On 2020-05-07 00:09:49, user Charles Warden wrote:

I think I saw something roughly similar in this Tweet:

https://twitter.com/manuelr...

However, I have the following questions:

1) How are you taking into consideration lack of exposure? If you looked for a difference in prognosis among infected individuals, then that would provide a control that you know all individuals have been exposed to the virus. I realize this may not be exactly what you are looking for, but I would expect a small proportion of individuals having been exposed to the virus will make achieving significance for infected versus uninfected individuals more difficult.

2) If you had antibody results, maybe this would help (even if that is also not perfect), but my understanding is that you are also not using that as a filter (which I am guessing is not available)?

3) It looks like you considered Exome data. I think that this may be good because I would have guessed you might miss a signal with SNP chip data, if the relevant variants are not common (or at least not well characterized as part of larger haplotypes). However, is it possible that variant calling for most genes is less optimal with these genes? Is there any way to go back to the raw data and see if the variant calling strategy can change anything among infected individuals?

4) If all of the above criteria are meet, do you need to consider non-genetic risk factors (such as age) into your model?

5) A lack of a significant result is not the same as saying with high confidence that a hypothesis cannot be true. I think that you should communicate what you have observed in some way, but I think some caution might be needed to avoid confusion. For example, a reader from the general public might think you are confident that you have found results that contradict reports that ACE2 (and/or TMPRSS2) may be important for COVID-19 infections. My guess is this is not what you meant, but I wonder if the limitations to these results need to be emphasized more.

If this provides me a way to ask these questions in a way that gets less attention from the general public, then I think it is good that you posted these results. Discussion about possible implications could be important, but my understanding is that this does not mean that this is strong evidence that the current public health recommendations should be changed (and I don’t want to cause any unnecessary confusion).

On 2020-01-27 07:01:41, user Perseus Smith wrote:

The paper points out valid concerns, but so do commentators below. However, the prediction model of such work is always contestable, as of today German researchers put the r0~3.

https://youtu.be/ehH0w0tkf28

Would be curious to see contesting research articles.

On 2020-01-27 17:32:05, user Iddo Magen wrote:

Another work of mine, focusing on classification of frontotemporal dementia by microRNAs in plasma. Was just submitted to JCI.

Highlight: a handful of microRNAs can classify FTD with high precision, using machine learning techniques (Figure 3)

On 2020-02-02 15:58:08, user Martin Modrák wrote:

Summary: The provided analysis can IMHO be a helpful complement to other efforts to estimate incubation rate of 2019-nCoV. The uncertainty of estimates of incubation rate and other intervals provided in the abstract is likely greater then what is reported, the numbers thus should be treated with caution. Only cases outside of Wuhan up to January 24th 2020 are included (31 - 43 cases are available for the individual subanalyses).

This review has been crossposted on pubPeer, medRxiv, prereview.org.

Disclaimer: I lack background in epidemiology to let me evaluate whether the proposed modelling approach is a standard one, if much better tools are available or if there are possible issues with the underlying data. In the following, I therefore focus primarily on the statistical aspects of the method employed, without considering alternative approaches.

The big picture:<br /> The main idea of the preprint is to use cases of 2019-nCoV reported in patients that spent only a short time in Wuhan to estimate incubation rate. The underlying assumption is that those patients could have been exposed to the virus only during their stay in Wuhan.

Strengths:<br /> The approach is interesting in that it removes the need to directly guess when/how the patients got into contact with the virus. It is also conceptually simple and requires few additional assumptions.

I find it great that multiple models for the time intervals are tested and reported. The fact that the models mostly agree increases my confidence in the results.

I further congratulate the authors on being able to put the analysis together very quickly and provide a clear and concise manuscript. I am thankful they posted their results publicly as soon as possible.

Limitations:<br /> The main disadvantage of the chosen approach is that it let's the authors to only use a fraction of the reported cases and that the approach is only valid on data from the early phase of the epidemic. Once more cases happen outside Wuhan, the number patients who have become infected elsewhere will increase and the approach of this preprint will no longer be applicable. This is however not strongly detrimental to the manuscript and it could hopefully serve as one of many approaches to estimate the characteristics of 2019-nCoV, each with its own strenghts and limitations.

There are however some specific points I find problematic in the manuscript.

1) Using AIC for model selection might be brittle, especially since the differences in AIC are very small and the AIC itself is a noisy measure. Using some sort of model averaging and/or stacking would likely be beneficial.

2) Also, no explicit effort to verify that the models used are appropriate has been reported. A simple model check would be to overlay the actual data over Figure 1 (e.g. the empirical CDF produced assuming both exposure and onset happend in the middle of the interval). Similar effort could be useful for Figure 2.

3) Taking 1 and 2 together implies there is substantial uncertainty about which model is the best. Further, no strong guarantees that at least one of the proposed models is appropriate were given. The uncertainty bounds computed using only the "best fit" model are therefore certainly overly optimistic as they ignore this uncertainty. While this is challenging to account for mathematically, I believe it should be reported prominently in the manuscript to avoid confusion.

4) While using only visitors to Wuhan makes sense to estimate the incubation period, the estimates of time from illness onset to hospitalization and/or death would likely benefit from including all cases. I don't see why only using cases outside of Wuhan for these other estimates is beneficial. I can however see why incubation period might be the primary focus of the paper and therefore a dataset with cases in Wuhan was not constructed.

5) For some reason the link to supplementary data is broken (probably not author's fault), so I cannot investigate the dataset. Code is also not available so it is hard to judge the modelling approach in detail.<br /> I have contacted the authors and will update this review if I receive that data and/or code.

The only issue I feel strongly about in this manuscript is with the abstract, which should IMHO clearly state that only a small number of cases has been used and that the uncertainty is likely larger than what was computed. Otherwise the paper seems to be a good contribution to the global effort to understand 2019-nCoV.

On 2020-02-10 13:03:42, user nCoV.su wrote:

https://ncov.su/ - this site has human-readable statistics for 2019-nCoV

On 2020-02-14 00:59:07, user acm_ian wrote:

Doesn’t the accuracy of the modelling depend on the input data. Identifying an unknown infectious agent in routine practice is not simple. It is feasible that the virus has been around longer without being recognized and the spread coincides with the usual winter influenza and other respiratory virus spread.

On 2020-02-14 11:34:58, user Igor Nesteruk wrote:

Dear friends,

On February 13 I have found tree different values of the cumulative number of confirmed cases (number of victims Vin my paper) on the official site Chinese National Health<br /> Commission:

46551; 59805 ; 59493

and the communications that they have changed the principle of cases

registration:

1) As of 12 February 2020, numbers

include clinically diagnosed

people not previously included in official counts. The definition of a

confirmed case changed to include clinically diagnosed people who had not yet

been tested for SARS-CoV-2.

2) Starting from February 12th, confirmed cases are now considered by officials as both tested confirmed cases as well as clinically diagnosed cases. All

percentage values that have this note tag, are calculated using the confirmed

cases values which are the sum of both the tested and clinically diagnosed

values. Thus any very large percentage value changes seen from the marked

percentage when compared to previous percentage values are caused by this.

I have put the new points (crosses) on the plot see attached file. I

think further statistical analysis is impossible. Please let me now, if you

have some recommendations.

Best regards,<br /> Igor

PS. Unfortunately, I cannot put any plots here. You can fint it on Research gate

On 2020-02-16 19:32:51, user Igor Nesteruk wrote:

Dear friends,

Number of coronavirus victims in mainland China is<br /> expected to be much higher than predicted on February 10, 2020, since 12289 new<br /> cases (not previously included in official counts) have been added two days<br /> later. See details in my preprint:

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

Best,

February 15, 2020

Igor

On 2020-03-02 11:50:39, user Igor Nesteruk wrote:

Dear colleagues,

We have good news. Yesterday, the number of accumulated confirmed cases in Italy was much lower that it was in Chinese on the corresponding day.

I put the new data from the official site of Italian Health Ministry.

http://www.salute.gov.it/po...

i.e.

February 25 <br /> - Vj = 332 tj<br /> =3

February 26 <br /> - Vj = 400 tj<br /> =4

February 27 <br /> - Vj = 650 tj<br /> =5

February 28 <br /> - Vj = 888 tj<br /> =6

February 29 <br /> - Vj = 1049 tj =7

To the Figure in

http://dx.doi.org/10.13140/...

Corresponding points are shown by red “stars”<br /> in the updated Figure, available on my FB page:

https://www.facebook.com/pr...

Black "triangles" show data<br /> for EU/EEA & UK +Ukraine (zero<br /> cases) from

https://www.ecdc.europa.eu/...

for the period February 22 – February 29 1058 new cases

for the period February 22 – February 29 1456<br /> new cases

You can see, that we can hope for<br /> the better scenario than in China.<br /> Let us check the development of the situation. Don’t forget to protect<br /> yourself!

Igor

March 1, 2020

On 2020-02-20 09:15:56, user Linh Ngoc Dinh wrote:

First off, I really appreciate this paper because it chose to fit time series of quarantined and recovered/death, which are less biased, to the model. However, I would like to be enlightened regarding some aspects:<br /> 1. w.r.t compartment P, based on what evidence do you think that a part of the population should be protected at alpha rate? We still have no vaccine yet.<br /> 2. w.r.t. the transition from compartment Q. Here I see there is only I (infectious/infective) can be moved to Q. However, if a person shows some symptoms but not become infectious yet (i.e. incubation period < serial interval), s/he is still considered as E (because not infectious), but might be quarantined. Should this one be included in your model?

Thanks much!

On 2020-02-21 07:57:05, user hym4063 wrote:

Good study. No new case is expected from mid March. In other words, it is still very DANGEROUS now!!

On 2020-03-05 11:51:10, user Luna Liu wrote:

If the ACE2 receptor can also mediate the entry of SARS into human cells, would it be useful to review the survivals of SARS and check if their kidney function and fertility?

On 2020-03-07 23:15:22, user Jens Schertler wrote:

Thanks for the publication!

1. Do you have data for symptom statistics by age group?
2. I suppose "Table 3: Risk factors for SAR-CoV-3 infection among close contacts" was a typo, it is SARS-CoV-2

On 2020-03-08 13:41:12, user White Blabbit wrote:

Could be the vaccines. Could also be that the normal disadvantage of immune system naiveté is removed since the Novel virus has never been seen in earth before. That paves the way for children's naturally more robust bodies (otherwise) to have a superior ability to fend off the deleterious effects of the virus.

On 2020-03-25 14:52:42, user Arturo Tozzi cns wrote:

Here you are a more focused writing on the issue of COVIFD-19/ mandatory childhood vaccinations: https://science.sciencemag....

On 2020-03-09 23:09:42, user Sasha Bruno wrote:

What was the total sample size analyzed? ...If it was solely data from “101 confirmed cases in 38 provinces, regions, and countries outside of Wuhan” that’s a statistically small sampling.

On 2020-03-17 03:45:06, user God Bennett wrote:

I foresaw this from February 9th, having started an ai based ct scan initiative:

https://github.com/JordanMi...

On 2020-03-17 19:53:18, user B. Lee Drake wrote:

Did the authors do any cross-validation? Machine learning should always have a data-split of 10-30% to evaluate the models generalizability. This is important and immediately consequential work - very much need to see some detail on how these models performed - it is not clear from the paper itself.

On 2020-03-22 20:13:37, user Sinai Immunol Review Project wrote:

This study retrospectively evaluated clinical, laboratory, hematological, biochemical and immunologic data from 21 subjects admitted to the hospital in Wuhan, China (late December/January) with confirmed SARS-CoV-2 infection. The aim of the study was to compare ‘severe’ (n=11, ~64 years old) and ‘moderate’ (n=10, ~51 years old) COVID-19 cases. Disease severity was defined by patients’ blood oxygen level and respiratory output. They were classified as ‘severe’ if SpO2 93% or respiratory rates 30 per min.

In terms of the clinical laboratory measures, ‘severe’ patients had higher CRP and ferritin, alanine and aspartate aminotransferases, and lactate dehydrogenase but lower albumin concentrations.

The authors then compared plasma cytokine levels (ELISA) and immune cell populations (PBMCs, Flow Cytometry). ‘Severe’ cases had higher levels of IL-2R, IL-10, TNFa, and IL-6 (marginally significant). For the immune cell counts, ‘severe’ group had higher neutrophils, HLA-DR+ CD8 T cells and total B cells; and lower total lymphocytes, CD4 and CD8 T cells (except for HLA-DR+), CD45RA Tregs, and IFNy-expressing CD4 T cells. No significant differences were observed for IL-8, counts of NK cells, CD45+RO Tregs, IFNy-expressing CD8 T and NK cells.

Several potential limitations should be noted: 1) Blood samples were collected 2 days post hospital admission and no data on viral loads were available; 2) Most patients were administered medications (e.g. corticosteroids), which could have affected lymphocyte counts. Medications are briefly mentioned in the text of the manuscript; authors should include medications as part of Table 1. 3) ‘Severe’ cases were significantly older and 4/11 ‘severe’ patients died within 20 days. Authors should consider a sensitivity analysis of biomarkers with the adjustment for patients’ age.

Although the sample size was small, this paper presented a broad range of clinical, biochemical, and immunologic data on patients with COVID-19. One of the main findings is that SARS-CoV-2 may affect T lymphocytes, primarily CD4+ T cells, resulting in decreased IFNy production. Potentially, diminished T lymphocytes and elevated cytokines can serve as biomarkers of severity of COVID-19.

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

On 2020-03-23 03:14:09, user Wen Minneng wrote:

Your conclusion is wrong. Both weather and public intervention could impact on the number of cases. How much does weather impact? How much does public intervention could impact?

On 2020-03-23 09:26:33, user Eric Henderson wrote:

Edit line 143 with correct sample age range if known.

On 2020-03-23 18:07:21, user Sinai Immunol Review Project wrote:

Summary:<br /> In an attempt to use standard laboratory testing for the discrimination between “Novel Coronavirus Infected Pneumonia” (NCIP) and a usual community acquired pneumonia (CAP), the authors compared laboratory testing results of 84 NCIP patients with those of a historical group of 316 CAP patients from 2018 naturally COVID-19 negative. The authors describe significantly lower white blood- as well as red blood- and platelet counts in NCIP patients. When analyzing differential blood counts, lower absolute counts were measured in all subsets of NCIP patients. With regard to clinical chemistry parameters, they found increased AST and bilirubin in NCIP patients as compared to CAP patients.

Critical analysis:<br /> The authors claim to describe a simple method to rapidly assess a pre-test probability for NCIP. However, the study has substantial weakpoints. The deviation in clinical laboratory values in NCIP patients described here can usually be observed in severely ill patients. The authors do not comment on how severely ill the patients tested here were in comparison to the historical control. Thus, the conclusion that the tests discriminate between CAP and NCIP lacks justification.

Importance and implications of the findings in the context of the current epidemics:<br /> The article strives to compare initial laboratory testing results in patients with COVID-19 pneumonia as compared to patients with a usual community acquired pneumonia. The implications of this study for the current clinical situation seem restricted due to a lack in clinical information and the use of a control group that might not be appropriate.

On 2020-03-26 15:14:37, user rx21825 wrote:

Does anyone know of data relating viral titre and symptoms? A qualitative assessment of viral presence is acceptable for clinical diagnosis but a quantitative assessment of viral load would enhance understanding of the drugs efficacy. In general terms and for ANY influenza infection, is the relationship of of viral titre and symptoms know?

On 2020-03-28 15:28:38, user timpin wrote:

Well, we'll soon know if this is correct. By my estimates there will be 7000 dead in the UK in 9 days time...

On 2020-03-30 22:55:25, user Pau Corral wrote:

None of the scenarios take into account immunity against second infection, or do they?

On 2020-03-31 00:14:04, user Alex wrote:

Have you analyzed West and East Germany?

On 2020-04-01 16:36:48, user japhetk wrote:

The study seems interesting.

However, the problems of this study's analyses, are as mentioned in the comments, <br /> they are not controlling when the infection spread in the country.

Other analyses are controlling that (for example, number of patients (or

deaths) 10 days after the 100th patients were detected, was used as a dependent measure).

Also, probably, the most accurate available BCG measure is "how long the country has advanced the BCG vaccination measure" (the year when the country stopped the BCG vaccination (or now, when the BCG vaccination is currently conducted in the country) - the <br /> year when the country started it). The current measure is not indicative as authors indicated.

I controlled these measures and have done the analyses.

The results were as follows.<br /> The partial correlation between "how long the country has advanced the BCG vaccination measure" and number of patients in the 10th day (when 1st day is 100th patients were detected in the country) after controlling the population of the country. P = 0.455, partial correlation coefficient = -0.116

The partial correlation between "how long the country has advanced the BCG vaccination measure" and number of deaths in the 10th day (when 1st day is the 100th patients were <br /> detected in the country) after controlling the population of the country. P = 0.111, partial correlation coefficient = -0.243

But the partial correlation between "how long the country has advanced the BCG vaccination measure" and when the 100th patients were detected in the country after controlling the population of the country was P = 0.078, partial correlation coefficient = 0.281.

Also "how long the country has advanced the BCG vaccination measure" is <br /> robustly and negatively correlated with GDP of the country after controlling the population of the country (p = 0.019, partial correlation coefficient: -0.292).

Also "how long the country has advanced the BCG vaccination measure" is robustly and negatively correlated with how fast the 100th patients were detected in the country (p = 0.078, partial correlation coefficient: 0.281).

But the correlation between GDP of the country and when the 100th patients were detected in the country after controlling population of the country was more robust (P = 0.001, partial correlation coefficient = -0.438).

And the correlation between "how long the country has advanced the BCG vaccination measure" and when the 100th patients were detected in the country disappeared when the population and GDP is also controlled (p = 0.322).

The partial correlation between "how long the country has advanced the BCG vaccination measure" and number of deaths in the 10th day after controlling the population and GDP of the country was P = 0.178, partial correlation coefficient = -0.210.

So, my guess is probably, there are number of spurious correlations happening in authors' analyses due to lack of important control variables, even if there are real correlations, they apparently should not be that strong (studies of the BCG's universal effects have not <br /> indicated such things either).

In Korea, China, Japan (diamond princess), the virus infected a lot of people in some regions or situations, too.

The countries of higher GDP can do more tests, they are more popular to the tourists from Asia, but they were perhaps less inclined to use masks, they were confident of their medical system and less alert. And those are the countries where BCG was "no longer necessary".

Remember one month ago, the coronavirus is an infectious disease of Asian people. Now it is an infectious disease of Western countries, who knows if it is not the disease of developing countries in the next few months.

On 2020-04-01 22:04:32, user Mr. Andrew wrote:

Singapore, Taiwan, Hong kong are litterally next to China and have only double digit death rates, all added, in total. WHY? All vaccinate their kids for BCG versus Tuberculosis. It's not a coincidence, all other countries do not vaccinate for it. Other BCG Vaccinating countries: Romania, Malaysia, Thailand..

check this map bellow (in the link) of countries which never had BCG. In entire Europe, Italy is the only one which never had BCG vaccination. Thus, they have a huge deathrate.

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

This is further proved by all countries with BCG at birth. Check out all countries and how bad they are doing with covid 19 by looking at their death rate and serious critical numbers, at the official WHO numbers: https://www.worldometers.in...

Till now all countries which have BCG at birth have extremely low death rate and people in serious critical condition, but huge infection rate (thus small percentage). Singapore, Taiwan Hong Kong were infected way back before Italy was infected.

=

Lets tell people about BCG and pressure more research on this, and if it actually is helpful give every other country which did not get it at birth: a shot. It might be a cure, I am predicting but the data does not lie.It provides viral immunity although its meant for bacteria. As your lungs are stronger. The data shows something, and look at all the countries death rate and serious critical numbers versus infected.

They are soo exceptionally good compared to all others like x30 times better. Would like your help to spread the word of BCG and more research to be done. So countries like Italy which never where vaccinated would get a shot. (the U.S is next, as they never vaccinated for BCG)

On 2020-04-02 12:47:23, user Anders Milton wrote:

The 70-plus Italians would have had BCG vaccinations when they were young, I believe. Still they die due to the covid-19 infection. How to explain that?

On 2020-04-22 23:33:18, user Silander North wrote:

2020-04-20: IHU Méditerranée Infection, Marseille, France: Early treatment of 1061 COVID-19 patients with hydroxychloroquine and azithromycin (PDF)

Quotes:<br /> BACKGROUND: Hydroxychloroquine (HCQ) and azithromycin (AZ) are promising drugs against COVID-19.

METHODS: We conducted an uncontrolled non-comparative observational study in a cohort of 1061 infected patients treated with HCQ+AZ combination for at least three days.

RESULTS: Good clinical outcome and virological cure were obtained in 973 patients within 10 days (91.7%). Prolonged viral carriage was observed in 47 patients (4.4%) and was associated to a higher viral load at diagnosis (p < 10-2) but viral culture was negative at day 10. All but one were PCR-cleared at day 15.

A poor clinical outcome was observed for 46 patients (4.3%) and 8 died (0.75%) (74-95 years old).

Mortality was lower than in patients treated with other regimens in all Marseille public hospitals (p< 10-2). Five patients are still hospitalized (98.7% of patients cured so far). Poor clinical outcome was associated to older age (OR 1.11), initial higher severity (OR 10.05) and low HCQ serum concentration. Poor clinical and virological outcomes were associated to the use of selective beta-blocking agents and angiotensin II receptor blockers (P<0.05). No cardiac toxicity was observed.

CONCLUSION: Early HCQ+AZ combination is a safe and efficient treatment for COVID19.

On 2020-04-23 03:15:30, user Zachary Blair wrote:

Mortality projections that are based on a sample limited to one of the wealthiest counties in the country will likely be dangerously flawed. This methodology ignores wealth-based health disparities and is totally irresponsible from a public health perspective. A comparative study needs to be done if you seek to make conclusions that are valuable on a national scale. These results will ALWAYS be geographically specific unless you broaden your sample.

On 2020-04-23 12:38:51, user Tomas Hull wrote:

There is a significant number of populations tested of which there is a large number, mainly younger population, who have cleared the virus out, and yet, no detectable levels of antibodies where found by the antibody test in their blood plasma.

By what mechanism those groups of people, mainly younger population, were able to overcome SARS-CoV-2 infection, if their immune systems didn't produce the detectable levels of antibodies?

Also, what does this phenomenon imply how widespread really the virus is, if many more people, who have been infected with SARS-CoV-2, are among the many of false-negatives for antibodies?

On 2020-04-18 12:39:09, user Tomas Hull wrote:

I will try to be the devil's advocate:

80% of population infected with COVID-19 have very mild symptoms or none.<br /> 75% of population with COVID-19-like-symptoms tested negative.

What's the likelihood of anybody from these 2 groups responding to the facebook ad, and participating in the study?

On 2020-04-18 15:49:32, user DickRuble wrote:

There are many question marks about this study re: population sampling and analysis.

I could find no info about the manufacturer of the test. Could it be that the test/antibody is not specific enough, and detects exposure to other coronaviruses, such as the common cold? I.e. many false positives?

On 2020-04-18 21:28:18, user Shiva Kaul wrote:

In the statistical appendix, the variance of the estimates of sensitivity and specificity seemingly have no dependence on the sample sizes. For example, if the empirically observed sensitivities \hat{r} of 91.8% and 67.6% were observed on samples 100x larger, the calculated variance would not decrease, though intuitively there would be less uncertainty.

Is there a missing factor of n, or have I just been sheltered-in-place too long?

On 2020-04-19 17:20:25, user James Kalb wrote:

Does anyone know which antibody test was used in this study? Maybe I overlooked it. I’m interested in the name of the manufacturer.

On 2020-04-19 20:28:34, user Tick Tock wrote:

Can we see an example of a set of positives, negatives and samples from the lateral flow immuno-blot used?

On 2020-04-20 08:46:50, user mendel wrote:

The study itself reports only the IgG specificity of the test kit, omitting the false error rate for IgM. It also sets the sensitivity at the lower of both values. This is mathematically consistent with accepting a sample as positive if it passes both the IgG and the IgM test. However, the package insert states that a test is positive if either one of these is detected, it doesn't require both.<br /> ,<br /> What did the study do? Page 6 states: "The total number of positive cases by either IgG or IgM in our unadjusted sample was 50". It comes down to a point of grammar: does "positive by either" mean the sample had to register positive by both?

This is a rather crucial point: if they counted a test as positive if just one of IgG and IgM was positive, the mathematical analysis is invalid and needs to be redone.

On 2020-04-20 13:43:09, user David Feist wrote:

What is the mathematical probability that all five seroprevalence tests are wrong? These tests now appear to be corroborated by many PCR tests showing 30% population infection rates (eg the "Boston homeless" results).

Isn't it possible that Swedish style policies naturally create 30% antibody immunity levels on top of existing 30% memory T cell immunity from prior common cold, corona virus infections? Virus specific, CD4 and CD8 memory T cells have been identified in recovered SARS patients - up to 4 years from infection:(https://www.ncbi.nlm.nih.go... "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4125530/)").

On 2020-04-20 18:38:21, user svenj wrote:

Academically interesting and a nice statistical exercise, but consider that COVID 19 is now the second leading cause of death in the US (https://www.dallasnews.com/... "https://www.dallasnews.com/opinion/editorials/2020/04/19/the-number-are-in-covid-19-is-worse-than-the-flu-and-is-now-a-leading-cause-of-death/)") and this is with social protocols in place. Kind of immaterial how many people statistically might have, or have had it. It's killing people at a faster rate than almost anything else right now. I can only imagine if you back off on distancing, those who don't know they have it will pass it on to more for whom it is a problem. Doesn't sound like a good idea.

On 2020-11-14 23:54:15, user Atomsk's Sanakan wrote:

The crippling flaws in this study have been pointed in many reputable sources, to the point that this paper cannot be considered an accurate estimate of seroprevalence and IFR population-wide in Santa Clara. The study over-estimates population-wide seroprevalence and under-estimates IFR.

For example, the study's recruitment method would favor those who volunteer for testing because they believe they're at higher risk of being infected (ex: they believe they were recently exposed to someone with symptoms). That's exacerbated by the fact that people need to travel to a site for testing, instead of doing self-testing at home or having researchers come to their home to test them, as done in other studies. The people more likely to put in the time and effort to travel to a testing site, are also more likely to have reason to think they were infected.

I recommend the following sources for those who want to learn about the scientific flaws in this type of study:

https://www.ncbi.nlm.nih.go...<br /> https://www.medrxiv.org/con...<br /> https://www.medrxiv.org/con...<br /> https://rapidreviewscovid19...<br /> https://bfi.uchicago.edu/wo...

And that's not even touching on some of the other problems with the study, such as the reportedly inaccurate information used to recruit some volunteers, alleged funding from an airline executive with a vested interest in making COVID-19 look less deadly so more people fly during the pandemic, etc. BuzzFeed has articles covering those points:

https://www.buzzfeednews.co...<br /> https://www.buzzfeednews.co...

In any event, there's a better designed seroprevalence study of Santa Clara underway:

http://med.stanford.edu/epi...<br /> https://www.ca-facts.org/

On 2020-06-26 13:40:46, user Eli Rosenberg wrote:

We note that this article was published in Annals of Epidemiology on June 17, 2020:<br /> https://www.sciencedirect.c...

We thank the medRxiv community for your interest in our work.

Eli Rosenberg<br /> Associate Professor<br /> Department of Epidemiology and Biostatistics<br /> University at Albany School of Public Health, State University of New York

On 2020-07-05 11:40:53, user OxImmuno Literature Initiative wrote:

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

On 2020-07-08 15:26:34, user Abhijit Mallick wrote:

The paper is informative. Good analysis of data is observed. The paper is believed to explore scope of future research.

On 2024-07-27 16:25:36, user Mahalul Azam wrote:

This article now have been published in an peer reviewed journal "The Southeast Asian Journal of<br /> Tropical Medicine and Public Health"

https://journal.seameotropmednetwork.org/index.php/jtropmed/article/view/1001

DOI: https://journal.seameotropmednetwork.org/index.php/jtropmed/issue/view/26

On 2020-04-15 23:02:06, user M. Ohta wrote:

It may not be simple overdose, because azithromycin is reported to <br /> inhibit, though weakly, cytochrome P450 3A4, which metabolizes <br /> chloroquine.

On 2020-04-16 20:32:36, user Tom Sakmar wrote:

With respect to UV decontamination, as noted earlier, it's not possible to use a UV-A/B meter (280-400nm range) to quantify the UV-C dosage from a germicidal LED (260-285nm). The dosage is clearly being underestimated. This is a major flaw. The preprint should be taken down and revised.

On 2020-04-18 15:07:55, user Bertrand DELSUC wrote:

Were there periods where the RT-PCR status of patients toggled between positive and negative and then back from negative to positive during the follow-up period of this study? If yes, did you report the last day positive in the LDVP column? Thanks

On 2020-04-18 20:15:52, user Tere mius wrote:

How are these results helpful if the analysis is conducted on nasopharyngeal swabs? We know that in asymptomatics the majority of viral load is present only in the upper respiratory tract, or am I not understanding what was reported in other papers? <br /> Furthermore, part of the symptoms are caused by the immune system itself, so a higher viral load will cause a different reaction anyway, am I right?<br /> Thanks if anyone can clarify this for me.

On 2020-04-18 20:32:07, user BaNe™ Machine wrote:

Does it matter that studies are finding antibodies in 85 times more people than reported being sick? How does this affect the models? If the spread rate is actually 85 times higher? Doesn't that make this nonsense? Thanks, please clarify. Studies out of Stanford Univ.

On 2020-04-19 10:09:36, user Ratnasingham Edward Shanthakum wrote:

I wonder if the humidity in very cold air facilitate the virus to be dried and suspended and taken further apart. Also would there be any electrical charge on the surface attract it to gas molecules.

On 2020-04-19 18:00:50, user Shang Tsung wrote:

This so called study is wrong all along.First i am surprised that the authors, among which supposedly there is an epidemiologist fail at the first basic premise of Epidemiology - non-linearity.Even looking back at Spanish Flu there were extremely strong non-linear occurences which do not lend itself to reasonable modeling ante factum.Same thing happened to attemtps to model SARS , then MERS , then Swine-Flu and so on.Also you claim that more elderly actually die from COVID than reported due to not testing, i claim exactly the oppositte, based on the current data.What i mean - in recent publications the lead pathologist of Hamburg , did a series of autopsies on alleged COVID deaths(tested positve post mortem) , what he found out that not a single person died solely or directly because of SARS 2 ! (https://www.mopo.de/hamburg...I "https://www.mopo.de/hamburg/rechtsmediziner--ohne-vorerkrankung-ist-in-hamburg-an-covid-19-noch-keiner-gestorben--36508928)I") strongly beleive that the case is the same for Sweden, just looking at the age structure of the deaths, the great majority of them are above 80, i would be more than curious if they do autopsies on a good number of the those alleged deaths , to check for real death cause.As of today 19.04, Stockholm reported the lowest number of new positive tests in a month, which completely support Anders Tegnell projection, that the Stockhlom peak is already due.

On 2020-04-20 01:20:55, user JWJ wrote:

Did I read that right, this model is predicting 96,000 dead in Sweden by June 29 2020?

On 2020-10-14 20:06:18, user Patrick Palmieri wrote:

Did a large number of COVID-19 infected people seeking health services at EsSalud know they were not receiving evidence-based interventions? In addition, were EsSalud leaders aware there were five different 'treatment groups' as well as a ‘control’ group? Thanks to the researchers of this manuscript, we can now ask for more details to address previous 'rumors' about poor clinical management of people with COVID-19. This point is especially important as the EsSalud hospitals have been sealed to family and visitors. As such, patients are left in these hospitals without advocates for their safety and wellbeing.

Although the current study presents an ‘observational cohort’ design, we need to ask about the circumstances, structures and processes, that resulted in the original data reported in this manuscript. The researchers clearly stated they ‘compare[d] five treatment groups to the standard of care treatment regimen, as a control group....’ To this point we need to ask how could these six groups (with significant variations from the care standard) be organically organized from the existing patient population at EsSalud? In this regard, I am not claiming the researchers did anything wrong, but we need to understand how EsSalud ignored or facilitated the reported situation. In fact, we should thank the researchers for exposing some potential issues only believed to be unsubstantiated rumors in the previous months. Again, we need to recognize the vulnerability of patients as there were no family members present to be advocates in the EsSalud hospitals.

In explaining the possibility for five treatment groups and one control group at EsSalud, the researchers added ‘...decision to administer one of these treatment groups depended on clinician’s own criteria guided by the Ministry of Health recommendations, which was changing over time according to updated evidence-based reviews...’ Given the fact that EsSalud is the social security system for workers in Peru with separate clinical practice guidelines claimed to be based on evidence, this statement indicates the quality of care and standards of medical practice were not monitored by EsSalud. Again, this is concerning as the patients were vulnerable, without family advocates, and there is no statement in the manuscript about the normal informed consent process for clinical management.

The current observational study indicates there was a single ‘standard treatment’ authorized yet five alternative protocols were implemented as seemingly 'n of 1' studies. How were physicians in EsSalud able to clinically manage patients with unapproved protocols that significantly varied from the evidence? Were EsSalud leaders aware of the deficiencies in clinical management that probably contributed to the deaths of many patients? As a matter of normal clinical management did each patient consent to the treatment they received understanding there were more effective treatments available?

From the information reported in this manuscript, the clinical management of patients with COVID-19 at EsSalud needs to be reviewed by external authorities to determine the possibility of clinical negligence, medical malpractice, unethical conduct, unauthorized research, and/or illegal activities. Again, I want to emphasize these comments are in no way focused on the researchers. I sincerely appreciate the researchers exposing several serious ethical concerns that have only been rumors for months.

On 2020-04-20 08:10:59, user Andrew the longwinded wrote:

For those that are thinking that this data puts the death rate of COVID-19 much lower than previously estimated, consider that these early cases may have been biased towards younger more socially active people, which would skew the mortality figures downwards.

Young CC Prof's comment puts NY city's deaths per million at 0.16% and climbing, which is consistent with Worldometer's figure of 933/Million (0.0933%) deaths per million for NY state, and is well above flu death rates.

On 2020-04-20 15:06:14, user PokeTheTruth wrote:

This article does not represent rigorous scientific study that conclusively proves the SARS-CoV-2 virus spreads from an asymptomatic person to susceptible (non-infected) people. The fact that active (symptomatic) carriers of the virus were in a closed environment (homeless shelter) sneezing, coughing, and exhibiting post-nasal drip prior to the 2-day study while they touched multiple surfaces everywhere cannot be overlooked as the collective "patient zeros." An analysis by an epidemiologist is only as good as the quality of data measured. Commingling active carriers with asymptomatic (inactive) carriers cannot be treated the same from a qualitative perspective and such information should be discarded from evaluation.

There is only one way to determine if asymptomatic carriers can infect a susceptible population and that is by a controlled experiment where one inactive carrier is introduced to a "clean" (non-contagious with any known airborne transmitted virus) group and observed clinically over the known incubation period

On 2020-04-21 19:31:05, user Pierre Balaz wrote:

Just to get all the details : which was the posology of then medications used ? (HCQ and AZT) ?

On 2020-04-22 09:23:55, user Dr Mubarak Muhamed khan wrote:

I keenly read this manuscript. My views. Following are the limitations before coming to conclusions <br /> 1. This is still not a published study in any top journals and must be taken back based on following points<br /> 2. Although a good write up, it’s retrospective study with hurried conclusions<br /> 3. Selection criteria is just based on hospitalised COVID19 patients. And at which stage drugs administered is not clear in all three groups<br /> 4. Outcome criteria is only either death or discharge. What happened to those who got discharged ? Whether there was any hastening in improvement due to these drugs? Whether there is shortening of duration due to drugs from COVID positive to negative?<br /> 5. Whether these drugs have been tried as prophylaxis? Or only used in hospitalised patients? <br /> 6. All patients included are with mean age at 70 and with many comorbidities <br /> 7. What dosages used for hcq and azythromicin ? How many days treatment given?<br /> 8. What type of pharmacovigilance noted for all groups?<br /> 9. Whether at anytime drugs discontinued due to side effects?<br /> 10. What side effects were obvious during the treatment period?<br /> 11. When patients succumbed to mechanical ventilation, how and what type of dosages of these drugs given?

*Although it is good retrospective study to know the effects of HCQ and HCQ+AZT in treatment of COVID 19 infected hospitalised patients.... it will be very much premature to conclude these drug’s role based on short experience and points raised above*

On 2020-04-22 05:16:57, user Marm Kilpatrick wrote:

This is published now in Lancet. Not sure why this isn't indicated above:<br /> https://doi.org/10.1016/S14...

On 2020-11-20 15:17:08, user sj hasnan wrote:

Excellent article. Unfortunately one huge confounding factor was the lack of face mask use recommendation by CDC and the Corona task force in the first two months of the study. Dr. Fauci himself did not use any face mask until the end of May. The number of new cases started to drop toward the end of May and June, until the 4th of July, when the public dropped all guards and pictures of swimming pools full of hundreds of reveling citizens appeared from many states. Also when we compare data from the Northern and Southern hemispheres with opposing seasons, there appears to be no relationship to the weather pattern.

On 2020-11-26 12:13:59, user Dr Gareth Davies (Gruff) wrote:

Thank you for this fascinating analysis! It brings together a great deal of very useful information, and the data were presented in useful and transparent ways, and the tables and graphs especially helpful in understanding the data.

I would like to offer some constructive feedback concerning the statistics and their interpretation, as some results appear to have been misinterpreted and this undermines this excellent work.

The use of term "statistically significant" (18 occurrences included negatives) is especially concerning and goes against best-practice. P values and confidence intervals are frequently misinterpreted by both review authors and readers. A lack of evidence is not evidence of lack of effect. This is especially concerning where interpretations of dose, frequency and trial length are interpreted, as they give the impression that some were demonstrably effective whereas others were demonstrably not effective and the latter is not something this study could ascertain and should definitely not conclude or discuss.

(Best practice recommendations from Cochrane Handbook for Systematic Reviews of Interventions version 6.1 C.15.3.2: "Review authors should not describe results as ‘statistically significant’, ‘not statistically significant’ or ‘non-significant’ or unduly rely on thresholds for P values, but report the confidence interval together with the exact P value.")

There is a great deal of heterogeneity in the studies that cannot be measured by an I-squared metric but are important and will affect. Differences in study populations, sizes, country, latitude, age ranges, comorbidities, length of trial, method of assessing outcome, dosing freqency, % participants <25nmol/L, year of study etc. can all introduce very large unmeasurable confounding bias that may strongly influence results in ways that cannot be accounted for by software calculating CIs, P values, or I^2 measures. I would strongly urge great caution in interpreting these as meaningful.

For example, in the group of studies where dose equivalent > 2000 IU/d, the studies vary enormously in almost every attribute and yet the I-squared metric suggests only moderate heterogeneity which is very misleading. It is especially telling that in some studies the reported incidence of > 1 ARI in the intervention and control arms is wildly different across studies: ~17% (Rake 2020) ~74% (Camargo 2020); ~96% (Murdoch 2012), casting strong doubt on the reliability of the measure to capture the outcome of interest to the study.

Berman 2012 showed a small population (N=124) of patients in Sweden (latitude 60°N) susceptible to ARIs (assessed with symptoms, range 40%-60%) and with measured high-prevalence of D deficiency (11.45%) responded positively to >2,000 IU with an odds ratio of 0.43 (CI 0.21 - .88). Among others, these results are combined with Camargo 2020 in New Zealand (40°S) in a very large population (N=5,056) of healthy adults with low prevalence of D deficiency (1.8%) where (ARIs self-reported cold/flu incidence ~75%) with an odds ratio 0.90 to 1.16; and Lehouck 2012 (adults with chronic obstructive lung disease).

It's hard to see how the data from these trials can be meaningfully combined. It's no surprise the comined CI was large 0.84 to 1.31 (in truth it will be far larger since bias and measurement errors have not been accounted for), but the only interpretation possible here is that we cannot interpret anything from these combined data and more research is needed.

The same problem occurs when combining individuals with deficiency (<25nmol/L) giving a combined CI of 0.53 - 1.16. This is reported as "a statistically significant protective effect of vitamin D was not seen in those with the lowest 25(OH)D concentrations" which is then wrongly interpreted to mean evidence of no effect which is simply not the case. All this means is the statistical power was too low to detect an effect with high confidence. Arguably, there IS a detectable effect if we use a lower confidence threshold. (I'm not suggesting this, I'm merely pointing out how careful we need to be interpreting statistics).

Results with CIs crossing null can say nothing about the existence or non-existence of an effect and should not be reported or interpreted as such, especially if the ranges are large. The inability to reject the null hypothesis is not proof of the null hypothesis. It's just lack of study power.

Statements such as "Greater protective efficacy of lower vs higher doses" has no evidential basis and should be removed. This analysis did not show a greater protective effect at lower doses! It showed an effect at lower doses and had insufficient data at higher doses to investigate the question. The subsequent musing over potential mechanisms to explain this imagined difference should also be removed.

I would also strongly caution against multivariable meta-regressions on trial characteristics. There are simply too many potential unmeasured confounders and sources of measurement error to trust that this method will produce meaningful adjustments. There's no telling if this would properly adjust, or conversely introduce bias and loss of precision.

I think if these issues were addressed the study contributes some very important and useful results confirming the positive beneficial effects of vitamin D, and suggests more research could help to answer the questions where the data were insufficient to cast light.

Congratulations on the paper and I hope this feedback is helpful!

Best wishes,

Gareth

On 2020-11-28 11:23:15, user Karl Pettersson wrote:

How are the final sizes of the epidemics calculated in this framework? Are they estimated by simulation? It seems that for the cases with full immunity after infection, you can use eq S31 in https://doi.org/10.1101/202... with ?=0 in ? (p. 5) for varying susceptibility and ?=1 for varying connectivity, but they need to be adjusted for the cases with partial immunity.

On 2020-11-30 19:51:40, user Igor wrote:

It's Santa Catarina, for god's sake...

On 2020-12-02 17:20:39, user Steven Luger wrote:

are there ways to furhter modify the input? Ex merv 8 filter?<br /> with full air turnover every 10 minutes but only 10% new outside air. <br /> ACH - does this refer to air change of recirculated indoor air passing through the merv 8? or does this mean full exchange of indoor air for outdoor air?

On 2020-12-05 20:36:08, user Anglo Svizzera wrote:

I'm not sure that this study is of much use at all when it comes to the conclusion "Vitamin C, zinc and garlic supplements had no association with risk for SARS-CoV-2".

This is especially the case when it comes to vitamin C and zinc as the amounts used would undoubtedly be relevant, particularly in the case of vitamin C.

Most OTC supplements of vitamin C are rarely over 1000mg and, if they are not liposomal or time-release, this may mean that much of the vitamin C is be excreted before being utilised. In fact, the UK RDA for vitamin C is a mere 40mg which is probably the amount found in many cheaper multivitamin supplements and, as such, would be unlikely to have any protective effect against Covid-19.

Many doctors have noted that patients with severe Covid disease appear to show symptoms of scurvy, thus indicating that their vitamin C levels are inadequate, which is unsurprising given that the demand for vitamin C by the body is vastly increased during viral infections.

Considering that mammals that produce their own vitamin C make far more than this amount on a daily basis, and even more when fighting infection, it would be interesting to see whether larger amounts of vitamin C spread out during the day may make a difference with regard to the susceptibility of Covid-10. Examples are that a healthy dog makes 18mg/kg a day, whilst a healthy goat makes 200mg/kg!

Regarding zinc supplementation, the forms of zinc supplements vary widely, some being more bio-available than others, so it may have been useful for the researchers to find out the amount of "elemental zinc" in the supplements taken.

Obviously this kind of study is not designed to discover whether larger amounts of vitamin C and/or certain forms of zinc might reduce susceptibility or morbidity from Covid-19 but it might clearly be worthwhile studying this in more detail.

On 2020-12-11 08:26:52, user Christian.Neuwirth@sbg.ac.at wrote:

Scientific Reports has published this manuscript:<br /> https://www.nature.com/arti...

On 2020-12-17 16:23:47, user G F wrote:

Our just published, see https://bmjopen.bmj.com/con... , detailed study protocol and data analysis plan may guide other biomarker exploration trials. You're welcome to get in touch with us (fuellen@uni-rostock.de) !

On 2021-01-03 12:28:23, user Richard Weller wrote:

Very pleased to see this randomised double-blinded controlled trial of vitamin D on COVID outcomes, and not altogether surprised that it shows no benefit. Rule 101 of epidemiology is that 'correlation does not equal causation' which is frequently overlooked by those advocating Vitamin D supplementation as a universal panacea. The only way of convincingly showing causality is by interventions studies such as yours. Numerous meta-analyses now published in the major journals showing no benefit of Vitamin D on cardiovascular disease despite the strong inverse correlation between measured vitamin D and CVS outcomes. This needs to act as a cautionary tale for those determined to imply that the link between low vitamin D levels and poor COVID outcomes is necessarily causal. 'Vitamin D tunnel vision' also has the unintended consequence of stopping researchers looking for alternative mechanisms. We have shown that non-vitamin D forming UVA inversely correlates with COVID mortality in USA/England/Italy https://www.medrxiv.org/con... This suggests that there are mechanisms independent of vitamin D by which sunlight might improve outcomes, and that we should be looking for them.

On 2020-11-18 12:34:24, user John Lambiase wrote:

Useless study. You have to dose the patient daily. The considered therapeutic range of >30.0 ng/mL is laughable. Anyone who has studied Vitamin D in a meaningful way understands dosage and therapeutic ranges of 40-60 ng/mL. There was enough anecdotal evidence out to point researchers in the right direction on how to dose patients. Not to mention where is the CBC differential results. It is interesting how all these critical covid patients had an avg platelet count over 300. Something is not right here because most critical patients are low in Platelets and Monocytes as well as lymphocytes.

On 2021-02-04 18:11:23, user Gina Assaf wrote:

Do the researchers have data on the percentage of those with autoantibodies who also developed IgM and IgG COVID antibodies from this cohort? My question is getting at if autoantibodies are mutually exclusive with antibodies. The reason I ask is we have a large cohort of long COVID patients who reported tested negative for antibodies in our patient-led research survey (most of them were tested post 3 months from infection though). https://www.medrxiv.org/con...

On 2020-09-16 07:52:15, user C'est la même wrote:

While this abstract may lead to catchy speculative headlines "60,000 undetected cases", unfortunately few will actually read this paper and realise the statistical and methodological limitations, meaning this is only of "suggestive" quality evidence and thus not generalisable.

"Perhaps there were Zero SARS-CoV-2 in Australia by July" sounds less catchy - yet the results also suggests this too.

I wonder why none of the journalists reporting on this article in the Australian media understand how to assess and communicate the quality of epidemiological studies on a simple scale of suggestive-moderate-high?

On 2020-09-16 17:38:21, user LB wrote:

This Causal Inference paper is brilliant. Why has this not been published?

On 2020-09-24 14:55:10, user Antonio Cassone wrote:

Enjoyable reading, instructive work, I warmly congratulate with the Authors for this excellent contribution to. this exciting research field of utmost importance.<br /> The Authors may wish to consider for their final peer-reviewed publication our report on D614G mutation in the first wave of Covid-19 epidemic in Italy, a country strongly and early hit by the virus soon after China<br /> (Evidence for mutations in SARS-CoV-2 Italian isolates potentially affecting virus transmission<br /> Domenico Benvenuto Ayse Banu Demir Marta Giovanetti Martina Bianchi Silvia Angeletti Stefano Pascarella Roberto Cauda Massimo Ciccozzi Antonio Cassone<br /> First published: 03 June 2020<br /> https://doi.org/10.1002/jmv... "https://doi.org/10.1002/jmv.26104)")<br /> The change conferred by the D614 mutation in the molecular structure and configuration of the S1 spike protein led us to hypothesize a role of it in enhancing SARS-2-CoV transmission. It was a short, initial communication and we clarified we had, of course, no proof of that supposition (must say, is there any real proof yet?) <br /> Antonio Cassone, MD, FAAM

On 2020-09-26 16:13:22, user Martin Balzan wrote:

thank you for this interesting study <br /> I have published this study on a possible association between phlebovirus prevalence (sicilian sandlfy fever group/bunyavirus, and low prevalance, mortality and case fataility in europe.<br /> Low Incidence and Mortality from SARS-CoV-2 in Southern Europe. Proposal of a hypothesis for Arthropod borne Herd immunity<br /> https://doi.org/10.1016/j.m...<br /> My hypothesis favours natural selection of populations with exposure to novel viruses of zoonotic origin over many generations.<br /> Flavirus (dengue), Coronaviruses (Covic), and Bunya viruses (pheloboviruses) all carry bat,<br /> and mammalian dna, in particular rodetns and hooved animals. <br /> These viruses have high mutation rate, and enhanced ability to cross species and I suspect that populations exposed to these arthropod bourne viruses have better innate and reactive <br /> immunity to SARS-COVID-2

See babayan et al <br /> 1. Babayan SA, Orton RJ, Streicker DG. Predicting reservoir hosts and arthropod vectors from evolutionary signatures in RNA virus genomes. Science. 2018;362(6414):577-80. <br /> https://www.ncbi.nlm.nih.go...

On 2020-09-27 12:30:11, user Saurabh Mandal wrote:

Dear Authors, <br /> You have mentioned that no single paper has been published by Bihar, a BIMARU state. This can be attributed for lack of medical or health science education and research institutions in this part.

I would like to know from authors, Could you please decribe that how many full-fledged research institutes are there in Bihar? How many active research groups are there who works in virus or virology research? How many virus research papers has been published from Bihar by 02/03/2020?

If authors compares with existing research status of any states and then compares with coronavorus research contribution, then it will be more robust to make attribution.

On 2020-09-28 19:59:31, user Laura Matrajt wrote:

This pre-print had a small error that was corrected during the review process. Please find final, corrected version of this work at:

https://wwwnc.cdc.gov/eid/a...

On 2020-10-03 15:16:25, user Dr. Amy wrote:

Thank you for this important contribution!! The particle number data points on the age and BMI graphs seem not to be the same. Eg The highest outlier superspreader who seems to be BMI 46 has 3000 particles and the highest outlier superspreader who is 63 has 3500 particles. 1) are all subjects’ particle numbers represented on both graphs? 2) what happens to the correlation coefficient if the outlier(s) is/are removed? 3) is it the same subject with different samples? Again, important information about droplet and aerosol behavior. It would be interesting to know if the same aerosol for Mation changes are noted with other respiratory infections or if SARS-CoV2 behaves differently.

On 2020-10-05 02:17:51, user Kiran Bhaganagar wrote:

I would like to bring to your attention a recent peer reviewed work in Environmental Research Journal on outdoor transmission, which will be very relevant to your work.

https://www.ksat.com/news/l...

On 2020-10-05 16:47:41, user Michael Sibelius wrote:

Very impressive work! Have you done any work on extending this to the second wave? Since your model could fit observed numbers of hospitalisations and ICU cases, it would be really interesting to see what it does for the second round of epidemic, now that children have gone back to school, and many people have returned to work.

On 2020-10-06 22:07:43, user Jema Rushe wrote:

A suite of interventions, including graded exercise and cognitive behavioural therapy, are<br /> needed to manage CFS and may be relevant to post infectious fatigue (57-59).<br /> Would you please consider revising the above sentence. The data from the original PACE trial (your reference 58) has been reanalysed and found not to support either graded exercise therapy (GET) or cognitive behavioural therapy (CBT) as an effective treatment for CFS. There is a general consensus among CFS physicians, researchers, and patients that both GET and CBT are often detrimental to CFS sufferers and there is a growing body of literature that supports this. The US CDC have dropped both from their list of recommended treatments for CFS. The UK are currently reviewing their NICE guidelines for CFS (update due April 2021) but they did release a statement July 2020 specifically advising people not to assume the current guidelines for GET as a treatment for mild-moderate CFS applies to post-COVID19 fatigue ( https://www.nice.org.uk/gui... "https://www.nice.org.uk/guidance/gid-ng10091/documents/statement)"). The HSE guidelines are currently under review and will presumably be published after April 2021. Thanks for your consideration.

On 2020-10-07 06:13:12, user Markku Peltonen wrote:

There were a number of comments on this manuscript on twitter early August, with concerns on errors in the calculations among others. Might be useful for others, so here is what I tweeted on August 5th 2020 (https://twitter.com/MarkkuP...: "https://twitter.com/MarkkuPeltonen/status/1290754970292281349):")

Recently there was a meta-analysis on the effects of masks conducted in Finland. A number of comments has been made about the quality of the piece, so I had a quick look at it. As the analysis was also mentioned at least in Sweden, few quick comments in English. 1/10

Background: the Finnish Ministry of Social Affairs and Health did a systematic review in May 2020 on the use of community face coverings to prevent the spread of Covid-19. There was no meta-analysis in the review, which focused on effectiveness. 2/10

The conclusion on that report was “very little research data available on the effectiveness of community face coverings in preventing the spread of COVID-19 in society.” and evidence “minor” or “non-existent”. 3/10

So, now then a formal meta-analysis, identifying the same 5 randomised controlled trials, showing an effect with relative risk estimate 0.61 (95% CI 0.39-0.96).<br /> Few points: 4/10

The meta-analysis focuses on efficacy; what is achievable potentially when perfect conditions. They do something which they call “account of bias caused by non-compliance”; ie. if persons in the mask-group did not were masks they “adjust” for this. 5/10

To me, this sounds quite controversial: In my world we look at intention-to-treat first, and then perhaps maybe on the “per-protocol”/“as treated”. <br /> Efficacy important, but this is now something different than what the original systematic review aimed at. 6/10

The problems of this accentuate in the Discussion, where the authors do not seem to understand the difference in efficacy and effectiveness, nor the fact that they are actually analysing something else than the original review, and making way too far-fetched conclusions. 7/10

There are other peculiarities, for example “Four of the analyzed studies evaluated the use of masks on respiratory infections directly, and in one the primary outcome was compliance with mask use.”. Hopefully an error, I don’t believe they actually mix the outcomes like this. 8/

. @jejkarppinen added the following comments after my initial post, which I agree with:<br /> - The potential biases in the original papers were not covered.<br /> - Quality of evidence was not evaluated at all.<br /> - Dissemination of the results did not consider the potential problems. 9/10

Finally:<br /> - I've not read the original 5 studies. <br /> - I’m not an expert on systematic reviews/meta-analyses. <br /> - I do think recommendation for masks is motivated, and the evidence is there (but not here..).<br /> - I do think we should be objective when evaluating evidence. 10/10

The original systematic review the Finnish Ministry of Social Affairs and Health in Finnish is here (english abstract only):<br /> http://julkaisut.valtioneuv...

Ps. Somebody noted the lack of preregistered protocol, which reminded me that the PRISMA-guidelines helpful when reporting systematic reviews and meta-analyses. <br /> Their checklist should be followed in reporting:<br /> http://prisma-statement.org

In addition, it was noted by Jesper Kivelä that there are errors in the calculations, these should be corrected (in Finnish):<br /> https://twitter.com/JesperK...

On 2020-10-14 16:22:00, user Yevhen Pylypenko wrote:

And then — https://www.medrxiv.org/con... :-|

On 2020-10-20 14:30:05, user Martin Dugas wrote:

In our paper serum was collected at the first patient contact (see material and methods). We used a validated commercial test kit to measure OC43 and HKU1 antibodies. We assessed 60 patients in three groups, age and gender matched. Yanqun Wang analyzed 12 severely ill and 11 mildly ill patients.

On 2020-10-14 18:44:44, user Darren Brown; HIV Physiotherap wrote:

The description of both episodic ("relapsing/remitting") symptoms plus uncertainty has specific relevance to existing literature from other health conditions eg: HIV. The Episodic Disability Framework considers the variable nature of disability, acknowledges uncertainty as a key component, describes contextual factors that influence experiences of disability, and considers life events that may initiate a major or momentous episode.

Here are some references that may be of value.

• https://hqlo.biomedcentral....
• https://www.ncbi.nlm.nih.go...
• https://pubmed.ncbi.nlm.nih...
• https://www.tandfonline.com...
• https://bmjopen.bmj.com/con...
• https://journals.sagepub.co...
• https://www.ejdisabilityreh...
• https://pubmed.ncbi.nlm.nih...

On 2020-10-15 08:32:49, user WOStavelot wrote:

Selling genetic testing is not the best formative domain for driving a clinical study. There should have been a broader base of information to permit placing symptomatology in a more useful perspective. Feels like this was patched on to an in-place system to get some additional funding. Nothing too wrong with that accept that thinking that large numbers of respondents will fix a poor design is an error. Disappointing.

On 2020-10-16 12:57:36, user Dieter Mergel wrote:

This article may help to explain a finding in:

https://www.medrxiv.org/con....<br /> Stipulating face mask wearing in Germany reduced the Covid-19 fatality rate although it did not affect the infection dynamics represented by the effective reproduction rate.

This is the most astonishing result of a data-scientific investigation: <br /> "Correlation between daily infections and fatality rate due to Covid-19 in Germany" and<br /> fits to the findings of 45% seroprevalence in Tokyo tempting to the breathtaking speculation that: <br /> "Wearing face-masks in densely populated areas is a sort of vaccination."

On 2020-10-17 19:50:42, user Martijn Hoogeveen wrote:

The paper has been accepted by Elsevier Science's journal Science of the Total Environment on October 11, 2020. Link will be shared when live.

On 2020-10-20 01:11:07, user Larry Weisenthal wrote:

Enrollment criteria are confusing: Abstract states that study subjects were symptomatic. Methods inclusion criteria state that the study was open to patients with CV19 diagnosis, but to my knowledge it wasn't clearly stated whether or not patients had to have lingering, long term symptoms.

We'd all like to know the denominator, i.e. of all young low risk patients, what percent go on to have long covid symptoms? This paper is confusing, with regard to this question.

Larry Weisenthal/Huntington Beach CA

On 2020-10-22 18:25:28, user helen colhoun wrote:

From Helen M Colhoun, AXA Chair in Medical Informatics & Epidemiology, University of Edinburgh. Honorary Consultant in Public Health Medicine.<br /> David McAllister, Senior Clinical Lecturer in Epidemiology and Honorary Consultant in Public Health Medicine, University of Glasgow.<br /> The authors should be commended on attempting to characterise long-COVID-19. Post-viral syndromes are a well- recognised phenomenon and it is important to accurately quantify the full range of the COVID-19 on health. The authors are careful to state that their reported risks pertain only to those with symptomatic COVID. However there are several reasons to think that even among those symptomatic that these results may be subject to serious bias. First of all there is a fundamental weakness of estimating risk based on a non-representative sampling frame, i.e. those who have chosen to use the app in the first place. Then after dropping around half of the 45839 persons who tested positive as being asymptomatic (the numbers in the first part of the flow diagram do not quite add up) a further 14443 are dropped because of starting to use the app whilst already unhealthy- it is not clear whether some of this represents people reporting symptoms well before diagnosis. Then 25% of those remaining are dropped for not persistently logging their symptoms (which could easily be much more common in people with no persisting symptoms than those without). <br /> Another major problem is the lack of specificity of the diagnosis. The disease state of long-COVID19 would appear to be defined as having “at least one symptom lasting more than one day” which has then been further categorised as LC28 or LC56 if symptoms persisted for these number of days. These symptoms include clearly non-specific symptoms such as “fatigue” , “unusual muscle aches and pains” and “skipping a meal”. No comment is made as to the prevalence of such symptoms in the other millions of users of the ZOE app. In the paper we find a hint of the lack of specificity in that in a matched set of test negatives we find that “Individuals with long-COVID were more likely to report relapses (16.0%)….In comparison, in the matched group of 139 SARS-CoV2 negative tested individuals, a new bout of illness was reported in 11.5% of cases.” This difference could easily be attributable to recall bias since at least a large proportion of those with positive tests will have known their result.<br /> Unfortunately this paper is being widely reported in the press as showing that “long COVID affects around 10% of 18 to 49-year-olds who catch the virus.” However those studied comprise just 15% of all those with evidence of infection and it is plausible that many of those not studied have no evidence of long COVID. That is even before we consider the problem that most people who have “caught the virus” don’t even get tested. It would be more correct to say this; “having excluded 85% of people with detected COVID-19 who were asymptomatic or did not continue to record their symptom status, we find that 10% of young people with a positive test report at least one symptom for 28 days and 2% report at least one symptom for 56 days.These symptoms are not specific for COVID-19 and are commonly found in the general population. “ We suggest that the authors to make this important distinction clear in the title of the final version of their manuscript or it will continue to be misquoted. We also suggest that they discuss the impact of the potential biases raised above more fully.

On 2020-10-23 02:17:54, user Pham Quang Tuan wrote:

Your data (Figure 1) does not support the conclusion that "As a linear trend from the first week of April, adjusted mortality risk decreased by 11.2% per week in HDU, and 9.0% in ICU". It may have been so at the beginning but has since slowed down, and would be nearer to 5% per week.

On 2020-10-24 02:24:38, user Elena wrote:

Dear author,

After reading your article, here are my comments. I will start out with positive; the title was great. It was straight to the point and exactly told what this paper is about. I like how it is broken down to individual parts in the abstract. It helped me navigate that section better. Your explanation of what studies were performed before were helpful. There were a couple of items that were missing. For example, why you chose peanuts and not of any of the other common allergens were missing. Also, in your methods, you said that you chose 3 families to get 2.5 or 5 grams and never explained why you chose 3 families and what that was for especially when all of the other families were feeding their infants one gram. I also feel that the methods and the results weren't well-organized. I feel like if you just reorganized those sections the paper would flow better.

On 2020-10-24 04:40:50, user gr2012 wrote:

I understand that very early use has some advantages.

On 2020-10-25 05:23:42, user Marston Gould wrote:

It would be interesting to run this analysis based on the GOP/Dem voting rate as of the 2018 election for the House of Representatives

On 2020-10-25 13:13:15, user John Roberts wrote:

Excellent analysis. The study mentioned that monoclonal antibodies will be added. At the top of the list should be leronlimab which is already in a phase 3 clinical study for severe/critical Covid patients with the primary endpoint mortality at 28 days. The interim analysis at 50% enrollment showed it is trending toward the endpoint and in fact another interim analysis was added at 75% enrollment.

On 2021-04-14 23:42:50, user fra setch wrote:

What is an anti-spike antibody, exactly. This doesn't match the P2 trials results for Covaxin after 1 dose.

On 2020-10-31 23:12:35, user Ron Conte wrote:

Many molecular docking studies have found that EGCG and other Green Tea Catechins are effective inhibitors of multiple SARS-CoV-2 viral components. However, dosage from green tea is lower than would be obtained by supplementation.

On 2020-11-02 00:40:26, user Md Mokhlesur Rahman wrote:

The peer-reviewed version of this paper has been accepted and published online. You can find the paper at the following link.

https://ieeexplore.ieee.org...

On 2020-11-02 02:33:32, user Atomsk's Sanakan wrote:

The paper calculates IFR using COVID-19 deaths 4 weeks after the median time at which antibody levels were measured. That's consistent with other papers that use at deaths 3 weeks or more after the median time. For example:

https://www.thelancet.com/p... (with: https://www.thelancet.com/j... )<br /> https://www.medrxiv.org/con...

The paper also notes that IFR for SARS-CoV-2 is substantially more than that of seasonal influenza:

"These results also confirm that COVID-19 is far more deadly than seasonal flu; indeed, the World Health Organization indicates that seasonal influenza mortality is usually well below 0·1% unless access to health care is constrained."<br /> https://www.medrxiv.org/con...

"Using the midpoint of that interval, we estimate that the total U.S. incidence of seasonal influenza during winter 2018-19 was in the range of 45 million to 93 million infections and hence that the population IFR for seasonal influenza was in the range of 0.04% to 0.08%--an order of magnitude smaller than the population IFR for COVID-19."<br /> https://www.medrxiv.org/con...

That is consistent with sources such as:

"The current data in Europe are consistent with an IFR of 0.5–1.0%, which is many times higher than seasonal influenza (<0.1%)."<br /> https://www.ncbi.nlm.nih.go...

"The calculated COVID-19 infection fatality rate is 1.63%, which is 10 to 40 times more deadly than the seasonal flu (fatality rate 0.04%-0.16%)."<br /> https://news.ochsner.org/ne...<br /> [for: https://wwwnc.cdc.gov/eid/a... ]

"In summary, we estimate that the overall COVID-19 IFR ranges from 0.14 - 0.42% in low income countries to 0.78 - 1.79% in high income countries, with the differences in those ranges reflecting the older demography of high income settings.<br /> [...]<br /> Our estimates of the IFR of COVID-19 are consistent with early estimates and remain substantially higher than IFR estimates for seasonal influenza (<0.1% in the USA) [...]."<br /> https://www.imperial.ac.uk/...

World Health Organization, in October:<br /> "Several of these analyses have used published or pre-print seroepidemiologic results and they all converge around a point estimate of around 0.6%.<br /> That may not sound like a lot but that is a lot higher than influenza [...]."<br /> https://www.who.int/publica...

On 2020-11-03 05:06:24, user KOTTAISAMY K wrote:

Hoe to download the dataset from Taiwan's National Health Insurance Research Database (NHIRD).kindly share the dataset link or share the dataset. its very useful for My Research works. Otherwise share the dataset to mail. THANK YOU..

On 2020-11-03 20:39:17, user Geoff wrote:

It's all hypothetical. The isolation and purification of the virus viewed under an electron microscope has never occurred. Then you have to show the virus you think you isolated and identified can multiply. Another step would be to introduce it to a host to see if it causes symptoms. Since none of this occurred to prove the virus exists, then small RNA or DNA segments collected from a cesspool sample can never verify if it is endogenous or exogenous in nature. And using computer programs to piecemeal a virus together is very suspect.

On 2020-11-10 00:27:08, user Mesut Erzurumluoglu wrote:

Now published in Environmental Research: https://www.sciencedirect.c...

On 2020-11-12 15:29:00, user Barbora wrote:

Figure 2 and the description on page 7 for teh late phase do not seem to match:

Description:<br /> "The sensitivities of the assays ranged from 40 to 86% for the early phase samples, 67 to 100% for the middle phase samples, and 78 to 89% for the late phase (Figure 2)."

Fig2: late phase goes up to 100%

On 2020-11-13 15:19:50, user Abhay Sharma wrote:

This finding that moderate adult COVID-19 patients administered a single dose of intradermal BCG achieve faster resolution of hypoxia, and significant radiological improvement and viral load reduction, without showing evidence of BCG induced cytokine storm, is supported by a recent epidemiological transcriptomic evidence that BCG vaccination induces persistent upregulation of antiviral defense response and downregulation of myeloid cell activation in blood cells (https://doi.org/10.1101/202... "https://doi.org/10.1101/2020.11.10.374777)").

On 2020-11-19 07:37:44, user Juan Miguel Antón Santos wrote:

I am glad to inform everyone that our paper is already peer-reviewed and published. It now includes information on 15111 patients.

You can find it here:

https://doi.org/10.1016/j.r...

Please cite this article as:

Casas-Rojo JM, Antón-Santos JM, Millán-Núñez-Cortés J, Lumbreras-Bermejo C, Ramos-Rincón JM, Roy-Vallejo E et al.. Clinical characteristics of patients hospitalized with COVID-19 in Spain: results from the SEMI-COVID-19 Registry. Rev Clin Esp. 2020;220:480–494.

On 2021-05-19 17:50:25, user marchenrion wrote:

This article has been accepted for publication in the International Journal of Epidemiology published by Oxford University Press.

The DOI for the version of record is https://doi.org/10.1093/ije....

On 2021-05-25 19:41:53, user rusbowden wrote:

A further point needs to be made, before we throw away our masks -- that masks do not help the individual wearer. This research barely touches on that, thus is open to be used politically to cause virus spread. And if an individual thought that this research showed that a mask does not help the wearer, it could cause many to shed their masks when it would be dangerous.

Here in my city, we just dropped off the "high risk" or "red" category last week, which means too many spreaders were walking around town, many willy nilly. Should I still be wearing a mask, can it stop the virus from getting to me? This study does not answer that question. If masks help as many studies shown or at least indicated, then public health officials need help now in getting the message out.

Let's backtrack and attempt the assumptive leap for a moment and try to affirm that this study points to a yet-proven truth, that all the wearing of masks was futile, because of the ineffectiveness of the mask only, which even causes problems for wearers. Let's try to affirm that with a thought experiment. Imagine with me that what got invented is the dream 100% mask back in April 2020, that (1) when worn, causes no discomfort or infection in the wearer and (2) completely prevents any wearer from being able to catch the covid virus. What if any difference would this have made to the results of this study? The hypotheses this leads to have to do with how human behavior and politics help viruses spread -- even in the face of mask-wearing.

For instance, consider the work of Ngaire Woods, founding dean of the Blavatnik School of Government and professor of Global Economic Governance at the University of Oxford. In his article, "What factors have determined how well countries have done in responding to the pandemic?," Richard Smith, a fellow of the Royal College of Physicians of Edinburgh, writes that "Woods identified three factors that had a strong influence on how well countries did," and, "The single most important factor, she argued, was effective collaboration between national and subnational governments."

Could this be translated for our purposes here, to be that there was little "subversion" by the populace too, that more successful countries had fewer parties or funerals where masks were shed, events which I witnessed and attended. It only takes one bullet to hit a target, so catch the target when the mask is off or down under the nose. In other words, if you were a virus, how would you enlist "subversive grandmother soldiers," say, to unmask around their grandchildren, allowing a snaking trail of the virus to spread from household to household. How many people broke their social bubbles?

On 2021-05-30 05:39:10, user FatBoy “JD” Diesel wrote:

Seeing people going back and forth here, but I'll just point this out:

If wearing masks are really that effective, as some claim, how are medical staff getting infected on the job despite the extra medical gear, protection, and protocols? The overwhelming majority of you can only provide conjecture at best. If medical staff adhering to strict sanitation protocols still get infected by a respiratory illness on site, what makes anyone believe that less effective equipment, or less physical coverage provided by said equipment, would be effective for a general population less equipped to adhere to stricter sanitation protocols?

On 2021-05-26 12:04:01, user zega wrote:

Paper has valid conclusions, but uses "work around" method to make that conclusion, this paper compares ACTUAL blood levels with positivity rate https://journals.plos.org/p...

On 2021-05-28 05:59:35, user ????? ????? wrote:

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 mispresented.

On 2021-05-30 16:14:37, user wj wrote:

The amount of misrepresentation of data in this meta-analysis as outlined in below comments reaches the level of scientific misconduct, given that the Niaee RR was corrected but the associated conclusion was not. I have faith that peer reviewers will reject based on this glaring falsity if the authors do not retract beforehand.

On 2021-05-30 17:55:22, user fatlas wrote:

When the data for Niaee study was corrected for version 2 the weights given to the studies were also changed. The weights in figure 2 changed in a way that is hard to explain by the Niaee data fix alone.

Was any other parameter adjusted for version 2 than data for Niaee study? If so, on what basis was the other parameters changed?

On 2021-05-28 13:37:01, user Jose Usme wrote:

Here you can access the last published version: https://wwwnc.cdc.gov/eid/a...<br /> DOI: 10.3201/eid2612.202969

On 2021-06-04 09:55:17, user Najmul Haider wrote:

This article is now published in the journal "Health Science reports". You can access to the full article free: https://doi.org/10.1002/hsr...

On 2021-06-04 14:47:57, user S Venkata Mohan wrote:

This pre-print has been peer-reviewed and published in 'Science of the Total Environment Journal', doi: https://doi.org/10.1016/j.s....

On 2021-06-04 19:54:51, user Nick Tustison wrote:

https://www.nature.com/arti...

On 2021-06-07 06:20:19, user Srinivasan Madhavan wrote:

What's the quantified value of Antibody. Which testing method was used...is it ELISA .or ECILA . How is Humoral response comparable between an infected- non vaccinated individual versus non infected vaccinated individual please.

On 2021-06-09 13:47:00, user Angela Kelly wrote:

‘Vaccine MRNA was not found in breast milk’ did you look for the spike protein it that may have already manufactured in the mother?

On 2021-06-09 19:49:55, user Emily Russell wrote:

It's important to note that a null statistical finding does not indicate that there is no evidence of association. With only 1,359 individuals who were previously infected and unvaccinated, I question that this study has enough statistical power to support the bold claims in the discussion. I would like to see a power analysis as part of the final published work.

On 2021-06-10 14:55:18, user J.A. wrote:

The statistical analysis is invalid suffering from an very clear immortal time bias. This is really basic stats 101. There is a reason this pre-print has not been accepted in a peer-reviewed journal.

For a Kaplan-Meier survival analysis, groups need to be defined at baseline. For persons to have received >80 mg/kg cumulative HCQ dosing (where a standard 600mg dose could is ~8mg/kg/day), persons need to survive for 10 days. So Figure 2, compares those who survive to receive 10 days of HCQ versus all others This is an immortal time bias.

As 88% of people overall received HCQ, likely a large proportion of those 40% who died before day 10 received HCQ (at max only ~12% could not have received HCQ).

The authors should re-do their analysis using a Cox-regression with a time-dependent covariate for receiving HCQ.

On 2021-06-11 10:21:12, user wolvverinepld wrote:

You not use correct mortality excess data per age adjusted:

"Many early reports comparing excess deaths resulting from the COVID-19 <br /> pandemic did not take account of population size, age distribution and <br /> focussed mainly on the first phase of the pandemic. Here, we provide <br /> updated estimates of excess mortality rates overall of 2020, <br /> standardised to a reference population"

https://excessmortality.shi...

https://www.cebm.net/covid-...

On 2021-06-14 04:23:29, user Eyad Qunaibi wrote:

This article has been peer-reviewed and published: <br /> https://elifesciences.org/a...

On 2021-03-19 14:42:27, user NermeenHM wrote:

I am so impressed with the extent of effort exerted on this paper and how it was deployed on such a large number of participants in the Arab world! Frankly, I never expected anything to come out of our part of the world with this high level of proficiency and accuracy + all this amount of participation. That’s quite a big number to be disregarded or dismissed particularly when, being a part of the Arab world myself, I know that it reflects a great deal of what we see around us and hear from friends, neighbors, colleagues and relatives. These results explain so much about how people think in this part of the world, also a lot more about the “why?” and the “who?” which were missing from the blurred and (usually) foggy picture when it comes to accurate figures and surveys. We’ re always “undocumented” in so many ways even with a pandemic looming high above our heads! Indeed, the issue of Covid vaccination is not something to be trifled with or treated lightly! I would highly recommend two things:

First: I quote the conclusion, “With the highly dynamic nature of the pandemic and vaccine production process and the interplay of ever-changing factors that affect vaccine acceptance, our study needs to be replicated at a later time to measure the change in public acceptance.” Yes please, that would be something really beneficial + informative.

Second: Due to the great importance of the topic and the rapid impinging of Covid on the<br /> world map and our societies, you should accept this paper to help others make use of its findings.

On 2021-06-18 19:40:48, user Ivan Sudofsky wrote:

Would it be ethical to quote directly from the study?<br /> "There were no notable differences in symptom duration or severity between the treatment groups over the 28 days."

It may clear virus from the limited compartment of the nasal mucosa, but fail to be effective at the lungs or the circulatory epithelia, where the most severe symptoms arise.

???

On 2021-09-29 22:14:43, user Hiam dodu wrote:

Saudi has very low rate of infection since the beginning and the conclusion contradicts what the manufacturer revealed.

On 2021-06-20 12:22:57, user Hlodovic wrote:

Has anyone given consideration to the fact that Ivermectin is administered to animals for intestinal parasites (worms, mainly)? Could it be that the ivermectin used in this study killed some kind of parasite common to most humans, thereby relieving the natural defense system from that fight and allowing it to attack the virus full force. Being already exercised by the battle of the parasites, the defense system would be strong. Suddenly relieving it of that battle could be like when you go in your car, but it seems to require that you give it a lot of extra gas. You realize the parking brake is on and release it. So the car suddenly takes off, throwing you back in the seat.

On 2021-06-24 15:13:53, user dami Onifade wrote:

In paragraph describing table 3. "these results would indicate effectiveness of 45% and 76% respectively for B.1.617.2" How have you derived these estimates?

On 2021-06-24 20:22:50, user Mikko Heikkilä wrote:

It seems that the Adjusted Odds Ratios are not all adjusted for covariates but are Crude Odds Ratios. This is for at least Macintyre 2015 and 2016 and Aiello 2010 and 2012 papers.

It is worth noting that even some of these are erroneous as for example Aiello et al. 2010 subtracted individuals with previous respiratory infections and therefore the intervention groups for mask and hand hygiene were actually 316 (367 in Ollila et al.) and for the mask only 347 (378 in Ollila et al.). As previously reported to Ollila et al for their first version (published 8/2020) if the aim is to define the effect of the mask as intervention using the results from both intervention groups does not render robust results.

Also, for the Abdin et al. 2005 paper the original intervention groups were 257 (mask and Health Education booklet), 292 (HE booklet only) and 446 (control). It is apparent that Ollila et al. used the compliance within the groups as redefining the groups: mask+HE booklet compliance 81.3% (209), HE booklet group compliance 51.7% (151) and control group using masks 33.6% (150). New intervention group 150+151+209=510 and control 995-510=485. Abdin et al. only reported the adjusted OR with these compliance based groups and no RR or OR based on the original Randomised Control Trial protocol. As Ollila et al. is by the authors a systematic review and meta-analysis of Randomised Control Trials these results should not be used as they have.

More, from the Simmerman et al. 2011 Ollila et al. chose only the Influenza Like Illness (ILI) results for their meta-analysis even though Simmerman et al. also reported PCR confirmed infections. For the sub-analysis with Adjusted Odds Ratios Ollila et al. preferred to use the PCR confirmed results from the same paper with OR 1.16 (95% CI 0.74-1.82) instead of the ILI results that were OR 2.15 (95% CI 1.27-3.62). The latter would push the pooled result much more towards the null hypothesis.

The same goes for the Cowling et al. 2009 where the authors reported respiratory infections based on three criteria: RT-PCR confirmed, Clinical definition 1 (2 symptoms) and Clinical definition 2 (3 symptoms). Ollila et al. only used the Clinical definition 2 results for their meta-analysis.

With these and all previously reported research flaws one can only conclude that the conclusions of Ollila et al. are not supported by the results if calculated correctly.

On 2021-03-14 17:30:22, user Vikingman wrote:

Hi<br /> Would there be an updated version of the table in page 7 of the PDF other than 29 January? Thanks. Keep up the amazing work.

On 2021-03-17 20:32:20, user Alessia Visconti wrote:

Now published in the British Journal of Dermatology (https://onlinelibrary.wiley... "https://onlinelibrary.wiley.com/doi/full/10.1111/bjd.19807)")

On 2021-03-19 17:43:57, user Kebbiet wrote:

Wait.....what were the mother's antibody titres BEFORE the vaccination? "Covid naïve" could be asymptomatic natural antibody formation after a year of frontline care provided during the pandemic. As with PCR testing, a negative result is only good at the time the test sample was obtained. As soon as the patient leaves the testing site, they may be exposed and turn positive from that exposure. To establish true Covid naivety, we would need to know whether there were detectable maternal antibodies immediately before administration of the 1st shot. A negative antibody test 6 months prior is of no value in establishing a direct connection of neonatal antibodies to the vaccine. We already know that Covid+ mothers have given birth to babies with detectable antibodies. We are missing a correlation step in this analysis.

On 2021-04-05 00:50:51, user Brandon Grant wrote:

The authors conducted secondary analyses to examine the effect of study type on the outcome of mortality for the studies of SARS-CoV-2 that were included in the primary analysis. However, the authors do not mention conducting secondary analyses to examine the same correlative relationship for any of the studies of any other virus besides SARS-CoV-2. This lack of equivalent data analyses and resulting lack of homogeneous comparisons of outcomes renders any conclusion based on the aforementioned data null and void.

On 2021-04-09 01:13:34, user Patrick Palmieri wrote:

Did a large number of COVID-19 infected people seeking health services at EsSalud know they were not receiving evidence-based interventions? In addition, were EsSalud leaders aware there were five different 'treatment groups' as well as a ‘control’ group? Thanks to the researchers of this manuscript, we can now ask for more details to address previous 'rumors' about poor clinical management of people with COVID-19. This point is especially important as the EsSalud hospitals have been sealed to family and visitors. As such, patients are left in these hospitals without advocates for their safety and wellbeing.

Although the current study presents an ‘observational cohort’ design, we need to ask about the circumstances, structures and processes, that resulted in the original data reported in this manuscript. The researchers clearly stated they ‘compare[d] five treatment groups to the standard of care treatment regimen, as a control group....’ To this point we need to ask how could these six groups (with significant variations from the care standard) be organically organized from the existing patient population at EsSalud? In this regard, I am not claiming the researchers did anything wrong, but we need to understand how EsSalud ignored or facilitated the reported situation. In fact, we should thank the researchers for exposing some potential issues only believed to be unsubstantiated rumors in the previous months. Again, we need to recognize the vulnerability of patients as there were no family members present to be advocates in the EsSalud hospitals.

In explaining the possibility for five treatment groups and one control group at EsSalud, the researchers added ‘...decision to administer one of these treatment groups depended on clinician’s own criteria guided by the Ministry of Health recommendations, which was changing over time according to updated evidence-based reviews...’ Given the fact that EsSalud is the social security system for workers in Peru with separate clinical practice guidelines claimed to be based on evidence, this statement indicates the quality of care and standards of medical practice were not monitored by EsSalud. Again, this is concerning as the patients were vulnerable, without family advocates, and there is no statement in the manuscript about the normal informed consent process for clinical management.

The current observational study indicates there was a single ‘standard treatment’ authorized yet five alternative protocols were implemented as seemingly 'n of 1' studies. How were physicians in EsSalud able to clinically manage patients with unapproved protocols that significantly varied from the evidence? Were EsSalud leaders aware of the deficiencies in clinical management that probably contributed to the deaths of many patients? As a matter of normal clinical management did each patient consent to the treatment they received understanding there were more effective treatments available?

From the information reported in this manuscript, the clinical management of patients with COVID-19 at EsSalud needs to be reviewed by external authorities to determine the possibility of clinical negligence, medical malpractice, unethical conduct, unauthorized research, and/or illegal activities. Again, I want to emphasize these comments are in no way focused on the researchers. I sincerely appreciate the researchers exposing several serious ethical concerns that have only been rumors for months.

On 2021-08-01 07:53:29, user Dlya Truby wrote:

Folks read "Supplementary Appendix" page 12: Deaths: Vaccinated group - 15; Placebo (unvaccinated): - 14.

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 2021-04-16 18:14:19, user Judy Hodge wrote:

I am curious what vaccine the mother that was tandem feeding received? Also am I reading your data correct that the Moderna elicited a higher IgA response in breastmilk?

On 2021-04-16 18:43:40, user Lee Rague wrote:

This paper has been published in the Journal of Nursing Management

https://doi.org/10.1111/jon...

On 2021-04-28 07:44:22, user Lee Rague wrote:

this paper is now published

https://onlinelibrary.wiley...

On 2021-04-17 17:36:18, user Mando Dido wrote:

This article available at:

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

On 2021-04-22 18:18:59, user friidrottare wrote:

What about the protection against the variants, such as the South-African ?

On 2021-04-23 13:52:16, user Dijon Mustard wrote:

The title says "up to 12 months", the abstract says "at least 12 months". Please change the title to accurately reflect the paper's findings.