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Source: medrxiv.org ·
medRxiv - The Preprint Server for Health Sciences
Ryan Selkis - @twobitidiot
2 weeks ago
This new study suggests what common sense & back of napkin math says:
+ true IFR is ~10% of headline fatality rates due to lack of testing and mild case rate.
+ That works out to something like 0.5-2.0% IFR
+ 2 billion infections would put lower end death estimate at ~10 million https://t.co/4zBx8b2X4e
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Michelle Varron - @michellevarron
2 weeks ago
RT @AdamJKucharski: New seroprevalence data from Geneva, estimating about 10 infections for every COVID-19 confirmed case. Overall this is consistent with our (and other groups) current rough estimates for the extent of under-reporting in Europe. https://t.co/2Mrg1V9oIc https://t.co/YVTri9d9hU
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John Lilic - @JohnLilic
2 weeks ago
RT @AdamJKucharski: New seroprevalence data from Geneva, estimating about 10 infections for every COVID-19 confirmed case. Overall this is consistent with our (and other groups) current rough estimates for the extent of under-reporting in Europe. https://t.co/2Mrg1V9oIc https://t.co/YVTri9d9hU
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ggumdol
2 weeks ago
Before this post getting inundated with uncountably many comments guesstimating the true IFR figure, I would like to urge you to have a look at this comment by u/merpderpmerp:
https://www.reddit.com/r/COVID19/comments/gf4iv0/repeated_seroprevalence_of_antisarscov2_igg/fprsj4e
As you might be aware, two serological survey results from Geneva and New York City are more reliable sources to base our IFR estimation due to their methodology and scale (i.e., high immunity prevalence). As shown in the above comment and another comment of mine in the following:
Rather surprisingly, two IFR estimates from Geneva and New York City almost coincide with eath other, i.e., 1.23%-1.29%. I think we are approaching some semblance of the true IFR figure finally.
I must add that, as many recent serological research results where the infected population is young and, sometimes, mostly female (which was the case for the study from Gangelt), have shown so far, we are likely to see relatively small IFR figures from research results conducted in areas with low seroprevalence because young people are active spreaders due to their high mobility pattern and thusly the infected population is relatively young. In this light, it is very dangerous to try to estimate the true IFR figure from these survey results with low prevalence, not to mention that the average inter-event delay between death (23.8 days) and antibody formation (14 days) must be considered.
0
csgoclockdrift
2 weeks ago
You don't even know the methodology used in the NYC study, how can you say it's more reliable based on its methodology? You have been biased from the start, and the fact that no scientist actually believes the IFR is above 1% yet alone 1.29% is indicating you have an agenda.
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lordDEMAXUS
2 weeks ago
How did you get 1.23% IFR for NYC? Did you do the same calculations that the user who calculated the IFR for Geneva did?
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XorFish
2 weeks ago
Please note that the confidence interval for the last week is 6.1-13.1% This is still quite a range. I hope they do a good analysis about the IFR by age that takes the distribution of the delays in antibody response and infection to death into account and propagates the uncertainty of the result forward.
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Smartiekid
2 weeks ago
So, 6% that have it are 0-19 8.5% are 20-49 and 3.7% are 50+?
So this thing is actually widely spread through younger ages but far far less deadly? I know the age difference made a big difference but that seems like a huge difference.. does explain the ongoing care home problems though. This is just what I'm getting from this I could be widely incorrect
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Smartiekid
2 weeks ago
Judging by the downvotes I'm incorrect? But instead of explaining it no one wants to?
2
merpderpmerp
2 weeks ago
I don't know, because you are right... I think the key issue in mitigation efforts is that as the incidence of infections increases, it becomes harder to protect elderly/care home. I suspect that's why IFR from low-prevalence studies tends to be so low, even after attempting to correct for specificity, while the IFR from studies from Italy/NYC is much higher.
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JenniferColeRhuk
2 weeks ago
Rule 1: Be respectful. Racism, sexism, and other bigoted behavior is not allowed. No inflammatory remarks, personal attacks, or insults. Respect for other redditors is essential to promote ongoing dialog.
If you believe we made a mistake, please let us know.
Thank you for keeping /r/COVID19 a forum for impartial discussion.
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JenniferColeRhuk
2 weeks ago
Low-effort content that adds nothing to scientific discussion will be removed [Rule 10]
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laprasj
2 weeks ago
This isn’t good or bad, this is expected. A very low ifr below 50 years old, basically 0 below 30. Scales up after 50. This .5 ifr is what studies have been pointing to. I think we are going to see a .3-.6 average by the end of this. As the studies come out we are seeing a larger fluctuation in ifr. This is also expected as demographics, care homes, air quality, overall health and social distancing measures are different for every area. As treatments come out we will also see this number go down.
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belowthreshold
2 weeks ago
Agreed except I’m not sure we can say social distancing impacts IFR. Total infections, sure, but not whether someone who gets COVID19 dies from it.
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laprasj
2 weeks ago
I was more going toward the overflow of hospitals and equipment. Mostly seen in Italy when it first hit. Which was a large factor for them.
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atomthespider
2 weeks ago
For me it’s just news and, judging by the reaction of some, quality science. Whether good or bad seems to depend on which side of the IFR line you fall on. But if the study is good then it becomes a strong reference point to compare future sero-studies to.
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mushroomsarefriends
2 weeks ago
Assuming that the presence of IgG antibodies is at least in the short-term associated with immunity, these results highlight that the epidemic is far from burning out simply due to herd immunity.
This will really depend on what the herd immunity threshold is. One recent study argued the threshold may be at 10-20%.
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mkmyers45
2 weeks ago
How does that square with real world data that has shown over 25% infected population in some localities with still growing epidemics. Chelsea, Massachusetts (32% - [https://www.bostonglobe.com/2020/04/17/business/nearly-third-200-blood-samples-taken-chelsea-show-exposure-coronavirus/](https://www.bostonglobe.com/2020/04/17/business/nearly-third-200-blood-samples-taken-chelsea-show-exposure-coronavirus/) ) and Alzaro and Nembro, Bergamo (62% - [http://www.ats-bg.it/upload/asl\_bergamo/gestionedocumentale/CSATSBG2020-04-30CSATSBG2020-04-30coronavirusesitisierologici\_784\_31010.pdf](http://www.ats-bg.it/upload/asl_bergamo/gestionedocumentale/CSATSBG2020-04-30CSATSBG2020-04-30coronavirusesitisierologici_784_31010.pdf)).
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notafakeaccounnt
2 weeks ago
Don't use chelsea's data. They only used one corner of a street for their sample.
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ghostfox1_gfaqs
2 weeks ago
Easy explanation for that, they all got infected rapidly, whereas larger cities will have a slower spread, much less geographical areas with low population density.
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ozzyosbournvita
2 weeks ago
Can't really get herd immunity in a prison where the whole population congregates regularly in close quarters during, for example, meals, assemblies, exercise sessions
Herd immunity is a concept that applies to geographical regions
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hpaddict
2 weeks ago
> Can't really get herd immunity in a prison where the whole population congregates regularly in close quarters during, for example, meals, assemblies, exercise sessions
That is exactly the situation in which this version of herd immunity is actually relevant.
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hairylikeabear
2 weeks ago
You could still get herd immunity, it just won’t mimic any of the conditions for herd immunity that occur outside of a self-contained environment.
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pastelsonly
2 weeks ago
NYC, Stockholm, Lombardy, Wuhan all hit figures over 10%; don't know how it could be 10%.
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jamesgatz83
2 weeks ago
Herd immunity isn't a hard stop. It's possible to significantly overshoot the threshold via rapid spread.
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TheNumberOneRat
2 weeks ago
But this is only true if the R value is very high. If R is effectively low due to clustering, so is the overshoot.
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hopkolhopkol
2 weeks ago
The percentage required for herd immunity only indicates when unmitigated spread falls below an R of 1. The velocity of infections before you reach herd immunity can create an overshoot by quite a lot.
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TheNumberOneRat
2 weeks ago
I'm not sure if this the case. Final Size Equations depend on the R value, however, if you're assuming that the herd immunity threshold is significantly less than that predicted by R, then the overshoot should also be massively less.
I'm not an expert, but I don't see how we can assume that clustering effectively reduces R, but doesn't effectively reduce the overshoot.
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hopkolhopkol
2 weeks ago
The herd immunity threshold is predicted by R0, which assumes unmitigated spread in a naive population. R or Rt is the value of R at any given time taking into account everything including immunized percentage. R decreases towards 1 as the actual percentage of immune people approach the herd immunity predicted by R0, and falls below 1 when it exceeds that value. Herd immunity does not mean zero spread, just decreasing rate of spread.
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TheNumberOneRat
2 weeks ago
I don't think that I explained myself clearly.
The difference between the Final Size and the Herd Immunity Threshold (the overshoot) is related to the R0 in a homogeneous population.
If the herd threshold is significantly reduced because of the population being heterogeneous, then the overshoot will also drop by a massive amount.
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hopkolhopkol
2 weeks ago
Gotcha. Modelling the overshoot is very difficult because it depends on both heterogeneity and velocity. I was simply explaining why New York could have a higher infection rate than the predicted herd immunity rate.
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matakos18
2 weeks ago
Indeed. The study you mention argues that this is due to variability in susceptibility.
The fact that younger people (who move around more, and therefore are more susceptible) have shown higher seroprevalence in this study is further argument to that.
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FC37
2 weeks ago
FYI: Euroimmun IgG immunoassays were independently validated as having 96% specificity and 67% sensitivity.
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notafakeaccounnt
2 weeks ago
[source](https://www.medrxiv.org/content/10.1101/2020.05.02.20080879v1.full.pdf+html)
In this study, they used a validation technique to maximize specificity. So you can trust the results.
This absolutely doesn't mean euroimmun on its own has 100% specificity. It has this high specificity with the combined validation technique.
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matakos18
2 weeks ago
From the paper:
We assessed anti-SARS-CoV-2-IgG antibodies using a commercially available enzyme-linked immunosorbent assay (Euroimmun AG, Lübeck, Germany # EI 2606-9601 G) targeting the S1-domain of the spike protein of SARS-CoV-2; sera diluted 1:101 were processed on a EuroLabWorkstation ELISA. An in-house validation study, using a set of sera from 176 pre-pandemic negative controls and 181 RT-PCR confirmed COVID-19 cases was conducted to estimate test performance and optimal thresholds for definitive results . Based on this validation study, which set thresholds to maximize specificity and inter-assay variability in test classification, we considered those with OD/CI >= 1.5 to be positive. This threshold resulted in a sensitivity of 86.2% and a specificity of 100%. For these analyses we treated all indeterminate results (>0.5 OD/CI <1.5) as negatives (Figure S1)
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XYHopGuy
2 weeks ago
Or rather specificity is likely > 99.5%, bounded by the number of controls they use.
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matakos18
2 weeks ago
IFR calculation(edited):
Deaths in Geneva: 178 239 (on 24.04)
Population: 499,480
IFR: 239/ (9.7%*499,480)= 0.49%
This is exactly in line with the Gangelt study
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jtoomim
2 weeks ago
Most of the cases in Geneva are recent. Seroconversion almost always happens within 15 days of first symptoms. The average date of death is about 23 days after first symptoms, and many deaths don't happen until 40-60 days after first symptoms. The current death count is an underestimate of the number of people who will die among those who have been infected so far. A quick approximate correction for this effect is to use the Week 2 (6.1%) or Week 1 (3.1%) sample for the infection rate. A better approximation (since it appears that new cases in Geneva have flatlined) would be to wait 2-6 weeks and use the death count then. Another reasonable approximation method would be to look at the (deaths / (recovered+ deaths)) ratio, as well as the number of ongoing cases, and project the number of future deaths by multiplying the two.
As /u/merpderpmerp mentioned, the Geneva outbreak seems to have been concentrated in younger people, unlike the outbreaks in Seattle and Lombardy which focused on older populations. Some age adjustment is necessary to get an accurate IFR for the general population.
> ~~This is exactly in line with the Gangelt study~~
The Gangelt study only counted 7 deaths, even though 9 had occurred by the time the study was published. Due to the small sample size, the confidence intervals were very wide -- [0.14% to 0.74%](https://old.reddit.com/r/COVID19/comments/gd7w7a/infection_fatality_rate_of_sarscov2_infection_in/fphwq2i/) using 7 deaths. However, the authors of that study appear to have made an error on their confidence interval calculation.
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ggumdol
2 weeks ago
Have a look at this comment by u/merpderpmerp:
[https://www.reddit.com/r/COVID19/comments/gf4iv0/repeated\_seroprevalence\_of\_antisarscov2\_igg/fprsj4e](https://www.reddit.com/r/COVID19/comments/gf4iv0/repeated_seroprevalence_of_antisarscov2_igg/fprsj4e)
Two IFR estimates from Geneva and NYC almost coincide with eath other, i.e., 1.23%-1.29%. I think we are approching some semblance of the true IFR figure finally.
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merpderpmerp
2 weeks ago
I would not put too much stock in that estimate/use it to inform an IFR estimate (NYC data is a different story), I just wanted to do some crude calculations on how much population-level IFR can change if younger people are being disproportionately infected compared to if infections occur evenly across the population regardless of age.
I don't think the latter assumption is true, infection rate will always be somewhat correlated with age, but I wanted to illustrate the potential IFR differences if we can protect the elderly versus what may more realistically occur if we have NYC-style uncontrolled spread.
And as we open up, I don't think we've taken steps that can convincingly protect the elderly as spread increases, so I expect IFR to increase compared to low-prevalence estimates.
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ggumdol
2 weeks ago
As pointed out by u/polabud:
>people are correctly incorporating personal risk into their actions.
I think that, at this juncture of the crisis, we are very likely to underestimate the IFR figure because old people become more careful so as not to be exposed to the virus. Relatively low infection probability of old people might be occurring in almost all serological survey results (e.g., Gangelt) unless the prevalence rate is so high that even old people cannot escape the danger. Neil Ferguson made a similar remark on this issue in the interview by UNHERD.
While we are observing lots of deaths in elderly homes, on the average, old people in general, i.e., 50+ years old, are actively incorporaing the risk of the virus into their actions. If the virus is allowed to spread like a wildfire, they will not be able to escape any more.
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merpderpmerp
2 weeks ago
The issue is that infections don't seem to be spread evenly across ages, and if more young people are being infected and are less likely to die, the estimated IFR will be lower than the average risk of death if a random person was infected. This is good, but does not imply this would be the IFR if Covid19 spread fully through the population to reach herd immunity and we were unable to shield the old. Below, I try to adjust for age, though the age-adjusted IFR seems high, so if anyone has better data on the Geneva age-distribution or deaths by age, I can update the estimates. Also, the estimate would be more accurate if I had more fine-grained age categories, especially if I could estimate a 70+ category.
Using the age-specific mortality posted in this thread (with 4 less deaths in the 50+ category to match the total number of deaths on 4/24), and using the Wikipedia age-distribution as of 2000 (if someone had better data on that, it would be helpful because I had to re-weight to get the age-categories to line up), I calculated these age-specific IFR's:
| age | seroprev | deaths | age dist | strata pop | infections | IFR |
|----------|----------|--------|----------|------------|------------|--------|
| 5 to 19 | 0.06 | 0 | 0.0182 | 9091 | 545 | 0 |
| 20 to 49 | 0.085 | 2 | 0.429 | 219105 | 18624 | 0.0011 |
| 50+ | 0.037 | 237 | 0.379 | 189469 | 7010 | 3.38 |
Infections is seroprevalence times the estimated age-strata population. Multiplying the age weights by the age-specific IFR, the pooled estimate is (0.0182x0 + 0.429x0.0011 + 0.379x3.38) = 1.29
Edit:
I would not put too much stock in that estimate/use it to inform an IFR estimate, I just wanted to do some crude calculations on how much population-level IFR can change if younger people are being disproportionately infected compared to if infections occur evenly across the population regardless of age.
I don't think the latter assumption is true; infection rate will always be somewhat correlated with age, but I wanted to illustrate the potential IFR differences if we can protect the elderly versus what may more realistically occur if we have NYC-style uncontrolled spread.
And as we open up, I don't think we've taken steps that can convincingly protect the elderly as spread increases, so I expect IFR to increase compared to low-prevalence estimates. I'd love to be proved wrong, and I'd love to see better policies around protecting high-risk individuals.
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Mei_Waku
2 weeks ago
Pickaway Correctional Institution in Ohio is up to 28 deaths out of a total population of \~2530, that's 1.11% of the total population so far. Total infected is \~1841 (1.52% dead). Would be interesting to see the age break down in that prison, I doubt it's an old folks home.
[https://coronavirus.ohio.gov/static/DRCCOVID-19Information.pdf](https://coronavirus.ohio.gov/static/DRCCOVID-19Information.pdf)
People like citing the Iceland data, this one prison has almost 3 times as many deaths as that entire country. Even the Diamond princess is up to 14 deaths.
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jumpmanuptosmth
2 weeks ago
Since you seem pretty knowledgeable I'd like to run a theory (that is probably dumb) by you.
We know that long term care homes / senior homes make up the majority of death most places. For example, in Canada 79% of deaths come from these LTC facilities. However, these tests are not representative of the prevalence in these homes as I assume they are not testing those in LTC homes. If so, would it not be inaccurate to lump those deaths in with the rest of the population, as they are not represented at all? Would it not be more accurate to have two IFR's: LTC and non-LTC? Cheers :)
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merpderpmerp
2 weeks ago
Not dumb at all! (And I'm knowledgable in epi but not specifically infectious diseases so don't listen to me too much).
Ideally, we'd have precise, highly stratified risk estimates for all risk factors and combinations thereof. So an estimate of IFR for every age, and estimates of IFR for every level of BMI, and estimates of IFR for every combination of age and BMI and heart disease status, etc. This kind of individual risk prediction is what machine learning is good for, but needs high quality data. It would also allow us to project population-level IFR with better accuracy when using data from, say, NYC, to estimate what a large outbreak in Salt-Lake City would look like, after adjusting for demographic/comorbidity differences. This could help in public health planning/risk mitigation.
However, it seems like testing is still a key bottleneck. The serology tests are useful to get a good sense of population-level prevalence of infection, and haven't been designed specifically to estimate IFR in high risk groups. In a sense it doesn't really matter what the exact IFR is currently in different populations, because we know the general range of IFR (much worse than the flu, but not apocalyptic), and we know local IFR will increase if high-risk groups get infected. So regardless of the exact IFR, we need to prevent infections in high-risk areas. It seems like that is very hard to achieve without limiting the overall spread of infection. I'm hopeful (and hopefully not naive) that as we relax lockdown, testing/contact tracing/mask-wearing/better voluntary social distancing can keep the rate of infection growth low in most locations.
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ggumdol
2 weeks ago
What do you make of the case of Diamond Princess?
[https://www.reddit.com/r/COVID19/comments/gf4iv0/repeated\_seroprevalence\_of\_antisarscov2\_igg/fpsi4n1/](https://www.reddit.com/r/COVID19/comments/gf4iv0/repeated_seroprevalence_of_antisarscov2_igg/fpsi4n1/)
As discussed by u/jtoomim in the above comment, the age-adjusted IFR figure of Diamond Princess is about IFR=1.0% which is considerably lower than 1.2%-1.3%. My hunch is that the passengers in Diamond Princess were probably wealthier and hence relatively healthy. Obviously, poor people do not have the luxury of getting on board a cruiser. Apart from the fact that there were only 7+7 deaths (the latter occurred after the corresponding study), do we have any other reasoning to explain this extraordinarily low IFR?
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jtoomim
2 weeks ago
> which is considerably lower than 1.2%-1.3%
I'm glad you said "considerably" and not "significantly."
DP had 14 deaths out of 712 infections, but it also had a population that was older and about 2.3x as likely to die. The 95% confidence interval (using the exact binomial distribution) for (14/712)/2.3 is 0.47% to 1.43%. That's not significantly lower than 1.2%-1.3%.
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ggumdol
2 weeks ago
After a bit of further investigation, the permanent link seems to be the following (Ministry of Health, Labour and Welfare in Japan):
[https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/kenkou\_iryou/kenkou/kekkaku-kansenshou/index.html](https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/kenkou_iryou/kenkou/kekkaku-kansenshou/index.html)
where all new data and information about the virus are updated by the Japanese health ministry. Here we should click the following expression:
"国内の状況、検査状況等について更新しました。"
Then there is usually a table with a **blue-shaded row** which is hyperlinked to a pdf file. The table is entitled:
"新型コロナウイルス感染症に関する入退院の状況"
I'm jotting down this for future reference.
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ggumdol
2 weeks ago
Thanks to u/notafakeaccounnt and u/n0damage, we have the following finding which seems to be the most reliable official source:
[https://www.mhlw.go.jp/content/10900000/000628024.pdf](https://www.mhlw.go.jp/content/10900000/000628024.pdf)
So far, 13 people died and it is quite astonishing that 8 patients are still in hospitals. We can speculate that a significant proportion of 8 patients in hospitals will not make it. It looks to me that the virus induces some sort of terminal illnesses which last months. I'm utterly shocked by the cruelty of the virus.
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ggumdol
2 weeks ago
u/notafakeaccounnt also noted that
>Also it has been about 2-3 months since DP and there are still 4 critical patients. I'm guessing these are people in coma where their loved ones refuse to pull the plug. If we assume them as dead that's 2.38% IFR.
So, there are 4 more potential deaths even after 2-3 months. The death count can be as high as 14+4=18.
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jtoomim
2 weeks ago
I wouldn't automatically guess that they're in a coma, but... yeah, that's troubling.
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merpderpmerp
2 weeks ago
I'm not sure I'd categorize that as extraordinarily low, but yeah it's a great dataset but not perfectly representative. As you mentioned, the population on a cruise is probably wealthier and therefore healthier, and the people who are vastly more likely to die from covid19 are much less likely to be traveling (very old/frail, immunocompromised, undergoing chemo, morbidly obese, etc.). But it's also an older population so its hard to generalize from.
Its also unclear to me if treatment has gotten better since then... I assume so but I don't know if it has been quantified.
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ggumdol
2 weeks ago
I just realized this. For the case of Diamond Princess, the **IFR figure is actually 14 (7 occurred after the study) / (301+318) = 2.3%**. Many redditors here took a wrong number in the first place.
[https://www.medrxiv.org/content/medrxiv/early/2020/03/08/2020.03.05.20031773.full.pdf](https://www.medrxiv.org/content/medrxiv/early/2020/03/08/2020.03.05.20031773.full.pdf)
**Just have a look at Table 2 on Page 5.**
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merpderpmerp
2 weeks ago
Yeah, I thought I remember that it was higher, but I think the age-specific rates are lower than NYC/Italy.
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toddreese23
2 weeks ago
Isn't this a really dangerous calc to make? We know the IFR for old people is really bad. It could very well be that people 70+ are more likely to get this because they are in super-spreading environments (like nursing homes) than people who are 50-70 (work from home, newly retired). So prevalence in the super old could be higher than the kinda old (50-70). If you apply 3.7% to the 80+ population, it implies that the 180 deaths = IFR of \~19% for 80+. That feels high, suggesting the prevalence in 80+ is likely higher than 3.7%.
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merpderpmerp
2 weeks ago
It is a really crude calculation... Ideally, I'd propagate uncertainty in each estimate to get a wide confidence interval around the point estimate.
I'll be very interesting to see age-specific serology estimates from a range of locations (especially hard hit versus low-prevalence area). What you've hypothesized makes sense, but I could also see the opposite, where those 50-70 are more likely to be out and about exercising/working/going to the grocery store. I just don't know, which is why I wish there were more fine-grained age estimates from this serology study, especially in older ages.
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toddreese23
2 weeks ago
I actually hope your opposite scenario is true. If the death rate for 80+ is \[50%\] just using a high number, this is not a big deal at all.
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merpderpmerp
2 weeks ago
Ideally we'll get really good age- and morbidity-specific IFR estimates which could be used to estimate years-of-life lost per death. There's been a lot of discussion on this sub about protecting nursing homes (which is right), but I'd also like there to be interventions to protect high-risk individuals in the general population, as I suspect some of those deaths cause a larger years-of-life-lost burden.
Hypothetical example (with very made up numbers): we shoot for herd immunity, while protecting care homes through extensive testing/PPE/quarantining staff at the facility. Lets say at one care home, these steps prevent 10 deaths of people 85+, each who would survive 2 more years, so 20 years of life gained and 10 deaths averted. But someone age 50 undergoing chemo gets infected and dies, who otherwise would have recovered and lived for another 30 years. So though nursing homes have way more deaths, I'm concerned about (though have little data about) high-risk people with treatable comorbidities in the general population.
I don't know what the solution is as we open back up. Government-paid hotel rooms with food delivery and on-site medical staff for diabetics/those needing dialysis/chemo patients/immunocompromised?
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toddreese23
2 weeks ago
Agreed. It just seems so obvious that this is what's worth understanding but no one is putting the data out there. Shut down the entire world for something that we know really impacts people over 50, but don't focus enough to understand how much it impacts them. That makes sense.
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ggumdol
2 weeks ago
I really appreciate your laborious yet brilliant work. You are one of the **"very few"** people in this subreddit who make scientific arguments. I am really surprised by your IFR estimate, 1.29%, because I and u/jtoomim also obtained 1.23%-1.26% (depending on two approximation methods used in the following comment) from the final serological survey result from New York City.
[https://www.reddit.com/r/COVID19/comments/gcb7cx/amid\_ongoing\_covid19\_pandemic\_governor\_cuomo/fpf93xr/](https://www.reddit.com/r/COVID19/comments/gcb7cx/amid_ongoing_covid19_pandemic_governor_cuomo/fpf93xr/)
As you might be aware, I also pointed out a critical flaw in the research result from Gangelt which is tirelessly and perpetually being cited by those who claim that IFR is as low as 0.3%-0.5%:
[https://www.reddit.com/r/COVID19/comments/gd7w7a/infection\_fatality\_rate\_of\_sarscov2\_infection\_in/fpgngb0](https://www.reddit.com/r/COVID19/comments/gd7w7a/infection_fatality_rate_of_sarscov2_infection_in/fpgngb0)
These **IFR figures from New York City, USA and Geneva, Switzerland are unmistakably similar, i.e., 1.29% and 1.23%-1.26%.** We are finally approaching some sort of consensus on the true IFR figure. Thanks for your great contribution again.
Lastly but most importantly, I must add that, as many recent serological research results where [the infected population is young and, sometimes, mostly female](https://www.reddit.com/r/COVID19/comments/gd7w7a/infection_fatality_rate_of_sarscov2_infection_in/fpgngb0) (which was the case for the study from Gangelt), have shown so far, **we are likely to see relatively small IFR figures from research results conducted in areas with low seroprevalence** because young people are active spreaders due to their high mobility pattern and thusly the infected population is relatively young. In this light, **it is very dangerous to try to estimate the true IFR figure from these survey results with low prevalence**, not to mention that [the average inter-event delay](https://www.reddit.com/r/COVID19/comments/g6pqsr/nysnyc_antibody_study_updates/fohxjrh/) between death (23.8 days) and antibody formation (14 days) must be considered.
PS: While I have no idea about Geneva, the virus has been considerably (not completely) suppressed in Switzerland in general:
[https://www.worldometers.info/coronavirus/country/switzerland/](https://www.worldometers.info/coronavirus/country/switzerland/)
In total, 1810 deaths occurred so far and the recent daily death count is just about 10-15. Thusly, I think the average inter-event delay between death (23.8 days) and antibody (14 days) probably does not significantly affect the above IFR estimate.
14
KyleEvans
2 weeks ago
I think getting that all-age IFR off the Geneva study is questionable simply because the dramatic difference in seroprevalence suggests we're never going to see all ages infected. Unless something fundamentally changes to expose the elderly more than they already are, the epidemic will be over before it gets to them in a proportion equal to younger ages. In other words, why would't the difference in seroprevalence not hold all the way to herd immunity or vaccination?
As such the \~0.5% reported without adjusting for hypothetical equal exposure of all ages may be the more useful number.
1
ggumdol
2 weeks ago
>In other words, why would't the difference in seroprevalence not hold all the way to herd immunity or vaccination?
I think it is indeed a valid question. Based on the assumption of IFR=1.0%-1.3%, it will take 2.5-3 years for most advanced countries in the world to achieve the so-called herd immunity level, i.e., 70%-80% immunity prevalence, without their hospitals and ICU beds being overrun (It depends on each country but it is close to 3 years for the case of Sweden). I think it is possible that old people get relatively less scathed *if the vaccine becomes available within one year or less*. On the other hand, if the vaccine development isn't finished within a year, the virus will permeate through all different age groups quite evenly because, if the overall immunity level reaches 20% or higher (c.f., New York City), it is practically impossible for them to evade the virus.
Neil Ferguson during the interview by UNHERD said:
*"There's not much evidence of that (old people get infected earlier than young people). In fact, probably the vulnerable and the older age groups have lower contacts than the average populations. It takes transmission longer to get to them."*
At this juncture of the crisis, we don't seem to have completely established scientific evidence on the difference in seroprevalence (i.e., high immunuty prevalence among young people) but the above statement is in line with what happens in similar sociological situations across many different fields. Old people exhibit low mobility pattern, hence they are usually infected later than young people while **young people are active spreaders** due to their mobility pattern. As u/polabud mentioned in other comment, old people also actively incorporate the risk into their actions due to the incomparably high risk.
As many recent serological research results where [the infected population is young and, sometimes, mostly female](https://www.reddit.com/r/COVID19/comments/gd7w7a/infection_fatality_rate_of_sarscov2_infection_in/fpgngb0) (which was the case for the study from Gangelt), have shown so far, **we are likely too see relatively small IFR figures from research results conducted in areas with low seroprevalence.** But, in the end, if the virus is let spread into all different age groups, the death rate will slowly increase to the true IFR figure.
2
KyleEvans
2 weeks ago
I think it's entirely possible that any blanket lockdowns may have served to keep the roughly 2:1 ratio in exposure between the 20 to 49s & the 50+ artificially *low* since normal might have led to an even higher ratio.
Historically, yes it seems to be true that younger people are infected earlier in a pandemic but I think you are neglecting to consider how herd immunity developed amongst the younger serves to protect the older. That would explain why historically we've seen the elderly constitute a decrementally *smaller* proportion of deaths as pandemics have worn on: https://academic.oup.com/jid/article/178/1/53/919896
1
stop_wasting_my_time
2 weeks ago
All the best and most complete data sets point to an IFR near (and possibly over) 1%, whereas <0.5% IFR has only been estimated by studies of populations with small outbreaks, insufficient data, skewed samples, bad methodology and antibody tests that lack the necessary accuracy for screening populations with low prevalence.
Unfortunately, since the Santa Clara preprint was plastered all over mass media, the myth is now taking hold that IFR is far lower than predicted, when in fact most prominent epidemiologists have long estimated an IFR near 1% in their models.
This has been packaged with other myths, like exaggerating the severity of comorbidities. By far the most common comorbidity is high blood pressure and that is an extremely common condition (100 million Americans have it) but when people hear the term comorbidity, they think most of these people must have been immunocompromised or had one foot in the grave.
The other myth is that somebody who "recovers" has got off scot-free, whereas people who become critically ill and suffer low oxygen levels could potentially sustain long term damage to the lungs or even the brain. The number of people who become critically ill is of course many times greater than the number that die.
All that may seem like a tangent, but I point this out because epidemiologists who estimated 1% IFR are now facing severe backlash for "fear mongering" and that is a baseless accusation. All the best data is backing their estimates up.
5
ggumdol
2 weeks ago
I cannot agree with you enough. I wrote something but the automoderator does not like my comment.
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1
jpj77
2 weeks ago
I would argue this IFR estimate is extremely high, when we have an IFR estimate from Diamond Princess of 1.3%, in a population that was skewed heavily towards the elderly. We also have a PCR test IFR estimate from Iceland of 0.6%, so anything substantially higher than that seems dubious.
2
ggumdol
2 weeks ago
>We can age adjust the data, but I'm not sure that will help your case. There's also a significant lack of infections in young people, and the IFR for that population is 0.
This is a quote from another comment of yours. If you look at the graph entitled "Number of infections per 1,000 inhabitants, by age and sex":
[https://www.covid.is/data](https://www.covid.is/data)
The virus certainly affected the middle age group disproportionately, i.e., 40-59. Obviously, the age groups 70+ has largely avoided the virus. As you might know, most deaths occur in this age group 70+, to the best of our knowledge. That is, the death rate in this group is incomparably higher. Please do some calculations.
2
ggumdol
2 weeks ago
>We also have a PCR test IFR estimate from Iceland of 0.6%, so anything substantially higher than that seems dubious.
The example of Iceland has been criticized **so many times** in this subreddit. Would you please have a look at the following graph entitled "number of active infections, recovered and deaths by age" ?:
[https://www.covid.is/data](https://www.covid.is/data)
Most infected people are very young because, in Iceland, the virus has not evenly spread into different age groups and was suppressed at the early stage. I can safely say that the infected population in Iceland is hardly generalizable to any other country. As a matter of fact, the entire population of Iceland is relatively young due to their recent immigration policies.
Iceland should not be used for estimation of the true IFR figure.
1
notafakeaccounnt
2 weeks ago
> when we have an IFR estimate from Diamond Princess of 1.3%, in a population that was skewed heavily towards the elderly
See this is why I don't like people parroting stuff.
DP's IFR is 1.83%, not 1.3%. Time has passed and I guess people don't check back to see the numbers.
Also it has been about 2-3 months since DP and there are still 4 critical patients. I'm guessing these are people in coma where their loved ones refuse to pull the plug. If we assume them as dead that's 2.38% IFR.
I'm not gonna argue that the 1.3% IFR for geneva estimate isn't high but I don't like it when people forget numbers are linear
7
n0damage
2 weeks ago
> Also it has been about 2-3 months since DP and there are still 4 critical patients.
Do you have a reference for this? I'd like to keep my eye on it and see how long it takes for these cases to fully resolve.
2
notafakeaccounnt
2 weeks ago
[https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/newpage\_00032.html](https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/newpage_00032.html)
[https://www.worldometers.info/coronavirus/](https://www.worldometers.info/coronavirus/) Diamond princess section
3
ggumdol
2 weeks ago
After a bit of further investigation, the permanent link seems to be the following (Ministry of Health, Labour and Welfare in Japan):
[https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/kenkou\_iryou/kenkou/kekkaku-kansenshou/index.html](https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/kenkou_iryou/kenkou/kekkaku-kansenshou/index.html)
where all new data and information about the virus are updated by the Japanese health ministry. Here we should click the following expression:
"国内の状況、検査状況等について更新しました。"
Then there is usually a table with a **blue-shaded row** which is hyperlinked to a pdf file. The table is entitled:
"新型コロナウイルス感染症に関する入退院の状況"
I'm jotting down this for future reference.
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ggumdol
2 weeks ago
Thanks for your good investigative work, u/notafakeaccounnt and u/n0damage. I tried to find them yesterday, but to no avail. Your searching capabilities are outstanding. The following finding by u/n0damage seems to be the most reliable official source:
[https://www.mhlw.go.jp/content/10900000/000628024.pdf](https://www.mhlw.go.jp/content/10900000/000628024.pdf)
So far, 13 people died and it is quite astonishing that 8 patients are still in hospitals. We can speculate that a significant proportion of 8 patients in hospitals will not make it. It looks to me that the virus induces some sort of terminal illnesses which last months. I'm utterly shocked by the cruelty of the virus.
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n0damage
2 weeks ago
Thanks. I had to dig around on the Japanese site but according to [this](https://www.mhlw.go.jp/content/10900000/000628024.pdf) there are still 8 patients hospitalized from the cruise ship as of May 6th.
Considering that these cases were diagnosed in mid-late February and they're still unresolved over 2 months later makes me question the general validity of comparing the death count vs antibody prevalence on the date the samples were taken, which seems to be what most antibody studies are doing.
Clearly there is a much longer tail to the death count, and assuming unresolved cases will result in survival is going to skew the IFR estimates downward.
5
moriteme
2 weeks ago
8% of the 20-29 tested positive, 8% of the 30-39, and 8% of the 40-49. Then it rises to 15% for 50-59, 19% for 60-69, 23% for 70-79, and 25% for 80+. It's obvious those PCR tests massively missed the great majority of infections in the 20-29, 30-39, 40-49 ages. If they're all infected at the highest rate (25%), that's 925 total infections. But if they massively missed the majority of the infected in the 20-49 ages why would they suddenly magically have caught all infected in the 60-89 ages? Probably 40% to 50% of all people got infected, for an unadjusted IFR of 0.8%, and an age adjusted IFR of 0.4%.
0
jtoomim
2 weeks ago
I also estimated [1.22% IFR for Chelsea, MA](https://old.reddit.com/r/COVID19/comments/g48p9t/concerns_with_that_stanford_study_of_coronavirus/fnx9jwv/) 17 days ago.
/u/merpderpmerp
4
merpderpmerp
2 weeks ago
Well as much as I like to be flattered, I'm not sure I agree! A lot of both good and bad science occurs here, in particular, while I know epi, I'm ignorant about pulmonary medicine/pharmaceutical and love reading about treatment developments here. Plus "the fog of war" around Covid-19 data makes it especially hard to find the data to do much more than crude estimates.
I would not put much stock in my estimates... the age categories are too broad, the number of deaths are low so very sensitive to change, and serology results are more robust from higher prevalence areas. I think the crude IFR I estimated and the one estimated from NYC excess mortality are similar by coincidence (though I expect the best-estimate IFR when the local epidemics are over to be >0.5). Due to population/comorbidity/healthcare differences between NYC and Geneva I would not expect the true IFR to be the same... I suspect NYC's will be higher, but that is uninformed conjecture.
14
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1
jtoomim
2 weeks ago
> I think the crude IFR I estimated and the one estimated from NYC excess mortality are similar by coincidence
Yes, I agree. The Geneva numbers also need to be corrected for date of infection, since deaths are often delayed by 4-8 weeks after first symptoms, and that interval has not yet elapsed.
On a separate note, 239 deaths out of 26179 infections is a somewhat decent sample size. Before age correction, the 95% confidence intervals are about ± 10%. So statistical noise doesn't seem to be a major issue here.
> I suspect NYC's will be higher, but that is uninformed conjecture.
My reading of the data suggests that the truth is the opposite of that conjecture. After correcting for both age and the inter-event delay for seroconversion vs death, Geneva's IFR looks to be higher than NYC's. I don't have any mechanistic hypotheses for why that would be true.
5
ggumdol
2 weeks ago
>After correcting for both age and the inter-event delay for seroconversion vs death, Geneva's IFR looks to be higher than NYC's. I don't have any mechanistic hypotheses for why that would be true.
That's true but it seems that the survey used testing kits with low sensitivity, which couterbalances the inter-event delay between death and antibody formation. I concede that it is a bit of a coincidence that we have similar IFR figures but it seems increasingly likely that the true IFR is between 1.1%-1.3%.
1
jtoomim
2 weeks ago
I don't think the sensitivity is very time-dependent. It's also something they corrected for, and it's something that is much smaller in magnitude than the time-delay of deaths.
In the Diamond Princess cohort, 7 deaths happened in the first 30 days of infections, and 7 deaths happened in the second 30 days. That's 50% underreporting at t=30, which is much more serious than ~15% underreporting at t=15.
> it looks increasingly like that the true IFR is between 1.1%-1.3%
See also [this comment](https://old.reddit.com/r/COVID19/comments/gf4iv0/repeated_seroprevalence_of_antisarscov2_igg/fpsi4n1/). When all known biases are corrected for, I've seen quite a few estimates fall into the 1.2% ± 0.3% range. That said, I was cherry-picking the results that fit that range in an effort to discredit the hypothesis that NYC was an outlier, so don't take that comment too seriously.
6
ggumdol
2 weeks ago
>It's also something they corrected for, and it's something that is much smaller in magnitude than the time-delay of deaths.
Did they correct the prevalence to compensate for the low sensitivity? If that's the case, the inter-event delay between death and antiboy must be playing a significant role here. On the other hand, while I have no idea about Geneva, the virus has been considerably (not completely) suppressed in Switzerland in general:
[https://www.worldometers.info/coronavirus/country/switzerland/](https://www.worldometers.info/coronavirus/country/switzerland/)
If the virus has been suppressed in Switzerland to some extent and the authors corrected the prevalence error caused by the low sensisitivey, I think it is not a coincidence that our IFR estimates from New York City and Geneva are similar. Also, as you suggested, the true IFR figure in Geneva might be very slightly higher than the estimate by u/merpderpmerp, i.e., IFR=1.29%, probably because New York City has relatively young population.
2
Tafinho
2 weeks ago
It *is* in line with the updated Gangelt study after the 9th death was factored in.
Even in Geneva, the value is likely to go higher as half the victims still on ICU are likely do die.
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JJ_Reditt
2 weeks ago
Doesn't this need to be adjusted for the fact that the groups catching it so far are those less likely to die?
>We found that 5-19 year-olds (**6.0%**, 95% CI 2.3-10.2%) had similar seroprevalence to 20-49 year olds (**8.5%**, 95%CI 4.99-11.7), while significantly lower seroprevalence was observed among those 50 and older (**3.7%**, 95% CI 0.99-6.0, p=0.0008).
4
merpderpmerp
2 weeks ago
Yeah, if anyone can find the age-specific Geneva mortality and the Geneva age distribution, the more correct approach would be to calculate age-specific IFR's then apply that to the age distribution of Geneva to get a pooled IFR.
Edit: Using the age-specific mortality posted in this thread (with 4 less deaths in the 50+ category to match the total number of deaths on 4/24), and using the Wikipedia age-distribution as of 2000 (if someone had better data on that, it would be helpful because I had to re-weight to get the age-categories to line up), I calculated these age-specific IFR's:
| age | seroprev | deaths | age dist | strata pop | infections | IFR |
|----------|----------|--------|----------|------------|------------|--------|
| 5 to 19 | 0.06 | 0 | 0.0182 | 9091 | 545 | 0 |
| 20 to 49 | 0.085 | 2 | 0.429 | 219105 | 18624 | 0.0011 |
| 50+ | 0.037 | 237 | 0.379 | 189469 | 7010 | 3.38 |
Infections is seroprevalence times the estimated age-strata population. Multiplying the age weights by the age-specific IFR, the pooled estimate is (0.0182x0 + 0.429x0.0011 + 0.379x3.38) = 1.29
11
tour__de__franzia
2 weeks ago
Did they give enough information to break out the 50+ group more?
Everything I have seen so far has been showing a much lower ifr for <50 vs 50+. I was expecting <= .1% IFR for <50. Obviously these numbers are much better but I'll wait for some more data before modifying my thoughts too heavily.
But I would like a better picture of how quickly ifr is rising with those older age groups.
0
merpderpmerp
2 weeks ago
Unfortunately not that I could find. Because I agree that it's important; I expect there to be a huge difference in IFR by decade, and some difference in seroprevalence.
3
punasoni
2 weeks ago
Realistically no disease will ever infect 100%. So I don't think the "corrected" IFR would not be realistic either in a real epidemic.
A number like that is interesting but it can't be compared to f.ex. influenza numbers as they are also crude numbers which also contain a selection of some sort. For example: Vaccinated health care personnel shielding the most vulnerable patients from infection which prevents infections in that group.
There'll always be some kind of selection for the infections which can't be predicted. Epidemics go into decline at some point between 10-90% of people infected. The exact point is probably impossible to predict for a new disease because of all the unknowns. In the end the some selection happened and it wasn't random or homogenous.
It is likely the most active and least shielded population will be the most infected and the least active and most shielded population least.
In practice this might mean that people working construction, industry, service and healthcare jobs will be most infected. White collar people who can telecommute and self-isolating pensioners can have less infections. The care homes can be shielded but it doesn't always succeed.
Also, the IFR estimates from serology studies is still the upper bound as it will also miss people who were infected but didn't show up in the test. That said, a good high specificity serology study gives a good lower bound of infections in any case. There can't be any less at least.
3
merpderpmerp
2 weeks ago
I completely agree, I don't expect any area to have an even distribution of cases, and I suspect that seroprevalence in the elderly will generally be lower even without mitigation due to their lack of mobility/crowded social interactions.
But I do think it is useful to calculate to give a range of possible severity, especially as I suspect, as Covid19 spreads, young people are infected first before spreading it to care homes, etc. So the IFR early in an outbreak may not be accurate as the age-distribution of infections spread, which may be why we are seeing lower IFR's from low-prevalence serology studies, even after correcting for specificity. And this estimate is not that far off the IFR calculated from NYC excess mortality.
In conclusion, I think IFR in most locations will be lower than this estimate/NYC/Italy, but we should hesitate to use IFR's calculated at low prevalence to estimate the potential burden of shooting for hed immunity.
5
punasoni
2 weeks ago
Fair enough. The numbers where covid19 epidemics turn to decline are still unknown. They might be well below the often mentioned 60-70% levels. That said, with unmitigated spread areas might overshoot the "necessary" level a lot.
1
n0damage
2 weeks ago
The current death count in Geneva is 254 per the official government stats here:
https://www.ge.ch/document/20094/telecharger
(There's also a chart with previous daily totals if you want to use an earlier date in April.)
22
ggumdol
2 weeks ago
Have a look at this comment by u/merpderpmerp:
[https://www.reddit.com/r/COVID19/comments/gf4iv0/repeated\_seroprevalence\_of\_antisarscov2\_igg/fprsj4e](https://www.reddit.com/r/COVID19/comments/gf4iv0/repeated_seroprevalence_of_antisarscov2_igg/fprsj4e)
Two IFR estimates from Geneva and NYC almost coincide with eath other, i.e., 1.23%-1.29%. I think we are approaching some semblance of the true IFR figure finally.
1
polabud
2 weeks ago
239 on 4.24 . Would give a crude IFR of ~~0.5% - but note huge difference in seroprevalence by age (all-age IFR would be higher).
28
ggumdol
2 weeks ago
Yes, I manage to recall that. Yet there are still a few epidemiologists claiming that IFR is 0.1%-0.2%. People follow them blindly because they are experts, irrespective of their competence or the lack thereof.
2
merpderpmerp
2 weeks ago
I calculate an all-age IFR of 1.29 below though it would be good to have others check my math, as that's higher than I expected. (https://old.reddit.com/r/COVID19/comments/gf4iv0/repeated_seroprevalence_of_antisarscov2_igg/fprsj4e/)
8
jpj77
2 weeks ago
Non age adjusted IFR on Diamond Princess was 1.3%, and that was a boat full of mostly old people. Anything in the realms of 1.3% shows an error in the study or calculation, especially when we know the all age IFR from PCR tests in Iceland is 0.6%.
0
Doggydogworld3
2 weeks ago
DP had 14 (eventually) die out of 712 positive tests for unadjusted IFR of 2%.
6
kbotc
2 weeks ago
Yea, and the opposite of that would be the Theadore Roosevelt with it's 0.08% mortality. (1 out of 1,150) with only 3 remaining hospitalized.
3
merpderpmerp
2 weeks ago
Eh, I agree that 1.3 is high (and I made a caveat in my post that my calculations are for illustrative purposes), but I'm not sure the Diamond Princess can be used to make an upper bound... the median age was high but people on cruises are probably wealthier than the general population and the old/infirm can't travel and are the most vulnerable. Local IFRs are probably going to vary wildly based on demographics/population health and the patterns of who is infected, so I expect most estimates to be <1% but would not be surprised to see a few very hard hit areas.... say a poor, rural, aging county where nursing homes were hit hard.
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jpj77
2 weeks ago
Agreed on localized rates being different. DP has a bunch of caveats - everything does. That's why it's very clear and easy to look at a study of ~2,000 confirmed cases and 10 deaths in Iceland and get a good estimate for IFR. Any other way of doing it involves too many assumptions to be taken too serioiusly. We can certainly age adjust Iceland's population if we need to.
2
merpderpmerp
2 weeks ago
Though that data has caveats too... one more death and IFR increases by 10%, and datasets with smaller numbers of infections like Iceland are more sensitive random error in who's gotten infected.
This is why systematic reviews/meta-analyses are so useful! But they are challenging and time-consuming to conduct especially as the evidence is evolving so fast with Covid19.
7
jpj77
2 weeks ago
Well for now, they have 36 mild cases. I'd expect 0.6% of those people to end up dying. These systematic reviews you speak of have 1300 samples - meanwhile Iceland has nearly 2,000 data points. I find that much simpler, fewer assumptions, and generally a signifcantly better source of information than extrapolating out antibody results to an entire population, age adjusting the distribution, and back-calculating the IFR based on lag time between deaths and antibody development. There is so much more potential for error in all of that than there is on the off chance that out of 1700 people, Iceland just happened to get really lucky.
0
merpderpmerp
2 weeks ago
Oh, when I say systematic review, I mean a study that aggregates and interprets results from many studies (including the Iceland data), and I certainly agree the Iceland data is one of the best datasets. It just suffers from generalizability issues. I don't look at the Iceland data and feel comfortable applying it to, say, Saudi Arabia, without a high degree of uncertainty due to differences in obesity rates among other differences.
5
Smartiekid
2 weeks ago
Does this mean the overall IFR is higher as a whole but the difference between younger people having it and showing symptoms/fatal cases is far less then the older population or am I reading it wrong
4
NarwhalJouster
2 weeks ago
That seems to be what this study is pointing to. One study isn't conclusive, obviously, but we're seeing a trend of low IFR in younger people that increases dramatically with age.
It's worth pointing out that there have been reports of adverse long term health impacts (other than death) even among young people, so even if you're young and healthy, make sure you stay safe.
3
toddreese23
2 weeks ago
has the canton of Geneva released the age distribution of deaths. One thing that is missing is a thorough analysis of deaths by age cohorts
4
julitoco
2 weeks ago
30-39 years: 2
50-59 years: 6
60-69 years: 11
70-79 years: 46
80+ years: 180
All others are 0
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julitoco
2 weeks ago
[Official Data Visualization (in french)](https://covid-19-schweiz.bagapps.ch/fr-1.html)
2
polabud
2 weeks ago
Wow, this is incredible. Incidence among >50 is less than half <50 but deaths are like 150x higher.
17
KyndyllG
2 weeks ago
I noticed this phenomenon in one of the areas I'm tracking and when I looked into it, I found a local news story in which a person was quoted saying that younger people were more like to come in and get tested, even though they were less likely to get seriously ill. This was several weeks ago - I don't recall if it was a medical professional or health department person being quoted. No link, as media links aren't wanted here.
3
SwimmingRoutine3
2 weeks ago
A major reason for wide differences in measured IFR is this varies so much from location to location. In Belgium they found nursing homes had a much higher rate of infection than the general population. That would cause a very different IFR to be measured even if the test was perfect and there were no sampling errors!
Instead of measuring a property of the disease, it appears IFR studies are measuring the effectiveness of local measures to protect high risk populations.
23
SoftSignificance4
2 weeks ago
that's what's going to happen when there is low prevalance. you are subject to more variation if infections hit certain groups more than others.
as infections grow though that's less of a concern which is why higher prevalance studies are valuable at the moment because you get a more accurate picture as infections hit more broadly. unless you think infections stop or that the only vulnerable population are those in nursing homes that's going to give a more accurate picture.
6
polabud
2 weeks ago
Yep, I think this is a very compelling way of interpreting things. We *have* to do a better job of protecting high-risk groups. Hopefully we're on our guard now, but who knows.
17
julitoco
2 weeks ago
Absolutely.
Doing really quick math:
IFR for under 50 years old: 0.006%
+50: 3.57%
37
julitoco
2 weeks ago
I took them from [https://ugeo.urbistat.com/AdminStat/en/ch/demografia/eta/geneve/25/2](https://ugeo.urbistat.com/AdminStat/en/ch/demografia/eta/geneve/25/2)
I splitted the 45-55 range in two as well, that's why I said it's really quick math and shouldn't be taken as 100% accurate at all.
​
Edit: Here is the exact 2019 stats from the goverment for anyone interested [http://www.ge.ch/statistique/tel/domaines/01/01\_01/T\_01\_01\_1\_03A.xls](http://www.ge.ch/statistique/tel/domaines/01/01_01/T_01_01_1_03A.xls)
​
Population under 50: 324.663
Population over 50: 144.647
0
Smartiekid
2 weeks ago
Theres just been a graph for ages and all deaths up to the 27th of April from the UK... The numbers are staggering, after the age of 40 things start pouncing up, I think 20-30s were 0.005-0.008% 90+ was 1/10
6
jpj77
2 weeks ago
People 90+ have over a 10% chance of dying regardless of a pandemic in any given year. Long term, it will be interesting to see if those people dying from Covid were dying because they were about to die anyway and Covid was the catalyst, or if Covid is taking a different subset of 10%.
5
mrandish
2 weeks ago
Yes, it's known as the "Harvesting Effect" and it's why some epis are saying we won't know the true death rate until afterward. I just read that Italy is already seeing all-cause mortality in recent weeks down 7% compared to prior years.
5
tour__de__franzia
2 weeks ago
Do you have a link to that graph? I'm pretty certain that rates rise enormously as age goes up, but I'd like to start getting a better understanding of what it looks like for each age group.
8
matakos18
2 weeks ago
Thanks!
I had just looked it up on Google, which maybe reports data from the city of Geneva instead of the canton of Geneva( where the study took place).
I looked again into the study to try to derive a new IFR calculation:
1. The test has ony 86% sensitivity (but 100% specificity!), meaning it undercounts positive cases.
2. They do not report exact dates, when the data was collected, only that it was collected in three successive weeks.
I think thus unfortunately any IFR calculation is going to be inaccurate.
On the one hand, the 254 deaths push the IFR higher, on the other hand, the possible lag from the date the data was collected, and the undercounting of positive cases, could push it further lower.
15
notafakeaccounnt
2 weeks ago
> The test has ony 86% sensitivity (but 100% specificity!), meaning it undercounts positive cases.
They used the same euroimmun assay but **they changed the threshold for positive to maximize specificity**. I love it.
[This is the validation study](https://www.medrxiv.org/content/10.1101/2020.05.02.20080879v1.full.pdf+html)
Don't worry about the sensitivity. You can easily adjust for sensitivity even at low prevalence as long you have 100% specificity. Because your results can't be completely wrong.
Also
>The canton of Geneva, Switzerland, reported its first confirmed COVID-19 case on February 26, with 5071 cases (10.15 per 1000 inhabitants) and 243 deaths as of April 30
The study gave you mortality information already. I strongly urge you to edit your comment to fit the study's mortality number instead of your own speculation please.
11
XYHopGuy
2 weeks ago
keep in mind with only 200ish controls they can only bound the specificity to >99.5% ish
10
matakos18
2 weeks ago
Thanks, edited.
Regarding mortality: This is the mortality at the time they were writing the paper, not when they collected the data. In the discussion in the paper, you can see that they compare against the number of cases on the 24th of April, so we can assume that is the date when the study was concluded. On the 24th there were 239, as also /u/polabud mentioned.
4
NarwhalJouster
2 weeks ago
I want to note that one of the challenges in determining IFR for m these antibody studies is that the delay between infection and development of detectable antibodies isn't always the same as the delay between infection and death.
Let's say you're using death data from the same day you collect your samples. If it takes two weeks from infection to antibodies (idk if this is the right number, but it doesn't matter for my point), if there were people who died who were infected less than two weeks before the sample collection, the IFR would be lower than reported. Conversely, if there are people who got infected two or more weeks before sample collection, but who die afterwards, the IFR will be higher than reported.
I would really like to see a proper analysis on the impact of death delays on reported IFR from seroprevalence studies, I think it would be really helpful.
4
ggumdol
2 weeks ago
The analysis you would like to see has been conducted in this subreddit. On the average, the random event of death occurs 10 days later than the random event of antibody formation.
[https://www.reddit.com/r/COVID19/comments/g6pqsr/nysnyc\_antibody\_study\_updates/fohxjrh/](https://www.reddit.com/r/COVID19/comments/g6pqsr/nysnyc_antibody_study_updates/fohxjrh/)
2
FormerSrirachaAddict
2 weeks ago
Forgive me, I'm a bit slow.
>the delay between infection and development of detectable antibodies isn't always the same as the delay between infection and death.
Meaning some could die without ever testing positive, and others would be infected while testing negative?
> if there were people who died who were infected less than two weeks before the sample collection, the IFR would be lower than reported.
Because there's many people yet to pass from the disease?
>Conversely, if there are people who got infected two or more weeks before sample collection, but who die afterwards, the IFR will be higher than reported.
Because they're all dying at the point you're collecting your samples?
1
polabud
2 weeks ago
They adjusted the projection for test sensitivity (see the later part of the paper) and used 4.24 as the date to project incidence.
12
analo1984
2 weeks ago
Seroprevalence studies are sort of converging towards 0.2-0.5%. A few outliers, but as of now it seems as if this is the interval.
11
twotime
2 weeks ago
A. 0.23% of NYC is dead already, and every study says that they are below 50% infection rate
B. quite a few "optimistic" studies which came up with 0.2% were later found questionable to put it mildly (Santa Clara study, Miami Dade study, etc)
1
jtoomim
2 weeks ago
No, they are not. There are quite a few that have low estimates like that, but most of them have errors. Here are some of the common errors in the studies:
1. Samples have not always been random, and have been susceptible to bias, in which sick people are more likely to end up being tested.
2. Surveys have often been performed in areas with low actual prevalence, which amplifies the effect of any errors. If a study has a 3% increase in observed prevalence due to systematic error from sampling bias, and the true prevalence is only 0.5%, that results in an 86% lower estimated IFR.
3. Surveys have often been small, or of small communities. For example, Gangelt only had 7 deaths and 422 cases. Diamond Princess only had 14 deaths and 712 infections.
4. Surveys have often used tests with low specificity.
5. Very few of the seroprevalence estimates account for the fact that deaths are significantly delayed relative to seroconversion.
2
sleeper_must_awaken
2 weeks ago
Depends on how deaths are counted. When taking the excess deaths as a metric in the Netherlands, it seems we come to an IFR of 1.2%
2
dumbfartlol
2 weeks ago
>0.2-0.5%
All seroprevalence studies that have come out that show this level of IFR either have had either one or more of the following: low sensitivity tests, small sample sizes, absurdly defined confidence levels, proven biased authors, incorrectly indexed for age sampling (studies has a sample with too many young people compared to the actual population), or poor validation methods.
I thought MAYBE after the NYC seroprevalence study which only showed that maaaaybe 20% of the population has antibodies with a very large sample size would finally get this subreddit to stop solely upvoting any post that says 0.2-0.5% IFR but alas. NYC currently has had 0.25% of its entire population killed by the virus - with strong indications of undercounting in both the confirmed death total and probably death cases as evidenced by a high level of excess mortality (3-4k extra deaths according to NYT) even when you factor in the confirmed and probably cases. If only 20% of the population has antibodies - in a city that under indexes for obesity and age, you have VERY strong evidence that the IFR will not be near 0.2 and more in the 0.8-1.5% IFR range.
21
larryRotter
2 weeks ago
Unpopular opinion around here, but I agree. South Korea's CFR is now above 2%. These 0.5% IFR estimates would indicated that they only caught less than a quarter of their cases. I just don't see how that's possible, and being able to contain the virus without a proper lockdown.
6
SwimmingRoutine3
2 weeks ago
If 20% of the general population is infected, what percentage of the at risk population is infected? Those 2 numbers are only loosely correlated. Some locations do a much better job than others at protecting their at risk populations. This will be directly reflected in the IFR for that location.
It is possible the results for NYC to be accurate and yet not be representative of other locations.
11
danny841
2 weeks ago
Right? If 20% of NYC is affected but 50% of the elderly NYC population is affected, the IFR will look...wait for it: massive.
7
CarlSagan79
2 weeks ago
NYC is an outlier just like Vietnam and Gibraltar with their regional IFRs of 0.0% are outliers.
There’s an unrecognized genetic component here. 6 regions are responsible for 75% of the deaths. The calamity predicted in CA/WA/FL that happened in NY has never materialized.
On the collection of roughly 50 studies we have at the moment, there’s a tight clustering around 0.0-0.3%. These are all in younger populations with long term care unaffected. When long term care is affected the IFR goes up to 0.5-0.7% in a region.
9
SoftSignificance4
2 weeks ago
there have been repeated issues amongst the studies with low prevalance numbers to the point that it's a bit dishonest not to include that when making broad proclamations.
and to dismiss higher prevalance studies at that. you're not even right on your <0.3 claim.
please source.
12
dumbfartlol
2 weeks ago
ty for helping me in the good fight but my original post is being downvoted into oblivion. this subreddit has really got a confirmation bias streak and its disappointing.
11
dxpqxb
2 weeks ago
Reddit is basically Confirmation Bias the WebSite. You can't expect good level of discussion on a site based around the upvote system.
1
dumbfartlol
2 weeks ago
It's not about popularity nor do I care about my score for my user account (look at my name) - these posts in a science board get *seen* less if they are downvoted on reddit. Meaning when people are making wild claims and someone rebuffs them - and then that person is downvoted for no evidence based reason, then the inaccuracy or critique is never discovered.
And the posts do matter. A random person on the net reads the post I was replying to and goes "Oh, 0.2% IFR. That's nothing! I'll tell my friends what I read and influence them!" and could wind up killing someone.
Maybe because I live in NYC, it doesn't feel as distant to me.
3
ggumdol
2 weeks ago
>these posts in a science board get seen less if they are downvoted on reddit.
Yes, we know and we are now used to it. Don't worry because we can't persuade those who are **unwilling** to see the truth, anyway.
1
SoftSignificance4
2 weeks ago
i also live in nyc.
you can't do anything about downvotes so no use complaining about it.
6
XYHopGuy
2 weeks ago
An ultra dense city with poor sanitation and a slow response is an outlier? Is new York really a surprise ????
The results of these studies are also compatable with a less infectious disease.
4
jamesgatz83
2 weeks ago
And there are huge differences between wealthy Manhattan and the significantly less wealthy Bronx, Queens and Brooklyn.
2
cakeycakeycake
2 weeks ago
Density, poverty, lots of elderly, conditions of NYCHA housing, and the prevalence of comordbities in the communities most impacted. But EVEN considering those we still end up with an IFR AROUND .5% with the antibody study. So I'm not sure what the argument is exactly? Like we all know these numbers won't be finalized for years most likely, but this is an expected and reasonable result. NYC is hit a bit worse for reasons that make perfect logical sense and still comes out around .5% IFR. If people want to guess that its actually higher....well okay? That makes sense? Its no surprise that its a bit worse in NYC and also no surprise that there are many more cases and asymptomatic cases than we knew about. I'm not understanding the debate here.
8
SoftSignificance4
2 weeks ago
ny is below median in obesity and age. density could explain infection rates but not fatality rates.
2
cakeycakeycake
2 weeks ago
Not the populations that are being hit disproportionately hard with fatalities though. Look at obesity, diabetes, and asthma among people in public housing in NYC, for example.
5
SoftSignificance4
2 weeks ago
what evidence do you have that infections are disproportionately hitting at risk groups?
2
dumbfartlol
2 weeks ago
sars-cov-2 virus has sensors that looks for fat new yorkers and old people
0
dumbfartlol
2 weeks ago
What are you talking about???? 0.5% IFR in NYC from that study? Let's go to the drawing board:
20,000 people in NYC have died from covid-19. That 0.25% of the population of NYC - roughly. That does not include uncounted deaths or excess mortality.
20% of NYC have antibodies according to that test.
So then 20,000 x 5 (because 20% were infected) = 100,000 or .... 1.25% IFR roughly.
4
cakeycakeycake
2 weeks ago
Wait are you talking about the city or the state? There are around 20k confirmed deaths in the STATE, not specifically in NYC? There are 13,000 confirmed deaths in NYC. Out of 1.8 million infected that is about .7. So maybe you're using different metrics or including suspected deaths but I was speaking about confirmed deaths.
ETA: about 14k confirmed as of this morning in the city.
0
cakeycakeycake
2 weeks ago
yoooo calm tf down. You're right- I was thinking of the initial calculation they published in the press conference from the 4/27 results, which given the death toll at that time and the 24.7% positive antibody result in NYC did suggest a CFR around .5%. You'll notice if you actually read my comment I mentioned we won't truly know these outcomes for years, but that NYC's results seem in line with everyone else's when you consider the conditions here. But you are still off a bit. 20% of the 9mil in NYC takes you to about 1.8 mil. Confirmed death toll of 19,400 gets you to around 1%- I think 1.1 to be more precise. Doing some in the head math so try not to lose your mind if I'm wrong.
2
XYHopGuy
2 weeks ago
That's fair, NY and the north east in general are very different than the rest of the us and I have a hard time believing that didn't play a role (we agree). I'm not convinced social distancing is even possible in that city. Grocery shopping at 6am under normal conditions is a nightmare.
However, due to lifestyle differences in NYC, I think a much more likely explanation is a lower r0. Not following you on .5% IFR for NYC. how do you get .5% IFR with NYC when .25% of the population is already dead from c19?
5
cakeycakeycake
2 weeks ago
I think you're right and my math is wrong. And I also think I was remembering the CFR Cuomo mentioned in the post-4/27 press conference when the suspected rate was 25% and the confirmed death toll was a bit lower at the time. The difference between confirmed death toll and suspected might be the issue too- two different numbers are being published from the city and state.
EDITED TO ADD: I think maybe I'm just using different metrics than you? I also think people are getting confused between the city numbers and state numbers. The current CONFIRMED death toll for NY CITY is about 13k. If 20% of NY CITY were infected that's a death rate about .7%. When we thought 25% had antibodies that would be around .5%. But again I totally think all these numbers need to be taken with a grain of salt. My only intended point was that this seems fairly in line with other studies and doesn't seem like a shocking outlier to me when everything else is taken into account.
So I live in NYC and completely agree with your point about social distancing. I live in a brownstone in a nice area of Brooklyn and can't check the mail without touching a surface that at least 20 other people have touched that day. And I'm in an affluent community with fewer incidences of obesity, diabetes, and asthma. Asthma in NYCHA housing is a longstanding problem. When you factor in how many low income people are crammed into tiny spaces, esp in the Bronx and parts of Queens, its no wonder we've seen terrible outcomes in those communities. Our latino population has also been hit very hard and its culturally quite common in NYC for some latino immigrant communities to live with the older generations which I bet has played a role- grandma and great grandma being in a three bedroom apartment with mom, dad, three kids and a grand kid is not an uncommon living situation here.
At the end of the day its density. We are denser than almost any European city I think? And certainly denser than the cities on the west coast that we thought would be hard hit. NYC is probably the most comparable to some of the density we see is some Asian cities.
4
XYHopGuy
2 weeks ago
Seems like we mostly agree! I'm with you, its difficult to trust any numbers at this point. The best we can do is bound the numbers, (which is why I'm so interested in the total deaths/capital.
My heart feels for you, I moved from park slope to california just before it all went down. I watched the new hospital buildilng get built, its crazy to think of what it ended up being used for :(
> Our latino population has also been hit very hard and its culturally quite common in NYC for some latino immigrant communities to live with the older generations which I bet has played a role- grandma and great grandma being in a three bedroom apartment with mom, dad, three kids and a grand kid is not an uncommon living situation here.
I've seen some speculation about this, I saw an article discussing the difference between corona and flushing. No numbers were included so it was really difficult for me to reason about, do you have any insights?
> without touching a surface that at least 20 other people have touched that day.
Interesting, I've started to think that surface-to-surface is probably not a great vector. But that probably changes based on frequency.
> At the end of the day its density. We are denser than almost any European city I think? And certainly denser than the cities on the west coast that we thought would be hard hit. NYC is probably the most comparable to some of the density we see is some Asian cities.
yep, and the response was super slow in NYC... feels like it hit critical mass before anything was put in place. But again, feelings, this is all speculation from my end.
0
SoftSignificance4
2 weeks ago
confirmed + suspected makes it way higher than .5. we are closer and might be over than 1% if you go by the excess death gap. but probably in the .8 range which would be conservative.
density only explains infection rates not fatality rates.
3
dumbfartlol
2 weeks ago
The outliers you mentioned with low IFRs have low sample sizes of a total of 400. NYC has nearly 200,000 confirmed cases. they are not equivalent outliers statistically am I losing my mind on this subreddit???? ?
6
dumbfartlol
2 weeks ago
Even in this TEST that is being paraded it admits that the sample is WAY oversampled on positive cases for people under 50.
3
cronos_qc
2 weeks ago
True. But is New York an outlier? The IFR seems higher than 0.5% based on death / setoprevalence.
22
jtoomim
2 weeks ago
> is New York an outlier?
New York's IFR [is about 1.2%](https://old.reddit.com/r/COVID19/comments/gcb7cx/amid_ongoing_covid19_pandemic_governor_cuomo/fpf93xr/?utm_source=reddit&utm_medium=usertext&utm_name=Coronavirus&utm_content=t1_fpr2ndl).
Chelsea, MA's IFR [is about 1.2%](https://old.reddit.com/r/COVID19/comments/g48p9t/concerns_with_that_stanford_study_of_coronavirus/fnx9jwv/).
Geneva's IFR [is about 1.2%](https://old.reddit.com/r/COVID19/comments/gf4iv0/repeated_seroprevalence_of_antisarscov2_igg/fprsj4e/).
The Diamond Princess's IFR [is about 1.2%](https://www.medrxiv.org/content/medrxiv/early/2020/03/08/2020.03.05.20031773.full.pdf), but only if don't correct for the DP's older age distribution or the fact that 7 more deaths happened after that study ended. If you correct for both, you get an IFR of 1.0%.
12
ggumdol
2 weeks ago
For the case of Diamond Princess, it's actually 14 / 619 = 2.3%. I think you took a wrong number in the first place.
[https://www.medrxiv.org/content/medrxiv/early/2020/03/08/2020.03.05.20031773.full.pdf](https://www.medrxiv.org/content/medrxiv/early/2020/03/08/2020.03.05.20031773.full.pdf)
**Just have a look at Table 2.**
2
jtoomim
2 weeks ago
Diamond Princess's population is about 2.3x as likely to die from infection because of their elderly-skewed population. If you correct for the younger age distribution for typical national samples (e.g. Mainland China), the age-adjusted estimated IFR is about 1.0%.
(The authors stated 0.5% in the abstract for that correction, but that was when they only had 7 deaths. Since there are now 14 deaths, I doubled 0.5% to get 1.0%.)
Note: there's a typo in the first sentence of the abstract. They swapped CFR and IFR. The numbers stated are IFR and CFR, respectively. I've confirmed this typo with the author, Tim Russel, by email. It has supposedly been corrected, but I haven't been able to find the corrected PDF anywhere.
2
ggumdol
2 weeks ago
Thanks for the clarification. I just took a glimpse into the paper one more time and I get your point. You are right. But you could edit your comment in the above because **the original IFR was 2.3%** which became **0.5% after age adjustment**, which became **1.0% after 7 additional deaths**.
It's terribly hard to keep track of what is happening!
1
mrandish
2 weeks ago
The median inferred IFR from all 27 antibody studies posted [so far is 0.2%.](https://www.reddit.com/r/COVID19/comments/gbmrof/seroprevalence_of_novel_coronavirus_disease/fp6zqkg/)
5
jtoomim
2 weeks ago
And if you do the simple mean instead of the median, you get 0.34%.
And if you do a weighted mean instead of a simple mean, you get something much higher. All of the big outbreaks -- NY, Italy, Madrid -- have higher IFRs in that table.
As far as I've seen, none of those studies correct for the inter-event delay between positive testing and death.
Many of those studies -- SF, Santa Clara, Kobe -- have well-known sampling bias errors, and drastically overestimated the number of infected. The Kobe study was sampling from people who were already at the hospital to have blood drawn, for example. That is NOT a random representative sample, and they could have gotten their results simply because 1 in 30 patients was a COVID+ patient who entered their hospital for COVID treatment via a path other than the emergency department or fever unit -- e.g. referral from a GP for blood tests.
And the SF study was sampling from a census district that was intentionally chosen by the authors to be a hotspot. The UCSF staff did not calculate an IFR because no fatalities data for Census District 229.01 is available, so the IFR must have been calculated by the spreadsheet author based on fatalities data from some other region, probably CA or SF as a whole. But both CA and SF have fewer infections per capita than CD 229.01, so there's no reason to believe that such an IFR is accurate.
When you take a bunch of garbage data, and run a median filter on it, you get garbage out. If most of the data suffers from the same biases (oversampling of sick people, who are more likely to seek out testing and medical attention), then that median filter can exclude the data that doesn't suffer from those biases, and you can get a result that is *more* biased than what you started with. With a median, it's not just GIGO. It's fresh garbage in, festering rotten maggot-infested garbage out.
New York City has had 14,162 confirmed deaths so far. If the true IFR for COVID were 0.2%, then there would be 7.1 million infections in NYC. NYC's population is only 8.4 million. It seems unlikely that 84% of New York's population has been infected.
New York City also has another 5,378 deaths classified as "probable COVID" -- these have clear symptoms of COVID (e.g. ground-glass opacities), but no PCR test. If you include those as well, then New York has 19540 deaths so far. If the true IFR is 0.2%, then there would be 9.77 million infections in NYC. It seems unlikely that 116% of NYC's population is currently infected.
Chelsea, MA has had 110 confirmed COVID deaths so far. If the true IFR for COVID were 0.2%, then there would be 55,000 infections in Chelsea, MA. Chelsea's population is 40,160. It seems unlikely that 137% of Chelsea's population is infected.
10
zippercot
2 weeks ago
Could be partially due to their healthcare system overload. Are there any seroprevalence studies from Italy or Spain yet?
1
notafakeaccounnt
2 weeks ago
[Italy](https://www.medrxiv.org/content/10.1101/2020.04.18.20070912v1)
1.29% IFR and that's with mostly testing people at high risk of exposure to the virus.
4
merpderpmerp
2 weeks ago
Coincidentally, that's the IFR estimated from this study when adjusting for age-skewed seroprevalence with the best numbers I can find: (https://old.reddit.com/r/COVID19/comments/gf4iv0/repeated_seroprevalence_of_antisarscov2_igg/fprsj4e/)
3
CBantPO
2 weeks ago
That's only an estimation of the IFR. While in Italy I'd imagine the IFR is closer to 1% due to age demographics and the health care system collapsing, we need to see some antibody studies to have a better idea
7
notafakeaccounnt
2 weeks ago
Nope, I confused it with another study but now I can't find the original study so I only have this
[https://bergamo.corriere.it/notizie/cronaca/20\_aprile\_30/coronavirus-bergamo-primi-risultati-test-61percento-positivi-85e8e36c-8a6b-11ea-94d3-9879860c12b6.shtml](https://bergamo.corriere.it/notizie/cronaca/20_aprile_30/coronavirus-bergamo-primi-risultati-test-61percento-positivi-85e8e36c-8a6b-11ea-94d3-9879860c12b6.shtml)
until I can find it.
1
zippercot
2 weeks ago
Sorry, my bad. I thought I read there were a couple of hospitals in Brooklyn or Queens that went over ICU capacity.
6
JenniferColeRhuk
2 weeks ago
Your post or comment has been removed because it is off-topic and/or anecdotal [Rule 7], which diverts focus from the science of the disease. Please keep all posts and comments related to the science of COVID-19. Please avoid political discussions. Non-scientific discussion might be better suited for /r/coronavirus or /r/China_Flu.
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1
mkiv808
2 weeks ago
The death data in NY appears to be messy too. It needs to be stratified further.
3
cakeycakeycake
2 weeks ago
Its worth taking it with a grain of salt, but even if you just consider the state numbers, which held steady around 13-14% week to week, you get a CFR around .5, which is roughly in line with these other results.
​
ETA bc some people are losing their minds- if you redo the math its around .7% in the city and state with updated numbers. And AGAIN- I think there are certainly unreported deaths and probably more infections than we know about even with the antibody study. So take this all with a grain of salt but my point is it seems fairly in line with other studies when you consider the unique factors at play in NYC.
8
SoftSignificance4
2 weeks ago
why wouldn't you trust the new york numbers? it has the highest prevalance.
are you accusing new york of doctoring the numbers? based on what?
11
analo1984
2 weeks ago
The NY sampling methodology is not random. There might be some kind of selection bias when you sample outside of grocery stores.
4
jtoomim
2 weeks ago
Here's another source from NYC. All pregnant women delivering babies were tested between March 22nd and April 4th. 13.9% tested positive for COVID-19.
https://www.nejm.org/doi/full/10.1056/NEJMc2009316
That's a different time point, and also not random, but it's consistent with the grocery store finding of 20% positive a few weeks later.
2
SoftSignificance4
2 weeks ago
it is random. it's possible that it could oversample certain populations but they are selecting people at random. they are also taking more samples than everyone which means if there is response bias then it's less of a factor than all the other studies that have even more questionable sampling methods.
0
DoctorZeta
2 weeks ago
If certain populations are oversampled it is *not* random. It is a simple as that. It is not random. Their selection protocol may be random but the statistical population from which the samples are drawn differs from the general population in a non-random way.
7
merpderpmerp
2 weeks ago
Technically, they randomly sampled their study population, but you can argue that those results aren't generalizable to their target population due to selection bias.
9
SoftSignificance4
2 weeks ago
you don't need perfect stratifcation in order for it to be random what are you talking about?
6
dumbfartlol
2 weeks ago
that was so absurd to read haha
can we all at least google statistics 1 before coming on here ...
0
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2 weeks ago
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1
matakos18
2 weeks ago
Abstract:
Background: Assessing the burden of COVID-19 based on medically-attended case counts is suboptimal given its reliance on testing strategy, changing case definitions and the wide spectrum of disease presentation. Population-based serosurveys provide one avenue for estimating infection rates and monitoring the progression of the epidemic, overcoming many of these limitations. Methods: Taking advantage of a pool of adult participants from population-representative surveys conducted in Geneva, Switzerland, we implemented a study consisting of 8 weekly serosurveys among these participants and their household members older than 5 years. We tested each participant for anti-SARS-CoV-2-IgG antibodies using a commercially available enzyme-linked immunosorbent assay (Euroimmun AG, Lubeck, Germany). We estimated seroprevalence using a Bayesian regression model taking into account test performance and adjusting for the age and sex of Geneva's population. Results: In the first three weeks, we enrolled 1335 participants coming from 633 households, with 16% <20 years of age and 53.6% female, a distribution similar to that of Geneva. In the first week, we estimated a seroprevalence of 3.1% (95% CI 0.2-5.99, n=343). This increased to 6.1% (95% CI 2.6-9.33, n=416) in the second, and to 9.7% (95% CI 6.1-13.11, n=576) in the third week. We found that 5-19 year-olds (6.0%, 95% CI 2.3-10.2%) had similar seroprevalence to 20-49 year olds (8.5%, 95%CI 4.99-11.7), while significantly lower seroprevalence was observed among those 50 and older (3.7%, 95% CI 0.99-6.0, p=0.0008). Interpretation: Assuming that the presence of IgG antibodies is at least in the short-term associated with immunity, these results highlight that the epidemic is far from burning out simply due to herd immunity. Further, no differences in seroprevalence between children and middle age adults are observed. These results must be considered as Switzerland and the world look towards easing restrictions aimed at curbing transmission.
27
polabud
2 weeks ago
The difference in seroprevalence between >50 and <50 is really fascinating. Lots of explanations possible, but first one that comes to mind is that people are correctly incorporating personal risk into their actions.
35
matakos18
2 weeks ago
Indeed. Also very interesting that children have the roughly the same seroprevalence as young adults. Meaning they also get infected but fight off the virus without showing symptoms. Could be also in line with Christian Drosten's (German state epidemiologist) finding that children show no difference in viral load compared to adults
23
KyleEvans
2 weeks ago
>matakos18
Drosten's study didn't sample randomly. Very difficult to draw any conclusions.
2
Sexylizzard
2 weeks ago
"Background: Assessing the burden of COVID-19 based on medically-attended case counts is suboptimal given its reliance on testing strategy, changing case definitions and the wide spectrum of disease presentation. Population-based serosurveys provide one avenue for estimating infection rates and monitoring the progression of the epidemic, overcoming many of these limitations."
We wanted to know how many people caught the virus and thought we'd use antibodies to find out.
"Methods: Taking advantage of a pool of adult participants from population-representative surveys conducted in Geneva, Switzerland, we implemented a study consisting of 8 weekly serosurveys among these participants and their household members older than 5 years."
We tested the blood of participants and their housemates over age 5 for 8 weeks straight. These folks represent the Geneva population pretty well.
"We tested each participant for anti-SARS-CoV-2-IgG antibodies using a commercially available enzyme-linked immunosorbent assay (Euroimmun AG, Lubeck, Germany)."
They tested participants for antibodies that take your body a while to make after exposure.
"We estimated seroprevalence using a Bayesian regression model taking into account test performance and adjusting for the age and sex of Geneva's population."
Because our test persons were not a perfect representation of the population, we had to do some math.
"Results: In the first three weeks, we enrolled 1335 participants coming from 633 households, with 16% <20 years of age and 53.6% female, a distribution similar to that of Geneva. In the first week, we estimated a seroprevalence of 3.1% (95% CI 0.2-5.99, n=343). This increased to 6.1% (95% CI 2.6-9.33, n=416) in the second, and to 9.7% (95% CI 6.1-13.11, n=576) in the third week."
We calculated that 3.1% of the population had antibodies in the first week, 6.1% in second, and 9.7% in the third.
"We found that 5-19 year-olds (6.0%, 95% CI 2.3-10.2%) had similar seroprevalence to 20-49 year olds (8.5%, 95%CI 4.99-11.7),"
We are 95% sure kids and teens have antibodies almost as often as young adults do.
"while significantly lower seroprevalence was observed among those 50 and older (3.7%, 95% CI 0.99-6.0, p=0.0008)."
We are 95% sure those over 50 don't have antibodies as frequently as young adults.
"Interpretation: Assuming that the presence of IgG antibodies is at least in the short-term associated with immunity, these results highlight that the epidemic is far from burning out simply due to herd immunity."
No herd immunity yet.
"Further, no differences in seroprevalence between children and middle age adults are observed. These results must be considered as Switzerland and the world look towards easing restrictions aimed at curbing transmission."
Y'all want to read our study before opening up schools.
I'm not a doctor or scientist, hope I got it right.
1
RipGuts415
2 weeks ago
When you have a moment please translate this as well as you can for someone who cooks for a living
4
Sexylizzard
2 weeks ago
- "Background: Assessing the burden of COVID-19 based on medically-attended case counts is suboptimal given its reliance on testing strategy, changing case definitions and the wide spectrum of disease presentation. Population-based serosurveys provide one avenue for estimating infection rates and monitoring the progression of the epidemic, overcoming many of these limitations."
We wanted to know how many people caught the virus and thought we'd use antibodies to find out.
- "Methods: Taking advantage of a pool of adult participants from population-representative surveys conducted in Geneva, Switzerland, we implemented a study consisting of 8 weekly serosurveys among these participants and their household members older than 5 years."
We tested the blood of participants and their housemates over age 5 for 8 weeks straight. These folks represent the Geneva population pretty well.
- "We tested each participant for anti-SARS-CoV-2-IgG antibodies using a commercially available enzyme-linked immunosorbent assay (Euroimmun AG, Lubeck, Germany)."
They tested participants for antibodies that take your body a while to make after exposure.
- "We estimated seroprevalence using a Bayesian regression model taking into account test performance and adjusting for the age and sex of Geneva's population."
Because our testing wasn't perfect, we had to do some math.
- "Results: In the first three weeks, we enrolled 1335 participants coming from 633 households, with 16% <20 years of age and 53.6% female, a distribution similar to that of Geneva. In the first week, we estimated a seroprevalence of 3.1% (95% CI 0.2-5.99, n=343). This increased to 6.1% (95% CI 2.6-9.33, n=416) in the second, and to 9.7% (95% CI 6.1-13.11, n=576) in the third week."
We calculated that 3.1% of the population had antibodies in the first week, 6.1% in second, and 9.7% in the third.
- "We found that 5-19 year-olds (6.0%, 95% CI 2.3-10.2%) had similar seroprevalence to 20-49 year olds (8.5%, 95%CI 4.99-11.7),"
We are 95% sure kids and teens have antibodies almost as often as young adults do.
- "while significantly lower seroprevalence was observed among those 50 and older (3.7%, 95% CI 0.99-6.0, p=0.0008)."
We are (more than) 95% sure those over 50 don't have antibodies as frequently as young adults. (The p number just means the findings are not likely to be a statistical fluke, I think, not sure).
- "Interpretation: Assuming that the presence of IgG antibodies is at least in the short-term associated with immunity, these results highlight that the epidemic is far from burning out simply due to herd immunity."
No herd immunity yet, we think.
- "Further, no differences in seroprevalence between children and middle age adults are observed. These results must be considered as Switzerland and the world look towards easing restrictions aimed at curbing transmission."
Y'all want to read our study before opening up schools.
I'm not a doctor or scientist, hope I got it right.
9
Sexylizzard
2 weeks ago
Seroprevalence - how often you find it (in this case antibodies) in their blood
Viral load - how much virus
1
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2 weeks ago
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