On 2020-05-05 21:07:04, user Bozo wrote:

Please review this preprint...figure 2 in the appendix says it all.

https://www.medrxiv.org/con...

On 2020-05-05 22:35:44, user Alan Bell wrote:

"If worse respiratory health and aggravated symptoms in polluted areas are the main channels of action, higher COVID-19 case hospitalization rates should also be expected in these locations."

Not sure that follows:<br /> If two locations each have 150 cases, some of which may remain asymptomatic and undetected.<br /> Location A with low pollution detects 80 cases, 4 of which are hospitalized.<br /> Location B with higher pm2.5 levels detects 100 cases, 5 of which are hospitalized.<br /> Location A and location B have the same hospitalization rate of 5% of detected cases but due to aggravated symptoms location B has a higher detection rate.

The analysis in 6.2.5 is interesting but I am not sure it is conclusive. Maybe random antibody sampling across the whole population will eventually reveal more information.

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

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

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

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

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

On 2020-05-06 20:06:21, user MS wrote:

Need to be clear on "false-negative RT-PCR". This study is looking at the presence of virus and the viral load in the upper respiratory tract through-out infection as much as it looks at sensitivity and is dependent on a deep sample taken correctly using appropriate sampling kits as well as the sensitivity of the test.

On 2020-05-06 20:25:49, user Frank Conijn wrote:

A furthermore well-written paper, in which in particular the section about the used dosage in vitro and in vivo is interesting.

But two crucial questions are not answered:

1. What determined that the HCQ group got it and the other group not? It's a single-hospital study, so that shouldn't be difficult to answer.

2. What other drugs, if any, did the groups get? HCQ has a strong antiviral effect in vitro, but in vivo seemingly also an immunomodulatory one, since it's effective in rheumatoid arthritis. So, several interactions may be possible.

On 2020-05-07 21:23:21, user Dan T.A. Eisenberg wrote:

How much spit did you collect and how much of it + the PBS was needed for sufficient sample for extraction. Thanks!

On 2020-05-08 14:37:27, user Merilee Brockway, PhD RN IBCLC wrote:

I think that you need to consider the possibility of retrograde flow contaminating the breastmilk from the infant's saliva. Sicker infants would likely have a higher viral load in their saliva/respiratory secretions. A study in the Lancet found that "The mean viral load of severe cases was around 60 times higher than that of mild cases, suggesting that higher viral loads might be associated with severe clinical outcomes." https://www.thelancet.com/j.... This may help to explain why the virus was present in the milk of mother 2, but not mother 1. Infant 2 was much sicker than infant 1 and as such the viral load was likely much higher.

On 2020-05-11 08:52:50, user Prof. Janusz Jankowski wrote:

Sounds like most people find this tool useful to date.

On 2020-05-11 19:29:55, user Charles Warden wrote:

Thank you for posting this pre-print.

I have a some questions:

1) Are the p-values significant after a Bonferroni correction?

2) Are you focusing on APOE because it is the most significant result for a relatively common SNP?

3) How are you defining the COVID-19 severity? Table 1 makes it look like you are comparing the proportion of positive cases for the 3 APOE genotype combinations (E3/E3, E3/E4, and E4/E4). However, that would be different than filtering for positive cases, and then looking for an association with a variable that describes the severity of the case.

4) I thought it was good and interesting that you excluded subsets of individuals to try to check for confounders. However, it looks like the number of APOE E4/E4 goes from 37 total (with none removed due to dementia) to 22 and then back up to 32 and 35. If you want to adjust for all individuals with chronic diseases, then I would have expected that to be cumulative. What happens if you remove all of the patients with chronic disease and then test within the highest age range?

5) I would expect most normalization to reduce but not completely remove the effect being adjusted for. Is is possible to look at older individuals as a separate bin (perhaps in a "Table 2") as evidence that the age-adjustment was effective? I could imagine this (along with what I suggested in 3)) might cause some issues with sample size, but there are usually some limitations for every study mentioned in the discussion.

6) Is there any sort of independent validation that you can do in another cohort? As more cases are known, do you plan to check the subset of samples that currently test negative but later test positive as a type of test dataset?

7) I usually think of the "Data Availability" as being for new data (rather than public data), but I am glad that you mentioned you UK Biobank application. However, since this wasn't quite what I expected in the Data/Code link, can you share the code that you used for analysis (assuming it can be reproduced by anyone else with similar access)?

Thank you again for sharing your research.

On 2020-05-12 04:19:04, user BentBollards wrote:

All info is peer reviewed and published numerous times since 1991.

"Nonpharmaceutical Intervention (NPI) published discovery to cure the refractory dry cough that spreads Coronavirus and influenza". The 325 year medical mystery of finding a cure for the dry refractory cough - solved by Dr. Miles Weinberger, M.D. 40+ year cough researcher.

I asked your esteemed colleague, Dr. Weinberger, what if there is no vaccine in sight? He said, "First, we need to cure the dry cough." [that spreads the virus.] Dr. Weinberger, M.D., 40+ year Immunologist and cough researcher, regarding mitigation and containment of cough aerosol droplet spray that is paralyzing the world. All references are peer reviewed and published multiple times in the most esteemed medical journals of the world. (Note: It does not cure any underlying disease - just the dry cough that is spreading the virus.)

www.NonpharmaceuticalInterv...

http://bit.ly/CureByProxy << Peer reviewed paper that started it all.

http://bit.ly/CoughCure2020 << Peer reviewed paper children AND adults.

Dennis Buettner<br /> Cough Research Manager for<br /> Dr. Miles Weinberger, M.D.

On 2020-05-12 11:12:30, user Guest wrote:

I’m an advocate for ignoring cases & case fatality rates at this stage. Why? Because of variance in testing & “at risk” populations.

I only watch deaths & “excess deaths.” I don’t see a benefit from arguing CFR at this point if excess deaths are much higher in most countries than would be expected from any other cause. The severity of disease can be seen on the multiple excess death numbers by various countries (See Ecuador’s excess deaths.)

From memory: U.S. Flu season tests ~10,000,000, uses ~1,000,000 for statistical analysis, Flu season starts at the 10% positive rate & only ~6,000 confirmed seasonal deaths. There are ~8750 daily deaths from all causes & as the rate goes higher during December, January & February they try to calculate how many could be due to influenza. Ie. From all these #s they produce statistical modeling that shows ~35,000,000 infected & ~36,000 deaths.

ADDENDUM<br /> CFR numerator & denominator. <br /> To be clear, CFR should be based on all deaths out of all cases from a disease. The Flu example above shows it is not 6,000 / 1,000,000 but 36,000 / 35,000,000. The former is CFR of 0.6%, the latter near 0.1%. The numerator & denominator on the former are low based on lack of testing (especially both asymptomatic & deaths at home, nursing homes, hospice, etc.) The latter deaths are calculated out of analysis of all excess deaths. If we use the former numerator, with the latter denominator it greatly lowers the CFR. CFR of Flu is not 6,000 / 35,000,000.

Law of large numbers. <br /> Taking one example of infection rates doesn’t show the variance in the country nor the globe. Most believe both the case counts & deaths are a multiple higher than are posted.

Current estimates are CFR ~0.5-0.9 which is 4-9X more deadly than the Flu. This really doesn’t need to be true to assess the dangers of an infection; CFR is but one variable. If, CFR is 0.1 but reproduction numbers are large & the # of cases & deaths are drastically larger than Flu, it would still support “social distancing” measures.

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

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

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

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

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

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

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

I will try to be the devil's advocate:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

On 2020-05-12 19:50:20, user Gaurav Jain wrote:

• When will COVID-19 end? Here are some predictions: https://covid19quarantine.i...

On 2020-05-13 16:17:09, user Algis Džiugys wrote:

Hi,

Because the number of Patients in Intensive Care Units depends on number of daily new infection cases, may be behavior of curve in Fig.3a can be explained by dynamics of daily new cases: https://www.medrxiv.org/con... (fig.11).

Best regards,

Algis

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 2020-05-14 10:44:33, user Dom Mcelhinney wrote:

In the at risk group for covid19 almost 50% of population are being treated for Hypertension and Hypercholesterolaemia. Can you explain why you have been unable to list this as a possible comorbidity.

On 2020-05-15 01:01:11, user Timeisrelative wrote:

This is not my field of study but I hope my comments are helpful to you. Thank you for publishing this important work.

The name "SD" for your metric is confusing for three reasons. 1) Standard deviation which is also used in the paper is commonly abbreviated as SD. 2)Recently less travel has *increased* what people commonly refer to as "social distancing", however your metric "SD" tends to *decrease*. 3)Mobility is only one aspect of the common definition of social distancing. Other aspects are not attending mass gatherings, standing at least 6 ft apart, not shaking hands, etc.(https://hub.jhu.edu/2020/03... "https://hub.jhu.edu/2020/03/13/what-is-social-distancing/)") These other aspects are not captured by your metric so again I think it's confusing to call it a "social distancing ratio" and use the abbreviation SD. Better names might be "Mobility Reduction" or "Relative Mobility".

Further, according to Wikipedia: "During the COVID-19 pandemic, the World Health Organization (WHO) suggested favoring the term "physical distancing" as opposed to "social distancing", in keeping with the fact that it is a physical distance which prevents transmission; people can remain socially connected via technology." (https://en.wikipedia.org/wi... "https://en.wikipedia.org/wiki/Social_distancing)")

Your metric SD is based on "the assumption that when individuals make fewer trips, they physically interact less." But you are not looking at the number of trips directly, instead you look at the deviation from normal levels of trips. Why not look directly at the number of trips? Different areas my have widely varying baseline numbers of trips and one would expect infection rates to vary correspondingly. By measuring the correlation between the actual number of trips and infection rates we could see if that is in fact true.

I'm having trouble understanding the calculation of GR. You state "A GR equal to zero indicates no new confirmed cases were reported in the last three days" However, plugging 0 into the all three Cj in the numerator of the GR calculation leads to log(0/3+0/3+0/3). The result is undefined(negative infinity) not zero. You also state " a value below one means that the growth rate during the last three days is lower than that of the last week" and testing some sample data does not produce this result. Perhaps I'm misinterpreting your formula?

FIG 3 What is the "Raw Date" line? In your description of GR you say "We use 3-day moving averages to smooth volatile case reporting data." Does that statement refer to the 3-day summation in the numerator of "GR" or is there an additional 3-day moving average taken after GR is computed?

The GR calculation itself introduces a lag due to averaging the previous 3 days of data in the numerator and previous 7 days of data in the denominator. This distinction is important as you state that the value of the 9-12 day lag "reflects the time it takes for symptoms to manifest after infection, worsen, and be reported." In fact the lag from the calculation itself is also a factor.

It's also unclear if your source data is the date a positive test was taken or the date the lab results came back. When we are talking about a lag on the order of 10 days, a 1-3 day delay for results could be significant. Further, source data including the date of symptom onset is available in some states and would be more useful as it would eliminate part of the lag which could be affected by test availability and speed.

Why are only the top 25 counties are analyzed? I would be interested in seeing the metrics calculated in other lesser affected areas. In other words, could mobility reductions result in the prevention of outbreaks or just in the reduction of major outbreaks?

The metrics you've chosen (SD and GR) follow very similar paths among all 25 counties analyzed. All 25 counties saw sharp drops in SD between March 10th and March 20th. All 25 counties saw sharp drops in GR a few weeks later. However, adding counties that didn't have a sharp reduction in SD during that time period would be revealing. Also adding counties that had GR paths that either dropped over different time periods or that grew much slower and steadier would also help reveal if GR and SD are correlated in wider situations.

Caption to Fig 2 has redundant text "(vertical dashed red lines)"

"King County, Washington is excluded because it precedes widespread social distancing and was driven by an infection source that differs from other outbreaks in the US." Previously you demonstrated that the SD metric is not well correlated with dates of implementation for local and state social distancing directives. King County shouldn't be excluded just because it precedes widespread social distancing. Also how is it known that the "infection source" is different from the outbreaks at the top 25 counties chosen?

"Last, the data used in this analysis does not differentiate amongst sociodemographic groups, and therefore may not representatively capture all groups such as the elderly, low income families and underrepresentative minorities, for whom social distancing may not be an option, or may not have cell phones." Everyone in those groups with a mobile phone and that has the apps and permissions required for teralytics to track them is expected to be included in the dataset. The dataset may not be representative of the population at large but that is not *because* the dataset doesn't differentiate between sociodemographic groups.

Conclusions: "In conclusion, our results strongly support the conclusion that social distancing pays dividends in the vital reduction of load on hospital systems in the United States." I think this conclusion is too broad. You show no data on load of hospital systems. Your data is on the reduction in reported cases correlating to reduced number of trips in severely affected areas not social distancing as a whole.

On 2020-05-25 16:35:51, user HT wrote:

By 24 May, the daily cases crossed 7000, and all projections have gone haywire.

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 2020-04-06 23:24:28, user Mastah Plannah wrote:

It is ridiculous that the model still says that Massachusetts has NOT implemented a Stay At Home order. Therefore the model is useless for Massachusetts.

Massachusetts did implement stay-at-home. They did it on the early side on March 23.

On 2020-06-23 15:26:34, user Gustavo Hernandez wrote:

it will be better to see the analysis as a match case control study instead (Death vs Discharged alive). Doing it as a cohort study makes no sense as its not clear the reason of receiving or not Ivermectin. Contrasting the characteristics of death and discharged alive patients with allow to weight the effect of the studied exposure

On 2020-06-23 16:30:13, user Sinai Immunol Review Project wrote:

Systems-level immunomonitoring from acute to recovery phase of severe COVID-19<br /> Rodriguez et al. medRxiv [@doi:10.1101/2020.06.03.20121582]

Keywords<br /> • COVID-19<br /> • cytokines<br /> • immunomonitoring

Main Findings<br /> In this preprint, Rodriguez et al. performed longitudinal, systems-level immunomonitoring on blood from 39 COVID-19 patients using mass cytometry (CyTOF) and Olink to better understand the mechanisms behind hyperinflammation in severe COVID-19. 17 subjects were inpatient; 22 were recovered patients. CyTOF was used to track immune cell populations over time while Olink was used to measure 180 plasma biomarkers from the acute disease phase and recovery. Importantly, none of the 39 patients in this study received any immunomodulatory therapies and therefore the data reflect the natural course of COVID-19 disease.

Several immune cell populations changed with COVID-19 disease progression. Neutrophils rose during the acute phase and decreased with recovery; in contrast, eosinophils, basophils, and all dendritic cell subsets all increased with recovery. Total CD4 and CD8 T-cells peaked at about 2 weeks into disease progression, with the largest increases seen in proportion of CD127+ CD4+ memory T-cells and CD57+ CD8+ memory T-cells. <br /> To further study the phenotype of the increased eosinophils seen with disease recovery, the authors used Partition-based graph abstraction to analyze changes in eosinophils on a single cell level. The authors report a transient expansion of CD62L+ eosinophils coinciding with IFN levels on days 2-6.

To determine the immunological correlates with IgG response, the authors used a mixed effect model using immune cell proportions and levels of plasma protein biomarkers. IFNg, IL-6, CXCL10, CSF-1 and MCP-2 negatively correlated with IgG response while CXCL6, CD6, SPRY2, CD16- basophils and CD16+ basophils positively correlated with IgG response. <br /> Next, the authors built a multiomic trajectory of recovery using multiomics factor analysis. This analysis identified decreasing levels of IL-6, MCP-3, KRT19, CXCL10, AREG, and IFNg with recovery while classical monocytes, non-classical monocytes, CD56dim NK cells, eosinophils, and gD T-cells increased with recovery.

Limitations<br /> Though the authors do a good job of balancing the sex ratio in their patient population, age ranges between symptomatic patients (40-77 yo) vs recovered patients (28-68 yo) may be contributing to immune phenotype. Median age of each group should be provided. While the authors state that the study captures longitudinal immune monitoring from acute to recovery phase, it is unclear which of the symptomatic patients, if any, were monitored through actual recovery. The authors’ claims would be better supported with paired analysis of symptomatic patients during their hospital course with the same patients after recovery, rather than a separate cohort of recovered patients.

The changes in immune cell populations over time reported in Fig. 3 would benefit from statistical analysis to denote which changes are statistically significant. Indeed, several of the trends reported, such as total CD4+ T-cells, CD127+ memory CD4+ T-cells and CD57+ CD8+ T-cells seem to be driven only by a few patients.

Previous work by Mesnil et al. 2016, as cited by the authors, report that CD62L+ lung resident eosinophils suppress excess Th2 inflammation after house dust mite (HDM) challenge in mice and have a more regulatory phenotype than CD62L- inflammatory eosinophils [1]. Here, Rodriguez et al. suggest that this increase in CD62L+ eosinophils may contribute to lung hyperinflammation in acute respiratory distress syndrome (ARDS) in COVID-19. While more studies are needed to address this potential contribution, one suggestion would be to see if there are differences in the number and phenotype of CD62L+ eosinophils between the ICU and non-ICU patients in Rodriguez et al.’s cohort. While it is possible the increased number of CD62L+ eosinophils may contribute to hyperinflammation, the more regulatory phenotype of CD62L+ eosinophils as reported by Mesnil et al. may instead point to a role for suppression rather than contribution to lung hyperinflammation.

In all analyses conducted, further stratification by ICU vs non-ICU patients may also be informative.

Significance<br /> This preprint provides system-wide longitudinal analysis of plasma biomarkers and immune cell populations from a cohort of inpatients with severe COVID-19. Because the patients were untreated with any immunomodulatory drugs, the authors are able to describe trends through the natural progression of COVID-19 in patients who ultimately recover.

Specifically, CD62L+ eosinophils are found to be expanded in the blood corresponding to a period of lung hyperinflammation in severe disease. Additionally, a higher abundance of circulating basophils is correlated to increased anti-SARS-COV-2 IgG response. Both findings warrant further investigation into the previously undescribed role of both eosinophils and basophils in COVID-19.

Furthermore, the authors show that biomarkers such as IFNg, CXCL10, and IL-6 negatively correlate with both humoral response and recovery. The negative correlation with IL-6 and IgG response is particularly surprising, given that IL-6 has been shown to promote antibody production in B-cells [2]. Moreover the authors cite Denzel et al. 2008, which shows that basophils with antigen bound to their surface enhance antibody production through IL-6, yet in this study basophils and IL-6 negatively correlate at recovery [3]. These findings further highlight the importance of studying the role of inflammatory cytokines in both the development of severe disease and recovery.

References

1. Mesnil C, Raulier S, Paulissen G, et al. Lung-resident eosinophils represent a distinct regulatory eosinophil subset. J Clin Invest. 2016;126(9):3279-3295.

2. Dienz O, Eaton SM, Bond JP, et al. The induction of antibody production by IL-6 is indirectly mediated by IL-21 produced by CD4+ T cells. J Exp Med. 2009;206(1):69-78.

3. Denzel A, Maus UA, Rodriguez Gomez M, et al. Basophils enhance immunological memory responses. Nat Immunol. 2008;9(7):733-742.

Credit<br /> Reviewed by Steven T. Chen and Alexandra Tabachnikova as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

On 2020-06-25 20:47:26, user J Chapman wrote:

Any testing on HLA B27?

On 2020-06-29 13:45:28, user Algur wrote:

Is there a timeline when this will be peer-reviewed?

On 2020-07-10 23:56:52, user John Pearson wrote:

IF the death rate is 0.04% = 0.0004 then 136592/.0004 = 341Million Americans who have already had the disease> Thus not only do we have herd immunity the entire country has already had the virus and we're all better!!! Yet we'll probably have 70,000 new cases today and the population of the US is 330 Million. In short this work is dangerously and clearly false.

On 2020-07-02 14:11:02, user Jason Jehosephat wrote:

If there had been 20 infections in the control group and also 20 in the experimental group, THAT would likely have been a statistically significant indication that the health clubs, in the manner in which they were being used, weren't a COVID-19 hazard. Results of 0 in one group and 1 in the other tell us nothing of statistical value at all about the safety of health clubs. Those results tell us that the duration of the experiment wasn't long enough or the group size wasn't large enough or both. It would have helped if the two groups hadn't been pre-screened.

On 2020-07-02 18:07:08, user 3b wrote:

Interesting paper and idea.

However, the main result is based on a correlation of two time-series. Time-series violates the iid assumption of the statistical test used due to the autocorrelation inherent in such data.It would be nice to see the analysis redone using proper methodology.

Here's an accessible paper on the topic: https://link.springer.com/article/10.3758/s13428-015-0611-2

See this blog post for a simple demonstration of this using simulated data.

And Yule, 1926 who first described the problem.

On 2020-07-02 20:07:46, user RT1C wrote:

This looks questionable to me. You can't calculate years of life lost based on life expectancy tables! We know that comorbidities are the key drivers of COVID-19 mortality; age, adjusted for comorbidities, is a minor factor. Thus, one really needs to adjust the life expectancy for any comorbidities present. For example, if the life expectancy of an individual in the tables is 75 years, but that individual suffers from obesity, COPD, CVD and diabetes, then independent of COVID-19, their life expectancy is significantly lower. Assume, for example, it is 65. Then if they died of COVID-19 at Age 64, their years of life lost is 1 year, not 11. Your methodology, which fails to account for comorbidities, overestimates years of life lost, possibly by a large margin.

On 2020-04-12 13:04:20, user japhetk wrote:

I think BCG studies' conclusions came from spurious correlations regardless of BCG has an effect or not.<br /> Anyway, now data from South America and Africa keeps coming and although, it may depend on the methods of analyses, my analyses show already the number death 13 days after the 100th case, and whether BCG is currently done is no longer significantly associated without correcting anything (p = 0.291, ANOVA).And after the number of tourists, population,total GDP, temperature of March, ratio of 65 years or older are corrected the associations show get even weaker (P = 0.621, ANCOVA).Among these covariates, the number of tourists has a robust significant effect on the number of deaths 13 days after the 100th case (0.00016), and the ratio of 65 years or older and population have significant effects, too (P= 0.024, 0.05, respectively). Total GDP (not GDP per capita) and the number of tourists have a close relationship (r = 0.82). <br /> The date when the 100th case was detected show more robust relationship with the BCG policy (currently performed or not), but after the correction of abovementioned covariates, this association also became insignificant )(p= 0.167). But this kind of relationship with the date of 100th case is seen in the case of variables that are specifically associated with Western countries, such as the consumption of wine)(the consumption of wine per capita shows robust association with the date of the 100th case after correction of population (p = 0.0002, more wine, the faster the detection of 100th case). <br /> So, my guess is that this spurious correlation mainly came from the fact the countries which abandaned BCG policies are more developed and more popular from tourists (which increased the faster and more and multiple spread of the virus) and also show greater aging (which increased the risk) and also they locate in western countries which were confident of their medical system and which were away from Asia and which were less alert to this infectious disease from China. The habit of wearing mask, hug, handshake or religious ceremonies might affect, too. <br /> In the cruise ship Diamond Princess, Japanese who were put in the same ship with Westerners show greater mortality rate than Westerners. And in a lot of Western European countries, the risk population (elderly) has experiences of BCG (they are classified as "past BCG", but in fact most of risk populations are experienced with BCG). So, the BCG hypothesis is not consistent with these facts, either. <br /> I am not saying BCG doesn't work, I am saying you cannot conclude anything from these uncontrolled studies which lacks in numerous potential confounding variables. Just let's wait for results of RCTs.

Here's my data if I haven't made any mistakes.You can see the apparent little association with BCG policy and number of the death (13 days after the 100th case) as of 11th April.

O Iran 291<br /> X Spain 288<br /> O China 259<br /> X Italy 233<br /> O Turkey 214<br /> O Algeria 130<br /> X United Kingdom 103<br /> O Indonesia 102<br /> O Brazil 92<br /> X France 91<br /> X Netherlands 76<br /> X United States 69<br /> O Dominican Republic 68<br /> X Ecuador 62<br /> O Portugal 60<br /> O Morocco 59<br /> O Philippines 54<br /> O Ukraine 45<br /> O Iraq 42<br /> O South Korea 35<br /> X Switzerland 33<br /> O Argentina 31<br /> O Egypt 30<br /> O Panama 30<br /> O India 29<br /> O Mexico 29<br /> X Canada 27<br /> O Hungary 26<br /> O Honduras 24<br /> O Peru 24<br /> O Romania 24<br /> O Albania 22<br /> O Greece 22<br /> O Ireland 22<br /> O Tunisia 22<br /> X Luxembourg 22<br /> O Bosnia and Herzegovina 21<br /> X Belgium 21<br /> O Burkina Faso 19<br /> O Macedonia 17<br /> X Andorra 17<br /> O Colombia 16<br /> O Poland 16<br /> O Afghanistan 15<br /> O Cuba 15<br /> O Moldova 15<br /> O Pakistan 13<br /> X Denmark 13<br /> O Bulgaria 10<br /> O Malaysia 10<br /> O Russia 10<br /> X Lebanon 10<br /> X Sweden 10<br /> O Lithuania 9<br /> O Mauritius 9<br /> O Azerbaijan 8<br /> X Austria 8<br /> X Israel 8<br /> O Chile 7<br /> O Kazakhstan 7<br /> O Venezuela 7<br /> X Australia 7<br /> O Croatia 6<br /> O Ghana 6<br /> O Japan 6<br /> O Thailand 6<br /> X Czech Republic 6<br /> X Norway 6<br /> O Jordan 5<br /> O South Africa 5<br /> O Sri Lanka 5<br /> O Taiwan 5<br /> O United Arab Emirates 5<br /> X Germany 5<br /> X Slovenia 5<br /> O Saudi Arabia 4<br /> O Uruguay 4<br /> O Armenia 3<br /> O Cote d'Ivoire 3<br /> O Uzbekistan 3<br /> X Finland 3<br /> O Costa Rica 2<br /> O Oman 2<br /> O Senegal 2<br /> O Estonia 1<br /> X New Zealand 1<br /> O Cambodia 0<br /> O Kuwait 0<br /> O Latvia 0<br /> O Malta 0<br /> O Qatar 0<br /> O Singapore 0<br /> O Vietnam 0<br /> X Slovakia 0

On 2020-07-06 09:56:53, user Moore wrote:

interesting but you find that there were no events in NSAIDs users not using paracetamol (figure 3) So that presumably all events were in patients using paracetamol (4.1%) or in combined paracetamaol+NASID users. The latter suggests chanelling of NSAIDs to more severe cases resisting to paracetamol, much as was shown for soft tissue infection by S Lesko.<br /> Unfortunately you do not give in figure 3 the number of patients concerned in each group, so that it is not possible for instance to look at poisson estimates (using the upper limit of the 95% confidence interval of 3 for no cases. Of course if all NSAIDs cases were in patients who associated paracetamol to NSAIDs, the conclusion is very different.<br /> Comparing use to non use is really misleading, since is cannot take into account confounding by indication (more severe cases get NSAIDs), and should not be used.<br /> Preferably in these cases where outcomes are associated with symptoms, the safest comparison is users vs user of drugs with the same indication, in this case paracetamol. It would be nice to see separately NSAIDs, paracetamol and NSAIDS+Paracetamol, and neither, and test for interaction.

On 2020-04-14 15:07:32, user Senad Hasanagic wrote:

Is there any difference between Eastern and western parts of Germany?

On 2020-07-08 19:13:51, user Will Jones wrote:

Many countries have ramped up testing in recent weeks. Does this not make case data largely useless as an indicator of infection levels? More generally do the constant changes in testing regimes not undermine the usefulness of case data?

When I have analysed death data in various countries I have usually found a brief period of exponential growth, for a week or so. For example there is a brief period of exponential growth between 17-23 March in the death data for London hospitals (by date of death rather than report, using 7 day rolling average). How does this fit into the Gompertz function model - is it too short to 'count'?

On 2020-07-10 16:44:40, user Miriam Marcolino wrote:

I would like to report to the authors that the registration number for ClinicalTrials.gov (NCT043235929) reported in the document have no registry correspondence in the ClinicalTrials.gov website. I believe it may be a typo. Best regards,

On 2020-07-14 03:14:19, user Robert Kernodle wrote:

I hope a statistical expert looks at this, because I suspect significant flaws in methodology that do not justify the conclusions. Based on physics, fluid dynamics -- the extension of these basic principles to the structure of woven cloth masks, in relation to infectious aerosols -- this supposed statistical study does not seem to hold up to reality.

On 2020-07-14 17:07:42, user Avnish Singh wrote:

i need email address of Lan-Juan Li. I am from www.meraupbihar.xyz

On 2019-07-04 23:42:29, user Guyguy wrote:

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

Thursday, July 4th, 2019

The epidemiological situation of the Ebola Virus Disease dated 3 July 2019:<br /> Since the beginning of the epidemic, the cumulative number of cases is 2,382, of which 2,288 are confirmed and 94 are probable. In total, there were 1,606 deaths (1,512 confirmed and 94 probable) and 666 people healed.<br /> 420 suspected cases under investigation;<br /> 13 new confirmed cases, including 4 in Beni, 2 in Butembo, 2 in Katwa, 2 in Kalunguta, 1 in Mandima, 1 in Biena and 1 in Mabalako;<br /> 8 new confirmed cases deaths:<br /> 2 community deaths, including 1 in Butembo and 1 in Mandima;<br /> 6 deaths in Ebola Treatment Centers including 3 in Beni, 2 in Mabalako and 1 in Katwa;<br /> 11 people cured out of Ebola Treatment Center including 7 in Mabalako, 3 in Katwa and 1 in Beni. <br /> 128 Contaminated health workers: One health worker, vaccinated, is one of the new confirmed cases in Beni. The cumulative number of confirmed / probable cases among health workers is 128 (5% of all confirmed / probable cases) including 40 deaths.<br /> Source: Ministry of Health press team on the state of the response to the Ebola epidemic in the Democratic Republic of Congo.

On 2019-07-21 03:12:01, user Guyguy wrote:

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

Saturday, July 20th, 2019

The epidemiological situation of the Ebola Virus Disease dated 19 July 2019:<br /> Since the beginning of the epidemic, the cumulative number of cases is 2,564, 2,470 confirmed and 94 probable. In total, there were 1,728 deaths (1,634 confirmed and 94 probable) and 726 people healed.<br /> 392 suspected cases under investigation;<br /> 18 new confirmed cases, including 7 in Beni, 3 in Mandima, 3 in Mabalako, 1 in Vuhovi, 1 in Butembo, 1 in Mambasa, 1 in Lubero and 1 in Masereka;<br /> 13 new confirmed cases deaths:<br /> 8 community deaths, including 4 in Beni, 2 in Mandima, 1 in Mabalako and 1 in Masereka;<br /> 5 Ebola Treatment Center (ETC) deaths, 2 in Mabalako, 2 in Beni and 1 in Katwa;<br /> 5 people recovered from ETCs, including 3 in Beni and 2 in Katwa.

NEWS

Minister of Health visits Beni<br /> The Minister of Health, Dr. Oly Ilunga Kalenga spent the day of Friday, July 19, 2019 in Beni where he visited the various field teams and the transit center whose capacity will be increased in the coming days.<br /> Following the resurgence of patients in Beni, Dr. Oly Ilunga said that one of the key lessons learned in this tenth epidemic is to rely on the health system. "If we really want to solve this epidemic and have a lasting impact, we need to strengthen the health system by working with the actors in this system and with the community," he said adding that this is how we can quickly stop this new outbreak in the city of Beni.<br /> He recalled that the declaration of this epidemic as an international public health emergency requires other countries to strengthen border surveillance, while for the response, the declaration recognizes the work that is being done and the performance of the response. managed to contain the epidemic in an extremely complex context.<br /> This statement also stresses the need for a response with greater coordination and consultation. Another point that Minister Oly Ilunga always insists on is the accountability of all actors on the ground, the sharing of information, the measurement of performance, and the use of data to guide and improve actions on ground.

168,746 Vaccinated persons.

76,632,731 Controlled people.

138 Contaminated health workers<br /> The cumulative number of confirmed / probable cases among health workers is 138 (5% of all confirmed / probable cases) including 41 deaths.

Source: Ministry of Health press team on the state of the response to the Ebola epidemic in the Democratic Republic of the Congo

On 2019-08-03 19:56:40, user GuyguyKabundi Tshima wrote:

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

Wednesday, July 31, 2019

The Epidemiological Situation of Ebola Virus Disease, July 30, 2019

Since the beginning of the epidemic, the cumulative number of cases is 2 701, of which 2 607 are confirmed and 94 are probable. In total, there were 1,813 deaths (1,719 confirmed and 94 probable) and 776 people healed.<br /> 293 suspected cases under investigation;<br /> 11 new confirmed cases, including 3 in Vuhovi, 1 in Mandima, 1 in Mambasa, 1 in Kalunguta and 1 in Nyiragongo (Goma);<br /> Continued search for the confirmed case in the health zone of Lubero dated 25/07/2019;<br /> 10 new confirmed cases deaths:<br /> 2 community deaths, including 1 in Beni and 1 in Mandima;<br /> 6 deaths at ETC, including 3 in Beni, 2 in Mabalako and 1 in Butembo;<br /> 2 deaths at the ETC of Beni;<br /> 6 people recovered from ETC, including 4 Mabalako, 1 in Katwa and 1 in Butembo;<br /> Two live health workers are among the new confirmed cases of Mambasa (non-vaccinated) and Vuhovi (vaccinated). The cumulative number of confirmed / probable cases among health workers is 148 (5% of all confirmed / probable cases), including 41 deaths.

Organization of the Coordination Workshop for the Ebola Response to the Ebola Epidemic

The Technical Secretariat of the Multi-sectoral Epidemic Response Committee of the EVD is organizing a coordination workshop from 31 July to 02 August 2019 to coordinate the response to the EVD epidemic at the Karibu Hotel in Goma in the province of North Kivu.<br /> This workshop aims to brief the Technical Secretariat of the Multisectoral Committee by coordinating the response on the organization of the current response in order to enable it to make informed decisions thus avoiding a major disruption of the response.<br /> It will enable the Technical Secretariat to inquire about the current epidemiological situation of EVD and the main challenges to be addressed, to learn about the current response structure (organization of the different levels of coordination) and the new strategic plan for the response (PSR4) and synergy with the security, humanitarian and financial sectors, as well as the operational readiness of DRC neighboring countries to create a favorable environment for the response.<br /> It will also allow to discuss challenges and perspectives related to priority themes (pillars). This workshop will result in the priority actions to be carried out over the next 90 days and the overall orientations on the response, as well as the new organizational structure of the response.<br /> It should be noted that under SRP-4, effective and coherent change in strategies, effective coordination, consistent standards and support for the most vulnerable communities are envisaged at risk in the provinces of North Kivu and Ituri while preventing the spread of the epidemic in other provinces and countries bordering the DRC

Death of the second confirmed case of Ebola in Goma

The second confirmed Ebola case from Goma died on Wednesday 31 July 2019 at the ETC Nyiragongo of Goma located in the General Reference Hospital of this city.<br /> This last case of Goma is a patient, who began to present the symptoms of EVD on July 22, 2019. On July 30, 2019 he went to the Goma General Referral Hospital (HGR) located in the Nyiragongo Health Zone, where he was directly transferred to the ETC for appropriate care. The ETC, being installed within this HGR.<br /> Previously, he was treated as an outpatient by a nurse in a private community health center in the Nyiragongo Health Zone.

180,558<br /> Vaccinated persons<br /> 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 19 May 2018.

80,118,963<br /> Controlled people<br /> 80 entry points (PoE) and operational health checkpoints (PoC)

148<br /> Contaminated health workers<br /> Two live health workers are among the new confirmed cases of Mambasa (non-vaccinated) and Vuhovi (vaccinated).<br /> The cumulative number of confirmed / probable cases among health workers is 148 (5% of all confirmed / probable cases), including 41 deaths.

Source: The press team of the Ministry of Health.

On 2019-10-16 12:50:12, user GuyguyKabundi Tshima wrote:

EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS AT OCTOBER 12, 2019<br /> Sunday, October 13, 2019<br /> Since the beginning of the epidemic, the cumulative number of cases is 3,218, of which 3,104 confirmed and 114 probable. In total, there were 2,150 deaths (2036 confirmed and 114 probable) and 1032 people healed.<br /> 429 suspected cases under investigation;<br /> 6 new confirmed cases to CTEs, including;<br /> 4 in North Kivu, including 2 in Beni and 2 in Kalunguta<br /> 2 in Ituri, including 1 in Mandima and 1 in Nyakunde;<br /> 2 new confirmed deaths, including:<br /> 1 community death in North Kivu in Kalunguta;<br /> 1 new confirmed death in CTE in North Kivu in Beni;<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

New health area infected with Ebola virus in Ituri<br /> - A new Health Area has been affected by Ebola Virus Disease in Ituri. This is the Maroro Health Area in the Nyakunde Health Zone;<br /> - Indeed, Nyakunde was already at 294 days without notifying a new confirmed case of the EVD and returned to zero following this new affection;<br /> - Of all the 6 cases reported this Sunday, October 13, 2019, none of them were listed as contact, nor monitored regularly or vaccinated;<br /> - It is also reported that the alerts of all these cases are coming back from the community and their contacts are being listed, the investigations are continuing, the decontamination of the patients' households is being carried out and the ring of vaccination has been opened around all these cases.

VACCINATION

• Since the beginning of vaccination on August 8, 2018, 237,632 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 travelers checked (temperature rise) at the sanitary control points is 105,518,454 ;
• 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 country's major cities 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 2019-11-14 14:53:08, user Guyguy wrote:

EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI ON NOVEMBER 12, 2019

Wednesday, November 13, 2019

• Since the beginning of the epidemic, the cumulative number of cases is 3,291, of which 3,173 are confirmed and 118 are probable. In total, there were 2,193 deaths (2075 confirmed and 118 probable) and 1067 people cured.<br /> • 508 suspected cases under investigation;<br /> • 4 new confirmed cases in North Kivu, including 2 in Beni and 2 in Mabalako;<br /> • No new deaths of confirmed cases have been recorded;<br /> • No cured person has emerged from ETCs;<br /> • No health worker is among the new confirmed cases. The cumulative number of confirmed / probable cases among health workers is 163 (5% of all confirmed / probable cases), including 41 deaths;

NEWS

Ebola Virus Disease Response Coordinator Meeting with North Kivu National Assembly Vice President on J & J Vaccine

• The General Coordinator for the Ebola Response to the Ebola Virus Disease, Prof. Steve Ahuka Mundeke, accompanied by a joint team of some members of the response and the consortium (National Institute of Biomedical Research-INRB, MSF / France and the London School), met this Wednesday, November 13, 2019 the Vice President of the North Kivu Provincial Assembly, the Honorable Jean-Paul Lumbulumbu, with whom they discussed the second Ebola vaccine called Johnson & Johnson.

• The Professor Steve Ahuka Mundeke, who requested the involvement of elected representatives in the community mobilization for this vaccination, welcomed the availability of the Provincial Assembly of North Kivu to support the activities that will begin on Thursday, November 14, 2019 in two health areas of Karisimbi, namely Kahembe and Majengo in North Kivu Province;

• In addition, the Honorable Jean-Paul Lumbulumbu promised to be among the first people to be vaccinated with the Johnson & Johnson vaccine, including members of the North Kivu Provincial Assembly, to serve as an example for their bases. To this end, he invited the people of North Kivu, particularly the sites concerned, to be vaccinated in order to protect themselves against any possible epidemic of the Ebola Virus Disease;

• Also in the context of the introduction of this second vaccine, a briefing session was organized on the same Wednesday in the meeting room of the general coordination of the response in Goma, for members of the Risk Communication. and community engagement (CREC) with some partners from the Ministry of Health.<br /> Training of Beni journalists on their role and responsibility in public health emergencies.

• The role and responsibilities of the journalist in the treatment of news in a public health crisis is at the center of this workshop held from 12 to 14 November 2019 in Beni, North Kivu Province;

• This workshop aims to equip about twenty media professionals with essential notions related to the treatment of information during a health crisis;

• At the opening of this meeting, the feather knights were trained on the risk communication related to Ebola virus disease and on the usual concepts in the response to this disease;

• The two speakers of the day, Dr. Bibiche Matadi, who is responsible for the surveillance pillar at the sub-coordination of the Beni response and Mr. Rodrigue BARRY of the WHO, emphasized the quality of the message to be given to because, according to them, the eradication of this epidemic is based on mastery of all contacts and on community involvement;

• The second day focused on journalist ethics and deontology in times of health crisis and on health - communication - media interaction;

• For this second topic, Ms. Miphy Buata, a journalist with the Congolese News Agency and communications officer of the Multisectoral Committee for the Response to the Ebola Virus Epidemic, recalled that the media remains the only channel of choice to restore and build trust between the (recipient) community and the health sector (Issuer), particularly in the context of Ebola Virus Disease;

• This workshop was organized by the Ministry of Health with WHO and was facilitated by UNICEF.

VACCINATION

• Since the start of vaccination on August 8, 2018, 251,079 people have been vaccinated;

• The only vaccine to be used in this outbreak was 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 travelers checked (temperature measurement ) at the sanitary control points is 116,596,285 ;

• To date, a total of 112 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities 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 2019-08-07 02:07:14, user Pranay Aryal wrote:

Aren't thrombosis biomarkers surrogate endpoints. Shouldn't we use meaningful endpoints like mortality and morbidity? Thanks.

On 2019-08-28 13:57:11, user Larry Parnell wrote:

MIR193B: Putative PPARG target miRNA genes showing associated PPARG binding in at least one of three datasets and upregulation above 2-fold during 3T3-L1 adipogenesis {John Wienecke-Baldacchino 2012 Nucleic Acids Res 40:4446, PMID 22319216}; Expression in supernatant from human adipocytes, inflamed by treatment with macrophage LPS-conditioned media, vs control adipocytes shows 0.37-fold change, per table 1 {Ortega Moreno 2015 Clin Epigenetics 7:49, PMID 25926893}; Of the 159 miRNAs identified from the initial pass designed to identify regulators of LDLR activity, 5 miRNAs (miR-140, miR-128, miR-148a, miR-148b and miR-193b) met the cut-offs, with miR-148a emerging as a strong positive hit {Goedeke Rotllan 2015 Nat Med 21:1280, PMID 26437365}

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

I think I saw something roughly similar in this Tweet:

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

However, I have the following questions:

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

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

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

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

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

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

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

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

https://youtu.be/ehH0w0tkf28

Would be curious to see contesting research articles.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Dear friends,

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

46551; 59805 ; 59493

and the communications that they have changed the principle of cases

registration:

1) As of 12 February 2020, numbers

include clinically diagnosed

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

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

been tested for SARS-CoV-2.

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

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

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

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

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

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

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

have some recommendations.

Best regards,<br /> Igor

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

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

Dear friends,

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

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

Best,

February 15, 2020

Igor

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

Dear colleagues,

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

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

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

i.e.

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

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

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

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

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

To the Figure in

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

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

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

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

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

for the period February 22 – February 29 1058 new cases

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

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

Igor

March 1, 2020

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

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

Thanks much!

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

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

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

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

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

Thanks for the publication!

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Edit line 143 with correct sample age range if known.

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

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

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

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

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

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

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

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

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

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

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

Have you analyzed West and East Germany?

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

The study seems interesting.

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

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

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

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

I controlled these measures and have done the analyses.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

=

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Best wishes,

Gareth

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Here are some references that may be of value.

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

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

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

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

This article may help to explain a finding in:

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

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

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

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

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

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

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

Larry Weisenthal/Huntington Beach CA

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

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

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

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

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

Dear author,

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

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

I understand that very early use has some advantages.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

That is consistent with sources such as:

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

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

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

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

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

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