On 2022-02-08 08:06:00, user kdrl nakle wrote:

Plus of course large portion of population gets annual vaccination for Influenza so there is no such drama about future flu as some would like us believe. But we better prepare for H5 coming.

On 2020-06-19 08:35:06, user Takeshi Miki wrote:

I am not convinced by the authors' conclusion at all. Please check my comments from the point of mathematical biology in PubPeer. https://pubpeer.com/publica...

On 2021-09-01 04:18:17, user John Smith wrote:

Surgical face masks at best have a 3.4 fold decrease in aerosols if worn perfectly, but in this case the typical imperfect fit would drop this down to about a 1 fold decrease. The math in this simulation is far off the mark compared to detailed peer reviewed experiments. Too many incorrect assumptions made in the simulation.

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

On 2020-08-23 16:11:32, user Ang wrote:

Hello there,<br /> below a question for someone with the right competence.

True the approach of this work is great, it might result that they are right or wrong we'll see, starting from the review outcomes. However a common person would ask: "Why can't we do a direct and conclusive experiment about transmissivity through aerosol?". A direct experiment is to put a never infected person in the same room with a SARS-CoV-2 ill person, without the physical possibility to exchange any particle between them except air/aerosol. 100 person would cover a good statistics in terms of age, gender, time of exposure and other characteristics of the volunteer. Is this possible? How can be that in the entire world we cannot find 100 voluntaries that are available for the following experiment. Why is this something not done yet?

On 2020-08-14 09:13:14, user Alexandre Júlio wrote:

Louis Pasteur taught us the importance of Pasteurization. Against air-borne epidemics, we are needing a safe indoor place to drink & eat. Up-down HEPA laminar flow is a speciality that I knew only in semiconductor clean rooms. Against Covid-19, my faith is going to UHT treated air, cooled to lower than 50ºC by heat-exchange with aspirated air. Further cooling may be provided by humidification with sterilized water and/or heat pump. What are UHT-air parameters used in Japan?

On 2021-08-01 21:16:57, user BiotechObserver wrote:

Ongoing for safety followup. The trial already met its primary endpoint for efficacy. There is no reason to continue it at that point and the participants would not have agreed to volunteer if it meant they'd have to wait 2 years to receive a vaccination if by chance they ended up in the placebo group. All trials get unblinded at the primary efficacy endpoint readout.

On 2022-01-14 14:59:21, user mike tomlin wrote:

Those ending up in hospital are more likely to be elderly who are more likely to be vaccinated, so it's not surprising more are in hospital. Presumably not dying as often as the elderly non vaccinated.

On 2021-10-19 01:20:29, user Sir Henry wrote:

From the study: "median age was 51 years"

On 2021-08-14 06:20:55, user john vegan wrote:

No, it does not say that they died "as s result".

However, it says that "Between 8 December 2020 and 11 June 2021, a total of 5,522 people died within 28 days of receiving a COVID-19 vaccine in Scotland".

All of them died "as a result" ? How can we be sure ?<br /> None of them died "as a result"? How can we be sure ?<br /> Some of them died "as a result"?, And, if yes, how many ?

On 2021-08-09 05:35:12, user Tim Freeman wrote:

You picked that row out of 26 other rows, so you have to correct for multiple analyses. To a first approximation that multiplies the odds of seeing something like that by accident by 26, so not significant.

On 2020-11-15 21:13:34, user Atomsk's Sanakan wrote:

Some flaws in this study that render it's IFR estimate unreliable:

1) He uses many studies that over-estimate the number of people that were infected [and thus under-estimate IFR], since these studies were not meant to be representative of the general population Ioannidis applies them to. He doesn't even follow PRISMA guidelines for assessing studies for risk of bias in a study's research design. "Bias" here does not refer to the motivations of the study's authors, but instead that the design of their study would likely cause their results to not be representative of the general population.<br /> 2) He exploited collinearity by sampling the same region multiple times, in a way that skews his results towards a lower IFR. He conveniently tends to avoid sampling an area multiple times when that area has a higher IFR.<br /> 3) He adjusts IFR downwards for reasons not supported by the analysis he cites for that adjustment.<br /> 4) He takes at face-value areas that likely under-estimate COVID-19 deaths, such as Iran, causing him to under-estimate IFR further.<br /> 5) He uses inconsistent reasoning to evade government studies that show higher IFR, even though governments are doing much of the testing needed to determine IFR. That includes Ioannidis ignoring large studies from Italy and Portugal that are more representative of the general population they sampled.<br /> 6) His IFR from a study in Brazil contradicts the study's own IFR, and his explanation for that makes no sense. This conveniently allows him to cut the study's IFR by about a 1/3.<br /> 7) His use of blood donor studies does not make sense, even if one sets aside the fact that blood donor studies would over-estimate population-wide seroprevalence. For example, he uses a Danish blood donor study that leaves out deaths from people 70 and older, to claim an IFR of 0.27% for adults. When those researchers performed a subsequent study in which they included people 70 and older, they got an IFR for adults that's 3 times larger than Ioannidis claims [0.81% vs. 0.27%].

And so on.

The sources below provide further context on this:

https://rapidreviewscovid19...<br /> https://rapidreviewscovid19...<br /> https://rapidreviewscovid19...

https://twitter.com/GidMK/s...<br /> https://twitter.com/GidMK/s... [ https://threadreaderapp.com... ]<br /> https://www.medscape.com/vi... { http://archive.is/O3vGs , https://threadreaderapp.com... }<br /> https://hildabastian.net/in...<br /> https://twitter.com/AVG_Jos...<br /> https://twitter.com/Atomsks...<br /> https://twitter.com/Atomsks...<br /> https://twitter.com/Atomsks...

"Estimation without representation: Early SARS-CoV-2 seroprevalence studies and the path forward"<br /> Not-yet-peer-reviewed: "Assessing the age specificity of infection fatality rates for COVID-19: Meta-analysis & public policy implications" (comments on "selection criteria")

On 2020-07-28 15:02:30, user Liam Golding wrote:

In regard to your sample size, do you think running in triplicate may answer some of the unknowns with UVGI?

On 2021-08-12 21:18:33, user Francis Fregoli wrote:

Well, if this data from 2018/2019 is any indication, it’s a negligible portion. Nice if you have a certain worldview, though!

https://www.statista.com/st...

On 2021-08-20 00:25:35, user troRx wrote:

So where are the "eMethods" they refer to? If this was driven entirely through Facebook, I would have some validity issues with applying it to the general population.

On 2021-08-13 10:16:11, user Earth Med Research wrote:

Please break down the PhD's according to what their field is, if it has not been done already. It would be very interesting if the sciences had stronger hesitancy, for example.

On 2021-08-14 17:36:32, user Matthias von Davier wrote:

Overclaiming, or the use of straight-lining is another option, as are other types of response biases.

In addition, the level of hesitancy for self-reported PhD (doctorate in the questionnaire) is at the same level of hesitancy seen in the group that chose to not answer the education question (missing education information also has 23.9% vaccine hesitancy).

On 2020-05-06 00:34:42, user mvandemar wrote:

It doesn't state in the study, but wouldn't people willing to venture out to be tested possibly be more likely to be ones who were ignoring social distancing orders, and if so a higher risk population? If that were the case would that skew their extrapolations to the community as a whole?

On 2020-03-18 17:18:28, user Teri Frevert wrote:

Publishing this article prior to peer-review is irresponsible. It has the potential to overwhelm our laboratories with blood type testing for paranoid individuals. Additionally, you cannot change your blood type so what would the benefit be? People with type A thinking it would be ok to ignore the rules put in place to slow the spread of CVOID-19? That would be a problem because they could still be a carrier. People with other blood types being more fearful? This is absolutely reckless.

On 2021-06-20 23:38:30, user Ran Talbott wrote:

It's confusing and uncertain.

All of the studies show that antibody levels decline somewhat over time, so there's some concern that immunity might eventually wear off, as it does with some diseases. But the studies have only lasted for 4-6 months, so it's too soon to tell. It does look like the doctor's advice that "you definitely don't need a vaccine until at least 6 months" is sound. It'll probably turn out that immunity lasts longer, maybe even for life, but we don't know yet.

A word about "immunity": vaccines or survival don't mean that you won't get infected (again). What they do is "educate" your immune system so it immediately springs into action, instead of waiting to recognize that "Hey! This thing is hurting me!". Notice that nearly all the cases reported in the vaccine trials are asymptomatic, only a small number are "mild", and none have been severe or fatal.

To allay your concerns, the fact that your parents were only recently infected means that there are tens of millions of people who are way ahead of them on whatever curve there might be. So, if it does turn out that resistance declines too much, they'll get lots of warning.

On 2021-07-03 12:53:03, user The Truth wrote:

It's a five month study!! What about a year from now?? In able to continue immunity do they need to become reinfected??

On 2020-09-16 12:25:37, user Paulo Portinho wrote:

Hello, I could not understand how the estimated standardized cumulative death rates are 0.23% for HCQ group and 0.22% for NONHCQ group.<br /> Deaths NONHCQ 477, sample 164.068 = 0,29%.<br /> Deaths HCQ 70, sample 30.569 = 0,228%.<br /> I know it is standardized, but shouldn't it be around the average number?

On 2020-04-15 23:59:04, user Mark .Minnery wrote:

The average time between symptom onset and randomisation was 16.6 days. Could the authors discuss the implications of this potential confounder. Was the long time before treatment because of delay between symptoms and presentation at hospital?

On 2025-04-10 21:41:43, user Will wrote:

line 273: "fReference is low" but I do not see "f" in Table 2.

On 2020-04-30 14:34:37, user Ivan Berlin wrote:

Rentsch CT et al. Covid-19 Testing, Hospital Admission, and Intensive Care Among 2,026,227 United States Veterans Aged 54-75 Years. <br /> medRxiv preprint doi: https://doi.org/10.1101/202... version posted April 14, 2020<br /> Review of the results concerning smoking related issues.<br /> Ivan Berlin<br /> The title is somewhat confusing. Only 3789 persons were tested for SARS-CoV-2.<br /> Data are extracted from the Veteran Administration (USA) Birth Cohort born between 1945 and 1965 electronic database. Between February 8 and March 30, 2020, 3789 persons were tested for SARS-CoV-2. Among them 585 were tested SARS-CoV-2 positive (15.4%) and 3204 SARS-CoV-2 negative. (Remark: the authors frequently confound testing for SARS-CoV-2 and having the disease: COVID-19.)<br /> Testing used nasopharyngeal swabs, 1% of the testing samples was from other unspecified sources. Testing was performed “in VA state public health and commercial reference laboratoires”, page 7. No further specification about the testing method is provided. Data are analyzed as if no between test-sources variability existed. However, it is unlikely that between test-source variability would influence the findings.<br /> Data extraction included diagnostics by diagnostic codes of comorbidities, non-steroid inflammatory drug (NSAID), angiotensin converting enzyme inhibitor (ACE) and angiotensin II receptor blocker (ARB) use, vital signs, laboratory results, hepatic fibrosis score, presence or absence of alcohol use disorder and smoking status.<br /> Smoking status data, never, former, current smokers were extracted using the algorithm described in McGinnis et al. Validating Smoking Data From the Veteran’s Affairs Health Factors Dataset, an Electronic Data Source. Nicotine & Tobacco Research, Volume 13, Issue 12, December 2011, Pages 1233–1239, https://doi.org/10.1093/ntr... used for HIV patients. According to this paper, the algorithm correctly classified 84% of never-smokers 95% of current smokers but only 43% of former smokers. The reported overall kappa statistic was 0.66. When categories were collapsed into ever/never, the kappa statistic was somewhat better: 0.72 (sensitivity = 91%; specificity = 84%), and for current/not current, 0.75 (sensitivity = 95%; specificity = 79%). Thus, classification error cannot be excluded in particular in classifying former smokers. <br /> In unadjusted analyses (Table 1) factors associated significantly with SARS-CoV-2 positivity were: male sex, black race, urban residence, chronic kidney disease, diabetes, hypertension, higher body mass index, vital signs but not NSAID or ACE/ARB exposure. It is to note, that among the laboratory findings, severity of hepatic fibrosis was associated with positive SARS-CoV-2 tests. <br /> Among those with alcohol use disorder, 48 (8.2%) tested positive versus 480 (15%) who tested negative (p<0.001).<br /> Among never smokers 216 (36.9%) tested positive vs 826 (25.8%) who tested negative. Among former smokers 179 (30.6%) tested positive vs 704 (22%) who tested negative. Among current smokers 159 (27.7%) tested positive vs 1444 (45.1%) who tested negative. Expressed otherwise, among SARS-CoV-2 negative individuals, there were less never smokers, less former smokers and more current smokers. To note: the reported OR for current smoking should be the inverse to that presented i.e. <1 and not >1. However, among individuals with SARS-CoV-2 positivity there were more persons with positive smoking history (former + current smokers): 57.8 % than with negative smoking history (never smokers): 36.9%. <br /> COPD, known to be strongly related to former or current smoking, was more frequent among SARS-CoV-2 negative (28.2%) than among SARS-CoV-2 positive (15.4%) individuals p<001).<br /> In multivariable analyses (Table 2), male sex, black ethnicity, urban residence, lower systolic blood pressure, prior use of NSAID but not ACE/ARB use and obesity were associated with SARS-CoV-2 positive test; current smoking (OR: 0.45, 91% CI: 0.35-057), alcohol use disorder (OR 0.58, 95%CI: 0.41-0.83) and COPD (OR: 0.67, 95%CI: 0.50-0.88) were associated with decreased likelihood of SARS-CoV-2 positive test. No association with age and SARS-CoV-2 positive test was observed. The association with hepatic fibrosis with SARS-CoV-2 positive tests remained significant in the multivariable analysis and the authors point out (page 15) that the “pronounced independent association with FIB-4 (fibrosis) and albumin suggest that virally induced haptic inflammation may be a harbinger of the cytokine storm.”, page 15. <br /> The main risk factors for hospitalization or ICU among SARS-CoV-2 positive persons are those that associated with a worse clinical signs (status). This is expected: clinical decision about severity is based on current clinical signs and not on previous history. <br /> Neither co-morbidities, nor smoking status or alcohol use disorder were associated with hospitalization/ICU. Surprisingly, age was inversely associated with hospitalization (Table 4) among SARS-CoV-2 positive individuals.<br /> Conclusion of the reviewer.<br /> This is the first report showing that there are less current smokers among SARS-CoV-2 positive persons. However, smoking history (former + current smoking) seems to be more frequent among SARS-CoV-2 positive individuals than never smoking. It is not known what is the percent of former smokers who were recent quitters; duration of previous abstinence from smoking is a crucial variable in assessing associations with smoking status. This raises the question of the validity of smoking status category classification. <br /> It is not known when smoking status is reported with respect of the SARS-CoV-2 testing. It is likely that individuals with clinical symptoms stopped smoking some days before testing and considered themselves as former smokers.

The fact that alcohol use disorder, which is frequently associated with tobacco use disorder, is also less frequent among SARS-COV-2 positive individuals raises the question of the specificity of the smoking finding and the raises the contribution of substance use disorders overall i.e. the finding about current smoking is part of a cluster of various previous or current substance use disorders e.g. cannabis use, potentially associated with SARS-CoV-2 negative test. <br /> COPD as well as current smoking are being reported to be more frequent among SARS-CoV-2 negative individuals raising the possibility that reduced respiratory function (entry of SARS-CoV-2 is by the respiratory tract) is associated with lower likelihood of SARS-CoV-2 positive tests. This hypothesis may suggest that reduced respiratory function and not smoking itself is associated with higher likelihood of SARS-CoV-2 negative tests. <br /> The paper does not report on analyses of smoking by clinical signs/co-morbidities interactions. It is likely that former smokers or those with alcohol use disoders are more frequent among individuals with comorbidities. Based on previous knowledge about smoking associated health disorders, one can assume that more severe clinical signs were associated with current smoking or among recent quitters; the smoking x clinical signs interaction is not tested. <br /> The authors conclude on page 14 “To wit, we found that current smoking, COPD, and alcohol use disorder, factors that generally increase risk of pneumonia, were associated with decreased probability of testing positive. While they were not associated with hospitalization or intensive care, it is too early to tell if these factors are associated with subsequent outcomes such as respiratory failure or mortality.”<br /> The reduced current smoking rate among SARS-CoV-2 positive individuals is an interesting but preliminary finding. It is likely that it is part of a more complex symptomatology and not specific to current smoking. Smoking status should have been assessed on a more detailed manner. The current findings, from a retrospective cross sectional analysis, certainly not support the hypothesis that current smoking protects against SARS-CoV-2 positivity.

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

Dear readers,

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

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

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

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

On 2022-01-05 04:00:17, user One bird one cup wrote:

According to this study, at least, vaxxed and unvaxxed were equally contagious

On 2020-07-05 17:14:25, user Research Explained wrote:

Unfortunately this study includes a great deal of speculation and very little evidence to back up the claims. The sample sizes were far too small and the primary outcome measure lacks statistical significance. The study also does not address the obvious confounding factor of the large health disparities between African Americans and other groups in the United States.

Check out our general public friendly explanation of this study:<br /> https://www.researchexplain...

On 2020-04-21 15:33:02, user ?????Ozymandias????? wrote:

I'm just an undergrad with no expertise, but based on Bayesian logic, when the sensitivity of a test isn't perfect, and given the low prevalence of a trait in a population, won't the test tend to elicit enough false-positives to cloud the results?

On 2020-04-22 05:08:10, user G.O.B. wrote:

Well, this changes everything...

“County of Santa Clara Identifies Three Additional Early COVID-19 Deaths”

First US death was in California over 3 weeks earlier than the previously reported first US death in Seattle on Feb 29.

https://www.sccgov.org/site...

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

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

On 2020-04-21 21:10:27, user Bruno Vuan wrote:

Article says, page 7,

"This study had several limitations. First, our sampling strategy selected for members of Santa Clara County with access to Facebook and a car to attend drive-through testing sites. This resulted in an overrepresentation of white women between the ages of 19 and 64, and an under-representation of Hispanic and Asian populations, relative to our community. Those imbalances were partly addressed by weighting our sample population by zip code, race, and sex to match the county. We did not account for age imbalance in our sample, and could not ascertain representativeness of SARS-CoV-2 antibodies in homeless populations. Other biases, such as bias favoring individuals in good health capable of attending our testing sites, or bias favoring those with prior COVID-like illnesses seeking antibody confirmation are also possible. The overall effect of such biases is hard to ascertain."

In summary sample has

Overrepresentation white woman 19-64<br /> Age imbalance not accounted <br /> Partial weighting by zip code, race and sex<br /> Biased favoring good health individuals and those seeking antibody confirmation

Conclusion: "overall effect of such biases is hard to ascertain"

1. Not balanced by age is a signal of impossibility of weighting by age without significative umbalance in the other dimmensions, as mentioned "result in small-N bins". Ignoring age balancing in a phenomena which is strongly age related is something that may bring a strong source of additional errors.
2. If authors recognize that these biases are hard to ascertain, and no further discussion appears, is that this uncertainty is not included in error range. So, error range of this experiment appears to be totally unknown for the authors.

Additionally

There is no discusion on sampling effect by facebook ads, as answering rates, impact of facebook ads algorithm which is optimized to get maximum amount of answers. It is well known that this convenience samples are non probabiistical, so this has to be included in error range evaluation, (1)

1. Baker R. et al, Non-Probability Sampling, AAPOR, June 2013 https://www.aapor.org/Educa...

On 2020-04-27 06:03:14, user think wrote:

The Stanford study, as well as the USC study, demonstrated that the case fatality rate is much, much lower than was projected and is currently being reported. The CDC is currently showing a cfr of 5.6%. The NYC survey also corroborates the conclusion that the cfr is orders of magnitude lower than what was projected and used to inform our policy decisions and drive our panic and mass hysteria. Obviously, different geographical regions with different demographics are going to have disparate outcomes in cfr. A finding of .36% most certainly does vindicate a finding of .2% when the figure being challenged by these tests is 5.6%

On 2020-08-20 15:31:30, user Matt Price wrote:

6 people had antibodies to SARS CoV2 prior to departure, 3 of whom had neutralizing antibodies (i.e., much more potent). Of the 3 who didn't have neutralizing antibodies, all developed symptoms and became PCR+ during the trip. I have a few comments for the authors, that I think would be helpful to address (unless I missed it):

1. Did those 3 without neutralizing abs later develop neutralizing antibodies?
2. Were they (as someone below suggests) the carriers who might have brought the virus on board, since PCR does have a high rate of false negatives (i.e., failing to detect virus when in fact it is there)? This might be difficult to test, as you don't have samples during the trip...
3. How many of the 104 with positive PCR and later seroconverted developed neutralizing antibodies (i.e., is it common that everyone develops potential immunity?)

Very nice work.

On 2020-05-07 04:29:09, user Paul Bollyky wrote:

Interesting paper but it's not clear to me what link there is to hyaluronan in these data. Hyaline membranes are made up of dead cells, surfactant, and proteins - not HA. HA staining and other specific tests would be need to be done to support the argument that HA is present and responsible for the disease manifestations of COVID-19.

On 2022-01-13 14:05:52, user Zsofi Igloi wrote:

now published in: https://pubmed.ncbi.nlm.nih...

On 2020-08-19 11:00:05, user AbsurdIdea wrote:

Have I understood this right: " Vitamin D dose was not significantly associated with testing positive for COVID-19."? So taking vitamin D does NOT reduce the probability of testing positive for CoViD-19...Then, why take it against CoViD-19? For the rest - correlation or causation? Healthier people are likely to have a higher probability of sufficient vitamin D, conversely, people in poor health for any reason are likelier to have low vitamin D. Also there is a difference between becoming infected i.e. the virus actually entering into a person and propagating and the degree of illness and complication once being infected. This study does not appear to address these factors. Finally the phrase "499 had a vitamin D level in the year before testing" does not make sense. All people have some level of vitamin D.

On 2020-04-03 00:55:22, user ???? wrote:

What I felt strange was, in Japan, though the number of the infected persons have been increasing, the fatality rate is apparently low in comparison with the corresponding numbers in the U.S. and in the Europe (except Portugal, in which the BCG vaccination is mandatory, while the fatality rate in Spain, where the vaccination is NOT mandatory, has become around 60 times more than in Portugal).

I think the number of the infected persons in Japan must be much higher than the one reported so far (i.e., there must be a lot of actually infected people not diagnosed with the new coronavirus); however, it cannot explain the low fatality rate in Japan.

In addition, it's notable that those who passed away due to the virus in Japan (except the foreigners, who account for as much as around 30% of the infected persons in Japan) are almost limited to elderly persons, while the BCG vaccination became mandatory in 1940s and 70-year-old or older Japanese tend not to have taken the vaccination.

On 2020-04-04 09:25:59, user Oleg Gasul wrote:

The main proof will be if you found statistic of dead by reason of virus with BCG and without.

On 2020-06-15 15:53:48, user OxImmuno Literature Initiative wrote:

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

On 2020-05-08 03:15:32, user Chao Jiang wrote:

Very well written, we could address all points except for the "bigger cohort" one.. which requires way more efforts than currently allowed. The study will unfortunately stay as an in vitro study, we will see if we can expand to more people as a follow-up study. Thanks

On 2021-08-23 13:36:18, user Leo G. wrote:

Oral and nasal hygiene with Povidone-Iodine is widely used in India & Bangladesh to prevent nosocomial transmission.

It is equally effective in community settings. This hygiene includes gargling, mouth rinsing, nasal drop or irrigation. They should be performed 2-5 times per day, and/or before visiting the clinical

Many Listerine & Crest mouthwash products can be used for gargling.

On 2020-03-20 09:22:51, user BertieInExile wrote:

Just wondering how this fits with the different picture we have from the Diamond Princess?

More than 80% uninfected. 50% of those asymptomatic.

tests of most of the 3,711 people aboard the Diamond Princess confirmed that 634, or 17 percent, had the virus; 328 of them did not have symptoms at the time of diagnosis. Of those with symptoms, the fatality ratio was 1.9 percent,

.<br /> https://www.sciencenews.org/article/coronavirus-outbreak-diamond-princess-cruise-ship-death-rate

On 2021-09-30 19:29:51, user Jakob Heitz wrote:

Did you account for the previous infected who died or never recovered, because of long covid?

This reminds me of a study the navy did of war planes in world war 2. They found the majority of bullet holes in returned planes were in the wing tips and central body. Therefore, they decided to reinforce those parts. What they forgot was that the planes that got shot in the engines never returned to base.

On 2021-09-04 14:10:39, user criticalscientist wrote:

It is likely that emergence of the Delta variant, which has 10 mutations in the spike protein, is due to leakiness of the mRNA vaccines based on the single spike antigen. I more worry about the Mu variant that appears to be completely resistant to monoclonal antibodies. Time to develop new vaccines using multiple viral proteins or develop an "attenuated" virus mimicking natural immunity.

On 2021-09-16 22:57:34, user Chadwick wrote:

Having read the paper, Ben, I can say with certainty that the very first experimental condition numbers showed that, by matching variables like sex, they created a vaccinated group that literally had 2.5x more immunocompromised people than the natural immunity cohort which they then assessed for reinfection. Go do the math. As for socioeconomics, the mean rank decreases from Exp. 1-3, while the +/- valences show being poor and being immune compromised make you less likely to be infected. (You have to look at more than p-values, and that includes the sign.) As for your reflexive condescension, I'll give your opinion the weight which it so clearly deserves.

On 2021-12-24 18:05:32, user Michael Ruimerman wrote:

Natural Immunity > Vaccinated Immunity!!! Governments should be considering EVERY unvaccinated person that has survived a COVID infection as "immunized", since they have a stronger, more robust resistance to another COVID infection.

On 2021-11-19 03:50:43, user VolComp wrote:

I recently found this thread as a critique leveled at the paper in question.

https://twitter.com/DGBassa...

Just wondered what your thoughts were

On 2021-09-08 13:40:29, user Kim Zuber wrote:

You seem to miss the point. Anyone who hasn't gotten COVID SHOULD get the vaccine, but anyone who has antibodies due to a past infection, should not or at least should be able to opt out. I tested exceedingly high after almost a year, even higher than someone who had the vaccine. Vaccine wanes after 6 months. It is pointless, even dangerous to get vaccinated if you have already had it.

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

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

On 2021-11-21 23:23:25, user Alberto wrote:

Vaccines are not injections of antibodies, they are injections of antigens. Please, no speculations, and specially of this kind. To try to explain a bad speculation with a worse one is not doing us any good.

On 2021-09-12 18:42:41, user Matt Jolley wrote:

Over 90 percent of Covid patients in hospital today in Oregon are not fully vaccinated. anti vaccination efforts are killing not only infected but others who are denied hospital services.

On 2020-04-21 16:21:44, user Kroger Martin wrote:

Related: Covid-19 Predictions Using a Gauss Model

https://www.preprints.org/m...

On 2020-03-29 23:51:59, user Shuang Gao wrote:

Just like to point out that <br /> "the latest estimates of the death risk in Wuhan could be as high as 20% in the epicenter of the epidemic whereas we estimate it ~1% in the relatively mildly-affected areas“<br /> This epicenter here means the epcienter in Wuhan not Wuhan itself. This expression is different from saying <br /> "the latest estimates of the death risk in Wuhan, the epicenter of the epidemic, could be as high as 20% whereas we estimate it ~1% in the relatively mildly-affected areas"<br /> Wuhan is big and impact of Covid 19 is different in different parts of Wuhan.

On 2020-10-30 23:23:26, user Leonidas Palaiodimos wrote:

This article has been reviewed by peers and published at Hormones-the International Journal of Endocrinology and Metabolism

On 2022-01-10 14:20:30, user Siguna Mueller, PhD, PhD wrote:

Dear authors,

thank you for your detailed results. Regarding the stats: an average patient just does not exist. Your SDs are rather big. Can you possibly say anything about characteristics of those individuals that exhibited negative efficacy? Is there any overlap to those groups that were excluded during the initial trials?

Thank you.

On 2020-07-16 21:40:47, user Marm Kilpatrick wrote:

Very nice study.<br /> Did you measure viral loads in patients? If so, would it be possible to include those to see if they might be implicated in 4 cases of infection? Sample size and power would be low, but it would be useful to at least take a look.<br /> It also wasn't clear if some HCWs had to engage in riskier activities (e.g. intubation) and this might have led to infection. Thank you!

On 2021-04-26 16:44:55, user Stuart Weisberg wrote:

This article has now been published with a revised title "Distinct antibody responses to SARS-CoV-2 in children and adults across the COVID-19 clinical spectrum". The PMID is 33154590

On 2020-03-28 11:05:53, user Aaron Richterman wrote:

In addition to potential age-varying susceptibility related to immunity, there are also important anatomic differences in the airways of children vs adults - this is why children tend to be higher risk for infections of the medium-size airways (bronchiolitis) relative to adults. SARS-CoV2 appears to involve the terminal airways in the lung. Relatedly, there may also be a differential distribution of ACE2, the target receptor for SARS-CoV2, by age.

On 2024-09-13 18:12:14, user Leandro Hermida wrote:

No one seems to realize this paper has done an incredible and time consuming job of manually curating, harmonizing, and standardizing all the drug names used in therapy in TCGA. GDC didn't harmonize the drug therapy clinical data. This resource makes a lot of research possible. As of Sept 2024 it's still virtually up-to-date when checked against GDC TCGA Data Release v41, I only had to add/fix a few entries! Excellent work Enrico!

On 2020-11-07 13:40:48, user kdrl nakle wrote:

Why would you need a surrogate? You did not explain anything about the relation between this virus and SARS-CoV-2. The title is misleading.

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

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

On 2020-11-09 11:47:26, user Thomas Grünebaum wrote:

I wonder if there is an influence on adults having childeren in kindergarden versus adults who do not have.

The reason I am asking this is becaus I assume that the immune system of parents is more trained and has a better response to covid.

Is there any study realated to this? I fully agree this might be too specific and quite dificult to determin due to lack of data.

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

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

On 2021-08-29 07:53:30, user Swami Ganesh wrote:

It is not clear how the average IFR (e.g. 0.21%) was obtained. Report says it was based on reported Covid fatalities. There is also mention of estimating IFR based on government mortality data for the subject area. How do you derive that? The standard way is to divide the cumulative death for the representative population (on, say, the date the sero survey collection ended), by the infected population (average seroprevalence X population). Of course, if one doesn't believe the reported fatalities, then some use excess mortality data, but that is a can of worms and strains credibility because the baseline mortality from pre-Covid years are equaally unrelaible if one doesn't trust the reported Covid deaths. Will appreciate your clarification. Thanks

Swami Ganesh (PHD, MBA) retired engineering professional, NY, USA

On 2020-04-23 02:46:06, user Raspee wrote:

(1) There appears to be a statistically significant imbalance in the arms with regard to disease severity.

“However, hydroxychloroquine, with or without azithromycin, was more likely to be prescribed to patients with more severe disease, as assessed by baseline ventilatory status and metabolic and hematologic parameters.”

The base line pulse oximetry data and baseline line absolute lymphocyte count (Table 2) - indicates a statistical difference at p = 0.024 - the subjects that received hydroxychloroquine had a worse baseline respiratory status - and a worse absolute lymphocyte count p = 0.021.

This is an inherent bias in the design that has not been adequately addressed. The analysis should compare treatment in subjects with the same disease severity.

(2) If we look at table 4 - (HC + AZ) - 82% were discharged without ventilation vs. 77% discharged without ventilation both in the HC and non- HC group - Apparently the HC + AZ group did better than the other two groups.

This is supported by the observation that the adjusted HR for ventilation is 0.43 (0.16 - 1.12) - It was better than the control arm with regard to disease progression and no different than the control for death.

So in patients that were sicker at baseline, HC + AZ appears to have had a better outcome - than the other two groups - with regard to being discharged without requiring an ICU admission.

(3) Please provide a better justification to exclude the 17 women Please go back and perform the analysis including the 17 women.

(4) What were the doses of azithromycin and hydroxychloroquine administered? How are the different doses and dose regimens adjusted in the analysis? Not everyone in the HQ and HQ + AZ groups were dosed in the same fashion. Is there a minimum number of doses that you used to include them in the treatment groups?

(5) If the control group had less severe illness at presentation, it stands to reason that the mortality rate would be lowest in the control group.

(6) Was there a sub analysis looking at impact of secondary bacterial pneumonia - which occurs in 5-15% of moderate to severe COVID-19 patients? Were the antibiotics utilized the same over the 3 cohorts or were they different?

(7) How many patients were on ace inhibitors and/or angiotensin receptor blockers? Were these medications balanced in the 3 arms? What about corticosteroid use in the 3 cohorts? Was corticosteroid use balanced?

(8) Please go back and re-run the analysis with an additional 14 days of COVID-19 data (using April 25th cut -off) as your sample size will undoubtedly be greater and we would expect that the HQ + AZ group will now have a p value < 0.05. for discharge without ventilation.

(9) Please include length of stay in your analysis as well

(10) Please include readmission rates to the hospital in your analysis

On 2020-03-25 14:25:55, user Sinai Immunol Review Project wrote:

Summary<br /> The use of heat inactivation to neutralize pathogens in serum samples collected from suspected COVID-19 patients reduces the sensitivity of a fluorescent immunochromatographic assay to detect anti-SARS-CoV-2 IgM and IgG.

Major findings<br /> Coronaviruses can be killed by heat inactivation, and this is an important safety precaution in laboratory manipulation of clinical samples. However, the effect of this step on downstream SARS-CoV-2-specific serum antibody assays has not been examined. The authors tested the effect of heat inactivation (56 deg C for 30 minutes) versus no heat inactivation on a fluorescence immunochromatography assay. Heat inactivation reduced all IgM measurements by an average of 54% and most IgG measurements (22/36 samples, average reduction of 50%), consistent with the lower thermal stability of IgM than that of IgG. Heat inactivation caused a subset of IgM but not IgG readings to fall below a specified positivity threshold.

Limitations<br /> Limitations included the use of only one type of assay for testing heat inactivated vs non-inactivated sera, and the use of the same baseline for heat inactivated and non-inactivated sera. The results indicate that heat inactivation affects the quantification of SARS-CoV-2-antibody response, specially IgM, but still allows to distinguish positive specific IgG. Therefore, the effect of heat inactivation should be studied when designing assays that quantitatively associate immunoglobulin levels (especially IgM) to immune state.

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

On 2022-01-09 12:17:20, user Christopher Hickie wrote:

What? No comments allowed? I am a pediatrician with a PhD in neuroscience. My comment earlier was valid.

On 2020-06-30 06:03:47, user Verde wrote:

This model suffers from an incomplete data problem resulting in an order of magnitude error. Easily resolved by either using all cause death data, or seeding the time series upfront with a large number and adding a constant to each day’s new deaths.<br /> https://drive.google.com/fi...<br /> https://drive.google.com/fi...

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

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

On 2021-02-11 15:28:29, user Robert Olinski wrote:

You are speaking about Ct values that are not part of clinical diagnostics. What were clinical symptoms of people post-vaccination with decreased viral load? Does it mean that the vaccination did not prevent infection?

On 2020-05-26 19:59:48, user Boonton wrote:

So 318 outbreaks were identified. At least 3 people are required to make an outbreak. The outbreaks were grouped into categories. So if a husband, wife, and grandparents all got the disease that would be categorized as 'home'. Fair enough.

Now this study is being cited as evidence the virus does not transmit outdoors. Is that consistent with the details of this study? If no one spent much time outside in China because it was bitterly cold, that doesn't seem to have tested outdoor transmission then. People gathered together at the beach betting that sun and warm air will keep them safe should not count on this study as evidence supporting that, or should they?

On 2020-11-17 21:24:47, user George wrote:

The two leading comorbidities associated with COVID-19 mortality, SCD and kidney disease, are mechanistic causes of selenium deficiency. Selenium deficiency is associated with hemolysis in SCD and has been strongly associated with mortality and other outcomes in 4 COVID-19 studies so far. High-dose sodium selenite infusion is safe and well-tolerated in dialysis patients.<br /> Vitamin D and dexamethasone both alter selenoprotein expression, and thus may be ineffective if selenium is deficient.

On 2021-08-11 01:19:08, user dustinst22 wrote:

Delta is infecting those who are not vaccinated at a much higher rate -- this tends to be those less at risk (i.e. younger). The older/vulnerable populations are vaccinated at higher rates and hence are infected with Delta in lower numbers and also experience less severe infection. This is why the CFR is lower -- younger population infected + less severity if infected with vaccination.

On 2020-08-26 18:09:14, user Ger Rijkers wrote:

Our manuscript has now been published by Journal of Infectious Disease. Please use that for future reference.<br /> Rijkers G, Murk JL, Wintermans B, et al. Differences in antibody kinetics and functionality between severe and mild SARS-CoV-2 infections [published online ahead of print, 2020 Jul 29]. J Infect Dis. 2020;jiaa463. doi:10.1093/infdis/jiaa463

On 2024-12-19 23:00:26, user pietje puk wrote:

This paper is based on a corrupted vaccine registration dataset.<br /> Details about this can be found in the following link:

https://virusvaria.nl/en/is-dit-de-smoking-gun/

In summary: vaccinees were registered in CIMS, but there often was a time lag of a few weeks between vaccination and registration in CIMS. Because of a ministerial decree from 19 November 2020, the following happened:<br /> If vaccinees died after vaccination, but before registration, they were not registered as vaccinated. In addition, for a period of time, registered vaccinees that died were actively removed from CIMS.<br /> This makes the data in CIMS not suitable for analyses.<br /> The authors should retract this paper.

On 2020-04-01 08:31:20, user Bob O'Hara wrote:

The paper refers to Supplementary Material which gives details about the model fitting. Could this be uploaded too, please?

On 2020-08-28 09:33:01, user Dr Aniruddha Malpani, MD wrote:

Shouldn't the Conclusion be exactly the opposite ?

Conclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines thelow risk of acquiring COVID-19 from surface and air contamination in managing COVID-19.

Conclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from surface and air contamination in managing COVID-19, and the need for effective use of PPE, social distancing, and hand/surface hygiene.

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 2020-10-27 03:12:46, user Critical Dissection 2 wrote:

Dear authors,

First I just want to say I think that it was great you pursued such an important topic. There were a lot of good things about your article like your clear abstract that very well laid out the different parts of the paper and the main summary of each section. I also like that you laid out the limitations of your study and how they should be solved for in further investigation of this topic. However, there is still some room for improvement in this paper. I thought that the introduction could use more background to contextualize the issue and put some scope into it to explain why people should expand upon your results and see if the data is helpful in the future. I also think that the figures need more explanation in the results section, unless a highly experienced physician is reading it, it is a little hard to tell what we are supposed to be looking at and drawing from the figure that supports your hypothesis. There was also an emphasis drawn between the two patients whose ablation was done with a little more targeting of certain factors compared to patients who underwent standard ablations that was only mentioned in the discussion but is a great point that I think should be brought up earlier maybe somewhere in the results section. I think with these changes you will have a good paper.

On 2020-10-27 02:05:30, user Critical Dissection wrote:

Dear author,

I enjoyed reading the article and I liked how the abstract was divided and broken down to introduction, methods, conclusion and results. I think that really helped me get an idea of what I will be reading. The methods section was detailed which was good. However, I had some difficulty and confusion when reading the paper. I thought the figures could be explained better because I had confusion dissecting them. Some issues with the methods were the reduced sample of the study and the lack of long-term follow up for atrial flutter relapse.

On 2021-06-25 18:19:47, user Xolo wrote:

Very good point about the masks. And why weren't all the previous outbreaks contained this way - shutting down everything? Probably the prior viruses weren't as contagious? Interesting to see how climate change, economic system reset all intersecting at the same time.

On 2021-06-01 23:55:02, user Justin Lovern wrote:

Could your refer to any good research purporting to show that masking reduces transmission rates? The burden of proof is on the maskers if they want to impose masking, not on those who don't want to wear masks to prove they don't work.

On 2021-05-27 04:38:06, user rusbowden wrote:

The study does not show what you say it does, and from your response, my fear that people will use it to think that unmasking is as safe as masking, means we have study here that puts people in peril.

On 2025-10-15 04:54:32, user CDSL JHSPH wrote:

Dear Author, This work presents an interesting analysis of the connection between the development of islet autoimmunity in early childhood and bile acid metabolism controlled by the gut microbiota. Understanding how early-life gut microbial and change in metabolism may affect type 1 diabetes risk is made possible by the long-term research design and the use of multi-omics methods, which combine metabolomics and metagenomics. Could gut microbiota regulation, such as with probiotics, prebiotics, or bile acid-targeted therapies, help normalize bile acid profiles and lower the risk of type 1 diabetes progression in children at risk, considering the noted changes in bile acid metabolism that occur before islet autoimmunity?

On 2021-01-14 00:01:25, user Emmanuel Aluko wrote:

Will such a study be done on older cohorts to show the age-dependent efficacy of Ivermectin on reducing mortality, for mortality is seen mainly in older patients with associated co-morbidities? Days to negative tests on such cohorts would also provide a better basis for accepting Ivermectin as a worthy COVID-19 therapeutic.

On 2021-09-22 19:56:45, user Steve E wrote:

Unfortunately, even your high-vaccination-hesitancy-level scenario, which leads to national vaccination coverage saturation at 70%, now seems too optimistic. Today's CDC vaccination data shows we may not even reach 60% by year's end (unless the Biden mandates change the situation dramatically). How do your projections change in a 60% scenario?

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

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

On 2020-04-18 16:08:42, user Tom wrote:

All Swedes have not died, but we predict that most soon will ? Or just the 96000 ? Actual at this point on April 17 is about 1511. Seems like a long way to go.

On 2021-06-20 08:07:15, user Stephen Smith wrote:

note bottom-left panel in Fig1 needs replacing with the correct scatterplot; have tweeted the corrected sub-panel and will update PDF here shortly.

On 2021-12-15 06:28:56, user Robert Clark wrote:

From the article:

We used two-month periods as our basic time interval for defining the sub-cohorts, but combined months 12 to18 for the Recovered cohort and omitted months 8 to 10 for the Vaccinated and the hybrid cohorts due to the small number of individuals.

And also:

Typically, infection rates among recovered or vaccinated individuals are compared to the infection rate among unvaccinated-not-previously-infected persons. However, due to the high vaccination rate in Israel, the latter cohort is small and unrepresentative of the overall population; furthermore, the MoH database does not include complete information on such individuals. Therefore, we did not include unvaccinated-not-previously-infected individuals in the analysis.

Frankly, I don’t think the researchers are being completely open about the real reason they aren’t including the unvaccinated/uninfected in their study. The vaccination rate in Israel is about 80%. At a population of 9 million, that would mean 1.8 million unvaccinated. Obviously they could get high statistical significance with that many people.

I think the real reason is they would find the same as what was seen in the UK and in Sweden, post 6 months the vaccine effectiveness is worse than being unvaccinated to begin with.

Stunning after this length of time so many countries are refusing to present this data. They’ll collect the data up to 6 months and find the vaccine has waned to having no effectiveness in comparison to the unvaccinated. But except for Sweden and the UK, they refuse to go beyond that point.

Robert Clark

On 2020-04-22 03:57:44, user IanM wrote:

Hi,<br /> Could you explain how you performed your quantitative RT-PCR?<br /> Also, could you comment on whether a recombinant or plaque purified version of each virus carrying a mutation of interest may increase the strength of these in vitro observations? Cheers!

On 2020-04-21 08:07:22, user Chao Jiang wrote:

The detailed number of mutations that are in the allele-frequency form should be indicated in the figure 1 of the manuscript.

On 2020-08-20 18:06:49, user PB wrote:

For campus opening in the US at present, the analysis in this manuscript is irrelevant. This analysis assumes a single starting infection in a completely susceptible (i.e. uninfected) population. A rough but reasonable order-of-magnitude estimate (but see https://mggg.github.io/esti... ) suggests that in a population of 10K student-age people in California, we would expect O(100) students who would test positive on day 1 if tests were available. That is, the outbreak is already there! It arrives with the students. The idea that it can be controlled with a single round of entrance testing is also wrong. Assuming 85% sensitivity (the value used by these authors, which is very optimistic but not crazy high), the test would catch 85 of those 100 positives, but miss 15. Again, the outbreak is already there. The analysis in this manuscript may be reasonable, but for managing campus infection in the US at the moment, it simply addresses the wrong question.

On 2020-06-04 00:48:58, user James Van Zandt wrote:

Vitamin C is a common supplement. I suggest you track whether patients had taken vitamin C (and how much) before or in the early stages of their illness. If it is helpful, then we would like to know when it is most helpful.

On 2020-03-23 20:09:08, user halrhp wrote:

wha is the mechnism that changes at 2 m? Gravity or particle mass or both. my guess is the combination. ahow large are COV19 virus droplets. Y. Li is very smart.

On 2020-04-04 14:57:41, user Alexandros Heraclides wrote:

Maybe better to refer to "differing Relative Risks for dying", rather than "differing mortality impacts"? The latter points to absolute risk difference, while you are referring to relative risks. Great paper though!

On 2020-03-30 21:04:58, user Sinai Immunol Review Project wrote:

Keywords<br /> death biomarkers, cardiac damage, Troponin, Blood type, respiratory failure, hypertension

Summary<br /> This is a retrospective study involving 101 death cases with COVID-19 in Wuhan Jinyintan Hospital. The aim was to describe clinical, epidemiological and laboratory features of fatal cases in order to identify the possible primary mortality causes related to COVID-19.

Among 101 death cases, 56.44% were confirmed by RT-PCR and 43.6% by clinical diagnostics. Males dominated the number of deaths and the average age was 65.46 years. All patients died of respiratory failure and multiple organs failure, except one (acute coronary syndrome). The predominant comorbidities were hypertension (42.57%) and diabetes (22.77%). 25.74% of the patients presented more than two underlying diseases. 82% of patients presented myocardial enzymes abnormalities at admission and further increase in myocardial damage indicators with disease progression: patients with elevated Troponin I progressed faster to death. Alterations in coagulation were also detected. Indicators of liver and kidney damage increased 48 hours before death. The authors studied the deceased patients’ blood type and presented the following results: type A (44.44%), type B (29.29%), type AB (8.08%) and type O (18.19%), which is inconsistent with the distribution in Han population in Wuhan.

Clinical analysis showed that the most common symptom was fever (91.9%), followed by cough and dyspnea. The medium time from onset of symptoms to acute respiratory distress syndrome (ARDS) development was 12 days. Unlike SARS, only 2 patients with COVID-19 had diarrhea. 98% presented abnormal lung imaging at admission and most had double-lung abnormalities. Related to the laboratorial findings some inflammatory indicators gradually increased during the disease progression, such as IL-6 secretion in the circulation, procalcitonin (PCT) and C-reactive protein (CRP), while platelets numbers decreased. The authors also reported an initial lymphopenia that was followed by an increase in the lymphocytes numbers. Neutrophil count increased with disease progression.

The patients received different treatments such as antiviral drugs (60.40%), glucocorticoids, thymosin and immunoglobulins. All patients received antibiotic treatment and some received antifungal drugs. All patients received oxygen therapy (invasive or non-invasive ones).

Limitations<br /> This study involves just fatal patients, lacking comparisons with other groups of patients e.g. patients that recovered from COVID-19. The authors didn’t discuss the different approaches used for treatments and how these may affect the several parameters measured. The possible relationship between the increase of inflammatory indicators and morbidities of COVID-19 are not discussed.

Relevance<br /> This study has the largest cohort of fatal cases reported so far. The authors show that COVID-19 causes fatal respiratory distress syndrome and multiple organ failure. This study highlights prevalent myocardial damage and indicates that cardiac function of COVID-19 patients should be carefully monitored. The data suggest that Troponin I should be further investigated as an early indicator of patients with high risk of accelerated health deterioration. Secondary bacterial and fungal infections were frequent in critically ill patients and these need to be carefully monitored in severe COVID-19 patients. Differences in blood type distribution were observed, suggesting that type A is detrimental while type O is protective – but further studies are needed to confirm these findings and elucidate if blood type influences infection or disease severity. Several inflammatory indicators (neutrophils, PCT, CRP and IL-6, D-dimer) increased according to disease severity and should be assessed as biomarkers and to better understand the biology of progression to severe disease.<br /> Reviewed as part of a project by students, postdoctoral fellows and faculty at the Immunology Institute of the Icahn School of Medicine at Mount Sinai

On 2020-10-25 19:08:24, user Daniel Haake wrote:

Dear study team,

Thank you for your study, which shows that the risk of COVID-19 death increases significantly with age. To improve the quality of the study I have some comments regarding the statistical analysis of the study. In the following I would like to go into it.

The time of the determination of the death figures

You write that antibodies are formed in 95% of people after 17-19 days. In contrast, 95% of deaths are reported after 41 days. That is a difference of 22-24 days. Nevertheless, you take the number of deaths 28 days after the midpoint of the study. Why do you take a later point in time than you yourselve have determined? Even with this approach, you are 4 - 6 days too late and overestimate the number of deaths. Why even this would be too late, I will explain in more detail below.

The 41 days were given for the USA. But what is the situation in other countries? In Germany, for example, there is a legal requirement that the death must be reported after 3 working days at the latest. Of course there can also be unrecognized deaths in Germany, where it takes longer to report. But this should be the minority. If we transfer however this fact of the USA to other countries, in which the risk of the long reporting time does not exist in such a way, you take up too many deaths into the counter of the quotient with. This leads to a too high IFR.

Counting the deaths 28 days after the study midpoint is also problematic because in the meantime, further deaths may appear in the statistics that were not infected until after the infected persons identified in the study became infected. This is because not all deaths take as long to report. These are then deaths that are not related to the study. You yourself write that the average value of the report of a dead person lasts 7 days with an IQR of 2 - 19 days. These figures speak in the statistical sense for a right-skewed distribution in the reporting of death figures. This in turn means that the majority of the deceased have a rather shorter reporting time. The procedure leads to a too high number of deaths. This is a problem especially with still existing infection waves, even with already declining infection waves.

You write: “The mean time interval from symptom onset to death is 15 days for ages 18–64 and 12 days for ages 65+, with interquartile ranges of 9–24 days and 7–19 days.”<br /> If we assume the 3 days reporting time for Germany, we receive 18 days for the age 18-64 and 15 days for 65+. In contrast, 95% of the antibodies are formed after 17-19 days, which is about the same or later than the time when the dead appear in the statistics. For other countries this may be different and would therefore need to be investigated. In any case, a blanket assumption from the USA is not possible for studies outside the USA.

Since the mean time interval from onset of symptoms to death is 15 days for the age 18-64 with the interquartile range of 9-24 days, but the midpoint of the range would be 16.5 days, this suggests a right-skewed distribution in the values. The same applies to the mean time interval from the onset of symptoms of 12 days with interquartile range of 7-19 days for the age 65+, where the midpoint of this range is 13 days. This also speaks for a right-skewed distribution of the values. This would mean that the majority of the values would be below the mean value in each case, making shorter times more likely. This also shifts the time too far back. Therefore it would be better to assume the median value, because it is less prone to outliers.

Your example infection wave from figure 1 also shows the problem with this procedure. As you say, antibodies are formed in 95% of people after 17 - 19 days. Now you have an example study with the median 14 days after the start of infection. At that time, only a few of the infected persons have formed antibodies at all, since just 14 days before the infection wave starts with low numbers and then increases. Only 4 days before is the peak of the infection wave. This means that the time period, which is very strongly represented, cannot have developed any antibodies at all. This leads to the fact that only very few infected persons are recognized as infected. In your example, 95% of the deceased are now infected, but only very few of the infected. This leads to a clear overinterpretation of the IFR.

Due to the problems mentioned, the number of deaths should therefore be taken at the median time of the study. Of course, it would be best if the studies took place immediately after the end of a wave of infection, where the death rates are stable and the expression of antibodies is complete.

Antibody Studies

You write: "A potential concern about measuring IFR based on seroprevalence is that antibody titers may diminish over time, leading to underestimation of true prevalence and corresponding overestimation of IFR, especially for locations where the seroprevalence study was conducted several months after the outbreak had been contained.“

You have made many assumptions about the death figures and adjusted the death figures (upwards) accordingly. Here you find that the antibodies disappear over time and that this can lead to an underestimation of the number of infected persons. However, you do not adjust the number of infected persons upwards, unlike your approach to adjusting the death figures. For example, a study by the RKI found that 39.9% of those who tested positive for PCR before did not develop antibodies (https://www.rki.de/DE/Conte... "https://www.rki.de/DE/Content/Gesundheitsmonitoring/Studien/cml-studie/Factsheet_Bad_Feilnbach.html)"). From this, we could conclude that the antibody study only detected around 60% of those previously infected and that the number of infected persons would have to be adjusted accordingly. But you have not done that. I can understand that you did not do that. I wouldn't have done it either, because we don't know how this is transferable to other studies. But in adapting the dead, you have transferred such assumptions to other studies. This should therefore also be avoided. There, too, we do not know how transferable it is. If you only make an adjustment in the dead, but not justifiably in the infected, this leads to an overestimated IFR.

PCR tests from countries with tracing programs

You write in your appendix D: "By contrast, a seroprevalence study of Iceland indicates that its tracing program was effective in identifying a high proportion of SARS-CoV-2 infections“.

In my opinion this is a wrong conclusion. In my opinion, it is not the success of the tracing program, but the number of tests and thus fewer unreported cases. To date, Iceland has performed almost as many tests as there are inhabitants in Iceland. Therefore they could keep the number of unreported cases lower. Other countries did not test as much. Therefore the results are not easily transferable to other countries. The PCR tests only show the present, but not the past and not the untested.<br /> You write it yourself: „(…) hence we make corresponding adjustments for other countries with comprehensive tracing programs, and we identify these estimates as subject to an elevated risk of bias.“<br /> Nevertheless, you leave these studies in meta-analysis, although for the reasons mentioned above this leads to severe problems. The figures for countries with tracing programs should therefore not have been included. The estimated number of unreported cases is not known and cannot be taken over by Iceland.

Study selection

You sort out some seroprevelence studies. These include Australia [63], Blaine County, Idaho, USA [67], Caldari Ortona, Italy [72], Chelsea, Massachusetts, USA [73], Czech Republic [75], Gangelt, Germany [79], Ischgl, Austria [81], Riverside County, California, USA [98] , Slovenia [101] and Santa Clara, California, USA [116]. For the most part, these studies are sorted out because there is no age specification for seroprevelence. Since this is the study's investigation, this is of course understandable. However, these studies in particular have shown calculated IFR values between 0.1% and 0.5%. At the same time, you leave the numbers of PCR tests from countries with tracing programs in the meta-analysis. As already mentioned, this is not correct due to the unknown dark figure and the transfer from Iceland is also not possible, as described before. This leads to the fact that studies with low values are sorted out, but at the same time uncertain numbers with high values are left in the study. This shifts the calculated IFR value upwards in purely mathematical terms.

It is precisely the outliers upwards that cause problems in the calculation. Since the numbers are rather small (in a mathematical sense), there can be no deviation as strong downwards as upwards. This means that there may be studies that deviate perhaps 0.2 percentage points downwards, but other studies that deviate upwards by 1.2 percentage points. This is a problem for the regression, because the regression then leads to too high values. Therefore, outlier detection should be performed upstream and the outliers should be excluded. You can also make it easier by taking the median value, since it is less susceptible to outliers. But then you would have only one value.

You write: “The validity of that assumption is evident in Figure 3: Nearly all of the observations fall within the 95% prediction interval of the metaregression, and the remainder are moderate outliers.”<br /> You can see it in figure 3, but due to the logarithmic scale it is difficult to estimate the ratios. Better suited is Figure 4, which would be desirable for the different age groups to be able to make a better estimation there. Figure 4 shows that many studies are outside the confidence interval, often to a considerable extent and to a greater extent also towards the high IFR values. Looking at the values and the confidence interval, these studies must have significant z-scores, which would show that these are clearly outliers that should not be considered. This leads to the fact that the regression will be brought further in the direction of high values, which results in too high IFR values.

Adjustment of death rates for Europe due to excess mortality

In Appendix Q you write: "In the absence of accurate COVID-19 death counts, excess mortality can be computed by comparing the number of deaths for a given time period in 2020 to the average number of deaths over the comparable time period in prior calendar years, e.g., 2015 to 2019. This approach has been used to conduct systematic analysis of excess mortality in European countries.[159] For example, the Belgian study used in our metaregression computed age-specific IFRs using seroprevalence findings in conjunction with data on excess mortality in Belgium“

I understand why you want to do this. But there are some dangers involved. The above statement may be true for Belgium, but it cannot be transferred to other countries in a general way. Especially since you cannot say in general terms that every dead person above average is a COVID 19 dead person. Mathematically, this would mean that there have been COVID-19 deaths in some of the last few years, because there have been periods with more deaths than the average. This makes the average straight. Especially since, as I said, you can't simply say that every death above the average is a COVID-19 death. The majority will be it, but not necessarily everyone. Thus, even cancer operations that did not take place or untreated heart attacks due to the circumstances and unnoticed visits to the doctor may have contributed a share. Whether this is the case, we do not know without a study. A blanket assumption that every death above the mean value is a COVID-19 death is not correct. From the statement "For example, the Belgian study used in our metaregression computed age-specific IFRs using seroprevalence findings in conjunction with data on excess mortality in Belgium", we could also conclude that the number of reported COVID-19 deaths is correct and can therefore be used as the numerator of the quotient for calculating the IFR. <br /> If you take this as a blanket assumption, how do you deal with those countries that do not have excess mortality but have several thousand COVID-19 deaths in the official statistics? Would you then correct the number of COVID-19 deaths downwards, perhaps even to 0? Certainly not.

Variation in the IFR

You write: "We specifically consider the hypothesis that the observed variation in IFR across locations may primarily reflect the age specificity of COVID-19 infections and fatalities.“

It is also possible that the variation in the calculated IFRs occurs due to still different dark figures. If, for example, the PCR tests are taken in countries with a tracing app, but an IFR based on Iceland is calculated there, this can lead to incorrect and too high IFR values. Also the adjustments of the death rates themselves or the late time of the death rate determination 4 weeks after the study center can lead to this high variance.

Conspicuous features regarding the correct determination of the death figures

In Table 1 you write that on July 15 there were 8 million inhabitants with a projected 1.6 million infections. According to my research there are 8.4 million inhabitants. You calculate the 1.6 million infected on the basis of the 22.7% infected in the study. However, the blood samples were taken between April 19 and 28, so the infections occurred before or until the beginning/middle of April. So you now take the number of infected persons from the beginning/mid-April or from April 24 (study midpoint) and insert them for July 15, i.e. just under 3 months later! In the meantime, however, not only people have died, but have also become infected and formed antibodies. They thus increase the numerator of the quotient, but leave the denominator unchanged, although the denominator would also be higher. So you shift the IFR upwards here as well.

The study on Gangelt, which was not taken into account, shows a similar picture. You write that at the end of June there were 12 deaths and therefore the IFR rises to 0.6%. That is 8 weeks (!) after the study center. This does not take into account that in Germany the deaths must be reported after 3 days. If you have proceeded in this way when calculating the other IFRs from other studies, this suggests that the IFR values are too high.

Calculation of the IFR of Influenza

You calculate the IFR of influenza based on the CDC figures for the 2018/2019 influenza season and indicate the IFR as 0.05%. Firstly, it should be said that statistically it is never good to look at just one value. The average of a time series should be considered. You calculate the value by looking at the estimated deaths and looking at how many were estimated to be symptomatically infected with influenza. You use a study according to which about 43.4% of cases are asymptomatic or subclinical (95% CI 25.4%-61.8%). You then take the mean value from the confidence interval with the value 43.6% and use this figure to calculate how many people were probably infected with influenza. Statistically it is not correct to take the average value of 43.6%. The value of 43.4% must be taken. Due to the small difference, this does not make much difference, but it shows the statistically imprecise consideration that runs through the study and generally leads to an IFR that is too high or, in the case of influenza, too low.

Now a statement on the selection of the 2018/2019 flu season, the CDC writes: "These estimates are subject to several limitations. (...) Second, national rates of influenza-associated hospitalizations and in-hospital death were adjusted for the frequency of influenza testing and the sensitivity of influenza diagnostic assays, using a multiplier approach3. However, data on testing practices during the 2018-2019 season were not available at the time of estimation. We adjusted rates using the most conservative multiplier from any season between 2010-2011 and 2016-2017, Burden estimates from the 2018-2019 season will be updated at a later date when data on contemporary testing practices become available. (...) Fourth, our estimate of influenza-associated deaths relies on information about location of death from death certificates. However, death certificate data during the 2018-2019 season were not available at the time of estimation. We have used death certification data from all influenza seasons between 2010-2011 and 2016-2017 where these data were available from the National Center for Health Statistics. (…)

The CDC writes the same for the 2017/2018 season, so the values, which were always only estimated anyway, were estimated even more due to missing data. Therefore we should have considered the figures for the seasons 2010/2011 to 2017/2017. If we calculate the IFR of influenza in this way and also use the confidence interval to calculate the number of people potentially infected per season, we get an IFR of influenza of 0.077%, ranging from 0.036% to 0.164%. Every single year prior to the 2018/2019 season was above the 0.05% and the average of 0.077% is also 54% above your reported value. This means that influenza is still not as lethal as COVID-19 has been so far, but the factor is not as high as suggested by your study.

It should also be noted that it is not possible to compare an IFR calculation that is equally distributed over age with an IFR of influenza that is not equally distributed over age. You do not do it directly, but by naming these numerical values, this has been taken up by the media. The IFR just indicates the mortality per actually infected person. Therefore the IFR of the actually infected persons of COVID-19 must be compared with the IFR of influenza. You can of course calculate a hypothetical IFR assuming that every age is equally likely to be infected. In this case, however, the calculation must be performed not only for COVID-19, but also for influenza.

I hope I can help you to improve the study in terms of statistical issues. I remain with kind regards.

On 2020-07-19 22:45:26, user George wrote:

" Lettuce consumption increased COVID-19 mortality."<br /> If you lack the commonsense to see how ridiculous that is, at least put it in non-causal language next time.

On 2021-12-22 19:07:52, user Pedro Abdalla wrote:

The complete paper was published on: https://rdcu.be/cDtQS or https://doi.org/10.1186/s13...

On 2020-08-01 22:03:19, user Pablo Richly wrote:

Could you please provide further details of the randomization of the control group?

On 2020-12-31 14:23:25, user Don Wheeler wrote:

Interesting. Additional research with a larger sample size featuring a broader cross section of the population will be most beneficial. Let's see where this leads.. Great work! @ComaRecoveryLab #covid19

On 2021-09-18 04:47:08, user Jack Albanese wrote:

big congrats to the authors on the publication in NEJM , september 15 <br /> as well as the press on Nature News

https://www.nejm.org/doi/10...<br /> https://www.nature.com/arti...

On 2020-04-30 20:54:07, user Frank Conijn wrote:

I don't have any objections against retrospective cohort studies, because they are sometimes all one can do, and they can give valuable insight. But the compared groups must have equal baseline disease severity. And am I overlooking something, or is that information missing?

Furthermore, the Brief Summary on page 2 says (emphasis by me): "The use of antiviral drugs (chloroquine, oseltamivir, arbidol, and lopinavir/ritonavir) did not shorten viral RNA clearance, especially in non-serious cases." But the text and figures show that that still concerned patients with pneumonia or worse. I don't find that non-serious cases. Those are moderate cases on a scale from light - mild - moderate - severe - critical.

On 2020-05-04 18:24:02, user Dan Kammen wrote:

The full data sets and code can also be accessed here:

https://rescognito.com/chec...

On 2021-05-25 18:52:14, user Stephen J. Collings wrote:

Looks like an error in the extraction of data from Niaee 2020 has led to an incorrect mortality conclusion. Please correct as a matter of urgency.

On 2021-05-25 20:24:16, user Green Ranger wrote:

The results and conclusions of this study are wrong. The authors mistook the ivermectin and control arms of one of the RCTs that they included. Look at figure 2. The results from Niaee 2020 are dramatically misreported. The actual results for that study are as follows:

Control groups: 11 deaths out of 60 patients.<br /> Ivermectin groups: 4 deaths out of 90 patients.

When this is corrected, the results of this meta-analysis confirm what other meta-analyses have found. Ivermectin use is associated with approximately 66% reduction in Covid fatalities. And this result is statistically significant.

A source for this.

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

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

Didn't check others.

On 2020-04-26 14:32:05, user Retelska wrote:

It would be useful to have a clear reference to each publication listed in table 2. Scientists would mostly use this table. What specificity an antibody should have to be used? I see studies that 2%, 3%, 6% of a population have been infected with covid, I would say it needs > 99% specificity, as an anitbody with 98% specificity would give 2% false positives.

On 2020-05-08 16:24:34, user Rich Dzikowski wrote:

If this virus can survive in the air for so long and still remain infectious, you don't need to be an expert to imagine that it will stick to clothing, skin, hair and other objects we carry with us. So what good is wearing masks, washing our hands and using a tracking app if we inevitably end up reaching for the virus-contaminated areas and infecting ourselves at home?

On 2021-07-08 13:48:55, user Eric wrote:

So is there a study that backs up 6 or 8 weeks for young and middle aged adults?

In Germany, the recommendation until last week was to hit exactly six weeks to stay within EMA licence but spread out the vaccine. Now, with more vaccine available and Delta looming, the allow 3 - 6 weeks but without any recommendation as to which end of that window to prefer.

EMA recommends 19 - 23 days but their reasoning is that 93% of trial participants fell into that bracket. So maybe they have simply no data to say that six weeks are better?

Unlike with the AZ vaccine, there is no vector immunity to overcome so it is not clear why a longer interval should be better.

Back to those seniors in this study here, is it even good for them to have more antibodies and less T-cells? My understanding is that they are typically T-cell challenged, so is it not better to boost T-cells?

On 2021-01-12 17:22:08, user Ann Marie Miller wrote:

Can any one tell me if the Herpes 6/7 loads could cause a false positive Hep. C test, during a flare up? Thank you!

On 2021-08-27 17:01:38, user Peter Novák wrote:

The selection may be more forceful in some countries applying apartheid rules - i.e. if unvaccinated must perform tests in order to get access to some settings where vaccinated are granted access without test.<br /> So the question on cohort selection is legitimate.

On 2022-01-10 09:16:10, user Roger Helgesen wrote:

How accurate are self-reported measures in determining actual illness?

Those who know that they have had a positive Covid test might report symptoms more often than those that know they have not had a positive Covid test due to confirmation bias.

On 2021-04-15 03:32:28, user Mark Czeisler wrote:

Note from the authors:

This paper was published in BMC Public Health on 15 March 2021 following peer review. Below is a link to the article, along with the PubMed citation.

https://bmcpublichealth.bio...

Czeisler MÉ, Howard ME, Robbins R, Barger LK, Facer-Childs ER, Rajaratnam SMW, Czeisler CA. Early public adherence with and support for stay-at-home COVID-19 mitigation strategies despite adverse life impact: a transnational cross-sectional survey study in the United States and Australia. BMC Public Health. 2021 Mar 15;21(1):503. doi: 10.1186/s12889-021-10410-x. PMID: 33722226; PMCID: PMC7957462.

On 2020-08-13 16:38:14, user SonOfAnOnion wrote:

If you look at just look HIGH RISK patients in this study, you will see that the medications cut the risk of a bad outcome in half.

So despite the given conclusion, when you drill into the ACTUAL DATA, this study supports the use of the medications.

On 2021-06-07 11:30:24, user Bridget Lockyer wrote:

Please be advised that this article has now been peer-reviewed and published in Health Expectations. <br /> https://onlinelibrary.wiley...

On 2022-08-25 21:34:09, user Ariana wrote:

What software did you use for the figures portraying the genetic correltion between the PDs? very cool, thank you!

On 2025-03-15 20:37:50, user Ali Molaverdi wrote:

The authors appear to conflate correlation with causation when attributing excess mortality to political populism, an interpretative leap that undermines the scientific validity of their conclusions and calls for a more cautious analysis.

On 2020-07-02 15:39:58, user Kamran Kadkhoda wrote:

The reported prevalence is very high suggesting high false positivity rate. The actual sero-prevalence for that county is estimated to be around 6% as of today (if we assume only 20% of cases are tested by for RNA). It would have definitely been much lower back in April. Another reason serology should not be used given it's high rate of false positivity mostly due to common CoVs like OC43 and HKU1.

On 2021-08-18 17:39:03, user John Baron wrote:

The first sentence states that this is a study of individuals who underwent SARS-CoV-2 testing, presumably (though not stated) during the study period. Unless there was population testing surveillance in place, there must have been selection factors (e.g. COVID exposure or symptoms) prompting testing. This would bias estimates of infection rates upward, though the bias is unlikely to differ between vaccine groups and may not differ much between vaccinated and unvaccinated groups. Tthe relative infection measures might be OK.

In the "final" paper, it would be important to account for the usual cohort issues: censoring, movement out of the health care system, etc. Also, the first paragraph of the Methods section should include unvaccinated individuals.

On 2020-04-15 18:51:04, user Greg Lambert wrote:

The study seems to incorrectly use a UVAB meter instead of a UVC meter to measure the exposure from their UVC lamp, consequently their UV exposure readings are wrong(low).

From the study:

"Ultraviolet light. Plates with fabric and steel discs were placed under an LED high power UV germicidal lamp (effective UV wavelength 260-285nm) without the titanium mesh plate (LEDi2, Houston, Tx) 50 cm from the UV source. At 50 cm the UVAB power was measured at 5 u W/cm2 using a General UVAB digital light meter (General Tools and Instruments New York, NY)."

Their lamp emits effective UV wavelength of 260-285nm but a General UVAB meter only measures from 280 to 400 nm with a calibration point of 365nm.

A

On 2021-04-29 12:05:31, user Anders Julton wrote:

Correct me if I'm wrong, but the actual air rate change efficiency of the HVAC alone in the hospital room seems quite low. My calculations yielded about 30% using eq. 2.1.6 in a book (linked below, linking is weird) on air rate change measurements. The control room had roughly 100% efficiency.

I used C(t = 10)/C(0) ~ 0.5 for the hospital room and C(t=40)/C(0) ~ 0.2 for the control room.

https://www.aivc.org/sites/...

On 2021-12-31 13:45:45, user Allan Lorentzen wrote:

What does negative effectiveness mean?

On 2021-12-29 02:31:04, user Jason wrote:

Have you seen results of other similar studies that align or contribute to further understanding?

If prior research on CoV already identified ADE as a potential issue.. and all vaccine makers are aware of the concept.. why aren’t there more studies looking for it and tracking larger data sets? Of all the things counted and measured isn’t vax effectiveness high on the list?

On 2021-08-27 06:09:19, user William Brooks wrote:

This is interesting paper showing that the first three states of emergency (SoE) and GoTo campaign didn't have much effect on the fluctuation on K. However, rather than conclude that the medical system is at no risk of collapse and the government should copy Texas and Florida and eliminate all business restrictions, the author calls for stricter border measures, lockdown, and more tests for healthy people despite it being clear for over a year that "Rapid border closures, full lockdowns, and wide-spread testing were not associated with COVID-19 mortality per million people" [1].

Since Covid's case fatality rate in Japan is now close to 0.1%, it's hard to see the point of spending even more time, money, and effort copying testing strategies that have been ineffective even in advanced countries like Germany [2] and Denmark [3].

[1] https://doi.org/10.1016/j.e...<br /> [2] https://www.ncbi.nlm.nih.go...<br /> [3] https://doi.org/10.1101/202...

On 2021-09-30 10:12:58, user Alberto wrote:

There seems to be something wrong with the numbers. Among the vaccinated, there are 82 infections, but 168 hospitalizations. Many undetected infections? False negatives? In contrast, among those with natural infection there are 28 infections and 19 hospitalizations.

Leaving the infections aside, among the vaccinated, the rate of hospitalizations was 0.45%, while among those with natural infection it was 0.34%. That's an overall better protection against hospitalization from natural infection than from vaccination, something completely missing in the abstract. It's true that among those above 65 of age, hospitalizations are a bit higher in the natural infection group (0.52% vs. 0.43%), a less significant difference especially given the low numbers (13 hospitalized in the 65+ natural infection group).

As for deaths, the numbers are too low in all groups to draw any conclusions. The total deaths in each group are 2 (0.036%) in the natural infection group and 8 (0.021%) in the vaccinated group. Among the 65+ groups, is 2 in the natural infection group (0.08%) vs. 5 in the vaccinated group (0.03%). But then in the <65 group it's 0 deaths in the natural infection group (0%) vs 3 deaths in the vaccinated group (0.02%).

The numbers and conclusions shown in the abstract are completely at odds with these real numbers shown in the paper itself. Not to mention the strange pattern shown in the numbers where most infections/hospitalizations in the natural infection group happened in June (with very low numbers in July and August), while in the vaccinated group most infections/hospitalizations happened in July and August, with very low numbers in June. So an analysis of June alone would agree with the abstract, but an analysis of July and August alone would be the complete opposite.

On 2020-06-19 06:18:19, user Kato Peterson K wrote:

This is an interesting study Nicholas, looking forward to you coming up with a harmonised country specific manual to guide nutrition education and counselling for T1DM in Uganda

On 2020-09-13 07:18:07, user OxImmuno Literature Initiative wrote:

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

On 2021-06-22 12:20:41, user PattonWasRight wrote:

Is this a concern now? The CDC is now recommending a lower PCR threshold for testing vaccinated patients. Are vaccinated patients showing less or more severe symptoms than unvaccinated patients with VOC infections? If vaccinated patients are showing less symptoms, then there’s risk here as the entire world is operating under the idea that vaccinated persons can travel freely (air travel) while while unvaccinated cannot.

I’m seeing a scenario where vaccinated are more likely to be spreading more virulent strains - especially if the vaccine keeps them from developing more severe symptoms and the new lower CDC threshold is missing positive cases -although I think the lower threshold should have been used this entire time because of false positives (false negatives are probably more rare).

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

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

On 2020-08-04 12:21:27, user Mike Dammann wrote:

Here are some of the important parts from the revised version of the NYP study highlighting the important of the Rh negative blood group: https://www.rhesusnegative....

On 2020-05-06 15:17:26, user Sinai Immunol Review Project wrote:

Summary: Based on peripheral blood samples from 117 COVID-19 inpatients and convalescent patients, the authors demonstrate that all patients sampled became seropositive with neutralizing antibodies within 20 days since onset of symptoms and stayed seropositive until day 41-53. Seropositivity of neutralizing antibodies was defined as a geometric mean titer (GMT) of 1:4 higher and the titer was calculated using a modified in vitro cytopathogenic assay where the dilution number of 50% protective condition from cytopathic effect of the virus represented the titer. The GMT of neutralizing antibodies (average: 1:271.2) was the highest at 31-40 days since onset and multivariate Generalized Estimating Equations (GEE) model controlling for clinical variables (i.e. gender, age, clinical severity, etc.) showed that antibody titer at 31-40 days was significantly higher than 10-20 days past onset. In addition, their multivariate GEE analysis showed that age- and clinical severity-dependent rise in antibody titers with the youngest (age 16-30) and patients with mild or asymptomatic conditions having a lower antibody tier than its elderly and moderately-sick counterparts.

Limitations: Several shortcomings limit the impact of this study. While it has been the intent of the authors to sample PBMCs from patients at various time points in order to establish a robust profile of antibody response against SARS-CoV2, in reality, sampling has been limited and inconsistent across different time points. For instance, PBMCs of only 12 out of 117 patients have been collected three or more times and it is not clear from the data whether samples from patients whose blood has been collected only once (n = 37) are evenly distributed across the time frames under analysis. Furthermore, the authors have tried to show differences in kinetics of antibody response between patients with mild and moderate conditions by sampling their blood at four different time points. However, not only do two of eight patients sampled in this study have only two data points, but also the authors have found that the antibody response varies considerably across individuals—further underscoring the need to have PBMCs sampled from each patient at multiple time points and normalizing their response before comparing the titers across individuals. In addition, due to the fact that patients were enrolled using convenient sampling instead of random sampling methods, it’s evident that the authors could not control for disease severity as they only had four patients in severe condition. Beyond the sampling issues, the modified cytopathic assay used to calculate the neutralizing antibody titers may be less sensitive and specific than ELISA-based assays that use purified antigens from the virus.

Significance of the finding: Limited. While it is informative to have descriptive studies like this one showing the dynamics of the antibody response against COVID-19, the failure of the study to collect samples in controlled manner prevents the reader from using the data to answer key questions regarding the humoral immune response against COVID-19: do differences in clinical severity manifest in different kinetics of antibody response? When controlled for age, is higher antibody titer predictive of their clinical severity and prognosis? Future studies may address those questions with more controlled experimental setup.

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

On 2020-05-18 14:25:25, user OxImmuno Literature Initiative wrote:

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

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 2020-07-18 10:11:08, user Richard Harrison wrote:

Much to discuss in this paper, including whether the hypothesis of a uniform prevalence across the country is consistent with more plentiful new confirmed cases data at LTLA level (showing significant variations in rates even in May), confidence limits on estimated R, possible self selection bias (related to low response rate), apparent differential response rates, and implications of high false negative swab rates for Test, Trace and Isolate strategy. Useful confirmation of a number of things, including high pre-/asymptomatic %, significant % of children infected and high infection rate of care home workers, with important recommendation for continuance of 'social distancing' measures.

On 2020-04-16 23:38:02, user Edwin wang wrote:

April 16, 2020

Just a short note, China has changed the quarantine period for imported travellers (14-day for a centralized quarantine plus another 14-day home quarantine).

just read the news here: http://news.creaders.net/ch...

It is in Chinese, the key sentence is '14?????????+14?????????'. you could enter it into Goole translator

https://www.google.ca/searc...

On 2020-05-26 02:41:41, user Frank Bentrem wrote:

Nice study. I have also looked into the COVID-19 connection to genetic ancestry. See the preprint <br /> https://www.researchgate.ne...

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

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

On 2021-01-07 08:06:09, user Giuseppe novelli wrote:

Congratulations, the work confirms and extends the considerations reported by us in two published papers, which happy if the authors could quote in an updated version of the manuscript Thanks

Giuseppe Novelli

1. Novelli A, et al., Analysis of ACE2 genetic variants in 131 Italian SARS-CoV-2-positive patients. Hum Genomics. 2020 Sep 11; 14 (1): 29. doi: 10.1186 / s40246-020-00279-z. PMID: 32917283

2. Latini A, et al., COVID-19 and Genetic Variants of Protein Involved in the SARS-CoV-2 Entry into the Host Cells. Genes (Basel). 2020 Aug 27; 11 (9): E1010. doi: 10.3390 / genes11091010. PMID: 32867305

On 2024-12-11 16:30:22, user Andrew Hagen wrote:

This preprint has now been published in its final form as:

Hagen AC, Tracy BL and Stephens JA (2024)<br /> Altered neural recruitment during single and<br /> dual tasks in athletes with repeat concussion.<br /> Front. Hum. Neurosci. 18:1515514.<br /> doi: 10.3389/fnhum.2024.1515514

On 2025-08-21 13:47:47, user J.C. Rome wrote:

The acronym makes no sense. STARD, meaning Standard, Accuracy, Reporting, Diagnostic? It should be SRDA, I understand that you're trying to make it into a word, but it doesn't work.

On 2022-12-22 13:04:03, user Howard Waitzkin wrote:

Here is another peer-reviewed article from the same project. Constructive comments welcome. Thanks.

Howard Waitzkin

Fassler E, Larkin A, Rajasekharan Nayar K, Waitzkin H. Using absolute risk reduction to guide the equitable distribution of COVID-19 vaccines. BMJ Evid Based Med 2022. doi:10.1136/bmjebm-2021-111789. [Epub ahead of print: 07 Mar 2022

On 2025-07-22 09:31:17, user Abdullah Jinah Ali wrote:

Boys under 18 and men over 65 can also be mistaken for combatants, and your data does show evidence ifvthus. Therefore it would be useful to have a gender breakdown among child and senior citizen fatality estimates.

In addition, I would like to emphasize that your survey missed the most vulnerable group - large households that did not leave Rafah and the north. They are more likely to be targeted by AI due to having more combat aged men, and more likely to suffer nonviolent deaths due to the poorer conditions in these governorates and the disproportionate number of children and the elderly in these larger households.