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

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

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

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

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

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

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

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

Many thanks<br /> Matt

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

On 2020-07-21 21:41:59, user Alfred_Packer wrote:

This research was done very early, and has the imperfections inherent in such an early effort- but they appear to have been pretty close to the actual IFR. At the time it was seen as impossibly low and a dozen researchers beat this study up. So how did such a flawed study get the right answer, basically?

On 2020-04-24 07:31:44, user Dennis Menace wrote:

The children they tested were brought in by their parents. These are also not independent, is this a problem ?

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

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

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

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

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

What hydrogen peroxide concentration was used?

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

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

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

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

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

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

It should say "...64-82% more deaths in the next 23 days." since 64% is the lowest value (for Brazil) among those countries in Table 1.

On 2020-07-17 13:12:12, user Liam Golding wrote:

What is the detection limit of this assay? I assume it's >log0.

On 2020-07-18 00:57:36, user Kamran Kadkhoda wrote:

Despite other reports such high seroprevalence in healthcare setting highly suggests false positivity. Ideally all positives should have been confirmed by neuralization assay. Since most were mild/moderate/asymptomatic and they admit 50% were confirmed this is an attestation to high false positivity of their screen test. I refer them to the large Wuhan study with 2% sero-prevalence as they confirmed all cases with neuralization. Most positives found here are probably from common CoVs...for the record specificity of 100% is a mathematical impossibility.

On 2020-07-18 08:58:40, user disqus_LHZMcrKY6P wrote:

LAMP has great potential as a screening tool, the limit of detection (100,000 c/ml ?) is at least 10 times less than most commercial and LDT assay based on traditional RT PCR methods. I note the authors suggest "If such a test were to be used for community screening outside of CLIA-certified diagnostic labs, effective interventions could be taken immediately while awaiting confirmatory tests at partner CLIA labs" When used in this way it Is a very good screening tool. It would be important in times of global supply issues and economical impact of testing versus impact to balance implications of duplication of swab collection.<br /> Those using and commissioning tests must be aware of the limitations of all assays and use them appropriately. It is very attractive to use tests that are high throughput and low cost. This assay would be well suited during peaks of pandemics where negative results are followed up by CLIA testing.

On 2020-07-18 09:38:23, user disqus_LHZMcrKY6P wrote:

Thank you for this informative paper, the statistical probabilities of result significance in the event of a negative result are very important in terms of the application of the test. Is there possibility to follow-up negative lamp results in both asymptomatic contacts and symptomatic cases with laboratory based assays RT-pcr assays (with LOD< 1000 cp/ml) and repeat LAMP assay at say 4-7 days later to evidence the statistical probabilities with true outcome? Do the authors recommend that LAMP assay result is routinely followed up with a more sensitive test for negative results?<br /> I cannot see units for analytical sensitivity is this copies/ml, per reaction or per swab? This is important for selecting a confirmatory test that has a more sensitive limit of detection and for comparing results across assay and finally for understanding the % of samples that would not be detected if this was performed as a single test based on current knowledge of viral loads in samples.<br /> The natural history of covid19 disease makes the means that interpretation of significance of low viral loads is reliant on a number of factors specific to the individual, while it is true that a low viral load is less likely to be infective, we cannot be sure at which stage of infectious course the individual is at and therefore low viral loads cannot be ruled as insignificant in terms of infectious potential nor clinical outcome.

On 2020-07-18 11:59:14, user Kevin wrote:

This paper appears to be a good quantitative assessment of the relative risk between full flights and flights with empty middle seats (~44% reduction with empty middle seats).

I believe the author’s attempt to quantify the overall probability of contracting COVID while flying to be highly flawed.

First, the Lancet study states its mask values are from (at worst) 12-ply cotton masks, and this paper assumes 100% mask compliance. Twitter is full of photos of non-compliant airline passengers, and with no federal regulations defining and requiring mask compliance, the author’s assumption skews the assessment.

Second, the author states he makes no attempt to account for duration of exposure. I assume the author believes this is acceptable, since as stated in the paper “the air in the aircraft cabin is constantly refreshed, so the cabin does not constitute a closed indoor space.” Unfortunately, FAA Regulation 25.831 (a) specifies a minimum fresh air volume requirement, but allows that to be mixed with filtered recirculated air. So while better than a room with no fresh air being circulated, it is not the same as standing in an open field. Again, not taking exposure time into account likely skews the outcome to make air travel appear safer than it is.

Third, and this may be the most important, the author does nothing to account for arguably the highest risk activities while aboard an aircraft: boarding and de-planing. As soon as the seatbelt light goes off, passengers jump out of their seats and crowd the aisle, huffing and puffing as they pull their suitcases out of the overhead bin, all while pressed up against fellow travelers. Boarding is a similar mess, but can at least be controlled through passenger metering at the gate and entry door. If the paper doesn’t consider the highest risk activities associated with flying, it can’t attempt to assign an overall probability to air travel.

In summary, this is not a comprehensive analysis of the probability of contracting COVID while flying, and the overall probabilities should not be presented and discussed since the underlying assumptions are both incomplete and likely flawed. This paper does appear to do good work comparing the transmission probability due to passenger proximity (QL), and should concentrate the findings in that area.

On 2020-07-19 10:16:32, user Shahar Seifer wrote:

The density of virions in aerosols may be different than the density in saliva. The argument on probability of transmission is based on the assumption that the two values are the same, which is doubtful.

On 2020-07-19 10:54:36, user C Ilie wrote:

The best way to test a theoretical model is to run an experiment. But, what if the experiment already took place? Princess Diamond Cruise analysis found the infection rate was below 20%. <br /> https://www.linkedin.com/pu...

On 2020-07-23 10:39:18, user Jeff Morris wrote:

I would love for these “dark matter” arguments to be true. I understand the mathematics, but is there any direct evidence that significant proportions of the population have immunity from exposure to previous coronaviruses or is it just hopeful speculation? Our antibody studies at Penn suggest our patient population has coronavirus antibody prevalence of about 1% — it is not even clear if these IgG titers indicate immunity — but even if they did the 1% would be negligible and big enough to produce this proposed “dark matter” effect.

On 2020-07-19 14:36:08, user Ulrich Müller-Sedgwick wrote:

Great to see this paper published with interesting results. I was the lead clinician for the CPFT Adult ADHD Clinic until March 2017 (when I moved to London). How should we screen for hoarding symptoms? Is there a screening version of longer questionnaires or 1-2 questions that we can ask in our clinical interview, especially in patients with procrastination as a main symptom?

On 2020-07-20 05:20:08, user Curbina wrote:

I have wondered if China published total mortality data that could be used as it has been done elsewhere to estimate excess mortality during the pandemic. This article is the closest to that so far.

On 2020-07-21 15:54:39, user OxImmuno Literature Initiative wrote:

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

On 2020-07-22 09:15:41, user Ben Berman wrote:

Is there an association with BRCA1/2 germline status? About 17% of TCGA cases are BRCA1/2 germline carriers.

On 2020-07-22 20:52:39, user Stuart Hameroff wrote:

Congratulations. I think the ultrasound mechanism of action is on microtubules and the microtubule-organizing-center (MTOC) which get hijacked by the virus for trafficking, filopdium growth to infect other cells, and cytokine storm. Ultrasound is mechanical vibrations in megahertz. Microtubules have resonance frequencies in megahertz, and cell studies show ultrasound rearranges microtubules into optimal configurations. Let's treat the microtubules on which the virus depends, e.g. those in vagus nerve, spleen and other areas. Ultrasound is painless, safe, inexpensive and widely available.

On 2020-07-23 01:12:23, user William Croft wrote:

Many reports surfacing online of reinfections (google covid reinfection). Within weeks or months of the 1st infection, after antibodies no longer exist. This would seem to caution against antibody based approaches. Unless the goal is re-vaccination every three months(!) Admittedly profitable for manufacturers. ;-)

On 2020-06-23 23:51:31, user Steve Moss wrote:

My initial concern after a very brief skim of the article, is that the raw data and statistical methods (including relevant code/parameters) aren’t available for reproducibility purposes. Could the preprint please be updated with links to these resources?

On 2020-06-24 08:42:37, user Canberk Baci wrote:

A study with a great prospect! Not only facilitates the interpretation of smears, but also provides the means of access to the opinions of specialists in this field. It would also reduce diagnostic costs.

On 2020-07-25 13:01:40, user Matthias von Davier wrote:

Something is missing here. Country level correlations can be inverse to individual level correlation (and they may be in this case). There's a long history of ecological fallacies and other types of wrong conclusions when making inferences from group level association to the individual level.

Andrew Gelman wrote https://www.amazon.com/Red-...

And the original Robinson 1950 article is here:

https://www.jstor.org/stabl...

On 2020-07-25 15:35:31, user wbgrant wrote:

Without information on the vitamin D dose and serum 25-hydroxyvitamin D concentrations of the participants, no concluusion can be made regarding vitamin D from this study. See<br /> Grant WB. Re: Preventing a covid-19 pandemic: Can vitamin D supplementation reduce the spread of COVID-19? Try first with health care workers and first responders. BMJ. April 1, 2020 https://www.bmj.com/content...

On 2020-07-26 13:21:08, user MaverickNH wrote:

“... we have no information on whether the excess firearms acquired were those used in violence.”

With an estimated 390 million guns in civilian hands in the US, it would indeed be difficult to attribute any estimated increase in criminal use of guns to those recently purchased during the pandemic. Unless, perhaps, the motivations of the purchasers was markedly nefarious. But as these excess purchases were estimated from NICS background checks, one might infer the purchasers to be *less* likely to use guns in criminal acts. As BJS surveys of prisoners found only 1.3% obtained the guns used in their crimes by purchased from retail sources, the connection between legal excess gun purchases and criminal use of guns is very tenuous. Beyond being unable to establish a causal relationship between excess gun purchases and increased criminal use of guns, the relationship is most likely fortuitous.

On 2020-07-26 17:29:35, user Dude Dujmovic wrote:

Great, too bad samples are so small but this is worth expanding.

On 2020-07-27 06:13:06, user OxImmuno Literature Initiative wrote:

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

On 2020-06-26 17:52:11, user abdullah Alsultan wrote:

Thanks for the great work, few questions regarding your PK study:

1) I think in the Perinel study, they used whole blood conc not serum

2) How did you determine the blood/plasma partitioning ratio? In your study it was 1.64, prior studies was closer to ~5

3) Would be good to add to figure 1, the predicted blood conc bases on the serum conc you observed

Thanks,<br /> Abdullah Alsultan

On 2020-06-27 15:36:00, user Dr SK Gupta wrote:

Researchers have rightly pointed out that plasma works in Non intubated patients and not so in Intubated patients meaning thereby.. plasma therapy if given late in course of disease may not be effective. Intubated patients obviously meant more sick and those with advanced disease who failed to respond to nasal oxygen inhalation, High flow nasal oxygen and CPAP... <br /> (Authors may please clarify the criteria of intubation if different.)<br /> Findings are in line with the observation that once the cytokine storm has done the damage, Neutralizing antibodies in plasma may not be able to reverse it. Cytosorb or ECMO is the only modality left for such patients apart from usual care.

Good interesting finding was effectively of use of plasma with titres 1:320 while most of the studies are done with titres 1:640.<br /> Study has the limitation of using retrospective cohort as control arm. Prospective selection for inclusion in Plasma arm and retrospective selection in control arm both can increase observer bias.

Nevertheless it is important be because it may not be practically possible to take consent of sick patient to be included into placebo group depriving him of so much advertised benefits of plasma therapy.

On 2020-07-29 18:21:23, user Alen Stojanac wrote:

When I've first noticed that anomaly in charts, my first thought was on the line of some of the comments - that it had something to do with the way reports were made. But this gives far more plausible explanation. Thank you.

On 2020-07-30 06:51:21, user Marm Kilpatrick wrote:

Thank you for this important study. Could you clarify if there is a typo in both Table 1 & 2 concerning the 2nd age group? It says 25-29 in both cases, but this would mean there are no data for ages 15-24. Should the age group in both cases be 15-29?<br /> thanks,<br /> marm

On 2020-07-30 13:59:42, user Pablo Richly wrote:

Could you please provide the rational of including the Rasheed et al study in the RCT group analysis since the authors stated in their paper that "21 of the patients were randomly chosen to take CP, while other age- and sex- matched 28 patients were under the conventional therapy as control group".?

On 2020-07-30 23:33:03, user Sluggo67 wrote:

The spatial pattern of Covid 19 deaths has substantially changed since the authors last updated the manuscript. Will the manuscript be withdrawn until the statistical analysis can be updated to incorporate all of the data from this evolving global health crisis?

On 2020-06-30 12:35:03, user Dude Dujmovic wrote:

May 4 to May 12. That was sort of ages ago. OK as a snapshot I guess.

On 2020-08-01 23:31:09, user Michael Verstraeten wrote:

I would like to add 2 comments on this article.

1. Specific governmental instructions to family doctors on testing, providing general care and hospitalisation criteria, affect also the randomness of the used samples. Patient with general complaints, but with a suspicion of Covid - 19, were refused or postponed for other needed care so that they were excluded from testing. In this way an unknown part of the population infected with Covid - 19 was excluded from your samples.

2. To state that "we are still far away from natural herd immunity" and "50 - 75 % of a population would need to have protective immunity (...) in order to achieve herd immunity mitigating subsequent waves of Covid - 19", you refer to 2 publications. One from D'Arienzo a.o., and the other from Liu.Y, a.o. Neither of them discuss the phenomenon of herd immunity. Both estimated in an early stage the R0 for Covid - 19. Taking in account your remark about eventual T-cell dependent immunity and your reference to the serology in Sweden, and considering the unknowns about Covid - 19, taking also in account the natural decrease of Rt in several countries before the confinements, there is no evidence that without confinement the overall infection rates in countries would ever reach 50 - 70 %, nor that herd immunity or other forms of immunity would not limit the overall infection rate for Covid - 19 to much lower percentages comparable to the historical infection rates for Influenza pandemics.

M. Verstraeten <br /> MBA

On 2020-08-02 11:49:02, user Rosemary TATE wrote:

Excellent paper. Thank-you so much for uploading the appropriate checklist. This seems to be almost always ignored!

On 2020-08-02 15:44:56, user Paul McKeigue wrote:

This manuscript has now been accepted for publication by PLoS Medicine

On 2020-08-03 13:56:54, user Charles R. Twardy wrote:

Another preprint out today finds risk reduction above 1000m in the US. Assuming both your effects are real and causal, possibly the same mechanism?

On 2020-08-04 19:47:00, user Kamran Kadkhoda wrote:

The specificity of 85% doesn't support your conclusion...

On 2020-08-04 22:41:06, user Mark Andrew Jones wrote:

Great paper. Did you consider also looking at (or at least discussing) "harm" outcomes e.g. mental health, unemployment rates, government handouts?

On 2020-07-03 08:50:08, user Roberto Avelar wrote:

Very interesting work. Thank you for citing CellAge! Just wanted to let you know we have published our database now ( https://genomebiology.biome... )

Cheers,<br /> Roberto

On 2020-08-05 10:32:37, user Beata Fonferko-Shadrach wrote:

This is an excellent review, however, as creators of the extraction of epilepsy clinical text system (ExECT),we would like to point out that that your statement of the way ExECT annotates and extracts seizure frequency is incorrect. Your review states that “The ExECT pipeline … identified the phrase or sentence within a clinical document that contained the seizure frequency but does not return a numeric value…” In our paper we clearly say that for seizure frequency we extract “the number of seizures in a specific time period” and then we give examples of phrases e.g. “two seizures per day”, “seven seizures in a year”, or “seizure free since last seen in clinic.” (Table 1 – Details on the categories of extracted information and criteria for manual review).2 Whether stated as numbers or words, numerical values are extracted as numerals, and the time period is extracted as a time period i.e. day, week, month, year. The number of seizures extracted from the last example given is 0 with a point in time of “last clinic”. Ref (doi: 10.1136/bmjopen-2018-023232)

Beata Fonferko-Shadrach

On 2020-07-04 02:28:38, user Experimental Methods wrote:

Do you have any breathability (differential pressure) data for this combination? It is easy to get high filtration efficiency, but if the material isn't breathable enough, most air inhaled will come from the side of the mask, defeating its purpose

On 2020-08-08 20:29:03, user Yiyun Shi wrote:

will there be an issue taking the average for each individual's value when pooling across different imputation datasets in contrast to using Rubin's rule?

On 2020-07-05 14:02:12, user Nataliya Skrypnyk wrote:

It would be interesting to see the actual range of IL-13 in the both groups of patients

On 2020-07-06 16:29:22, user OxImmuno Literature Initiative wrote:

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

On 2020-07-07 16:30:21, user Rosemary TATE wrote:

I took a quick look at this. Looks useful, but just a couple of comments from a statistician. 1. Spearman correlation coefficients are called rho - not r (well done for using Spearman). It's the coefficient that is important so no need to report the p-value as even a small correlation will be significant whit a large number of observations. Also, you are looking at the death rates, not the numbers so need to make this clear.

On 2020-07-09 08:53:02, user Ahmed wrote:

Applying the logistic growth model to the second period (scenario) separately is unprecedented approach as modeling should include the entire data of a single outbreak. It will be useful to model (scenario 3) which includes the whole period.

On 2020-08-14 04:52:30, user Melimelo wrote:

Great paper, could you please say which hygroscopic materials you tested? (salt? rice? something else?)

On 2020-07-09 20:12:01, user scott kelley wrote:

Where is the trial with immediate treatment at time of positive test in patients over 65 without ekg abnormalities. Like all antivirals, early treatment is the key.

On 2020-08-14 22:32:38, user Andrew Foers wrote:

Really nice work, and a great resource! Looking forward to your future reports.

If I was reviewing this I'd ask for more information about the control subjects. Without details as to how the control subjects were processed, we are unable to judge if preparation differences in the control cohort contirbute to sample classification.

On 2020-07-10 21:22:21, user Dimy Fluyau wrote:

The paper presents quantitative data on the efficacy of some pharmacological classes of drugs( medications) to manage or treat benzodiazepine( BZD) withdrawal. BZD withdrawal is a life-threatening condition, and its treatment requires the immediate use of BZDs. However, beyond the use of BZD for the management of BZD withdrawal, other drugs( medications) can also manage or treat the withdrawal. Some of them present less risk of withdrawal, tolerance, or dependence. Thus, their use may be recommended.

On 2020-08-15 14:44:07, user Rakeshkumar Yadav wrote:

Which PEG was used ? PEG9000?<br /> Why the sample volume is variable and not fixed?

On 2020-07-13 09:48:00, user Rosemary TATE wrote:

Interesting, but I think the major limitation (which you do mention in the discussion) is that the "actual" number of cases is probably not that at all, due to lack of testing, or reporting. It would seem more likely that the projections for the number of cases are accurate, but the severity of symptoms is not, and so the cases are not being identified. <br /> I have never seen the following mentioned as a limitation! ".. statistical tests, p-values and confidence intervals carry their share of limitations and insufficiencies that need to be mentioned and accounted for." Could you please expand?

On 2020-07-14 04:46:01, user AJ wrote:

Interesting paper. Gives an important view of CD8 physiology following infection. As expected, but not reassuring- the CD8s are more differentiated on a background of lymphopenia. Concerning to say the least.

On 2020-07-14 12:41:27, user David Simons wrote:

There are ongoing questions about a potential protective effect of nicotine. You report tobacco use and the risk of hospitalisation which includes both current and former tobacco use. There are multiple confounders associated with these groupings (i.e. those who have increased comorbidities are more likely to be former compared to current smokers). Could you consider exploring this in further versions of your work?

On 2020-07-15 10:54:18, user quanta renormalized wrote:

I seriously wonder weather the author know the definition of power-law. Fitting any arbitrary polynomial function to a time-series data doesn't amount to a power-law.<br /> https://en.wikipedia.org/wi...

On 2020-09-07 11:05:36, user Raffaele Loffredo wrote:

Why not controlling for age distribution of cases (possibly of infected people)? In Italy and other countries the high mortality during the firs peak can be, at least partially, explained by the fact that the pandemic has been first and foremost an hospital-acquired infection. Consequently the pandemic during the firs peak has hurt the most vulnerable part of the population. This can explain the higher mortality and ICU occupacy rates during the first peak (also the harvesting effect can be at play).

On 2021-07-10 02:06:06, user Marcus wrote:

No mention of strain or variant specific data, even in limitations discussion.

On 2021-05-16 11:40:32, user ingokeck wrote:

This is a very interesting preprint, especially as they authors state the LOD which many others never give. 0.007 /ml TID50 for ORF1ab mean that you need to ingest 1ml/0.007=142 ml to have a 50% chance of infection (ignoring for one the immune protection in the mucus) at the ct vale of 33. Or, the other way round, a ct vale of 33-7 = 26 would correspond to 1ml of sample that would infect 50%. At the threshold the authors selected of ct 30, one would still need 17ml of sample to infect 50%. <br /> Also, ORF1ab is interesting because it should be much more near to the actual amount of infectious virion than for example the N-gene which gets copied many sizes of magnitude more often than the ORF1ab gene.

On 2021-05-17 21:44:07, user Red Lawhern wrote:

In your paper you write

"drug monitoring programs (PDMPs) reduce prescribing rates 8.7%, while <br /> mandatory PDMPs increase death rates from opioids 16.6%, heroin and <br /> fentanyl 19.0%, cocaine 17.3% and all drugs 10.5%"

This phrasing seems to assert demonstrated cause and effect. It is not at all clear to me that the techniques you are applying have the capability to establish that.

Likewise, there is an important question in this process that you do not address and I would hope you may be willing to attempt: recognizing that predominant modes of overdose mortality involve multiple legal and illegal opioids in the great majority of deaths, is the contribution of opioids prescribed by doctors to their patients (not diverted) statistically significant in the overall mortality rate due to all opioids, legal and illegal? Based on the relative rarity of addiction to medically managed prescription opioids, I would suggest that it is not.

Your comments are invited.

On 2021-05-18 18:17:37, user happydog wrote:

NOTE from the authors: The Brazil data used in the analysis (the data associated with the Hallal et al. (2020) serostudy; see https://doi.org/10.1016/S22... ) must be reevaluated in light of new results about the sensitivity of the Wondfo antibody tests (Silveira et al.; see: http://dx.doi.org/10.2139/s... ). We are currently working on a revised analysis with this in mind.

On 2021-05-20 03:04:05, user kdrl nakle wrote:

Your samples are way too small for sweeping conclusions you made.

On 2021-05-22 00:45:46, user Michael Plank wrote:

The model and results in this pre-print are now part of an article published in the Journal of the Royal Society of New Zealand, available open access at: https://doi.org/10.1080/030...

On 2021-05-23 07:56:10, user Sabina Pfister wrote:

Table shows significant difference in distribution of 1-2 symptoms and 3 or more symptoms between seropositive and seronegative. Yet the discussion combines the two categories into 1 or more symptoms. Looking at 3 or more symptoms separately could lead to different conclusions, with the caveat of low numbers.

On 2021-05-25 06:21:14, user japhetk wrote:

The clinical site of this trial defines case as following and positive serology alone should be counted as a case. But instead in this manuscript, positive serology is not counted as a case.

Positive serology is greater in BCG group (7/148) compared with placebo (2/153). But in the result this is apparently not counted as a case as BCG group has only 2 cases.

If my under standing is correct, this is not a proper research, not to mention. I would like an explanation.

Primary Outcome Measures :<br /> Positive for the respiratory <br /> questionnaire consisted of questions concerning the appearance of <br /> symptoms possibly, probably and/or definitively related to COVID-19 on <br /> visit 3. [ Time Frame: Visit 3 (90 +/- 5 days) ]

This is set on visit 3 (90 ± 5 days from the date of visit 1). The two <br /> groups of vaccination are compared for the primary endpoints which is <br /> composite. Patients who meet any of the following will be considered to <br /> meet the primary endpoint:

O Positive for the respiratory questionnaire endpoint when at least one of the following <br /> combination is met either at visit 2 and/or at visit 3:

-One situation definitively related to COVID-19<br /> -All four questions of symptoms possibly related to COVID-19<br /> -At least two questions of symptoms possibly related to COVID-19 as well as need for admission at the emergency department of any hospital and/or need for intake of <br /> antibiotics<br /> -At least four questions of symptoms probably related to COVID-19 one of which is "need foradmission at the emergency department of any hospital and/or need for <br /> intake of antibiotics"

OPositive IgG or IgM antibodies against SARS-CoV-2

On 2021-05-25 14:13:52, user max wrote:

what about deep sequencing of the sperm? or snp for covid pieces?

On 2021-05-25 15:23:14, user Dinofelis wrote:

The results for Niaee are inverted between control group and ivermectin group. Control group 11 death, IVM group: 4 death. See https://www.researchsquare....

Didn't check others.

On 2021-05-27 09:29:55, user MarcWathelet wrote:

What a joke, after your "mistake" inverting the control and IVM arm of the Niaee study, the RR goes from 1.11 to 0.37 yet you dare to not change a single word in your conclusion:<br /> In comparison to SOC or placebo, IVM did not reduce all-cause mortality,<br /> length of stay or viral clearance in RCTs in COVID-19 patients with mostly mild disease.<br /> IVM did not have effect on AEs or SAEs. IVM is not a viable option to treat COVID-19<br /> patients.<br /> The new "scientists": we don't care about data contradicting our preconceived interpretation, we stick to our guns! Propaganda Abteilung!

On 2021-05-27 14:01:51, user unscientific science wrote:

Dr.Niaee posted this on the previous version of this article: Hi, I'm Dr.Niaee and I was surprised that even basic data from our RCT is completely mispresented and is WRONG. We had 60 indivisuals in control groups and 120 in intervention groups and even this simple thing is mispresrntated.

You can read all the comments to this article if you click on "View comments on earlier versions of this paper".

On 2021-05-26 09:43:39, user Laura Potts wrote:

Many thanks for this article. I appreciate this is a pre read but would be grateful for clarification as within the text it states “Age was significantly associated with Long-COVID (LC28) rising from 9.9% in 18-49 year olds to 21.9% in those aged >=70” using the data in table 1 looking at over 70s 24/96 gives 25% rather than 21.9% and the sums of the ages don’t add to the overall count for both overall and L28. It would great if these numbers could be updated.

On 2021-05-26 19:18:16, user Gudsson wrote:

This study might be crucial when explaing the susceptibility to the symptoms of Long Covid.

On 2021-05-27 06:20:33, user Mike Stevens wrote:

Well, it’s not whether there is a mandate in place, is it?<br /> It’s whether the mandate is adhered to.<br /> And when people actually comply, and wear the masks, Covid spread declines.<br /> https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0249891

Funny how a disease spread primarily through droplet spread can be halted by methods that stop droplet spread, isn’t it?<br /> ...Who would have thought it?

On 2021-05-27 20:05:21, user NC91 wrote:

No one mentions the caution at the top of the article.

On 2021-05-28 12:42:16, user Barbara Elizabeth wrote:

For those who have commented here that wearing the mask (mandate or not) is what lowers numbers... another study on NIH site disagrees (and you've surely heard of this) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7680614/. The conclusion states: <br /> "The existing scientific evidences challenge the safety and efficacy of wearing facemask as preventive intervention for COVID-19. The data suggest that both medical and non-medical facemasks are ineffective to block human-to-human transmission of viral and infectious disease such SARS-CoV-2 and COVID-19, supporting against the usage of facemasks. Wearing facemasks has been demonstrated to have substantial adverse physiological and psychological effects. These include hypoxia, hypercapnia, shortness of breath, increased acidity and toxicity, activation of fear and stress response, rise in stress hormones, immunosuppression, fatigue, headaches, decline in cognitive performance, predisposition for viral and infectious illnesses, chronic stress, anxiety and depression. Long-term consequences of wearing facemask can cause health deterioration, developing and progression of chronic diseases and premature death. Governments, policy makers and health organizations should utilize prosper and scientific evidence-based approach with respect to wearing facemasks, when the latter is considered as preventive intervention for public health."

On 2021-05-28 12:53:12, user rusbowden wrote:

Earlier, I pointed out that the study does not show that mask use fails to protect the individual, mostly looking at more meta data about mask mandates versus virus spread. It's simply not what the study is about. It is about the effectiveness of mandates. The data the researchers used for mask use comes from Washington IMHE, which urges mask use. And here I will point that the researchers seem to be outside their areas of expertise. This study also supports the idea that masks cause delays in upswings, which may mean that contracting the virus took place more while unmasked. We cannot make the leap to say that wearing a mask does not help the individual. It is irresponsible to do so.

There's a further problem with the robustness of this study, which goes beyond simple timing and groupings. It has to do with not holding other factors constant. For instance, did mask use cause people to breach social distancing guidelines, and how much enforcement was used in different states. There's a tale of 2 cities here in Massachusetts, Lowell, which was lax in enforcement and attitudes, and Cambridge, which levied fines. When you entered Cambridge, you see most people wearing masks -- especially during the day -- not so in Lowell. Surreptitious unmasking did take place at night in Cambridge, especially among men (who should be wearing masks if for no other reason then to make others feel safe around them -- another psych/social, which were not allowed discussed). But, Cambridge, a more congested city of the same population size, had less cases per population than Lowell -- which as I mentioned earlier only got off the high-risk level a couple weeks ago.

On 2021-05-27 18:54:42, user alejandro wrote:

Maybe this is interesting for you https://www.biorxiv.org/con...

On 2021-05-28 00:56:34, user Jian Zhang wrote:

According to Figure 2B, there is clearly a two- to three-fold increase of anti-Syncytin-1 after vaccination (especially 1-4d and 6-7w) compared to Day 0.

On 2021-05-31 06:51:28, user Luca Prosperini wrote:

Where is the TABLE 1?<br /> Thank you

On 2021-06-02 08:09:56, user Le Bon wrote:

The idea of looking at patients who had at least a substantial cumulated dose is great, but there is an important immortal time bias, since the patients can't die the first 10 days in the HCQ group, as seen on the Kaplan-Meïer

I suggest to calculate again the results with excluding the first 10 days death in the control group.

Due to the small group size, you will probably loose significance, but it still will be usefull for pooled analysis.

On 2021-06-09 16:25:49, user livefreeinTX wrote:

It's heartbreaking to me that all those hundreds, perhaps thousands, of NYC patients in the spring of 2020 were left to sit around at home until they could barely breath and were then hospitalized and put on ventilators, and were denied HCQ, which may have saved hundreds of lives. Truly sickening, imo.

On 2021-06-10 04:44:14, user John Jay wrote:

Sorry if I missed it, but was there a discussion of how the demographics (ie age, co-morbidities, status before treatment, etc.) differed or didn’t differ between the 3,000mg HCQ + 1,000 AZM group and the rest of the patients?

On 2021-06-02 17:51:00, user Jørgen K. Kanters wrote:

The paper has now been accepted and published in Scientific Reports 11(1) 1-11 (2021)

On 2021-06-03 18:02:57, user Peter Ellis wrote:

"We conclude that it is almost certain that there is increased transmissibility that will rapidly lead to B.1.617.2 becoming the prevailing variant in the UK."

This was posted on the day Public Health England released a report showing that the Delta variant (B.1.617.2) is currently 73% of sequenced cases - i.e. it was 73% of new cases a couple of weeks ago. S gene proxy data is only a week out of date and has the prevalence of Delta at 85.4%

Forget journals not being able to keep up, even preprints can't keep up with this.

On 2021-06-08 03:53:14, user Emma Tan wrote:

What is the PPA and NPA if a PRNT cut -off of 1:20 is used instead (1:20 is recommended as positive result)?

On 2021-06-09 16:22:28, user Vojtech Huser wrote:

Great paper about a tool that is used by OHDSI researchers.<br /> Relationship to Achilles Heel prior tool would be a good added discussion. Contribution from the community to the tool (besides core authors) can also be described.

On 2021-06-11 15:37:05, user Bob Leon wrote:

Below is an excerpt from the full text stating that the purpose of the study was to prove that it was beneficial for the previously infected to still receive the vaccine. Thankfully the researchers had the ethics to report that they found the opposite of what they purposed to find.

"A strong case for vaccinating previously infected persons can be made if it can be shown that previously infected persons who are vaccinated have a lower incidence of COVID-19 than previously infected persons who did not receive the vaccine.

The purpose of this study was to attempt to do just that,"

On 2021-12-30 11:33:49, user Suruchi wrote:

CORRECTION: The crude seroprevalence was 89.5% (95% CI 89.1, 89.8).

Dr. Suruchi Mishra

On 2021-06-14 03:42:27, user Jones Onigbinde wrote:

Hi. Great work. I do however have an issue with the way you injected political dichotomy into the COVID-19 outlook. In your DAG diagram you inferred that having a right wing populist idea somehow contribute to the spread of the disease but you do not mention the radical left wing rioting that occurred all around the world in the summer of 2020. You forgot that people who are right leaning are more likely to believe that the virus actually came out of a Wuhan lab which left leaning media, scientists, and politicians termed to be a conspiracy theory for the past one year. Now the theory is becoming more and more plausible. My advise is that you should keep politics out of science. Follow the evidence wherever it may leads, that's science. It appears to me that 2020 was the year science came to die because of the propensity of people like you to tailor science to politics.

On 2021-06-17 16:13:50, user Hao Yin wrote:

Note: Contents in this draft are our preliminary results. The final results of this study are published on the journal, Lancet Planetary Health, please check out our online peer reviewed paper: https://doi.org/10.1016/S25...

On 2021-06-18 23:08:13, user Number Six wrote:

A question on the definition of "infectious" people in your model.

Are you using positive test results and defining those as infectious people?

On 2021-06-22 15:51:27, user Amit Kumar wrote:

The paper has been published in a peer-reviewed journal Aerosol and Air Quality Research with the following DOI number.<br /> https://aaqr.org/articles/a...

On 2021-03-22 13:21:54, user Stephen B. Strum wrote:

The Gorial et al. study in my opinion is a weakly positive study re ivermectin. The IVM group had 25% of patients with co-morbidities vs 45% in the non-IVM group. Gorial's reference 14 is a retracted paper (Patel). Positive findings re IVM were shorter times ot – PCR with 7 days for IVM vs 12 for non-IVM. Mean hospital days 42% less in IVM arm. I wish Gorial would have detailed the 2 fatalities in the non-IVM arm. Someone should have proofed this paper; it is very sloppily written. In contrast, the paper by Krolewiecki et al. is very impressive re IVM & the importance of pharmacokinetics.

On 2021-03-24 09:22:16, user Sarwah Al-Khalidi wrote:

This paper fills an essential gap by surveying the hesitancy rates of being vaccinated against the COVID-19 virus among Arab-speaking individuals, and investigating associated hesitancy factors. It is the first study of this scale in the Arab world, with over 36 thousand individuals from all 23 Arab countries and beyond.

The multidimensionality and the well-thought out plan of both the survey and the analysis are truly impressive. The use of 29 objective points to measure the level of Hesitancy gives this paper great power. The importance of this study is evident form results that indicate that 60% of the Arab population are hesitant to take the vaccine. This is a striking percentage to anyone fighting against this pandemic. Using multivariate analysis to deconvolute key factors effecting hesitancy makes results more comprehendible. Interestingly, results of the multivariate analysis show that people typically classified as high-risk (above 60 or have a chronic illness) are the least hesitant to take the vaccine, which could be reflective of the media and government’s influence on people’s decision.

Among the tested factors that could be affecting a person’s attitude, the frequency of taking the flu vaccine seems most convincing, and could be indicative of a person’s confidence or knowledge about vaccines. It is surprising that the hesitancy among health workers is not significantly less than that of those who don’t work in the health system.

By revealing the main barriers to taking the vaccine against COVID-19, results published in this paper are an essential step forward towards tackling the pandemic in the Arab world.

On 2021-03-24 12:15:54, user Rogerblack wrote:

This studies depression and anxiety measure ASSUMES A HEALTHY PATIENT. 'little energy', 'trouble concentrating' 'moving slowly' = a minimum score of 3 due to physical symptoms of longcovid/fatigue. If very exhausted, this can easily rise into the 'severely depressed' range.

It is not unreasonable to use the PHQ-9 or similar as a screening measure of disease severity.

To use it in a patient population suffering from fatigue, concentration problems, ... is guaranteed to cross-read between those symptoms and anxiety - it is useless without a careful assessment of each question to find if you are measuring MH, or physical symptoms.

It absolutely cannot justify sentances such as "The physical, cognitive and mental health burden experienced by COVID-19 survivors was considerable. This included symptoms of anxiety and depression in a quarter" without much more work, as it will lead to the conclusion that treating depression may benefit the patient when there is no depression, and it's a scale artifact.

PHQ9 and similar scales are designed for patients without significant physical comorbidities to the mental state they are trying to measure. The normal scale cuts are only valid for this purpose.

I note similar concerns to those raised with the C-MORE paper. (https://www.medrxiv.org/con...

Edit: response to the promoter account on twitter in July raising this issue.

https://twitter.com/SithEle...<br /> '@PHOSP_COVID<br /> What is the current analysis plan and instruments (BDI,SF36) planned to be used to measure health? I am concerned that instruments can be misinterpreted and cross react between physical and mental health.'

On 2021-03-24 15:30:41, user Stephen B. Strum wrote:

I am puzzled. The full article I found with the exact same title is different from the article found on this website. The abstracts are not the same. Here's the conclusion from the pdf I found before finding this site with the exact same title and authors:

"Conclusion: The results of our target trial emulation match with previous findings of randomized clinical trials and observational studies, which showed no beneficial effects of hydroxychloroquine, ivermectin, azithromycin, or their combinations."

Compare the above with the conclusions found on this site:

"Conclusions Our study reported no beneficial effects of hydroxychloroquine, ivermectin, azithromycin. The HCQ+AZIT treatment seems to increase risk for all-cause death."

Why is there this dicrepancy?

On 2021-03-29 19:49:55, user killshot wrote:

This paper needs major review. Statins do not "improve endothelial function". If anything they are anti-inflammatory. Also there is very little discussion of randomization. If the group is not randomized minimally with vitamin D levels, the whole study is meaningless.

On 2021-04-09 01:46:20, user Yq Zhang wrote:

Is it possible to make the data public in addition to the code?

On 2021-04-10 11:14:32, user John Smith wrote:

I have been following your studies and find it fascinating, you've done an excellent job. Would be interesting to see Turkey's figures due to their rapidly rising infection rate. I see on the Economist/NYT websites they only have Istanbul figures with an undercount ration of 1.55. Is it possible to obtain the whole country's figures do you think?

It's a shame that a lot of people look to the Worldometer site for their figures when so much of the information is incorrect/incomplete. It would be a great idea if they had a separate column showing 'total deaths including excess mortality' next to the 'total deaths' column to show a truer picture.

On 2021-05-12 13:15:26, user John Smith wrote:

Hi, I see the IHME have just published their excess death figures:

http://www.healthdata.org/s...

I was wondering how they came to their figures on Japan and Kazakhstan which differ from yours substantially.<br /> They have Japan as 108,320 excess deaths and Kazakhstan as 81,696. They also differ with many others also. Interesting reading.

On 2021-04-12 14:22:11, user Okan Bulut wrote:

Our study has been accepted for publication in the Journal of Mixed Methods Research. Please see the full citation, including the title change for our study below:

Poth, C., Bulut, O., Aquilina, A., & Otto, S. (In press). Using data mining for rapid complex case study descriptions: Example of public health briefings during the onset of the COVID-19 pandemic. Journal of Mixed Methods Research.

On 2021-04-13 07:54:42, user helene banoun wrote:

Infections within 14 days of vaccination are not taken into account: experts have warned that ADE can occur in the first few days when vaccine antibodies are at low levels and low affinity

Why were people tested in PCR? were they control PCRs, were they sick, hospitalized?

The maximum Ct to consider a positive PCR is set at 33, it has been published that from 28-30 no live virus can be cultured: why not choose this threshold?

The matching may have led to the elimination of people carrying virus fragments because their Ct was higher than 33.

On 2021-04-16 12:52:13, user bluenoser2 wrote:

Peer reviews of the study are now available at Rapid Reviews: COVID-19 https://rapidreviewscovid19...

On 2021-04-14 08:04:37, user Muhammad Yousuf wrote:

Implications of SIREN Study regarding immunity and reinfection after documented SARS-CoV-2 infection

According to this study (1) done in the UK in Health Care Workers (HCWs), the cohort having evidence of previous documented SARS-CoV-2 infection had the following observations:<br /> 1. The immunity was noted up to 7 months after the incident COVID-19 infection<br /> 2. 155 infections were detected in the baseline positive cohort of 8278 participants (1.87%).<br /> 3. The cohort with past COVID-19 after reinfection were mainly asymptomatic or had milder symptoms with no mortality.<br /> This augurs well regarding immunity in most people who have recovered from COVID-19. Immunity may last for over 7 months (more follow up of this cohort will be more informative to assess the long-term immunity. However, most of such HCWs were female and younger. The immunity duration after SARS-CoV-2 infection will need more such studies in people >65 years particularly in males who are mainly being affected by COVID-19 pandemic.

1. SARS-CoV-2 infection rates of antibody-positive compared with antibody-negative health-care workers in England: a large, multicentre, prospective cohort study (SIREN).<br /> Published Online April 9, 2021 https://doi.org/10.1016/S01...

On 2021-04-15 11:01:12, user Ian Viney wrote:

Interesting paper, and although an approximation I think it makes a good point. Having conducted similar studies to reconstruct research income, I share the methodological frustration that funders do not always provide basic financial details for awards (exceptions include UKRI, Wellcome, NIH and many others), institutions do not always provide details of the research projects they secure (exceptions include Kings College London, Edinburgh University and many others), publishers don't capture structured authoritative grant information in their articles (despite the efforts of FundRef etc.), and authenticated information cannot be easily re-used/compiled (despite the efforts of ORCID). As a result there are clearly a lot of investments that your study is missing. One element is the support that Oxford and other UK and international institutions will have provided to the work. The financial details for the grants you have identified will in the main be the amounts awarded by the funder, not the full economic costs of the work. Your FOI might have been an opportunity for you to collect the full cost of each project, and I'm surprised that Oxford didn't comment on this. As most government funded grants are awarded on the basis of 80% of full-economic costs, the institutional contribution may have been ranked third in your analysis. Of course the institutional contribution to the work will be supported from a variety of sources with one major element being the UK funding council grant, so also substantively publicly funded. This would therefore have not have changed your overall conclusion. Some nice context to the public and charity funding for UK health research, with an overall estimate of the contribution from various sectors can be found in the recent UKCRC report at www.hrcsonline.net.

On 2021-04-19 11:59:37, user lotrus28 wrote:

Has this study been submitted to a journal?

On 2021-04-20 16:21:52, user Laurie B wrote:

Thank you for conducting this important research work and making your results available online. This information must be widely communicated. My dad received his final Retuxin infusion January 2021. Shortly after he received Covid vaccination 1 and then February 4th the second. While still following most covid precautions he had certainly let his guard down as a "fully vaccinated" person. Turns out, he was not. He is in the ICU with covid. Please pursue a press release. Please let me know how I can help disseminate your findings. Thank you for your work!

This is how our family found out: https://www.nytimes.com/202...

And from an ICU Doc who shared: "Rituximab specifically target cancerous B cells and helps our immune system destroy them. But B cells are the very cells that make our antibodies so his response to the vaccine is going to be muted with or without the Rituximab."

On 2021-04-21 15:08:18, user Rafael wrote:

Interesting work. We also found Prevotella sp. associated with COVID-19 severity in https://www.frontiersin.org...

On 2021-04-26 00:57:11, user jgas wrote:

Intrigued by the 1-21 days pre-vaccination data.<br /> If the 5% of total positive PCR in the 21 days pre-vaccination (compared to 85% occurring in those more than 21 days pre-) is just down to people increasing behavioural shielding -extra NPI caution because "daft if I get Covid19 now when I'm so close to getting protection", what explains the reduction in self-reported 'typical Covid19 symptoms' i.e cough/fever/anosmia in the positives from this same period?<br /> If extra caution -> lower rate of positives and also less symptomatic positives is this mediated by scant innoculum?<br /> How does the timing of the big post-Christmas wave and ensuing lockdown interact with these data?

On 2021-04-26 13:56:45, user Zorak wrote:

2 deaths in 200 cases. As far as we know, it is the same ratio of "untreated" cases. There's no improvement with this treatment.

On 2021-04-27 05:35:58, user SSS wrote:

Ok so there are at least 100 articles showing vaccines cause thrombosis. you can start by looking at the references in this article. youre welcome. https://www.ncbi.nlm.nih.go...

On 2021-04-27 19:49:26, user louiea wrote:

The assumption: "we assume constant mask filtration pm over the entire range of aerosol drop sizes." is a bad assumption and supported by the referenced papers. Optimal mask efficiency can only be achieved with N95 masks that are properly worn (no gaps), All other masks types have filtration efficiencies around 50% or less. (ref 69).

On 2021-04-30 11:11:21, user Kontrolletti wrote:

I am surprised by some of the numbers in the Introduction section, specifically "...the cumulative hospitalization rate has exceeded 1300 persons per 100,000 since early 2020 (2). Hospitalized patients account for 1% of COVID-19 patients...".

Today the source given for the cumulative hospitalization rate gives a number of 531.5 persons per 100.000 for the week ending April 24. Also today, the CDC report a number of roughly 32 million total cases reported and a number of 2.1 million total new hospitalizations, which would mean that hospitalized patients would account for 6.5% of COVID-19 patients (https://www.cdc.gov/coronav... "https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html)").

On 2021-05-04 16:08:58, user Don Lotter wrote:

Could much of COVID vaccination resistance be due to the person having had the virus & belief that they are immune?<br /> What portion of that vaccine hesitant 30% are people who have had the coronavirus and, quite justifiably ( https://www.nature.com/arti... "https://www.nature.com/articles/s41590-021-00923-3)"), feel that they are already immune and don't need the shot? Why isn't this question being asked? Because it is an important one in the herd immunity calculation.<br /> And yes, I realize that there are no hard stats on previous infections, but an estimation could at least be made based on studies. It might pull the 30% down to, say, 20%, that 20% being those who were<br /> likely never infected and yet are hesitant. It would push the total immune percentage up in the herd immunity quest.

On 2021-05-07 16:36:27, user Gustavo wrote:

It would be very interesting if the researchers compared the levels of vitamin D (25OHD) in the two sisters. Vitamin D is actually a hormone with an immunomodulatory effect. A 2010 survey identified that sufficient levels of vitamin D are crucial for the activation of CD8 + killer T lymphocytes:

https://www.sciencedaily.co...<br /> von Essen et al. Vitamin D controls T cell antigen receptor signaling and activation of human T cells. Nature Immunology, 2010; DOI: http://dx.doi.org/10.1038/n...

• T-cells really are the superstars in fighting COVID-19 - but why are some of us so poor at making them?<br /> https://www.bmj.com/content...

This finding was confirmed by these two recent studies:

-Circulating Vitamin D levels status and clinical prognostic indices in COVID-19 patients<br /> https://t.co/FpTJ0vwWkc

-The association between vitamin D levels and the clinical severity and inflammation markers in pediatric COVID-19 patients: single-center experience from a pandemic hospital

https://t.co/Cb8kYk5wo5

On 2021-05-08 08:06:03, user Dennis Kleid wrote:

It seems to me that the model needs to take into account what folks are doing in that room. In a restaurant or a nice dinner party, people are eating, talking, laughing, and having fun over their meals. No masks, lots of aerosols; let's say just one person is sick.

The issue is: "In the presence of a quiescent ambient, they (e.g. the particles with virus), then settle to the floor". In this room, much of the exposed "floor" is interrupted by everyone's plate or food. The virus will enjoy the landing and stay for a while.

"Hey, Uncle Joe [Namath/Montana, at the other end of the table], please pass the spaghetti". Infection via the mouth needs to be considered. Airborne transmission?

On 2021-05-08 20:45:10, user greenorange041 wrote:

This is a very thorough analysis indeed. But I think it is extremely dangerous just to assume that all excess mortality is due to the COVID infection (and you do exactly this if I got it right). It may well be indirectly caused by COVID, but in fact be a more direct consequence of various restrictions imposed by governments and reduced economic activity.

When people lose jobs or their business becomes effectively banned, they do not necessarily expect to die from hunger immediately. But when usual sources of income become unavailable, it puts people under strong pressure and can quickly cause psychological problems that may make them vulnerable to other diseases and in the worst case can lead to suicides. This is especially true when the situation of high uncertainty persists over months without much hope of returning to normality quickly. And this is the moment when the risk of dying from hunger gets higher.

This is also why poorer nations are in general much more affected because their population has less savings on average and is often dependent on richer nations because this is where a considerable amount of their citizens work, including the industries affected by the restrictions (and richer countries could afford especially strict lockdowns). Even if they work in other sectors, travelling between countries became more cumbersome, which is why some of those people had to stay in their home countries having no clear prospects of finding work.

You account for none of these factors. Arguably, it is difficult, but to separate the negative effect that COVID itself has caused from the negative effect all government measures have caused is absolutely necessary for a meaningful analysis.

On 2021-05-10 09:54:41, user Auroskanda Vepari wrote:

Do the findings suggest that patients who suffered a natural infection resulting in detectable anti-spike antibodies do not necessarily require a single or double does of a vaccine?