On 2021-09-03 13:05:33, user Jeremy R. Hammond wrote:

Indeed, I was not misinterpreting the study's findings. They did not demonstrate a significant benefit of people who've acquired natural immunity getting a single dose of vaccine. Instead, their result only attained statistical significance when they included people who received a single dose of vaccine and then were vaccinated.

The media are falsely reporting this study as showing a significant benefit of vaccinating people with natural immunity. Science magazine, for example, reported this (bold emphasis added):

"The researchers also found that people who had SARS-CoV-2 previously and then received one dose of the Pfizer-BioNTech messenger RNA (mRNA) vaccine were more highly protected against reinfection than those who once had the virus and were still unvaccinated."

That original version of the article is archived here:

https://web.archive.org/web...

I confronted the author and the magazine on Twitter, and the author acknowledged that their claim was incorrect. She said they had "corrected" it, but all they did was remove the "then" that I've bolded, so that anyone reading it would still assume that it meant that the study found a significant benefit of vaccinating people with natural immunity.

I replied:

"No, you did not correct it. You just deleted "then" so that instead of your statement being outright false, it is now just highly misleading. You need to tell the truth: people who had infection THEN got vaccinated were NOT shown to have received a benefit from the shot."

But she insisted that it was now "accurate" and refused to clarify for Science readers that they did not find a statistically significant benefit of vaccinating people with natural immunity. That Twitter thread is here:

https://twitter.com/jeremyr...

News Medical, too, is falsely characterizing the findings of this study, saying: "Results showed that a single vaccine dose with natural immunity provided greater protection against reinfection than people with natural immunity alone."

https://www.news-medical.ne...

I have also called on the author of that piece and the publication to correct their false claim, but as of now, they have not responded.

In fact, the study authors explicitly state in the body of their paper that they found no statistically significant benefit when individuals with natural immunity received a single dose of vaccine. The result only achieved statistical significance when including individuals whose immune systems were primed by vaccination and who then were infected.

Thus, rather than suggesting a benefit of vaccination for naturally immune people, the findings suggest a benefit of infection for vaccinated people.

It is little wonder that the media are falsely characterizing the findings of the study given that the authors themselves mischaracterize their own findings in the abstract, stating:

"Individuals who were both previously infected with SARS-CoV-2 and given a single dose of the vaccine gained additional protection against the Delta variant."

The only logical interpretation of that statement is that they found a statistically significant benefit of vaccination for people with natural immunity, but they might just as well have summarized, "Individuals who were given a single dose of the vaccine and infected with SARS-CoV-2 gained additional protection against the Delta variant."

The responsible thing for the authors to do would be to publicly clarify their findings and to correct their wording so that the abstract accurately summarizes their actual findings so that the media don't continue to falsely claim that the study found a significant benefit of vaccination for people with natural immunity.

On 2022-01-15 12:05:39, user Anzola wrote:

Reading for comprehension? Why is everyone missing the critical text at the top that says this is a preprint and has not been peer-reviewed?

MedRxiv:<br /> “We also urge journalists and other individuals who report on medical research to the general public to consider this when discussing work that appears on medRxiv preprints and emphasize it has yet to be evaluated by the medical community and the information presented may be erroneous.”

On 2021-10-31 12:17:40, user Heath wrote:

The study had a bizarre design.

The agency’s researchers looked at 200,000 people who had been hospitalized<br /> with “Covid-like” illnesses from January through August in nine states.<br /> Right away, this choice sets up the study in a problematic way; for <br /> most of that time, people who had received Covid vaccines believed <br /> (because the CDC and others told them) that they were at VERY low risk <br /> of getting Covid, and certainly symptomatic Covid. Thus they may have <br /> been less likely to go to the hospital at all, or be tested for Covid <br /> once they arrived.

But put that aside.

Then the researchers decided to compare two groups - people who had <br /> definitely had Covid at least 90 days before and received another Covid <br /> test around the time of their hospitalization and people who had been <br /> fully vaccinated at least 90 days (but no more than 180 days) before and<br /> received a Covid test around the time of their hospitalization.

This choice is also bizarre. Those of you who have been paying attention <br /> will know that this date range is designed to make the vaccines look as <br /> good as possible by testing in the happy vaccine valley, the short <br /> period when mRNA vaccines are at maximum effectiveness (in fact, they <br /> are probably starting to lose it by the sixth month).

But more importantly, this criteria excluded the VAST majority of the <br /> people hospitalized with Covid-like illnesses or tested for Covid.

Only about 1,000 people out of the 200,000 people hospitalized for <br /> Covid-like illnesses over the eight months had a previous documented <br /> Covid infection. (Given the fact that at least 20 percent of Americans, <br /> and probably more like 40 percent, had had Covid by the spring of 2021, <br /> this is a strikingly small percentage - and certainly doesn’t suggest <br /> long Covid is much of a threat.)

And only 89 of those 1,020 people with natural immunity tested positive. In<br /> contrast, 324 out of the 6,328 vaccinated people who met the study’s <br /> criteria tested positive.

But isn’t 324 more than 89?

It sure is. And the CDC didn’t have - or didn’t publish - figures on how <br /> many people were actually in the two groups - those with natural <br /> immunity and those infected. Instead it compared the PERCENTAGE OF <br /> POSITIVE TESTS in the two groups.

But why would the percentage of positive tests matter, when we don’t know how many people were actually at risk?

Great question.

But, amazingly, the statistical manipulation then got even worse.

The natural immunity group had an 8.7 percent positive test rate. The fully<br /> vaccinated group had a 5.1 percent positive test rate. So the natural <br /> immunity group was about 1.7 times as likely to test positive. (1.7 * <br /> 5.1 = about 8.7.)

With such a small number of people in the natural immunity group, that raw <br /> “rate ratio” may well have failed to reach statistical significance. (We<br /> don’t know, because the CDC didn’t provide an unadjusted odds ratio <br /> with 95% boundaries - something I have never seen before in any paper.)

Instead, the CDC provided only a risk ratio that it had adjusted with a variety <br /> of factors, including “facility characteristics [and] sociodemographic <br /> characteristics.”

And finally, the CDC’s researchers got a number that they could publish - <br /> hospitalized people who had previously been infected were five times as <br /> likely to have a positive Covid test as people who were fully <br /> vaccinated. Never mind that there were actually four times as many <br /> people in the second group.

Science!

By the way, buried at the bottom of report is some actual data. And it’s bad.

The CDC divided the hospitalizations into pre- and post-Delta - January through June and June through August.

On 2021-08-27 15:57:52, user Jacky wrote:

The study does not account for survivor bias (i.e., those who got COVID and died; however, hardly anyone--probably nobody--who got a <br /> vaccine died); the estimates they report are confounded and not <br /> interpretable. Also, it does not account for time differences of when <br /> the person was vaccinated and when when the person got COVID. If most <br /> individuals were vaccinated say 6 months ago and they are compared <br /> individuals that got Delta recently, then of course the latter will have<br /> more antibodies than the former (antibodies will wane in both groups). <br /> Thus, this study has sever methodological challenges.

On 2021-09-12 15:11:23, user Ben Veal wrote:

I don't think you've read the paper very carefully. Socio-economic class is matched between the treatment groups, and also used a covariate in the model (i.e. the results take economic class into account).<br /> What makes you think there is a difference in the amount of immunocompromised people between the treatment groups? Important health risk factors are accounted for.

On 2021-12-03 13:45:03, user Ben Veal wrote:

Those are not flaws, they are limitations (which are also mentioned in the paper). You have not highlighted any errors, you have just mentioned one possible way that the results could be misleading if certain circumstances were met.

On 2021-10-11 16:26:26, user sentner wrote:

It's not relevant because this study isn't about survivability of the disease. It's about how likely one is to be infected a second time.

On 2021-08-26 20:37:24, user David Anfinrud wrote:

This is mostly common sense. But again to get people to understand that those that had COVID are better protected and do not need the vaccine you have to have a study. This information is just a repeat of science seen over the Decades. Vaccines help but the best protection is natural immunity

On 2021-09-11 16:32:55, user Chadwick wrote:

Red Flags all over the place, like there are 2.5x more immunocompromised in normalized comparison groups, being immunocompromised makes re- or breakthrough infection LESS likely, Economic class swings between comparison groups, being wealthier makes infections more likely...

On 2021-12-30 12:21:53, user Raster wrote:

It was not ignored, it simply wasn't confirmed by evidence.

On 2021-12-24 21:24:16, user Shannon Rowland wrote:

How much shorter was the duration in the vaxed group vs unvaxxed? And those who took something other than Moderna- can I assume that they didn’t have the same benefit?

On 2020-05-27 16:52:06, user Gordon Freeman wrote:

Can you please share the source code....

On 2020-08-24 17:50:51, user Puvvada Rahul krishna wrote:

We would like to withdraw the article from MedRxiv. The reason for withdrawal was plagiarism issue while publishing to other journal's

On 2020-05-24 21:33:11, user Tim Tarr wrote:

DOXY was suggested as replacement for azithromycin for those with heart issues. Seems azithromycin may compound HCQ risk to the heart. Now put Zinc in the mixture.<br /> DOXY+HCQ+Zinc sulfate <br /> The lab work should be run for vitamins D&C deficiency and Zinc. Lab work on kidney & liver status is pretty standard for admission.Also if heart function not known that should be checked, also usually a basic admission process.

On 2020-04-22 19:08:57, user Brian_Dell wrote:

This is analogous to the "sputum" tests widely used in Germany, right?

On 2020-06-24 11:34:40, user Renzo Huber wrote:

This is a nice review that might also be valuable to the field of layer-fMRI. <br /> I think the manuscript might benefit from an additional brief discussion of the related laminar connectivity findings from non-invasive human fMRI studies:

-> layer-dependent connectivity in Fig. 6 and 7 of the following study: <br /> Huber L, Handwerker DA, Jangraw DC, et al. High-Resolution CBV-fMRI Allows Mapping of Laminar Activity and Connectivity of Cortical Input and Output in Human M1. Neuron. 2017;96(6):1253-1263.e7. doi:10.1016/j.neuron.2017.11.005

-> layer-dependent connectivity with gppi in this study: <br /> Sharoh D, Mourik T van, Bains LJ, et al. Laminar Specific fMRI Reveals Directed Interactions in Distributed Networks During Language Processing. PNAS. 2019:1907858116. doi:10.1101/585844

-> layer-dependent hierarchical connectivity discussed in this study: <br /> 1. Huber L, Finn ES, Chai Y, et al. Layer-dependent functional connectivity methods. Prog Neurobiol. 2020:in print. doi:j.pneurobio.2020.101835

On 2020-08-21 04:26:05, user N M C wrote:

Very odd FDA put this on hold,

On 2020-08-24 18:08:50, user Frank Harrell wrote:

A discussion is taking place here: https://discourse.datametho...

On 2020-04-21 22:06:22, user Senad Hasanagic wrote:

Totally flowed retrospective analysis because baseline Clinical characteristics are missing ( not reported) in significant number of patients. So adjustments not possible.

On 2020-04-22 07:37:25, user Dominique Joly wrote:

Major issue: the number of days between the first symptom and the beginning of hosp/treatments is not indicated.

On 2020-04-22 20:38:01, user David Swiff wrote:

Macrolides can prolong the QT and QTc interval and cause cardiac arrhythmias, including TdP, ventricular tachycardia, and ventricular fibrillation, via their effect on the IKr potassium channel.

On 2020-04-22 12:57:03, user Jorgen Schultz wrote:

Interesting - and chocking, I must say. I hope the report I have read on medRxiv is not final, because I am missing the following:<br /> 1) As I understand it, the combo-treatment is effective in treating patients with Coronavirus BEFORE it is "to late". Giving a treatment with known side effects during late stage infection is recommended by?<br /> 2) The screening done (on patients) prior to treatment - I must have missed it or? Just to compare: as I understand it, in IHU's treatment (besides patient-groups being not comparable) screening is done prior to any medication, and patients in risk form a kind of control group as best as can be in this effort to safe lives. <br /> 3) Dosage and duration of treatment?<br /> 4) Did patients with cardiovascular symptoms receive hydroxychloroquine? Likewise with patients showing symptoms of "Cytokine Storms"?<br /> 5) According to "Cytokine storm and immunomodulatory therapy in COVID-19: role of chloroquine and anti-IL-6 monoclonal antibodies" by Ming Zhao, Hydroxychloroquine is mentioned for its effect to inhibit viral replication. Is that not very much prior to the case of the patients in this study? <br /> Did other - and more relevant drugs - replace the use of Hydroxychloroquine if the later stages of the infection?<br /> 6) Why study a subject you have already discarded and emotionally distanced yourself from (in "Background" it describes the use of Hydroxychloroquine as "anecdotal")?

As the French President said: We are at war. <br /> But here - another war seems to be fought!<br /> With human sacrifice and casualties as a result.<br /> Colatteral damage?

I am chocked and saddened by the loss of life during this study.

On 2020-05-04 11:11:16, user Will Jones wrote:

What was the median number of days between admission and death?

On 2021-08-29 20:54:09, user peter_wark wrote:

Thanks again Recovery trial.<br /> Participants admitted with COVID19; unable to maintain SpO2 <94% despite FiO2 0.4.<br /> Mean age 57yrs<br /> Primary outcome was intubation or mortality at d30.<br /> CPAP HR 0.72 (0.53-0.96) p=0.03<br /> HFO2 0.97 (0.73-1.23) p=0.85<br /> The number needed to treat for CPAP was 12 (95% CI, 7 to 105) and for HFNO was 151 (95% CI, number needed to treat 13 to number needed to harm 16).

On 2020-04-25 14:15:03, user Wouter wrote:

End of the week. Agreed ;-)

On 2020-05-13 17:00:14, user Sinai Immunol Review Project wrote:

Main Findings<br /> The immunity of the mucosa between the mother and the newborn against COVID-19 was tested. The secretory antibody -IgA of breastfeeding milk, shown immune response to the Receptor Binding Domain (RBD) of SARS-CoV-2 Spike protein.

Limitations<br /> Further studies are needed to understand the types of vaccines and routes of administrations in terms of protective antibodies against SARS-CoV-2 in human milk.

Significance<br /> Immune-modulating factors in breast milk may exert a significant impact on the infant’s developing immune system preventing or mitigating SARS-Cov-2 infection.

Reviewed Martinez-Delgado Gustavo as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

On 2022-02-04 14:45:12, user Mukhtar wrote:

Hey, we have three affected individuals and found a very convincing homozygous missense mutation in KCNC2. The variant co-segregates with disease phenotype. Parents are heterozygous carriers. The phenotype of patients is vision impairment. I am just wondering if your patient also has some vision defects.

On 2022-02-09 20:37:04, user anon wrote:

This is completely nonsensical after a population wide study by Patone et al. showed the pooled risk of developing myocarditis to be higher after any mrna vaccine series compared to covid infection for males <40. Yet 1000x higher from covid? I have to say, even among covid long hauler communities, compared to communities with vaccine side effects, the prevalence is much higher in the vaccine communities. Where am I missing the extra 1000 people for every extra person in the vaccine communities?

On 2021-11-07 19:23:55, user Eleutherodactylus Sciagraphus wrote:

It is relevant to note that this preprint (along with other two from the same group) includes data from human subjects that are under ethical scrutiny. The majority of patients enrolled were not informed nor agreed on participating in the study. The Brazilian National Comission for Research Ethics (CONEP) has been bypassed and is now investigating this case.

https://brazilian.report/li...<br /> https://www.emergency-live....<br /> https://www.dire.it/14-10-2...<br /> https://www.matinaljornalis...<br /> https://g1.globo.com/rs/rio...

On 2020-09-18 14:18:31, user mark.goldberg@mcgill.ca wrote:

Similar to the ecological studies on mortality fro covid and air pollution this study is biased. You may want to read the paper by Paul Villeneuve and myself in Environmental Health Perspectives: "Methodological Considerations for Epidemiological Studies of Air Pollution and the SARS and COVID-19 Coronavirus Outbreaks", https://doi.org/10.1289/EHP...

On 2021-12-16 11:48:59, user Timm wrote:

Congratulations to this important study! Why do you think the data hints towards affinity maturation and not just higher titers?

On 2021-03-24 21:24:49, user Erik von Elm wrote:

Could the authors please clarify whether the study has been commissioned by a corporate client (as suggested at https://idalab.de/success-s... "https://idalab.de/success-stories/improving-rd-productivity/)") or not? In the manuscript they state that "no funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."

On 2022-06-02 18:37:12, user Nils Yang wrote:

This paper was accepted at Lancet Child & Adolescent Health.

On 2020-04-12 23:44:57, user Xavier de Roquemaurel wrote:

Please re-run the analysis strain by strain. Regrouping all countries using Tokyo172 together, all Copenhagen 1331 together, all BCG Russia together and all the other strains together. You may achieve an extreme correlation with Tokyo172.

Then, if you can, run all possible other factors: social distancing, masks usage, confinement, average % of population in overweight, blood group predominance in population, ethnicity (for enzyma differences not for skin colours), % of population smoking, level of air pollution... or other factors that may come to your mind.

Let’s get the BCG hypothesis fully checked and then let’s act according to the results. Thanks!

On 2021-06-07 07:55:12, user David Curtis wrote:

The confounding effects of ancestry on genetic risk prediction are well established. See for example:

Polygenic risk score for schizophrenia is more strongly associated with ancestry than with schizophrenia. <br /> https://journals.lww.com/ps...

On 2021-11-20 23:32:38, user Gordon V. Cormack wrote:

Were the previously infected also vaccinated, either before or after their infection?

Edit: I think I answered my own question: <br /> When we examined HCWs (n=423) with infections occurring before vaccination, no re-infection was observed, accumulating 74,557 re-infection-free person-days (starting 10 days after initial infection and censoring at the date of receiving their first vaccine dose). Further, after vaccination, previously infected HCWs did not contribute any breakthrough infection events among the vaccinated HCWs.

On 2020-04-11 08:07:44, user Jeff Aronson wrote:

This looks like an interesting study, with an important warning about the combination of hydroxychloroquine + azithromycin, which people are beginning to use. But why misleadingly title the paper "Safety of hydroxychloroquine …" when what is being reported is serious adverse events, i.e. unsafety? I hope that when the authors prepare their paper for peer review, they will use a more accurate description.

On 2025-10-06 18:12:00, user David Maxen wrote:

I am confused as to why k=1,...,13 in equation 1. I accept that the transition probabilities T(A->A)+T(A->T)+T(A->C)+T(A->G)=1, and likewise for T, C and G. This explains 4 of the 13 values of k, but not the others. I am also confused as to what is meant when k=13; in this case, s ranges from 49 to 52. But there are only 16 dinucleotides, T^52 t->t+1 doesn't make sense either. Otherwise I am very interested in this paper, so I am very interested to hear your response. Best wishes, David

On 2020-09-30 18:53:22, user James Rubin wrote:

Please note the authors have identified an issue with the underlying dataset that was analysed for this pre-print. Specifically, around 3% of respondents in the dataset reported having been contacted by NHS contact tracers and asked to quarantine. According to official data from NHS Test and Trace, this should be less than 1%. Given this difference, it is likely that we will revise our interpretation of the quarantine data in the final peer-reviewed paper. Until then, as noted in the manuscript, the data relating to quarantine should be treated with caution.

On 2022-01-29 14:34:02, user Alberto wrote:

Thank you for highlighting this problem. It's amazing that indoor air quality has been so ignored apart from some "open the windows" advise.

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

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

On 2020-05-01 15:58:00, user Diego Fleitas wrote:

First, thank you for your research.<br /> Second, I have some doubts about your weighting criteria. Because of: it is not known the relation between contagion and socio demographic aspects; weighted results are almost three times higher than raw prevalence what looks out of scale; and also the relation of 80 times fold between actual diagnosed and projected looks a bit out of scale.<br /> Best

On 2020-05-02 07:17:53, user MichaelWoon wrote:

Do you think nobody in the test was positive?

On 2021-01-05 14:01:12, user Lianna Martin wrote:

Hiya - I had suspected covid 11th March and still can barely smell. My nasal passage more recently has become painfully crusted up coinciding with most things with a strong smell coming across like bleach or petrol to me. Taste comes and goes. I can live with symptoms, but would love to be part of a study...

On 2021-08-11 15:21:36, user circleofmamas wrote:

They need to include vaccination status of the cases, hospitalizations and deaths. Canada is one of the most widely vaccinated countries in the world, and we can see from Israel data and UK data that the vaccinated are particularly vulnerable to the Delta variant.

On 2021-04-19 18:28:52, user lbaustin wrote:

Has this been submitted (or accepted) in a journal yet?

On 2020-02-15 18:42:07, user Giuseppe Lapadula wrote:

How was determined the index date for calculating the incubation period? How can it be 0 in some patients?

On 2020-03-03 14:31:32, user palazmarkazi wrote:

So if the assumption is correct, then we should see those who suffer from it decline the following week

On 2020-03-02 16:40:24, user Abed Ghanbari wrote:

We estimated that 18,300 (95% confidence interval: 3770 – 53,470) COVID-19 cases would have had to occur in Iran, assuming an outbreak duration of 1.5 months in the country, in order to observe these three internationally exported cases reported at the time of writing.

How did you reach to these numbers?

On 2020-08-25 09:01:02, user Tjabbe wrote:

Evidence for what, that it doesn't work for late stage covid in hospitalised patients? Is that even news? How come at this stage in the pandemic we are still publishing reports that claim medication be ineffective "for treating covid19" when in fact it was only tested for patients with severe covid19 already in the hospital. We all know patients will not be sent to a hospital in the Netherlands for covid unless they have progressed pretty far. <br /> The report describes hcq being used on patients when deteriorating in several of the hospitals, affecting mortality, and media outlets conveniently leave out this part of the puzzle.

If you want to curb covid, or if you want to write off medication as being useless "for covid" , start doing trials on early outpatient treatment.

On 2021-05-29 20:55:44, user Robert Clark wrote:

Seriously, it’s like some researchers opposed to the concept of EARLY treatment of COVID will go to any lengths to provide evidence against it, even if it crosses the line of scientific ethics.

Sorry, to have to say this but the authors no longer have any credibility on this issue.

Extremely important to recognize the importance of this: to provide evidence against IVM researchers have had to change data to fit their conclusion.

What does that tell you about the effectiveness of IVM?

Robert Clark

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

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

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

On 2020-11-16 08:49:06, user Mike Maglothin wrote:

I've seen several studies attributing all excess deaths to CoVid. I agree... BUT.. what they show in their modeling is a correlation to CoVid. The excess deaths could very easily be from delayed procedures or people being unwilling/unable to get a procedure done in a timely fashion. I know many procedures were delayed, especially during the beginning of the pandemic when hospitals were being "reserved" for CoVid. Would be interesting to see the Excess death correlation only after August.

On 2020-04-08 00:15:34, user Sinai Immunol Review Project wrote:

Clinical features and the maternal and neonatal outcomes of pregnant women with coronavirus disease 2019

Keywords

Pregnancy, SARS-CoV2, neonatal and maternal Covid-19 outcome

Key findings

33 pregnant woman and 28 newborns were included in this retrospective multi-center study, conducted at 5 hospitals in Wuhan and Hubei province, China, between January 1 and February 20, 2020. All women were diagnosed with Covid-19 by qPCR or viral gene sequencing based on the Chinese New Corona Pneumonia Prevention and Control Program, 6th edition, and were further subdivided into four groups based on clinical severity: (1) mild, presence of mild clinical symptoms without radiological abnormalities; (2) moderate, fever or upper respiratory symptoms as well as radiological signs of pneumonia; (3) severe, at least one of the following: shortness of breath/respiratory rate >30/min, resting oxygen saturation SaO2<93%, Horowitz index paO2/FiO2 < 300 mmHg (indicating moderate pulmonary damage); and (4) severe-acute, acute respiratory distress with need for mechanical ventilation; systemic shock; multi-organ failure and transfer to ICU. Maternal admission to ICU, mechanical ventilation or death were defined as primary outcomes; secondary study outcomes comprised clinical Covid-19 severity in both mothers and newborns, including development of ARDS, neonatal ICU admission as well as mortality.

Maternal characteristics and outcome: 3 out of 33 women were in their second trimester of pregnancy (17, 20 and 26 weeks), and 15/33 (45.5%) had a previous history of underlying chronic health disorders including cardiovascular, cerebrovascular or nervous system disease. Common Covid-19 symptoms at presentation were fever (63.6%), dry cough (39.4%), fatigue (21.2%), and shortness of breath (21.2%). Less common symptoms included diarrhea, post-partum fever, muscle ache, sore throat and chest pain. 4 (12.1%) pregnant women had no apparent symptoms. The majority of cases were classified as mild (39.4%) or moderate (57.6%); however, one woman developed severe Covid-19. 40.6% of women were diagnosed with bilateral pneumonia, 43.8% presented with unilateral pneumonia, and 15.6% showed radiological ground-glass opacity. 87.9% of women required oxygen administration, and one (3%) woman had to be put on non-invasive mechanical ventilation (primary outcome). 81.5% of women had a C-section and only 5% had vaginal deliveries. Obstetrical complications were seen in 22.2% of women, including three cases of preterm rupture of membranes, two cases of hypertensive disorders of pregnancy, and one case of spontaneous preterm labor. Five pregnancies were ongoing at the end of the observation point of this study; one woman decided to have her pregnancy terminated. Neonatal outcome: Out of 28 newborns included in this study, 35.7% were born preterm at <37 weeks of gestation with Apgar scores ranging from 8-10/10 at 1 min and from 9-10/10 after 5 min, indicating normal heart and respiratory rates. 17.9% of newborns were of low birth weight (not specified) and 14.3% showed signs of fetal distress (also not specified). According to the authors of this study, none of the newborns presented with clinical Covid-19 symptoms. However, one newborn, delivered at 34 weeks of gestation, was diagnosed with (apparently Covid-19 unrelated?) ARDS and transferred to NICU (secondary outcome). Of 26 newborns tested for SARS-CoV2, only one was found positive and showed radiological signs of pneumonia, but no clinical symptoms of Covid-19. It remains unclear whether this was the same case as the newborn diagnosed with ARDS. The affected newborn did not require any treatment and was discharged at 16 days post birth. In summary, the primary outcome “mechanical ventilation” in pregnant women was rare (3%), no other primary outcomes were reached. Most Covid-19 cases in pregnant women were described as mild to moderate. Only one of 28 (3.57%) newborns was diagnosed with ARDS (secondary outcome).

Potential limitations

Major limitations of this study are its small size and the rudimentary and at times inadequate description of patient specifics. For example, underlying health conditions that might be affecting Covid-19 outcome in pregnant women should have been clearly specified (other than being of be listed (not just <37 weeks). Given that maternal infection status seemed mostly unknown at the time of birth and, more importantly, that the majority of cases in this study were clinically asymptomatic or mild to moderate, it remains unclear whether the C-sections performed were a medical necessity or elective procedures. This is of importance and should have been discussed. With regard to neonatal outcome, it is also not apparent whether the newborn found to be infected with SARS-CoV2 and the case diagnosed with ARDS were the same individual. If this was the case, it would be incorrect to refer to all newborns as asymptomatic. Additionally, it seems somewhat unlikely that a newborn with a near-perfect Apgar score would present with ARDS immediately after birth. Likewise, any individual diagnosed with ARDS would certainly be expected to receive supportive treatment including (invasive) mechanical ventilation. While it is highly relevant that overall clinical outcome in pregnant women diagnosed with Covid-19 seems better than in SARS or MERS (as discussed by the authors), it nevertheless needs to be stressed that more than 37% of newborns in this study were delivered preterm and that the obstetric complication rate of 22% seems higher than non-Covid-19 average.

Overall relevance for the field

Observations in this study confirm some of the findings published in a case series by Yu N et al. (Lancet Infect Dis 2020; https://doi.org/10.1016/ S1473-3099(20)30176-6). However, due to the relatively small study size of 33 pregnant women and 28 newborns, this study lacks statistical power and final conclusions on Covid-19 outcomes in pregnant women and newborns cannot be drawn. Yet, the data collected here are important and should be incorporated into larger data sets for more insight. Understanding the clinical course and effects of Covid19 in both pregnant women and newborns is essential, and while there are some recent publications on vertical SARS-CoV2 transmission between mothers and newborns (Dong L et al, JAMA March 26, 2020, doi:10.1001/jama.2020.4621; Zeng H et al, JAMA March 26, 2020, doi:10.1001/jama.2020.4861) as well as on neonatal infection at birth (Zeng L et al, JAMA March 26, 2020, doi:10.1001/jamapediatrics.2020.0878), our knowledge of how these patient subsets are affected is still very limited.

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

On 2020-06-12 15:57:05, user Rosemary TATE wrote:

What is the correlation hypothesis? With all due respect I think that a course in statistics could be very beneficial for the author.

On 2021-05-26 02:24:19, user ????? ????? wrote:

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

On 2025-02-23 14:32:17, user Shin jie Yong wrote:

"Recently, a subset of non-classical<br /> monocytes has been shown to harbor S protein in patients with PVS [18]" - reference #18 cited Patterson et al. (2020), which might be an error since this study examined long covid participants only. My apologies if I'm mistaken, however.

On 2021-09-27 21:24:14, user pwlg wrote:

"Dror Mevorach, head of internal medicine at the Hadassah University Medical Center, and his colleagues identified 110 myocarditis cases among 5 million people in Israel who had received two doses of the Pfizer-BioNTech vaccine in the month before their diagnosis. That translates to about one in 50,000 vaccine recipients, a number that isn't concerning given the background rate of myocarditis in the general population where it is typically triggered by viral or bacterial infections." Science June 1, 2021.

On 2021-10-06 16:56:58, user zega wrote:

Baseline is 1/100000/365days https://www.myocarditisfoun... vast difference, they an be off by 8000times and still below...

On 2020-03-21 21:53:33, user Joel Joonatan Kaartinen wrote:

There's data showing that someone who gets the asymptomatic version of this disease tends to fight it off in under 10 days. So if asymptomatic covid-19 was the case for >90% of patients, it'd actually be quite plausible that all people aboard the ship had it and were totally virus free by the time they were finally tested.

That being said, until this paper gets some significant peer review, I'm not going to give it much weight, personally. Indirect analysis is easy to fumble in many ways.

On 2021-11-04 03:14:20, user Hiromichi Suzuki wrote:

We thank you for your comments, The reagent was currently approved in October, 2020. We changed the year of approval from 2021 to 2020, which is the mistake of description.

On 2020-07-16 18:54:38, user Marm Kilpatrick wrote:

This is a fantastic dataset. Could you possibly show the data relative to date of onset of illness, both for SARS-COV-2 & for other viruses? Also will the raw data be shared when paper is published? This could help quite a bit in syntheses on several key topics. Thank you!

On 2021-02-18 23:34:23, user Max van Berchem wrote:

There is a mistake in the discussion part. Moderna is the one that had 30 severe cases, all in placebo and Pfizer 9 severe case in placebo and 1 in vaccine arm.

On 2023-09-14 09:01:23, user Chris Iddon wrote:

CO2 measurements are taken for one occupied day in November 2022 and compared to PCR positive cases during the period Aug21 to Aug22. There is no data presented here to suggest that the single day CO2 reading is representative of the room ventilation during the whole period of Aug21 to Aug22. Also there doesn't appear to be any record of how many of the occupants were PCR positive prior to Aug21 and therefore have some level of prior immunity, nor how often the occupants are tested by PCR.

On 2022-01-14 06:53:34, user Andy Bloch wrote:

The hazard ratios for ICU admission and mortality were unadjusted. This is not clear from the abstract, but the full text explains: "The observed number of patients meeting each of these endpoints was inadequate for multivariate analyses due to the absence of counts within multiple covariate strata." Considering that non-SGTF (Delta) were more likely to be 60 or older, nearly twice as likely to be unvaccinated (49.7% v. 26.6%) and about 1/3 as likely to have had 3 shots boosted (4.6% v. 13.4%) there should have been some adjustment or stratification made, perhaps using rates from other studies. The CDC is citing this study as showing a "91% reduction in risk of mortality" and that is very misleading since the figure is unadjusted.

On 2022-01-13 17:45:28, user T S wrote:

There is a calculation error in Table S8 for the Hispanic noSGTF. The percentage listed in parenthesis is incorrect and appears to be a transposition of the line above it for Black non-hispanic.

Also, I would hate for this article to appear biased upon publication but listing the actual value for increased risk of Omicron infection as compared to Delta for people with prior Covid diagnosis "4.45 (3.24-6.12) fold higher" yet listing a general statement of just "higher" when presenting that same data for prior vaccines leads an astute reader to assume the authors are using framing to present a bias to the data in favor of vaccines. With our top health officials playing politician by misrepresenting studies and dodging questions it is ever more important that actual scientists and peer reviewed studies are above reproach or we risk further deteriation of the rapidly declining public trust in scientists and studies.

"Among cases first ascertained in outpatient settings, adjusted odds of documented prior SARS-CoV-2 infection >=90 days before individuals’ first positive test during the study period were 4.45 (3.24-6.12) fold higher among cases with Omicron variant infections than among cases with Delta variant infections infection (Figure 1; Table 2). <br /> Similarly, adjusted odds of prior receipt of each vaccine series (1, 2, or 3 doses of BNT162b2/mRNA-1973, or Ad.26.COV2.S with or without a booster dose of any vaccine) were higher among cases with Omicron as compared to Delta variant infections."

On 2025-11-13 16:48:17, user Kevin Davy wrote:

Interesting paper! Is self-reported health in 80+ yo really "superaging"? The most common definition is ~ 80 plus year olds who have the memory capacity of individuals 30 years younger. Mortality has been studied as an outcome since the inception SRH measures. Don't quite a few studies report lower mortality among those who self-report better health?

On 2021-04-27 06:36:03, user Miguel Pedrera Jiménez wrote:

Hi, I'm Miguel Pedrera, from Hospital Universitario 12 de Octubre in Madrid, Spain. Great job, very interesting manuscript. We have recently published an article about a methodology to obtain useful data for clinical research from EHRs, applied to COVID-19. I am sending you the link in case it is of your interest: https://www.sciencedirect.c...

On 2025-07-25 12:31:33, user Evolutionary Health Group wrote:

We at the Evolutionary Health Group ( http://evoheal.github.io ) really enjoyed this paper.

Here are our highlights:

Evaluating co-occurrence across different surfaces in the built environment led to the identification of 15 pathogens as indicators for elevated SARS-CoV-2 abundance.

Methodology was flexible, including the evaluation of both cellular and viral genomes.

Study potentially lays the groundwork for improved hospital surveillance of antibiotic resistance gene abundance applying the same approach in a different context.

On 2020-03-12 20:49:50, user personfromreddit wrote:

Thank you for the well-designed and informative experiments and paper. The implications are critically important to managing COVID-19 patients and preventing further outbreak. However, in seeking publication for this paper I strongly recommend that the authors bolster their discussion -- specifically on the topic of in vitro vs. in vivo aerosolization. I am not a virologist, but I understand that just because SARA-CoV-2 can be aerosilized in similar systems that you used in your paper, that it may not mean there is clinical aerosolization and viral spread. While you touch on this in the paper and state that it is "plausible" that aerosol may contribute to spread, I think there needs to be more nuance to this discussion as to how likely this may be. To my understanding there are notable cases where in vitro systems such as the one in this study have found steady viral aerosols, but clinical aerosolization was not significant enough to present a means of viral spread (i.e. ebola).

I think discussing the topic of aerosolization in more detail are especially important for this paper during this time period -- one where many non-virologists and laypeople are reading high-brow virology literature. A more careful and detailed discussion of the implications of these findings will help prevent undue interpretations of these results.

On 2021-07-30 08:33:39, user Rob Leeson wrote:

The most important end point is death, as there is no pre hospital treatment for covid in the UK and the most at risk group was the over 65s surely an over 65s split 50-50 with placebo would have been more realistic. This looks a bit like the Tamiflu studies where there was a shorter time to recovery but NO protection for flu progressing to pneumonia and death.

NHS England removed the original link early 2021.

https://web.archive.org/web...

On 2022-01-05 09:52:18, user Zacharias Fögen wrote:

Dear Authors,

Thank you for this in-depth analysis, yet you draw conclusions that lack physiological plausibility.

First, you draw conclusions from serum neutralisation to transmissibility. As any SARS-COV-2 variant replicates in nasopharyngeal epidermal tissue, and protection from these kind of viruses is solely reliant on sIgA or IgM but not on monomeric IgG, you cannot draw this conclusion.<br /> This has been well researched in animal coronavirus vaccinations and is also the reason why Polio vaccines are given p.o. instead of s.c. in endemic regions.<br /> Furthermore, you are describing the variant as hyper-transmissible, yet again, there is no physiological explanation for this. Entry into epithel cells is only a micrometer of the distance needed to move from one person to another. Changes in spike protein have no effect on the men-to-men transmission. <br /> Furthermore, you are relying on studies which do not control for differences in contacts between the group now infected with Omicron variant and those previously infected with Delta variant. There are less restrictions now in England and Denmark then when Delta variant arrived. Statistics also show that age group 20-30 is primarily infected with Omicron, and as those have the most variable contacts, there is clear indication of bias.

Best,

Zacharias Fögen

On 2021-09-28 18:41:49, user Tigist Workneh wrote:

You can find the published article on EJHS journal following the link https://www.ethjhealths.org...

On 2020-06-13 03:34:34, user kpfleger wrote:

Why are the 25(OH)D levels reported here (43 or 44 nmol/L w/ IQRs of 32 or 31 respectively for the n=580 C19+ and n=723 C19- UK Biobank cases) so much higher than those reported in Hastie et al, "Vitamin D concentrations and COVID-19 infection..." Diabetes Metab Syndr., 2020: https://pubmed.ncbi.nlm.nih...<br /> which reported median 25(OH)D of 29nmol/L w/ IQR 10-44 for C19+ & 33 IQR 10-47.<br /> This is a huge difference for 2 papers with online publication dates 2 days apart both pulling data from the same source.

See also D'Avolio et al, "25-Hydroxyvitamin D concentrations are lower in patients with positive PCR for SARS-CoV-2", Nutrients, 2020 and Meltzer et al, “Association of vitamin D deficiency and treatment with COVID-19 incidence”, medRxiv, 2020 for 2 different studies that found in contrast to the top level conclusion here, that low D was associated with higher C19 incidence. Discussion of all 4 paper of these papers in the "D deficiency might be associated with higher infection risk" of the review: "Low vitamin D worsens COVID-19": http://agingbiotech.info/vi...

On 2022-08-17 07:08:46, user Partho Sen wrote:

This paper is now published in iScience, Cell Press. Here is the link of the journal pre-proof. https://www.sciencedirect.c...

On 2021-04-25 15:46:55, user Annette Toledano wrote:

Congratulations on advancing the understanding of the range of symptoms in hospitalized COVID-19 patients. I am surprised that pain was not a common symptom in the population you examined. I found elsewhere (Persistent neurologic symptoms and cognitive dysfunction in non-hospitalized Covid-19 “long haulers.” E. Graham, March 2021, Annals of Clinical and Translational Neurology), pain afflicted 43% of "long haulers." <br /> Are you aware of a review paper on the clinical manifestations of non-hospitalized COVIS-19 patients? <br /> It appears the Cov-Sars-2 virus affinity to target organs varies in different populations. In some people, the innate immune system's initial response in endothelial cells can lead to lung or vascular symptoms. In others, infected nerve cells can lead to pain.

On 2020-04-24 01:46:41, user Mike wrote:

Note: the original version has been previously discussed at length. medrxiv is redirecting that paper to v2 (this paper), making those comments are no longer available. This is a link to those comments: https://disqus.com/home/dis...

Below is my original comment with updates for this version of the paper

This was certainly an interesting paper. They’ve done a lot of work and the findings are notable. IMHO it warrants as much attention as the original pro-HCQ study via Dr. Raoult (~3/15). While it is entertaining, I will add that it is not conclusive, nor without fault. A double-blind study is still required.

It is important to note that this is version 2 of a previously released paper and it is much the same, with no major differences in the conclusions reached compared to v1. Therefore, my previous comment still holds true. Below I’ve included them followed by a new list of observations.

Observations/Questions (updated)

1. "hydroxychloroquine, with or without azithromycin, was more likely to be prescribed to patients with more severe disease” (p.12)<br /> 2. “as expected, increased mortality was observed in patients treated with hydroxychloroquine, both with and without azithromycin” (p.12) — I assume it’s expected because the patients given drugs were in a more severe state (and more likely die regardless of treatment)<br /> 3. "we cannot rule out the possibility of selection bias or residual confounding” (p.13)<br /> 4. demographic: 100% male, 66% black, median age ~70 (59 youngest); (Table 2, p.17)<br /> 5. uses PSM, which despite a common practice, could be considered controversial (https://gking.harvard.edu/f... "https://gking.harvard.edu/files/gking/files/psnot.pdf)")<br /> 6. Unless I missed it, I didn't see any specifics about how the treatments were administered.<br /> - How long before death were patients treated?<br /> - What was the quantity/frequency/duration of the treatments?<br /> - Were the treatments consistent between hospitals?<br /> 7. The rate of ventilation was less in HC+AZ (half of the HC and no-HC rates). Why was that? What does that suggest?<br /> 8. Although they were statistically insignificant, what was the result of the 17 women not included in the study?<br /> 9. Why does the paper seem to address political points? It seems like the Abstract is editorialized, which I'm not used to. The result seems to address topical issues of the times, having awareness of other similar studies being conducted, rather than a standalone independent study of its own. I interpret this as potential for some analysis/deciphering bias. I don't mind in the Discussion sentence as it's normal, I'm just not as accustomed to seeing it in the Abstract.

New List of Observations

1. There seems to be some bias in the number of healthy people with no-HC treatment, but left in the study. Those people are going to be unlikely to die to begin with. This is not a comparison of apples to apples.??

To clarify:<br /> ?- Dramatic difference in percentage of people people that had fever temperatures (38.1-39.0ºC / 100.58-102.2ºF); HC:11.3%, HC+AZ:11.5%, no-HC:7.6%. There’s ~4% difference between treated and untreated fever temps (more likely to die) in favor of untreated cohort. ?<br /> - Compare that with the percentage of people that had normal temperatures (35-37ºC/95-98.6ºF);HC:56.7%, HC+AZ:52.2%, no-HC:61.4%. There’s a 5-9% difference between treated and untreated normal temps (likely to not die) in favor of untreated cohort.

??So in this study, there was a larger proportion of people that did not have fevers, suggesting the data may be padded. In absolute numbers it's approx. a 40 person swing, which is a fairly large percentage in such a small study/survey. Similar observations are for systolic blood pressure and breaths per minute. There appears to be more healthy people? again.

2. Creatinine is created when muscles breakdown creatine. It’s a waste product removed by the kidneys. Levels are elevated when the kidneys begin to fail. Notice, there’s a much larger presence in the HC-groups, which suggests there was a larger percentage of these patients experiencing kidney failure.?

3. There are an awful lot of missing data in solely the no-HC group for statistically significant criteria. For instance Erythrocytes (red blood cells that transport oxygen), there 11.4% (!) missing in no-HC patients, yet that category has a P-value of 0.001 (<0.05 is statistically significant).??

Hermatocrit is the same way (missing 11.4% for no-HC). It’s also related to red blood cells, it is the ratio of red blood cells to the blood volume. Same missing amount for Leukocytes (white blood cells) test. And also Lymphocytes —white blood cells in lymphatic system, which transports fatty acids from the digestive system and white blood cells from the lymph nodes into the bones— not only has a lot of missing data, but the disproportional low count (<800 per mm^3) may warrant further investigation.

4. Even looking at the statistically significant Cerebrovascular disease, there are a much larger percentage of HC-only patients per its cohort.?

5. Table 4 describes a greater percentage of people using HC+AZ being discharged (recovering) w/o ventilation; 5% more than no-HC patients. Keep in mind that 30% of the no-HC patients were given AZ.

On 2020-12-05 11:14:49, user Robert Brown wrote:

A very useful study, but more questions than answers surely? I respectfully suggest an alternative one-line summary might read:

“Administration of a significant single oral bolus of Vitamin D3 (200,000iu) in COVID-19 patients, close in timeline to onset of ARDs, with Baseline 25(OH)D values averages of 21.0ng/ml and 20.6ng/ml, where significant numbers are on Corticosteroids and or PPI (which reduce magnesium and zinc (via zinc fingers), both ‘D’ co-factors), when almost all established risk factors were skewed against the treatment arm, did not result in any benefit in hospital stay or mortality.but did show tendency to reduced oxygen requirement.”

See lower post for detail.<br /> Erratum reference 2 is a TCT and not RCT – apologies.

On 2021-07-20 01:11:22, user The Davidtollah wrote:

From your pull quote: "our simulations"<br /> The study "proves" nothing but that these researchers plugged assumptions and numbers into a simulation and the simulation spit out results. Real-world analysis (such as in the study here at this site) and clinical studies show that masks do not work against airborne viruses. (Studies that are usually referred to that claim efficacy for masks are mechanistic, which, like simulations, can only show how something might work, and do not demonstrate whether they actually work or not.)

You are wrong, and your message demonstrates you don't know the difference between a simulation and an analysis of real-world results. Simulations must always be compared to actual data, and if they don't match, the simulation is obviously flawed and must be rejected.

On 2021-05-26 18:19:28, user rusbowden wrote:

Keep looking, Norbert-man. Seems you cherry pick, which is what I suspect these researchers of doing too. That's politics, not science. Aerosol can get through, which means the effectiveness is not 100%. Unless there is smart steering on board the tiny particles, or the mask material actually repels them, the mask will stop some. (It would be interesint for someone to find a smart mask material that draws the aerosol.) Note too, how vapor saturation of masks from the wearer's breath increases mask effectiveness. This is out there as well.

On 2021-05-30 17:20:05, user Badger Vanamburgh wrote:

It’s astonishing that anyone thinks that this study is in any way scientific, controlled, with conclusions based on anything but bias. It’s alarming that such nonsense writings like these are even taken seriously enough for someone to decide to publish it, spread it around the internet. The times of truth, fact and reality ruling the day, are behind us. They are destroying science and destroying our democracy. That is very sad indeed.

On 2023-01-03 01:06:03, user Myssi Graves wrote:

How can they say it was unexpected that increased doses would increase risk when there’s decades of evidence of this with flu vax. It was an obvious outcome to anyone educated on the topic. Sadly anyone who mentioned this possibility was vilified.

On 2021-06-04 19:32:52, user Anil Gulati wrote:

This article is published in DRUGS. DOI https://doi.org/10.1007/s40...

On 2020-04-03 12:58:04, user Kate wrote:

How can you find a correlation in an observational study? I'm not even touching on the issue of not controlling for any confounding variable

On 2020-04-13 15:17:54, user Advocatus Diaboli wrote:

It is one thing to talk about "observational studies" in general, and quite another to talk about a study on an ongoing and rapidly evolving global crisis.

They controlled for certain factors (e.g., wealth of the country, which can reasonably be considered a proxy for the development of the health care system). If they had other factors that they could have considered but did not, then I would agree that it is a weakness. However, given the paucity of data coming in, I seriously doubt that they had access to much more information across the full data set that could have allowed a more nuanced analysis.

They presented a hypothesis that seems both interesting and relevant (both in terms of projections of impacts and possible response options). As long as it is presented as a hypothesis and not a fact (the latter would be misleading), I believe it is better to have it out. Others can (and should) repeat the analysis, and with the progression of the virus we should get a clearer picture. If you are aware of data that they could have used but did not, you would be more than welcome to improve the analysis and publish your results.

On 2024-08-06 18:36:26, user Cindy wrote:

I would like to include some feedback regarding data analysis (full disclosure, I work for Olink), which I hope will be beneficial to both authors and others who are analyzing similar data from Olink:<br /> We recommend calculating Limit of Detection (LOD) according to manufacturer’s guidelines, specifically LOD for Explore HT should be calculated based on actual project data rather than use of estimates of LOD from unrelated validation data. <br /> The best practice is to use Olink Analyze functionality to determine the LOD for each project (available in the latest version!). <br /> Replacing values below LOD with LOD/2 is not recommended. It will artificially inflate coefficient of variation (CV) values. Instead, apply an LOD cutoff specific to the project data. Removing values below the project-specific LOD when calculating CVs ensures a more accurate representation of data variability.<br /> I am happy to coordinate any discussions with the Olink team to facilitate.

On 2021-01-23 16:10:09, user Prasad Kasibhatla wrote:

Do the top and bottom panels of Figure 1 represent different ambient conditions?

On 2020-04-14 10:07:38, user Rob wrote:

Thank you for this paper, as usually there is plenty of time to consider and/or analyse all angles within the context of Covid-19 I acknowledge the challenges. Beside, solid suggesting already mentioned. Please consider this as influencing the effect seen in relation to smoking and Covid-19 severity. Age (65+) are most risk of severe covid-19. Often smoking rate in 65+ is well below the national average. In the US ~14% is the smoking prevalence whereas 8.4% for 65+, in the UK ~16.5% is the national smoking prevalence whereas 10% 65+ (figures from CDC and ONS). Life Smokers die younger or likely to have comorbidities especially if they have smoked. Combines with other factors e.g under reporting and other mentioned already in the comment. Finally the effect size may be further attenuated by air pollution often severe in cities where most Covid-19 cases are found. Even if the paper shows a real effect, this would def change the effect size. In any case, it would not neccesarily show that smoking is protective

On 2021-08-05 01:41:37, user Tony Weddle wrote:

Those deaths are all cause deaths. In fact there were twice as many COVID-19 deaths in the placebo group as in the vaccinated group, though the figures are very low (1 and 2 respectively). By the way, there were 15 all cause deaths in the vaccinated group, 14 in the placebo group.

On 2021-08-18 18:30:23, user Steve Kirsch wrote:

There were two people in the placebo group who got the drug after the unblinding. The paper never talks about the cause of death from those two people. This is EXTREMELY important. Does anyone know?

On 2021-08-07 20:35:21, user vinu arumugham wrote:

Table S4 shows 4 deaths in the vaccine arm and 1 death in the placebo arm due to cardiac arrest. <br /> The probability that this outcome is a chance occurrence is 1.5%.

(((21999÷22000)^21996)×((1÷22000)^4)×(22000!))÷(21996!×4!) =0.0153 or 1.5%. <br /> So 98.5% chance that the vaccine CAUSED the excess cardiac arrest deaths.

Table S4 also shows 1 excess COVID-19 related death in the placebo arm.<br /> So to prevent 1 COVID-19 related death, the vaccine causes at least 3 deaths due to cardiac arrest.

On 2020-04-01 16:13:26, user Rosemary TATE wrote:

Hi Knut, I enjoyed reading your paper and found the models and your suggestions very interesting. However, I agree with Aaron, I was disappointed to find no evidence to back up the claim of the title. A much clearer justification should be made (with data and appropriate references) or the title should be changed. I suggest the latter as evidencing different strains seems to be a very secondary aim. <br /> I totally disagree with your that almost nobody considers epidemiological data for decision making. This is simply not true. Starting with John Snow there are far too many examples to name.

On 2020-03-29 10:45:38, user Bob O'Hara wrote:

Someof us had some problems with this manuscript, so we wrote a response: https://doi.org/10.32942/os...

Abstract: The ongoing pandemic of the severe acute respiratory syndrome <br /> coronavirus 2 (SARS-CoV-2) is causing significant damage to public <br /> health and economic livelihoods, and is putting significant strains on <br /> healthcare services globally. This unfolding emergency has prompted the<br /> preparation and dissemination of the article “Spread of SARS-CoV-2 <br /> Coronavirus likely to be constrained by climate” by Araújo and Naimi <br /> (2020). The authors present the results of an ensemble forecast made <br /> from a suite of species distribution models (SDMs), where they attempt <br /> to predict the suitability of the climate for the spread of SARS-CoV-2 <br /> over the coming months. They argue that climate is likely to be a <br /> primary regulator for the spread of the infection and that people in <br /> warm-temperate and cold climates are more vulnerable than those in <br /> tropical and arid climates. A central finding of their study is that <br /> the possibility of a synchronous global pandemic of SARS-CoV-2 is <br /> unlikely. Whilst we understand that the motivations behind producing <br /> such work are grounded in trying to be helpful, we demonstrate here that<br /> there are clear conceptual and methodological deficiencies with their <br /> study that render their results and conclusions invalid.

On 2020-04-18 04:34:06, user Zev Waldman MD wrote:

I agree with prior commenters that people who suspected that had or were exposed to Covid would be more likely to seek antibody testing. I see that participants were asked about prior symptoms, but it would also have been nice to ask about prior possible exposure concerns, If both numbers are very low, that would provide some reassurance about this possible bias.

I really wanted to address another issue: the calculation of the infection fatality rate, i.e., estimated deaths/cases. It seems that a lot more thought went into trying to get an accurate case count than an accurate death count. They seem to take it as a given that 50 people died of Covid in the county as of April 10; however, like case counts, there are multiple reasons to suspect this number of deaths might be higher:

1. Reporting of deaths is well-known to be delayed - i.e., date of reporting does not equal date of death

2. People who actually died of Covid may never have been tested, and thus may not be included as cases or deaths

3. The doubling time of deaths that was used to project to April 22 is based also on reported deaths; if reporting of deaths is delays, the doubling time may appear slower.

I did appreciate the authors' efforts to validate the antibody testing. That's useful information.

I worry that, because these results support their prior beliefs, some readers may take these results at face value and push them for policymakers to use before they have been more widely vetted by the scientific community.

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

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

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

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

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

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

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

On 2020-04-19 14:45:07, user Tomas Hull wrote:

1. This study is not perfect but no studies ever are.

2. The study shows pretty close estimates of the much lower mortality rate than previously estimated, likely slightly above the seasonal flu of 0.1% and lower than the German study estimates of 0.37% of a town of 12,000 inhabits where an accelerated infection likely happened due to the town carnival 2 months earlier.

3. More similar study results will soon be published, including L.A. County, MLB organization from 27 cities, and many European countries, which will probably confirm that CoV2 is much more widely spread than initially thought, and with the infection mortality rate slightly above the seasonal flu of 0.1% and below 0.37% from the German town of Gangelt, where the much faster infection rate initially occurred due the the town festival in February.

On 2020-04-22 04:47:58, user G.O.B. wrote:

Comparing a nasopharyngeal swab viral PCR test to a qualitative serum antibody test is apples and oranges. The NP swab has a high, user dependent false negative rate (low sensitivity), as most of those who perform the test are only get a nasal swab and not reaching the nasopharynx.

Here’s an idea for a study: test serum antibodies on those with a history of negative PCR of NP swab - you’re likely to find a high false negative rate (likely due to improper swabbing technique)

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

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

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

On 2021-04-16 16:18:49, user S Wood wrote:

Since we first posted this a couple of results from direct statistical measurements have come out that are broadly in agreement with the paper's results on incidence and its timing relative to lockdown. Figure 1 of this REACT-2 report shows the reconstructed time course of symptom onset, which lags infection by 5 to 6 days. Reported ONS incidence reconstructions from statistical infection survey data tell a similar story. This paper, now published in Biometrics, also produces similar results on incidence and R, by a different approach.

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

This paper is under review in Experimental Biology and Medicine

On 2022-06-06 19:03:39, user Chris wrote:

During this period, the test positivity rate was about 7%. Some of those tests may be random (schools, needed for travel, etc.), but it seems odd that the rate of having COVID would be lower for people who go get a test than for people who just pick up the phone and answer a survey - even if it related to a 2 week period. Note the COVID tests would pick up people who were infected over some period in the past as well.

On 2020-09-17 11:50:43, user Brian Kennedy wrote:

Semi - comment, also a question.

I am an economist living in Bangkok, Thailand, so this is pretty far out of my area of expertise. Thailand was the first country to get the virus outside China, but it never took off here, at this date still less than 4,000 total cases, and 60 deaths. I think there were a variety of things that led to this, but clearly early and near universal mask usage was part of it.

Your paper looks very interesting, but I could not really follow all the math. So I will trust you on it :)

My question is one of emphasis by public health officials in the U.S. Why has there not been a push on the issue of Viral Load? It seems to me that it is very important concept - even if using the mask doesn't always prevent you from getting infected, it will still reduce the viral load, giving your body more time to deal with the virus, significantly increasing your bodies chances of fighting it off.

Why has this issue been (it seems to me) largely absent from the public sphere, and from the arguments public health officials use to promote mask usage? Note if it has been there and I am missing it from far away, just say so.

Thank you for helping a laymen in your field understand :)

On 2021-10-06 23:34:39, user kdrl nakle wrote:

Excellent, this is what we are afraid of and this is why we need updated vaccines.

On 2021-08-22 22:00:56, user MeandMyShadow2 wrote:

Link?

On 2021-08-12 19:41:47, user Steven Ramirez wrote:

I wonder if the study disentangled time since vaccination. If protection diminishes over time for both vaccines it needs to be controlled for.

On 2021-08-12 12:38:42, user Peter Colin wrote:

Has there been any analysis of vaccine effectiveness in (1) persons who tested positive for Covid prior to vaccination, versus (2) persons who did not test positive prior to vaccination (negative and/or untested)?

On 2021-12-23 12:09:50, user Matthew Nelson wrote:

This is a superb paper, especially the careful approach to CNV calling and the Bayesian methods used throughout. Searching through the supplementary material, there is one important piece of the story that appears to be missing. Where are the counts of de novo missense, PTV, and CNV mutations per gene? These were nicely captured in the Satterstrom and Kaplanis NDD publications. These are really important for understanding potential population sizes for prioritizing therapeutic discovery and development. Have I missed them?

On 2022-08-22 03:33:38, user BGThree wrote:

In Section 2.1 "Cell culture system to express SC2 proteins from synthetic mRNA-1273" the authors disclose incubation of mouse and human cells with 200uL vaccine, but do not disclose corresponding controls. Specifically, mouse and human cells should be incubated WITHOUT addition of vaccine. This control is needed to demonstrate the proteins extracted from lysed cells are transcribed from vaccine mRNA not innate mouse or human mRNA.

On 2022-01-02 22:26:52, user madmathemagician wrote:

I believe the dataset used in this article is

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

but it is not linked in the article.

On 2020-10-19 16:03:32, user Rogerblack wrote:

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

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

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

It absolutely cannot justify sentances such as 'A significant proportion of COVID-19 patients discharged from hospital experience ongoing symptoms of breathlessness, fatigue, anxiety, <br /> depression and exercise limitation at 2-3 months ' without much more work, as it will lead to the conclusion that treating depression may benefit the patient when there is no depression, and it's a scale artifact.

On 2022-10-24 17:42:56, user CDSL JHSPH wrote:

Dear Mekkes et. al.,

Thank you for sharing your work with us! Creating models to predict neuropathological diagnosis based on temporal signs and symptoms is very significant research, and I’m looking forward to seeing where this heads in the future! I enjoyed reading this paper, especially since it introduced me to techniques and concepts I was previously unfamiliar with. That being said, while reading I did notice some parts that I think could be given further clarity in order to make this paper more accessible to those not within the immediate field. There are a lot of abbreviated disorders mentioned, and I noticed that some were explained in the introduction however I could not find the proper matching terms for disorders abbreviated in later sections. I think it would be a great benefit if there were a word key with the disorders and their corresponding abbreviations. Especially since different disorders may be represented by the same acronyms, so googling it may not provide the reader with the correct one. Also, I was wondering if you plan to do another study focusing on optimizing these models to diagnose mental illnesses and psychiatric conditions in a separate paper? I understand the main focus on brain disorders and neurodegenerative diseases since those can be linked to prominent neuropathological changes, but when reading the abstract I was given the impression that mental illnesses would be focused on to a larger degree than I noticed in the paper. I would love to see any future steps you take with this, especially since the alterations in cognition and behavior associated with mental illnesses can be observed from live patients, and don't necessarily have to be inspected retroactively like from brain donors.

On 2020-04-10 14:54:38, user Neil Lancastle wrote:

For clarity: Figure 5 is countries with BIGGEST falls in mean growth... from the text on p7...'growth rates have fallen most compared to earlier period' and, excluding China, these countries are France, Spain, Switzerland, Italy, UK and Norway.

On 2021-10-01 11:24:21, user Mahalul Azam wrote:

This work now published in the Int. J. Environ. Res. Public Health with the DOI https://doi.org/10.3390/ije...

On 2020-08-12 20:39:28, user David Curtis wrote:

There may be dozens of groups around the world doing studies like this on 30 or 40 patients. Some of them are naturally going to show "statistically significant" results. There's an argument that it's unethical to perform clinical trials with small sample sizes. This study is impossible to interpret. If you'd done the study with 100 cases and 100 controls we'd have something useful.

On 2020-08-12 06:05:43, user Christian Heebøll-Nielsen wrote:

It seems most of the differences between groups could be explained by the difference in time since symptoms onset. Have you made any effort to control for that?

On 2021-05-21 22:52:28, user Ilmari Haavisto wrote:

The beginning state of medication of disease is very important. Was it equal to both groups?

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

Is this Peer Reviewed?

On 2021-09-19 18:43:06, user 4qmmt wrote:

While interesting and adding to our knowledgebase, and thus possibly important for something in the future, these quantitative titer studies don't seem to be useful for predicting outcomes or comparing protection. Many are now using these titer comparisons in vaccine studies in what appears to be the hope of predicting future immune response for both vaccine and or previous infection.

But the authors of the study Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection in Science point out the obvious and logical problem with these studies. pg 6:

While immune memory is the source of long-term protective immunity, direct conclusions about protective immunity cannot be made on the basis of quantifying SARS-CoV-2 circulating antibodies, memory B cells, CD8+ T cells, and CD4+ T cells, because mechanisms of protective immunity against SARS-CoV-2 or COVID-19 are not defined in humans.

pg 2:

A thorough understanding of immune memory to SARS-CoV-2 requires evaluation of its various components, including B cells, CD8+ T cells, and CD4+ T cells, as these different cell types may have immune memory kinetics relatively independent of each other.

https://doi.org/10.1126/sci...

On 2022-01-06 23:28:48, user Greg Nelson wrote:

This is encouraging, would love to see another paper comparing to the PCR negative population (2346 respondees - 951 with PCR positive (study population) = 1395 people) to see baseline frequency of symptoms

On 2020-01-24 20:05:36, user Warren Li wrote:

How could the ascertain rate be as low as 4.8-5.5%? I don't see clearly the basis or fitted evidences for this assumption?

On 2020-04-03 16:31:40, user Alexander Siegenfeld wrote:

These projections likely severely underestimate the number of deaths and hospitalizations because they assume that any state that has implemented three out of four interventions they consider (school closures, non-essential business closures, travel restrictions including public transportation closures, stay-at-home recommendations) will see an epidemic trajectory similar to that reported in Wuhan, China.

The Imperial College report released on March 30 that quantifies the impact of nonpharmaceutical interventions in Europe predicts that even with the complete lockdowns implemented by 10 out of the 11 countries studied, the number of new infections may still increase. Given that the response in even the U.S. states implementing all four of the interventions considered by IHME may be less effective than the European lockdowns, there is a distinct possibility that without action beyond that assumed by the IHME study, the rate of new deaths and hospitalizations may not only not peak and decrease as quickly as IHME predicts but may also continue to exponentially increase (albeit at a slower rate).

See our full comment here: https://tinyurl.com/yx8xxqsv

On 2019-10-16 13:01:03, user GuyguyKabundi Tshima wrote:

EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS AT OCTOBER 13, 2019<br /> Monday, October 14, 2019<br /> Since the beginning of the epidemic, the cumulative number of cases is 3,220, of which 3,106 confirmed and 114 probable. In total, there were 2,150 deaths (2036 confirmed and 114 probable) and 1033 people healed.<br /> 383 suspected cases under investigation;<br /> 2 new confirmed cases at CTE in Ituri in Mandima;<br /> No new confirmed deaths have been recorded;<br /> 1 person healed out of CTE in Ituri in Mambasa;<br /> No health workers are among the newly confirmed cases. The cumulative number of confirmed / probable cases among health workers is 161 (5% of all confirmed / probable cases), including 41 deaths.

NEWS

Governors of North Kivu and Ituri Raise Awareness on Ebola Virus Disease in Biakato, Ituri<br /> - The Technical Secretariat of the Multisectoral Ebola Epidemic Response Committee (CMRE) in collaboration with the Governor of North Kivu, Carly Nzanzu and Ituri, Jean Bamanisa, organized this Monday October 14, 2019 an awareness raising day on Ebola Virus Disease in Biakato, Ituri;<br /> - This tripartite awareness-raising aimed to share the experience of North Kivu on Ebola Virus Disease and to show that the movement of people between the two provinces can encourage further spread of this epidemic in the region, as much as the last four cases recorded in North Kivu (in Beni and Kalunguta) came from Biakato;<br /> - The governor of North Kivu has indeed responded favorably to the invitation of the Technical Secretariat of the CMRE because he wants to reinforce the surveillance in his province and refuses to see his province plunge into the epidemic;<br /> - To achieve their objectives the two governors were accompanied each by a strong delegation, where one finds the presidents of their provincial assemblies and some influential deputies of their respective countries;<br /> - In addition, the Ebola Virus Epidemic Response Coordination Team, which has been in the Mambasa Health Zone in Ituri for the past week, has been monitoring Bavalakaniki Control Points and Mabakese in this health zone.

VACCINATION

• Since vaccination began on 8 August 2018, 237,956 people have been vaccinated;
• The only vaccine to be used in this outbreak is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, following approval by the Ethics Committee in its decision of 20 May 2018.

MONITORING AT ENTRY POINTS

• Since the beginning of the epidemic, the total number of checked travelers (temperature rise) at the sanitary control points is 105,840,505 ;
• To date, a total of 111 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the major cities of the country. countries and prevent the spread of the epidemic in neighboring countries.

As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number;
3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.
5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of # Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.

On 2021-07-12 20:50:31, user David Epperly wrote:

Some good details. Should add diary info from each of the infected as available to include: recollection of interactions with patients 0a and 0b and 0b's recollection of all interactions with all infected. Each interaction should include distance, time, and environment. More info about tent structure, identity blurred photos of event wide-shots, and historical prevailing wind conditions for area. Help us understand how transmission likely occurred. Also state relationships of each infected (spouse, close friend, acquaintance, ...) to help understand likely interaction levels. It appears 0a and 0b are related. Tell us. Move info on Delta into a supplement and focus on details of the event. Create a supplement for additional event / relationship / transmission details. Capture more of what happened.

On 2020-11-18 00:52:30, user Peter James wrote:

I am very much concern with ethics of this research. The authors stated that the local authorities approved the study. Although it is good to have a local buy in in such research, I believe that it would have been prudent and ethically appropriate to seek proper ethics approval from a recognised body either from a university or from the national ethics committee. There are ethics committees or IRB. The most popular one is the one in the ministry of health and sanitation (Sierra Leone ethics review and scientific committee). I believe that those local authorities, many of which are less educated and have little or no idea about research ethics and so getting their consent does not equate to addressing the ethics concerns of such study. I think this research should not published given that the authors did not adhere to the tenets of good research ethics. As a Sierra Leonean researcher, I have observed such issues, especially during the Ebola outbreak where western researchers disguising as NGO workers tend to exploit our system or take advantage of the weak quality assurance system when conducting research in Sierra Leone. I believe such cannot be done in Italy.

On 2020-05-19 01:55:12, user Subhradip AIIMS New Delhi wrote:

Testis has a very poor expression of TMPRSS2 , a critical determinant for virus entry. I suppose the entire molecular machinery must be present to make possible the virus entry and replication. Drawing a conclusion based on just 1 -2 target proteins will be an over simplification.

On 2025-11-24 05:16:17, user WILLY CESAR RAMOS MUÑOZ wrote:

The paper has been published in the journal Acta Tropica. DOI: 10.1016/j.actatropica.2025.107919

On 2021-01-26 14:17:45, user Rune wrote:

How is sensitivity and specificity of 69.7% and 99.5% for RAT calculated?<br /> 87.0% of PCR sensitivity of 90%-95% equals 78%-83%.<br /> 98.6% of PCR specificity of >99% equals >98%.<br /> PCR values according to SST "Information om PCR test for COVID-19 til almen praksis".

On 2020-07-27 06:43:06, user OxImmuno Literature Initiative wrote:

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

On 2021-02-09 23:10:30, user Robert van Loo wrote:

Why do the authors talk about overdispersion as some infections seem to occur in clusters, and for me that would mean underdispersion.

On 2020-05-06 21:53:31, user laurent moulin wrote:

Looks very interesting. Small Typo on figure 3 (I Guess...) with inversion of case and death on the France panel.

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

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

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

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

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

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

On 2020-04-17 19:08:16, user Jacob G Scott wrote:

See github repo for live updating and opportunities to contribute data: https://github.com/TheoryDi...

On 2020-04-30 13:53:43, user Alan Beard wrote:

Could the authors clarify about Smoking status please . Is it <br /> Current Smoker only OR <br /> Current and Former Smokers(which would include current Vapers)

On 2021-11-15 13:29:28, user Gary Porter wrote:

How long does this reduction in transmissability last?

On 2025-04-17 08:53:34, user Nitzan Paldi wrote:

For this research project to be considered valid, a few items are missing and should be provided by the authors. The most pertinent outstanding issues are:<br /> 1) The analysis of wMel was done in the months July-October, but the 2023 and 2024 summer wMel prevalence data is missing. This is important because almost all previous studies, including those done in Niteroi, have shown a very large dip in Wolbachia detection in the summer. <br /> 2) The analysis done to demonstrate the impact on dengue compares an arbitrary 10 year-period and takes into account the extremely high 2013 outbreak in Niteroi to demonstrate how Niteroi moved from one of the highest dengue cities in the state of Rio to the lower part of the spectrum. However, the period from 2013 onwards marks a massive reduction of dengue regardless of intervention or non-intervention status, and is identical in almost all >100k population cities in a 100 km radius around Niteroi. Moreover, the adjacent city of San Goncalo, that had half the level of dengue in the 2024 outbreak, had an almost identical reduction since the 2013 epidemic and all of this without any connection to Wolbachia releases. <br /> 3) The authors compare the dengue cases of Niteroi in 2024 with other cities in the state, but do not make a comparison with the city of Rio, across the bay, where another massive Wolbachia project had no impact on the prevalence of dengue in 2024, and resulted in a massive dengue epidemic of 1300 cases per 100k population. Omitting, or more likely “conveniently forgetting” this project underscores the unacceptable “cherry picking” of this manuscript. <br /> When these comparisons with the adjacent cities of San Goncalo (no Wolbachia – low dengue, half from Niteroi in 2024), and Rio de Janeiro (massive Wolbachia project- identical high epidemic dengue to non-intervention areas) are both taken into consideration, the conclusion is that the Wolbachia project had no demonstrable impact on dengue prevalence.

On 2020-06-22 14:22:44, user Valentina Viego wrote:

Forthcoming in Ciencia e Saude Coletiva

On 2020-04-24 20:18:46, user Diego B. wrote:

This article has been published on JAMA Network: <br /> https://jamanetwork.com/jou...

On 2020-10-28 17:44:49, user Andrea Camperio wrote:

Here you finally can find our research on the covid pandemic revealing that the first wave which was early modeled by Giordano et al., .(2020 reference in the text),and influenced the Italian government decision toward lockdown, using SIDDARTHE algorithm, was dramatically worse than what actually happened.

This suggests that there is good hope against the pessimistic perspectives of this second wave will be disattended as well. We are still actively developing new strategies to counteract virus effects. We are in the brink of implementing vaccination, new medicines are becoming available, older ones have been rehabilitated, so there is good hope for winning new battles to defeat the virus.

My personal predictions are that when the whole country, 60 million people, will have been infected, 2% of them will need special and intensive care( data on present fraction of infected needing intensive care about 2%), that means 1.2 million people, if intensive care will be available and sufficient, only 2% or less will die (data on at present survival rate in intensive care with Sars-Covid19), that means around 12,000 people, once everyone is (and if) exposed to the infection. However, if the intensive care, wont be sufficient, then at present 40% of the worst cases (1.2 millions) will risk their lives, without intensive care support, equal to about 400,000 people.

All depends on the evolution of the virus. The virus is evolving, in two directions, as all other aereal virus that affect humans, such as flu. First direction, that we already have seen, the virus is evolving toward being less and less lethal, because harming the host means extinguishing its self as well. The second direction, however, is more dangerous, and it is going toward faster and faster diffusion in new human hosts.

All human flu are very fast spreading, within 3-5 month they affect a very large portion of the population (30-40%), and very low mortality, usually 8.000 to 12.000 lives every year, mostly old and fragile individuals (about 0.0002 % of infected individuals). The Sars-Covid19 virus at present is killing at 0,1 % without the support of intensive care, and 0,002 with the supplement of intensive care, which means that is between 1000 and 10 times more lethal than a normal flu. In other words, if the virus will affect the whole population, in ten years or more (very improbably slow) the rate of people in intensive care will be below 10.000 per month, and affordable by our present health system. On the other extreeme, if the virus will spread to the whole population in just one year (extremely fast given the present rate), there will be ten times more people needing intensive care that the ones available, which will mean, not more, but around, 400.000 people at risk of failing with present rates.

Hence my personal prediction is that this pandemic in Italy, will take between less 10,000 and 400,000 lives more, before transforming in a normal human flu, depending on virus evolution regarding the speed of infection and the decrease of lethality.

On 2021-01-12 09:56:32, user Hein De Waele wrote:

How far are you with the peer reviews. When do you expect publication?

On 2025-10-13 14:50:42, user James L.Meisel wrote:

This article has now been published. Please point towards its final publication:<br /> Meisel JL, Navedo DD, Opole IO, Cohen GM, Bernard SA, Carmona H, Nahas AH, Eiduson CM, Papps N. From Reductionist Skills to Meaningful Learning: Trust and Humility in Bedside Cardiac Assessment. Adv Med Educ Pract. 2025 Aug 5;16:1305-1316. doi: 10.2147/AMEP.S520398. PMID: 40791751; PMCID: PMC12335936.

On 2022-07-23 03:21:31, user Jodi Schneider wrote:

Thanks for an interesting paper.

Distinguishing post-retraction citation would be useful. I can't understand that now from what you write: You note that 893/1036 (86%) of citations did not identify that the RP was retracted or raise any concern - but some of these citations appear to be BEFORE the retraction. It appears that you looked at publication dates (two paragraphs later), but the info isn't explicit enough for me to, say, extract numbers for a meta-analysis of post-retraction citations.

When you do publish this, I recommend depositing the data - citation contexts, sources, and your ratings of them could be relevant for other researchers (like me!)

For Figure 1 clarify whether the blue correspond to preprints, online first articles, etc.

A flow diagram (analogous to a PRISMA diagram) would help in the methods for a concrete example of what this might look like, see https://doi.org/10.1371/jou...

In the methods when you say you "extract citation information" - are you talking about sentences from the papers (citation contexts)? Try to be more clear when you revise this.

On Google Scholar ranking, it's a black box but today the About page says "Google Scholar aims to rank documents the way researchers do, weighing the full text of each document, where it was published, who it was written by, as well as how often and how recently it has been cited in other scholarly literature." - https://scholar.google.com/... <br /> And for instance its language bias is widely recognized (see e.g. https://doi.org/10.3390/fi1... ) - not a problem for your study, but something to be aware of.

For background I'd recommend including Frampton's recent paper - which gets at the visibility of retracted publications: Frampton, Geoff, Lois Woods, and David Alexander Scott. “Inconsistent and Incomplete Retraction of Published Research: A Cross-Sectional Study on Covid-19 Retractions and Recommendations to Mitigate Risks for Research, Policy and Practice.” PLOS ONE 16, no. 10 (October 27, 2021): e0258935. https://doi.org/10.1371/jou...

There is also a recent analysis of reasons for retraction - which gets into the other angle on quality (e.g., has there been a rush to public)<br /> Rubbo, Priscila, Caroline Lievore, Celso Biynkievycz Dos Santos, Claudia Tania Picinin, Luiz Alberto Pilatti, and Bruno Pedroso. “‘Research Exceptionalism’ in the COVID-19 Pandemic: An Analysis of Scientific Retractions in Scopus.” Ethics & Behavior online first (June 7, 2022): 1–18. https://doi.org/10.1080/105....

These are likely the most interesting - though you can see other papers in my bibliography of Empirical Retraction Lit: the category "Analysis of Retracted COVID-19 articles" has 14 (there are more - the bibliography is so far only up to July 2021).

-Jodi<br /> https://orcid.org/0000-0002...

On 2020-08-27 13:31:15, user Joe Psotka wrote:

Using data from Florida creates misleading expectations because Florida's decrease in March and April was largely from Snowbirds' and part time residents' departure from the State. Some people estimate that one-third of Florida's winter population leaves in the Spring to avoid the summer heat.

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

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

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

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

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

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

That is consistent with sources such as:

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

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

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

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

On 2021-06-25 00:23:11, user Otheus wrote:

While I would like to believe the results of this study, the details and the presentation of their numbers leaves much to be desired for the numerically astute/obsessed. A CI of "0 to infinity" means someone has not really done the statistics right at all. In the online preprint of the article, which may be an older version, the article cites -- somewhat deceptively -- provides the percentages in terms of the number of infections in one group with respect to the number of infections in another group. So, 99.3% of the infections were from people not previously infected and not vaccinated and 0.7% of infections were from vaccinated group. The problem with doing it that way is that you have a much larger population of people not infected and not vaccinated than the other sub-populations.

Imagine you have two drawers of socks, and in each drawer, there is the same ratio of white socks to red socks -- let's say 1 red sock for every 9 white socks, ie, 10% red. You then pull out 10 socks from each drawer. From the first drawer, you pull 5 red socks and 5 white socks, and from the second drawer, you pull 1 red sock. It would seem the first drawer had a greater percentage of red socks. In fact, the first drawer had 500 socks and therefore 50 red socks, while the second drawer had exactly 10 socks and only 1 red sock. The probability of this happening in each case is about the same. The number of red socks to socks drawn from the first drawer is 50%, but for the second drawer, it is 10%. But if you look carefully, the first drawer has 5x the number of red socks as the second. The ratio of red socks drawn to total *red* socks in that drawer is 10% in both cases.

At any rate, from the math given, I calculate that the rate of those got sick among those who were not vaccinated and did not have previous infection was about 10%, while the rate of those who got sick among those who either had been vaccinated or had a previous infection was about 1.2%. Since 0 people with a previous infection reported getting sick, we get 0%. What's significant here is that the population of those unvaccinated and not infected was 10x higher than that last group.

An important sentence from the paper sticks out: "Not one of the 2579 previously infected subjects had a SARS-CoV-2 infection, including 1359 who remained unvaccinated throughout the duration of the study." Those numbers appear high enough with respect to the lower bound of the infection rate (1.2%) to have enough statistical power. You'd expect to find at least 31 cases for the null hypothesis. It seems quite improbable to get 0 results unless previous infections provide very strong protection.

On 2021-07-13 23:48:27, user Meditate wrote:

Where is the reinfection by variant data available? Please share, would be grateful for the link