong. Beacause Arak airport is closed for 2 years and before have some Internal flight.<br /> Pollution arrived of other sorces.

On 2020-09-19 13:39:00, user kdrl nakle wrote:

The fundamental UK study on dexamethasone has made the same conclusion so you are really not breaking any new ground with this.

On 2021-03-10 08:27:46, user Eduard Baladia wrote:

This is one of the problems with preprints, there is no minor revision. This is an ecological fallacy and a spurious correlation. This is really misleading information!

On 2020-03-16 22:24:16, user Leslaw Milosz Pawlaczyk wrote:

Is this dataset available somewhere? I would be interested in helping developing this method further as I have lots of experience in that area.

On 2020-03-20 16:43:20, user Cathy Gansen wrote:

The final conclusion doesn't seem right based on the reported stats. Or am I reading it incorrectly? Conclusion says: "CONCLUSION People with blood group A have a significantly higher risk for acquiring COVID-19 compared with non-A blood groups, whereas blood group O has a significantly lower risk for the infection compared with non-O blood groups." However, their stat summary is: "MAIN OUTCOME MEASURES Detection of ABO blood groups, infection occurrence of SARS-CoV-2, and patient death RESULTS The ABO group in 3694 normal people in Wuhan showed a distribution of 32.16%, 24.90%, 9.10% and 33.84% for A, B, AB and O, respectively, versus the distribution of 37.75%, 26.42%, 10.03% and 25.80% for A, B, AB and O, respectively, in 1775 COVID-19 patients from Wuhan Jinyintan Hospital." Stats indicate that AB has the lowest, not O. Is this a typo?

On 2022-02-17 20:47:23, user RT1C wrote:

Another point of confusion: "An individual was considered protected by natural immunity 14 days after testing positive for COVID-19 by a nucleic acid amplification test (NAAT). If not previously infected, a person was considered protected by vaccine induced immunity 14 days after receipt of the second vaccine dose of an mRNA vaccine. " and "A vaccine booster was defined as at least 1 dose of any COVID-19 vaccine at least 90 days following COVID-19 infection for those with natural immunity (i.e. those previously infected), or a third dose of a COVID-19 vaccine at least 90 days following the second dose of an mRNA COVID-19 vaccine for those with vaccine-induced immunity (i.e. those not previously infected)."

This is all very confusing, stemming from your broad use of "natural immunity" to include those who were vaccinated before or after infection. Figure 4 is entitled with "natural immunity" but includes people with 0, 1, 2 or 3 doses. Based on the definitions in the text quoted above, that doesn't seem possible. Did they get infected and then receive 0-3 doses AFTER infection and still called "natural immunity" subjects? What about people who received 1 or more doses before infection? Are they counted among the vaccine-induced immunity subjects? In my opinion, your definitions and uses don't seem consistent or understandable.

Furthermore, because other research has shown a difference in immune response when people are vaccinated then infected vs. infected then vaccinated, you should not combine these as one group. Did you make any attempt to compare these situations? Shouldn't you?

Look at Fig. 2, for example. I assume that many of the subjects included in the curves on the left ("Natural Immunity") actually were vaccinated at some time, since Fig. 4 shows that many with "natural immunity" were vaccinated by 0-3 doses. How, then, are we to interpret Fig. 3? Is the weaker immunity with longer durations since POIC to be interpreted as time since infection, or time since vaccination (which would count for resetting POIC)? Is the weaker immunity with longer durations due to decay of natural immunity as the text seems to imply, or is it due to confounding with vaccination? (After all, doubly vaccinated have higher susceptibility in Fig. 1). These issues make it difficult to understand your study.

I think you probably have the data for an informative analysis, and your method of analysis looks promising (I prefer it to the "person-days" approach used in some other work). Please consider reexamining the dataset with a clarified definition of "natural immunity" that accounts for all combinations of vaccination and infection including sequence.

On 2020-03-24 13:29:19, user Sharon Tracy wrote:

Can anyone explain why the endpoint on copper is so much higher than that of the other materials (cardboard, plastic)? Does it matter that there is a higher concentration of virus on copper over a longer time than the other materials where the end concentration is lower?

On 2020-10-28 03:32:39, user David Epperly wrote:

Was any genomic study done? Since the patients are all co-located, this could help identify whether or not there may be a genome relationship to the activity identified.

On 2020-03-30 14:10:51, user Sinai Immunol Review Project wrote:

Study description: Data analyzed from 52 COVID-19 patients admitted and then discharged with COVID-19. Clinical, laboratory, and radiological data were longitudinally recorded with illness time course (PCR + to PCR-) and 7 patients (13.5%) were readmitted with a follow up positive test (PCR+) within two weeks of discharge.

Key Findings:

At admission:<br /> o The majority of patients had increased CRP at admission (63.5%).<br /> o LDH, and HSST TNT were significantly increased at admission. <br /> o Radiographic signs via chest CT showed increased involvement in lower lobes: right lower lobe (47 cases, 90.4%), left lower lobe (37 cases, 71.2%).<br /> o GGO (90.4%), interlobular septal thickening (42.3%), vascular enlargement (42.3%), and reticulation (11.5%) were most commonly observed.

After negative PCR test (discharge):<br /> o CRP levels decreased lymphocyte counts (#/L) increased significantly (CD3+, CD3+/8+ and CD3+/4+) after negative PCR.<br /> o Consolidation and mixed GGO observed in longitudinal CT imaging w different extents of inflammatory exudation in lungs, with overall tendency for improvement (except 2/7 patients that were readmitted after discharge with re-positive test) after negative PCR.

Seven patients repeated positive RT-PCR test and were readmitted to the hospital (9 to 17 day after initial discharge):<br /> o Follow up CT necessary to monitor improvement during recovery and patients with lesion progression should be given more attention.<br /> o Dynamic CT in addition to negative test essential in clinical diagnosis due to nasal swab PCR sampling bias (false-negatives).<br /> o Increase in CRP occurred in 2 readmitted patients (and decr. in lymphocytes in one patient), but was not correlated with new lesions or disease progression vs. improvement (very low N).<br /> o Patients readmitted attributed to false-negative PCR vs. re-exposure.

Importance: Study tracked key clinical features associated with disease progression, recovery, and determinants of clinical diagnosis/management of COVID-19 patients.

Critical Analysis: Patients sampled in this study were generally younger (65.4% < 50 yrs) and less critically ill/all discharged. Small number of recovered patients (N=18). Time of follow up was relatively short. Limited clinical information available about patients with re-positive test (except CRP and lymph tracking).

On 2020-03-30 15:38:19, user Sinai Immunol Review Project wrote:

Main findings<br /> This is the first report to date of convalescent plasma therapy as a therapeutic against COVID-19 disease. This is a feasibility pilot study. The authors report the administration and clinical benefit of 200 mL of convalescent plasma (CP) (1:640 titer) derived from recently cured donors (CP selected among 40 donors based on high neutralizing titer and ABO compatibility) to 10 severe COVID-19 patients with confirmed viremia. The primary endpoint was the safety of CP transfusion. The secondary endpoint were clinical signs of improvement based on symptoms and laboratory parameters.

The authors reported use of methylene blue photochemistry to inactivate any potential residual virus in the plasma samples, without compromising neutralizing antibodies, and no virus was detected before transfusion.

The authors report the following:<br /> ? No adverse events were observed in all patients, except 1 patient who exhibited transient facial red spotting.<br /> ? All patients showed significant improvement in or complete disappearance of clinical symptoms, including fever, cough, shortness of breath, and chest pain after 3 days of CP therapy. <br /> ? Reduction of pulmonary lesions revealed by chest CT.<br /> ? Elevation of lymphocyte counts in patients with lymphocytopenia. <br /> ? Increase in SaO2 in all patients, indicative of recuperating lung function. <br /> ? Resolution of SARS-CoV-2 viremia in 7 patients and increase in neutralizing antibody titers in 5 patients. Persistence of neutralizing antibody levels in 4 patients.

Limitations<br /> It is important to note that most recipients had high neutralization titers of antibodies before plasma transfusion and even without transfusion it would be expected to see an increase in neutralizing antibodies over time. In addition to the small sample set number (n=10), there are additional limitations to this pilot study:<br /> 1. All patients received concurrent therapy, in addition to the CP transfusion. Therefore, it is unclear whether a combinatorial or synergistic effect between these standards of care and CP transfusion contributed to the clearance of viremia and improvement of symptoms in these COVID-19 patients. <br /> 2. The kinetics of viral clearance was not investigated, with respect to the administration of CP transfusion. So, the definitive impact of CP transfusion on immune dynamics and subsequent viral load is not well defined.<br /> 3. Comparison with a small historical control group is not ideal.

Relevance<br /> For the first time, a pilot study provides promising results involving the use of convalescent plasma from cured COVID-19 patients to treat others with more severe disease. The authors report that the administration of a single, high-dose of neutralizing antibodies is safe. In addition, there were encouraging results with regards to the reduction of viral load and improvement of clinical outcomes. It is, therefore, necessary to expand this type of study with more participants, in order to determine optimal dose and treatment kinetics. It is important to note that CP has been studied to treat H1N1 influenza, SARS-CoV-1, and MERS-CoV, although it has not been proven to be effective in treating these infections.

On 2020-05-22 13:37:38, user Dan Elton wrote:

The original version can be viewed here: https://www.medrxiv.org/con...

On 2020-04-02 02:41:56, user H RC wrote:

Is it possible that the right hand side of the fifth ODE in equation (1) has a mistake?, It has "S" instead of "R"

On 2020-05-23 07:34:51, user Chris Valle-Riestra wrote:

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

A simple thought experiment will demonstrate this. If an entire <br /> population is split into two sub-populations of equal size, and the <br /> individuals in one of the sub-populations all have low susceptibility, <br /> effective R just for that sub-population can be well below 1.0, in spite<br /> of a generally high virulence of the virus. Very few in this sub-population will ever become infected. The other half of the full <br /> population will be highly susceptible, and a substantial majority of <br /> that sub-population would be expected to become infected over time. <br /> Adding it all up, something well under 50% of the total population will <br /> 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 <br /> suppression efforts were inadequate to suppress. Basically, either <br /> suppression would work or it wouldn't. It would be surprising to find <br /> that that the virus had enough power to infect a major fraction of the <br /> 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 <br /> susceptibility is based on his social conditions, that may change over <br /> time. An individual living far out in the country may have little <br /> connectivity, and therefore little susceptibility. If he moves into the<br /> heart of a city, that may change. This implies that herd immunity is <br /> likely to "erode" over time. COVID-19 is likely to remain endemic and <br /> 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 <br /> officials and political leaders have been seriously misinterpreting the <br /> progress of epidemic. This has major implications for public policy <br /> 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 2020-11-12 15:40:04, user Martin Dugas wrote:

Further evidence is available:

https://www.jci.org/article...

On 2022-10-25 02:28:09, user Zainab Sikander wrote:

I would like to start with saying thank you for addressing the issues of comorbidity and overlapping symptoms in your research paper. It is a big problem for psychologists and psychiatrists to accurately make a diagnosis. This study makes me believe there is a solution and maybe this can be used in an actual medical setting to diagnose patients accurately. This brings me to my question, which system did you use for defining the symptoms and disease? I know about DSM-5 having criteria for diagnosis and the diagnosis has to be based on the most updated revised version of it, so what was used for this study? The summaries from the brain bank were how old and if they were defined based on a system like the DSM? Also, I would like to see a more depth study on mental illnesses. My initial impression was that those will be discussed in as much depth but they weren't focused on as much. Is there going to be a future research? Will this tool be upgraded and approved to use in behavioral health centers and medical settings?

On 2022-11-22 13:29:29, user Tusabe Fred wrote:

Dear Authors,<br /> Thank you for conducting this very important review.<br /> I noticed you cited our work, grateful that it was of importance during your write-up. The current citation is number 58 and cited as ‘Tusage Fred. Bacterial Contamination of Healthcare worker’s Mobile Phones; a 574 Case Study at Two Referral Hospitals in Uganda. 2021', this was wrongly cited.<br /> The correct citation should be 'Fred Tusabe, Maureen Kesande, Afreenish Amir, Olivia Iannone, Rodgers Rodriguez Ayebare & Judith Nanyondo (2022) Bacterial contamination of healthcare worker’s mobile phones: a case study at two referral hospitals in Uganda, Global Security: Health, Science and Policy, 7:1, 1-6, DOI: 10.1080/23779497.2021.2023321'

Looking forward to your corrected version.

On 2020-05-27 01:18:30, user Pablo wrote:

It is not rigorous enough but contains useful hypothesis.

On 2022-12-19 02:33:05, user Charles Warden wrote:

Hi,

Thank you very much for posting this preprint. I think this is an interesting topic to learn more about.

I was surprised at what seemed to me like a relatively small amount of difference for the EA-PGS versus the rare alterations (such as in Figure 2), but I am not sure if that is because of certain associations that I might have with “rare” and “monogenic”. I apologize if some of these questions are naïve and certain assumptions may be more or less relevant in certain situations.

For example, I wonder if the phenotypic “deviators” might relate to part what I am trying to describe, and I think it would be good for me to take some time to better understand resources like Developmental Disorder gene panel from Gene2Phenotype.

Nevertheless, as a starting point, I hope that you can help with at least some of the following questions:

1) I could find Supplemental Figure S1. However, I see references to Supplemental Tables, when I can’t find additional uploaded “Supplemental” files.

Am I overlooking something, or do the Supplemental Tables need to be added in a “v2” version?

2) I thought Figure 1 was very helpful in gauging the relative influence on the different characteristics.

However, the abstract mentions a “threshold for clinical disease”. I apologize that I don’t know the best full list of known genetic/genomic alterations that would provide a good reference, but I would be interested to know how the predictive power (which I assume is correlated with the beta values) might compare to something like trisomy 21 for Down Syndrome (although that affects many genes on chromosome 21).

Reading this preprint helped me in terms of gaining familiarity with earlier publications, such as the AJHG Kingdom et al. 2022 publication. In that paper, I was interested in the set of 25 “known highly penetrant genes.” However, my understanding is that those were not used in a main figure because of sample size for sufficiently rare variants/alterations. For example, I recognized “Williams syndrome” and “Prader-Willi/Angelman” in Table S2 of the other publication, but I believe there were less than 20 UKB subjects for each of those diseases.

I also apologize that I think that I am confusing the results a bit, since the other publication includes copy number alterations and Figure 1 in this preprint mostly describes “variants”. However, I hope that provides some sense of what I am asking about.

For example, I don’t know if sample size is an issue, but I would be interested in seeing an additional category to compare the current results to something of comparable clinical utility can be defined (if possible, from individual variants in certain genes). I apologize if I am overlooking something.

3) In this preprint, I also see “monoallelic (i.e., autosomal dominant)” in the methods. So, I think this already answers one of my question/questions, but I wanted to still say something to try and find the better way to describe what I would call “monogenic” diseases. It is possible that there might be some other explanation like needing additional characterization of individual variants (and I believe the earlier paper mentioned “very few” pathogenic variants were in ClinVar), but I would like to start with the possibility that perhaps there is a more precise way to communicate my thoughts.

In other words, I would expect the clinical disease for monogenic diseases to be recessive in certain situations, and the questions that I had earlier related to whether there could be a noticeable number of developmental disorders that follow a recessive inheritance pattern (such as whether a homozygous variant counted as “2 variants”).

Likewise, in terms of downstream effects, I thought Phenylketonuria/PKU affected some of the traits mentioned in this study (if dietary changes were not made in time). I don’t know if that was precisely considered a “developmental disorder,” but that is an example of what I think of when I see “rare” and “monogenic”.

Please let me know if I have misunderstood anything, but my understanding is that this may go against the assumed dominant inheritance pattern for the genes. However, the question that I am trying to ask is how to categorize what I would call “monogenic” disease like PKU (as I understand it) and then compare that to the effects described for the associations in this preprint? I think one other way to describe this might be something like “positive predictive power” and yet another way might be something like “what are the proportion of individuals meeting the threshold for disease among carriers (if significantly higher than controls)”? However, I don’t know if there are issues with my use of terminology (and/or sample size, criteria to volunteer and consent, or something else).

4) In terms of the clinical importance, I am not sure if there might be caveats/limitations to suggesting “PGS may provide some clinical utility by improving diagnostic interpretation of rare, likely pathogenic variants that cause monogenic disease”? For example, if a large enough number of individuals can be functional adults (or even adults with excellent health/success), then I might worry about the stigma of test results provide early in life (if predictive power was over-estimated). Based upon Figure 3, my understanding is that there is a reasonable chance that could happen with the EA-PGS? If that was true, then I hope that there is or can be a different term/classification to separate such a risk assessment from the genetic tests for diseases similar in severity to what I tried to list above (if I understand correctly).

I hope that I can learn more, and I hope that these questions might also be helpful to some other readers.

Thank you very much!

Sincerely,

Charles

On 2020-04-06 11:57:49, user Sinai Immunol Review Project wrote:

Main findings<br /> It has been previously reported that COVID-19 patients exhibit severe lymphocytopenia, but the mechanism through which this depletion occurs has not been described. In order to characterize the cause and process of lymphocyte depletion in COVID-19 patients, the authors performed gross anatomical and in situ immune-histochemical analyses of spleens and lymph nodes (hilar and subscapular) obtained from post-mortem autopsies of 6 patients with confirmed positive viremia and 3 healthy controls (deceased due to vehicle accidents).

Primary gross observations noted significant splenic and LN atrophy, hemorrhaging, and necrosis with congestion of interstitial blood vessels and large accumulation of mononuclear cells and massive lymphocyte death. They found that CD68+ CD169+ cells in the spleens, hilar and subscapular LN, and capillaries of these secondary lymphoid organs expressed the ACE2 receptor and stain positive for the SARS-CoV-2 nucleoprotein (NP) antigen, while CD3+ T cells and B220+ B cells lacked both the ACE2 receptor and SARS-CoV-2 NP antigen. ACE2+ NP+ CD169+ macrophages were positioned in the splenic marginal zone (MZ) and in the marginal sinuses of LN, which suggests that these macrophages were positioned to encounter invading pathogens first and may contribute to virus dissemination.

Since SARS-CoV-2 does not directly infect lymphocytes, the authors hypothesized that the NP+ CD169+ macrophages are responsible for persistent activation of lymphocytes via Fas::FasL interactions that would mediate activation-induced cell death (AICD). Indeed, the expression of Fas was significantly higher in virus-infected tissue than that of healthy controls, and TUNEL staining showed significant lymphocytic apoptosis. Since pro-inflammatory cytokines like IL-6 and TNF-? can also engage cellular apoptosis and necrosis, the authors interrogated the cytokine expression of the secondary lymphoid organs from COVID-19 patients; IL-6, not TNF-?, was elevated in virus-infected splenic and lymph node tissues, compared to those of healthy controls, and immunofluorescent staining showed that IL-6 is primarily produced by the infected macrophages. In vitro infection of THP1 cells with SARS-CoV-2 spike protein resulted in selectively increased Il6 expression, as opposed to Il1b and Tnfa transcription. Collectively, the authors concluded that a combination of Fas up-regulation and IL-6 production by NP+ CD169+ macrophages induce AICD in lymphocytes in secondary lymphoid organs, resulting in lymphocytopenia.

In summary, this study reports that CD169+ macrophages in the splenic MZ, subscapular LN, and the lining capillaries of the secondary lymphoid tissues express ACE2 and are susceptible to SARS-CoV-2 infection. The findings point to the potential role of these macrophages in viral dissemination, immunopathology of these secondary lymphoid organs, hyperinflammation and lymphopenia.

Limitations<br /> Technical<br /> A notable technical limitation is the small number of samples (n=6); moreover, the analysis of these samples using multiplexed immunohistochemistry and immunofluorescence do not necessarily provide the depth of unbiased interrogation needed to better identify the cell types involved.

Biological<br /> The available literature and ongoing unpublished studies, including single-cell experiments of spleen and LN from organ donors, do not indicate that ACE2 is expressed by macrophages; however, it remains possible that ACE2 expression may be triggered by type I IFN in COVID-19 patients. Importantly, the SARS-CoV-2 NP staining of the macrophages does not necessarily reflect direct infection of these macrophages; instead, positive staining only indicates that these macrophages carry SARS-CoV-2 NP as antigen cargo, which may have been phagocytosed. Direct viral culture of macrophages isolated from the secondary lymphoid organs with SARS-CoV-2 is required to confirm the potential for direct infection of macrophages by SARS-CoV-2. Additionally, it is important to note that the low to negligible viremia reported in COVID-19 patients to-date does not favor a dissemination route via the blood, as suggested by this study, which would be necessary to explain the presence of virally infected cells in the spleen.

Relevance<br /> Excess inflammation in response to SARS-CoV-2 infection is characterized by cytokine storm in many COVID-19 patients. The contribution of this pathology to the overall fatality rate due to COVID-19, not even necessarily directly due to SARS-CoV-2 infection, is significant. A better understanding of the full effect and source of some of these major cytokines, like IL-6, as well as the deficient immune responses, like lymphocytopenia, is urgently needed. In this study, the authors report severe tissue damage in spleens and lymph nodes of COVID-19 patients and identify the role that CD169+ macrophages may play in the hyperinflammation and lymphocytopenia that are both characteristic of the disease. It may, therefore, be important to note the effects that IL-6 inhibitors like Tocilizumab and Sarilumab may specifically have on splenic and LN function. It is important to note that similar observations of severe splenic and LN necrosis and inflammation in patients infected with SARS-CoV-1 further support the potential importance and relevance of this study.

On 2023-06-18 16:46:29, user Matt Kmiecik wrote:

This paper was published online in PAIN in April 2023: 10.1097/j.pain.0000000000002909

On 2023-09-09 17:35:30, user Leonardo Fontenelle wrote:

It is refreshing to see scientometrics used for something else than ranking!

While each one has its own objectives, I'd like to point the authors to another study using a bottom-up approach, "Research themes of family and community physicians in Brazil" (https://doi.org/10.1101/202... "https://doi.org/10.1101/2021.12.22.21268269)"), which is approved for publication in the AtoZ journal. Its reference list includes two more articles leading to it.

In brief, we listed the country's family doctors, listed their journal articles, grouped the articles and the corresponding keywords in research themes, and then described the postgraduate trajectories leading to the main themes. Like this new work, ours valued the reproducibility and sharing the analytic code, while inevitably need some manual data curation.

On 2023-11-22 08:39:05, user Author wrote:

This article is published as of 28 September 2023 - https://onlinelibrary.wiley.com/doi/full/10.1002/jrsm.1675

On 2023-12-24 09:20:57, user Seyed Amir Ahmad Safavi-Naini wrote:

The published version is: https://doi.org/10.1016/j.h...