Coronavirus Statistics: Cases, Mortality, vs. Flu

Mendel

Senior Member.
you forgot to tell us how Covid fatality rates relate to flu fatality rates. (granted i havent read the last few pages of this thread, but isnt that the general topic?)
I tend to think of this thread as mostly "statistics, cases, and mortality", but flu vs. Covid-19 mortality isn't difficult to compare with any more even without any rates.

image.jpegimage.jpeg
https://www.cdc.gov/flu/about/burden/index.html
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https://www.worldometers.info/coronavirus/country/us/
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https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html#hospitalizations
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Z.W. Wolf

Senior Member.
Feud over Stanford coronavirus study: ‘The authors owe us all an apology’
Angry statisticians dispute Santa Clara County research that found high infection rates
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https://www.mercurynews.com/2020/04...irus-study-the-authors-owe-us-all-an-apology/

... the test kit, which is not FDA approved and has a 90 to 95% accuracy rate.

The Stanford study’s authors said they adjusted for the test kit’s performance and their limited sampling techniques to estimate the prevalence of the virus in Santa Clara County.

But over the weekend, some of the nation’s top number crunchers said their extrapolation of the results rests on a flimsy foundation.

They contended the Stanford analysis is troubled because it draws sweeping conclusions based on statistically rare events, and is rife with sampling and statistical imperfections.

Gelman of Columbia University called the conclusions “some numbers that were essentially the product of a statistical error.”

“They’re the kind of screw-ups that happen if you want to leap out with an exciting finding,” he wrote, “and you don’t look too carefully at what you might have done wrong.”
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From the lab of Erik van Nimwegen of the University of Basel came this: “Loud sobbing reported from under Reverend Bayes’ grave stone,” referring to a famed statistician. “Seriously, I might use this as an example in my class to show how NOT to do statistics.”

“Do NOT interpret this study as an accurate estimate of the fraction of population exposed,” wrote Marm Kilpatrick, an infectious disease researcher at the University of California Santa Cruz. “Authors have made no efforts to deal with clearly known biases and whole study design is problematic.”
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Others accused the authors of having agendas before going into the study. Back in March, Bhattacharya and Bendavid wrote an editorial in the Wall Street Journal arguing that a universal quarantine may not be worth the costs. Their colleague John Ioannidis has written that we lack the data to make such drastic economic sacrifices.

One major problem with the Santa Clara County study relates to test specificity. It used a kit purchased from Premier Biotech, based in Minneapolis with known performance data discrepancies of two “false positives” out of every 371 true negative samples. Although it was the best test at the time of the study, that’s a high “false positive” rate that can skew results, critics say — especially with such a small sample size.

With that ratio of false positives, a large number of the positive cases reported in the study — 50 out of 3320 tests — could be false positives, critics note. To ensure a test is sensitive enough to pick up only true SARS-CoV-2 infections, it needs to evaluate hundreds of positive cases of COVID-19 among thousands of negative ones.

This potential error in the test can easily dominate the results, they said.
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Statistician John Cherian of D. E. Shaw Research, a computational biochemistry company, made his own calculations given the test’s sensitivity and specificity — and estimated the proportion of truly positive people in the Stanford study to range from 0.5% to 2.8%.

Adjusting for demographics, Cherian’s calculations suggest that prevalence could plausibly be under 1% and the mortality rate could be over 1%.

The “confidence intervals” in the paper – that is, the range around a measurement that conveys how precise the measurement is – “are nowhere close to what you’d get with a more careful approach,” he noted.
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Biostatistician Natalie E. Dean of the University of Florida called it a “consent problem.” The Facebook ad might have attracted people who thought they were exposed to the virus and wanted testing.

“The prevalence drops off quickly when adjusted for even a small self-selection bias,” wrote Lonnie Chrisman, chief technical officer at the Los Gatos data software company Lumina Decision Systems.
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Further Reading: https://statmodeling.stat.columbia....-in-stanford-study-of-coronavirus-prevalence/

I think the authors of the above-linked paper owe us all an apology. We wasted time and effort discussing this paper whose main selling point was some numbers that were essentially the product of a statistical error.

I’m serious about the apology. Everyone makes mistakes. I don’t think they authors need to apologize just because they screwed up. I think they need to apologize because these were avoidable screw-ups. They’re the kind of screw-ups that happen if you want to leap out with an exciting finding and you don’t look too carefully at what you might have done wrong.
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deirdre

Senior Member.
I tend to think of this thread as mostly "statistics, cases, and mortality", but flu vs. Covid-19 mortality isn't difficult to compare with any more even without any rates.
your post is great but may be a bit hard for general readers to figure out as they have to search for numbers.

the bottom line is we have no idea what the Covid-19 mortality rate is.
but Connecticut covid deaths (in one month) are 11x higher than flu this year (august 25, 2019-last sunday)
note: we started stay-at-home stuff (ie. vaccine-protection substitution) approx. March 20th.



flucovid.png
 

Oystein

Senior Member
thanks. understanding "z scores" is too much for my brain at this time. so I gave up and skipped that.
It's not really necessary to have in-depth understanding of z scores - a layperson can read the graphs without such detail knowledge. The short is that it is a method to standardize and normalize data such that several data sets with underlying differences in total size, base variation etc can be compared on the same scale.

So what we see there is that some countries (Belgium, France, Italy, The Netherlands, Spain, Switzerland, England, Scotland) see mortality increases peaking during this Covid season considerably higher than during their most recent flu seasons (or any country's flu seasons), while places like Finland, Luxembourg or Germany have so far avoided that.
 

deirdre

Senior Member.
The short is that it is a method to standardize and normalize data such that several data sets with underlying differences in total size, base variation etc can be compared on the same scale.
oh. well that is incredibly easy to understand. thanks for putting it in simple English!

I still don't understand the other graphics in post #183, (but it took me 6 months to figure out what I was looking at on the Appleman contrail Chart!) but I trust you so will take your word for it. thanks!
 

deirdre

Senior Member.
some countries (Belgium, France, Italy, The Netherlands, Spain, Switzerland, England, Scotland) see mortality increases peaking during this Covid season considerably higher
ok I went to the website itself and zoomed in.. I didn't realize it was multi year dates across the bottom. the new MB software isnt letting us see the "bigger image" like it used to. :(
so the pink section is the 2019-2020 flu season we are now in and the peaks at the end are the covid bumps.

thanks!
http://www.euromomo.eu/index.html
1587499321916.png
 

Mendel

Senior Member.
so the pink section is the 2019-2020 flu season we are now in
You put the pink line right in the center of the 2018/2019 flu season (which was mild).
Flue season is winter! The age chart on the EuroMomo page shows that very clearly as well (look at Portugal), you may want to have a look there.
The influenza season is pretty much over everywhere on the Northern hemisphere.
In #201, I posted a chart for the USA that works the same way: you can see the 2017/2018 season on the left, and if you look at the flu pyramids, that was a big one. But Covid-19 is rising much more sharply much higher, and the only way for it not exceed the 2017/18 flu is to go down again very quickly (and why would it).
 

deirdre

Senior Member.
But Covid-19 is rising much more sharply much higher, and the only way for it not exceed the 2017/18 flu is to go down again very quickly (and why would it).
in Connecticut our fatality rate for 2017/18 flue season was 184 people. we are already above 1,100 covid deaths (in one month).
 

Agent K

Senior Member
I'm not doubting you, but do you have a source for that?
I'm surprised that worldometers still lists 55 active cases, I'd have thought they'd be all closed now, but maybe these are crew?

I posted it in the other thread for a different reason, but I'll repost it here
The CDC said the following about the Diamond Princess on March 26. No mention of airborne spread, just fomites.
https://www.cdc.gov/mmwr/volumes/69/wr/mm6912e3.htm
The results of testing of passengers and crew on board the Diamond Princess demonstrated a high proportion (46.5%) of asymptomatic infections at the time of testing. Available statistical models of the Diamond Princess outbreak suggest that 17.9% of infected persons never developed symptoms. A high proportion of asymptomatic infections could partially explain the high attack rate among cruise ship passengers and crew. SARS-CoV-2 RNA was identified on a variety of surfaces in cabins of both symptomatic and asymptomatic infected passengers up to 17 days after cabins were vacated on the Diamond Princess but before disinfection procedures had been conducted (Takuya Yamagishi, National Institute of Infectious Diseases, personal communication, 2020). Although these data cannot be used to determine whether transmission occurred from contaminated surfaces, further study of fomite transmission of SARS-CoV-2 aboard cruise ships is warranted.
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Mendel

Senior Member.
@Agent K Thank you!
The study they cite is "Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020"
https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.10.2000180
In this study, we conducted statistical modelling analyses on publicly available data to elucidate the asymptomatic proportion, along with the time of infection among the COVID-19 cases on board the Diamond Princess cruise ship.
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They're guessing, they're guessing of a mean incubation period of 6.4 days, which is high, their final result is not included in the range they report when they vary the mean in in their model between 5.5 and 9, and several published estimates for the mean are lower than 5.5.

From the study:
image.jpeg
Obviously, they tested and removed the cases with the severe symptoms from the ship first, to treat them. But that gave asymptomatic cases time to fight the infection and become negative. For people who have symptoms, the virus concentration in the nose goes down from the day they first have symptoms, and at some point, you can only find virus in their lungs any more. So if you assume that for everyone they removed from the ship, an asymptomatic patient stayed on board and maybe had no detectable virus 5 days later, you get a large number of asymptomatic cases that were never detected.
They are also assuming for their model that the PCR test detects infections immediately, which is not supported by evidence.

tl;dr do not trust that number.
 
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deirdre

Senior Member.
not sure which thread, but his i think applies to statistics (and seems like pretty big news as far as asymptomatic or mild cases possibly being more wide spread. )

Washington Post today

At least two people who died in early and mid-February had contracted the novel coronavirus, health officials in California said Tuesday, signaling that the virus may have spread — and claimed lives — in the United States weeks earlier than previously thought.
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https://www.msn.com/en-us/news/us/a...reviously-thought/ar-BB131mhb?ocid=spartanntp
 

Mendel

Senior Member.
seems like pretty big news as far as asymptomatic or mild cases possibly being more wide spread
The Medical Examiner-Coroner performed autopsies on two individuals who died at home on February 6, 2020 and February 17, 2020. Samples from the two individuals were sent to the Centers for Disease Control and Prevention. Today, the Medical Examiner-Coroner received confirmation from the CDC that tissue samples from both cases are positive for SARS-CoV-2 (the virus that causes COVID-19).
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https://www.sccgov.org/sites/covid19/Pages/press-release-04-21-20-early.aspx

First, aside, the headline in my mind here is "CDC takes 2 months to process tissue samples". ;-P
The fact that the medical examiner conducted an autopsy, and especially the fact that these people died, is evidence that they did have symptoms.
It also illustrates that people can be diagnosed posthumously.

But it's also a clear sign that community transmission was going on undetected, because these people did not have a travel history. This does imply many other people were asymptomatic, and it illustrates how difficult it was in China to recognize this epidemic: you have cases that look like the flu, that aren't connected to other cases that had the flu because they were asymptomatic or mild and not sick at the time when the contact occurred. The medical examiner clearly suspected something, but were they able to combat the spread?
 

deirdre

Senior Member.
The medical examiner clearly suspected something, but were they able to combat the spread?
this is just an off topic aside (since your comment is an off topic aside), but I think California has so far done a remarkable job! with the spread. I'm super impressed myself. but then ive got diblasio's NYC fiasco spilling into our state (and it's not good) so maybe im a bit jealous and biased.
 

Trailspotter

Senior Member.
The problems with Stanford's Santa Clara Study (this; https://www.medrxiv.org/content/10.1101/2020.04.14.20062463v1 ) are even worse than I thought.
Without disputing the above, I'd like to quote a very recent Nature News article suggesting that coronavirus was in Santa Clara County early than thought.

https://www.nature.com/articles/d41586-020-00154-w

22 April 17:05 BST — Deaths suggest coronavirus was in the US weeks earlier than thought

The first US COVID-19 death may have occurred in California on 6 February — more than three weeks before the first reported death occurred in Washington state.

Three people who died in Santa Clara County between 6 February and 6 March have now been confirmed as COVID-19 deaths after autopsies, according to a statement released by the county’s department of public health on 21 April. The updated statistics include two people who died at home and a third whose location of death was not specified. Previously, the first COVID-19 death in the county was thought to have occurred on 9 March.

The revised cause of death shows that the deadly disease had footholds in the United States earlier than previously thought. Similar reports have surfaced elsewhere in recent weeks. In late March, a non-peer-reviewed epidemiology study of the Lombardy region in northern Italy found that the virus may have been circulating there for more than a month before it was detected.
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Mendel

Senior Member.
Without disputing the above,
You're actually supporting my original point, as did Deirdre when she posted about it in #213. ;-)
First, he picked the county that had the earliest cases in California and had the outbreak the first, ensuring that the population would be undertested. This means that it's likely that every other county in California has fewer unregistered infections than Santa Clara.

On the subject of excess mortality, the NYT has published an article comparing this year's mortality to the historical average for various countries, and also checked if the reported Covid-19 deaths fit these statistics. Covid-19 deaths tend to be underreported, actually.
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https://www.nytimes.com/interactive/2020/04/21/world/coronavirus-missing-deaths.html
I suggest using a private tab to follow this link, as the NYT paywall counts your visits.

US mortality data by state or region is available through https://gis.cdc.gov/grasp/fluview/mortality.html .
 

Trailspotter

Senior Member.
A new study, highlighted in Nature News article:
https://www.nature.com/articles/d41586-020-00502-w

23 April — Intensive testing finds a small town’s many silent infections

A large proportion of people with COVID-19 have no symptoms, according to research in a small Italian town.

On 21 February, the town of Vo’ reported Italy’s first COVID-19 death, leading authorities to ban movement in the town and end public services and commercial activities there for two weeks. Andrea Crisanti at Imperial College London and his colleagues swabbed almost every resident of Vo’ for viral RNA at the beginning and end of the lockdown.

The team found that some 43% of the people infected with SARS-CoV-2 in the town reported no fever or other symptoms (E. Lavezzo et al. Preprint at medRxiv http://doi.org/ggsmcj; 2020). The researchers observed no statistically significant difference in potential infectiousness between those who reported symptoms and those who did not.

Asymptomatic and pre-symptomatic individuals have a key role in COVID-19 transmission, which makes it difficult to control the disease without strict social distancing, the authors say.
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Arugula

Member
There's a very interesting op-ed in the Scientific American taking a critical look at the the CDC's flu mortality algorithm, which I had no idea extrapolates to include deaths related to pneumonia and other respiratory and circulatory causes even where influenza isn't listed as a cause of death. It makes sense to account for unreported deaths, because the flu is extremely widespread and people aren't often tested for it before death, but inherently comparing a projection to counted deaths in the case of COVID is very misleading and has clearly created a lot of confusion for people.

This may be helpful to share with people who are still conflating the virus with the flu:

The 25,000 to 69,000 numbers that Trump cited do not represent counted flu deaths per year; they are estimates that the CDC produces by multiplying the number of flu death counts reported by various coefficients produced through complicated algorithms. These coefficients are based on assumptions of how many cases, hospitalizations, and deaths they believe went unreported. In the last six flu seasons, the CDC’s reported number of actual confirmed flu deaths—that is, counting flu deaths the way we are currently counting deaths from the coronavirus—has ranged from 3,448 to 15,620, which far lower than the numbers commonly repeated by public officials and even public health experts.

There is some logic behind the CDC’s methods. There are, of course, some flu deaths that are missed, because not everyone who contracts the flu gets a flu test. But there are little data to support the CDC’s assumption that the number of people who die of flu each year is on average six times greater than the number of flu deaths that are actually confirmed. In fact, in the fine print, the CDC’s flu numbers also include pneumonia deaths.

The CDC should immediately change how it reports flu deaths. While in the past it was justifiable to err on the side of substantially overestimating flu deaths, in order to encourage vaccination and good hygiene, at this point the CDC’s reporting about flu deaths is dangerously misleading the public and even public officials about the comparison between these two viruses. If we incorrectly conclude that COVID-19 is “just another flu,” we may retreat from strategies that appear to be working in minimizing the speed of spread of the virus.

The question remains. Can we accurately compare the toll of the flu to the toll of the coronavirus pandemic?

To do this, we have to compare counted deaths to counted deaths, not counted deaths to wildly inflated statistical estimates. If we compare, for instance, the number of people who died in the United States from COVID-19 in the second full week of April to the number of people who died from influenza during the worst week of the past seven flu seasons (as reported to the CDC), we find that the novel coronavirus killed between 9.5 and 44 times more people than seasonal flu. In other words, the coronavirus is not anything like the flu: It is much, much worse.
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https://blogs.scientificamerican.co...u-deaths-is-like-comparing-apples-to-oranges/
 

Mendel

Senior Member.
This may be helpful to share with people who are still conflating the virus with the flu:
I don't think it's helpful. It's not helpful to erode trust in the CDC on acocunt of an article that probably had no input from the CDC, so it's a bit onesided; and it's not helpful because these 61000 people died of *something* in the year 2017/18, and the comings and goings of the various strains of flu that are logged by the flu surveillance do explain why that happened that year, and some years nobody seems to die in the winter other than the baseline. I mean, we posted the graph here in this thread. Something is happening, and I feel we shouldn't dismiss an explanation we haven't fully understood without having a better one.

(Flu deaths have age risk, same as Covid-19. People who die from it are likely to die in the care home, not in the emergency ward. If people die "from pneumonia", that pneumonia probably started with an influenza infection.)

But you can now say that this epidemic is worse than a war: the US have now lost more citizens to Covid-19 than to the Vietnam war.
More Americans have died from Covid-19 than were killed during the Vietnam War, a grim milestone coinciding with Hanoi officially reporting zero deaths from the coronavirus.
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https://asiatimes.com/2020/04/why-vietnam-won-and-us-lost-their-covid-19-wars/
 

deirdre

Senior Member.
to counted deaths in the case of COVID
not all people being listed as a covid death has had a test either. my state (Connecticut, and New York) just changed their criteria and the number of deaths rose a lot in one day.


On Tuesday, the city's health department released a revised COVID-19 death count that included those who were not tested but were presumed to have died from the disease. That added an additional 3,700 people, bumping the city's total count well over 10,000 coronavirus fatalities.
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https://news.yahoo.com/york-city-revises-coronavirus-death-205328540.html
 

Mendel

Senior Member.
https://www.medrxiv.org/content/10.1101/2020.04.14.20062463v2
The Santa Clara study pre-print has been revised, and they added some extra data. The study still struggles severely with self-selection bias. They also added a bunch of validation data for their test kits that doesn't really answer what we need to know.
The specificity trials on page 19 are not normal.
7 trials show 100%, with N=30,70,1102,300,311,500,99, sum 2412.
The 6 remaining trials:

368/371 = 99.2% (97.7-99.8)
198/200 = 99.0% (96.4-99.9)
29/31 = 93.6% (78.6-99.2)
146/150 = 97.3% (93.3-99.3)
105/108 = 97.2% (92.1-99.4)
50/52 = 96.2% (86.8-99.5)

Pooling these, I get 896/912=98.3% (97.2-99.0).
We use the pooled test performance based on the available information:
Sensitivity: 82.8% (exact binomial 95CI 76.0-88.4%)
Specificity: 99.5% (exact binomial 95CI 99.2-99.7%)
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There is no trial that has exactly 1 false positive. There are 3 trials that don’t have 99.5% in the 95% CI (4 trials if you include 1102/1102). There is no trial that falls inside the 99.2-99.7 range (one straddles it). The specifity range they’re using is an empty space between the values that the trials are actually at. This is not a normal distribution.

187 samples had loss of smell and taste in the past 2 months. This is a very specific indicator for Covid-19, ~70% of patients (well, 33,9–85,6%, depending on the study, e.g. Mons/Belgium, Heinsberg/Germany) have that, and I don’t think this kind of nerve affliction has been reported for any other common illness. Yet only 11% of these samples test positive. For the 59 more recent samples, it’s 22%.

This looks like the prevalence this study should have measured is 267/3330 = 8%, and the test failed to pick up on that. It would fail to pick up on recent infections, because they wouldn’t have seroconverted (created enough antibodies) yet, and it would fail to pick up on infections that happened too long ago (because the antibody levels would have fallen off below the sensitivity of this test). This study really needed a more sensitive test, like an ELISA, which is actually available at Standford, and is able to detect much lower levels of antibodies.

This kit has not been validated against people who had the infection a month ago.

The presence of false positives is an indication that cross-reativity with outher cold viruses exists. If you test a sample with few people who haven’t had a severe cold recently, which probably includes most samples taken of people who check into the hospital for elective surgery, or samples taken in the summer months, you get an optmistic sensitivity that does not apply to the general population in early spring.

The WHO Early investigation protocol (Unity protocol) for the investigation of population prevalence mandates the use of an ELISA test, or the freezing of samples until a time when such a test becomes available. The WHO does not endorse the use of lateral flow assays for this kind of testing.

tl;dr I don't trust Lateral Flow Assays to answer the question "have you been infected", since they're not sensitive to older infections, and also pick up common cold viruses. The question they might actually answer might be something like "have you had Covid-19 or a HCoV cold between 1 and 4 weeks ago", and even then they probably ignore many paucisymptomatic cases (presenting with few symptoms).

P.S.: No study that does not measure prevalence in the older population where the majority of deaths occurs should speak on fatality rates. This study had 2/167 positives in the age 65+ population, that’s 0.1-4.3% (95% CI), a 30-fold spread, and hardly a representative sample, since I don’t expect residents from care homes were able to attend the drive-through testing.
 

deirdre

Senior Member.
..loss of smell and taste in the past 2 months.This is a very specific indicator for Covid-19, ~70% of patients (well, 33,9–85,6%, depending on the study, e.g. Mons/Belgium, Heinsberg/Germany) have that, and I don’t think this kind of nerve affliction has been reported for any other common illness.

I haven't looked for statistics, but I think it stands to reason if you have a stuffy nose then [you feel] you can't taste. Like when you drink Nyquil, ..hold your nose before you even pour it, then rinse mouth out with juice, follow with drinking some juice to clean the throat before you let go of your nose. This way you barely even taste the Nyquil.


Causes of taste disorders and a loss of taste include:
  • upper respiratory infections, such as the common cold
  • sinus infections
  • middle ear infections
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The new coronavirus isn’t the only infection that causes a loss of taste and smell.

For example, similar symptoms were reported during the SARS outbreak in 2003, and changes in taste or smell are also common for viral illnesses like the common cold or flu.
Content from External Source
https://www.businessinsider.com.au/why-coronavirus-may-kill-your-sense-of-taste-and-smell-2020-3
 

Mendel

Senior Member.
if you have a stuffy nose then [you feel] you can't taste.
This is not the symptom. This is being unable to smell anything, even a dirty diaper, because of a nerve disconnect caused by the virus.
The businessinsider article says it's caused by an inflammation in the nose. Thank you for looking that up!
 

deirdre

Senior Member.
This is being unable to smell anything, even a dirty diaper, because of a nerve disconnect caused by the virus.
I'm not sure what you are saying. I must have missed a study in this thread? They've proven nerve damage with Covid?

but as far as the Santa Clara study, how do you know -if they were interviewing people who experienced something 2 months ago -that it was nerve damage? You said only 11-22% tested positive for Covid.
 

Mendel

Senior Member.
I'm not sure what you are saying. I must have missed a study in this thread? They've proven nerve damage with Covid?

but as far as the Santa Clara study, how do you know -if they were interviewing people who experienced something 2 months ago -that it was nerve damage? You said only 11-22% tested positive for Covid.
If you have a stuffy nose, the smells don't reach the smell sensor cells because they're gunked up.
If you have a cold/flu, the sensor cells are inflamed and don't work (says the businessinsider article).
I don't think anyone really knows why this happens with Covid-19, but it's not because of the gunk.
The sensor cells are nerve cells, which is why I think it either attacks these cells, or the connection from the cells to the brain, which is nerve cells either way.

It's just a separate symptom from having a stuffed-up nose.
 

deirdre

Senior Member.
It's just a separate symptom from having a stuffed-up nose.
ah. I see. thanks.
my mistake, I didn't really mean "gunked up". I never gunk up ( I wish I did then I could just blow it out!) but I did hoard a years worth of Benedryl (only 3 boxes) because of my sinus issues.
 

Agent K

Senior Member
The good news is that the social distancing measures quickly caused a huge drop in fevers, according to this creepy smart thermometer company.

https://www.kinsahealth.co/social-distancing-and-its-effect-on-reducing-the-spread-of-illness/
https://healthweather.us/

Following up on Kinsa fever tracking, the fevers are all but gone across the U.S. now. The % ill is close to zero, which is the new normal that's lower than the past normal level of around 2% for this time of year.

https://healthweather.us/?mode=Atypical

1588809613011.png1588809669131.png

Note that the Expected curve has a big drop that didn't exist before. They just added this update to account for social distancing that caused big drops in fevers across the country, which changed expectations. They also said that their fever trackers did correlate with COVID-19 cases and deaths, not just with influenza.
https://www.kinsahealth.co/modeling-social-distancing-effects-and-real-time-atypical-illness/
To better account for the effects of widespread social distancing, we updated our expected illness curve on Healthweather.us today. Our cumulative atypical illness map has also changed as a result. The new map correlates even better with COVID-19 cases and deaths than before. We’re also adding a new mode to the map, real-time atypical, which shows real-time levels of atypical illness.
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Leifer

Senior Member.
Elon Musk, when on the Joe Rogan Experience podcast... complained of seemingly un-associated causes of deaths being attributed to Covid.....which Elon feels is somehow significant.

Here, Source: https://www.youtube.com/watch?v=RcYjXbSJBN8

@ 1:27:00
@ 1:34:00
Elon (he is not the only one to voice this opinion) claims that a significant amount of reported "naturally occurring deaths (like heart attacks or other typical causes of deaths) are erroneously being reported as "Covid deaths"...... just because the deceased was post-tested positive for Covid.....and .so that the Covid death stats are skewed somehow.
He humorously describes (paraphrased) "If a human is eaten by a shark, and his arm is tested positive for Covid..... .he is deemed to have died of Covid."
I kinda doubt that, and it sounds like an emotional conclusion, not a statistical one.
Here are stats for typical causes of death, year 2018.....(before Covid)
https://www.cdc.gov/nchs/products/databriefs/db355.htm
(figure 2)
age-adjusted_deaths_2017_18.jpg

If we look at the above link, typical rounded #s for "Top 10 causes of death" in a typical 12 month years of 2017 or 2018 before Covid, we find about 750 USA deaths per 100,000.
But we are looking at a 6 month period (so far), so it's 375 deaths per 100k....of "un-associated Covid deaths".
(Cancer, heart and lung disease, etc, including accidents and suicide)

The US death rate from Covid is steadily approaching 90k or 100k.... and that's for a 6 month recorded period.
That leaves us with about 375 possible "natural and unrelated deaths" among 90k-100k Covid deaths.
That's a small number, and not as significant as Elon and others are claiming.
 
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Leak

Member
The US death rate from Covid is steadily approaching 90k or 100k.... and that's for a 6 month recorded period.
That leaves us with about 375 possible "natural and unrelated deaths" among 90k-100k Covid deaths.
That's a small number, and not as significant as Elon and others are claiming.

Aren't you comparing micro-apples to apples here, i.e. 90.000-100.000 deaths per ~330 million in 6 months vs. 375 deaths per 100 thousand in 6 months?
 

Mendel

Senior Member.
The Covid-19 death rate per 100,000 is ~25 in the US at large and ~100 in New York state.
I'd say the majority of these deaths occurred mid-March to mid-May, so 2 months. Taking your US average of 750/100,000, that's 125. So we'd expect the number to approximately double. But since NYC is harder hit, and the deaths skew to late April, the mortality graph for NYC actually looks like this:
image.png
Article:
As of Sunday, the city had attributed 16,673 deaths to coronavirus, either because people had tested positive for the virus, or because the circumstances of their death meant that city health officials believed the virus to be the most likely cause of death.

But there remains a large gap between the 16,673 figure and the total deaths above typical levels in the last six and a half weeks: more than 4,200 people whose deaths are not captured by the official coronavirus toll.

The counterargument is now that the social distancing measures cause people to die in large numbers. It's purely speculative and not supported by data. Counterevidence would be the mortality data for Hessen and Berlin on EuroMomo: Germany had social distancing with only essential businesses open, care homes quarantined and public meetings limited to 2 (two) people, and there is no spike in mortality, compared to Sweden with less restrictive measures (note the yellow area is incomplete data):
image.jpeg
Source: https://www.euromomo.eu/graphs-and-maps/

The offical Covid-19 mortality is 4.5/100,000 for Berlin and 6.7 for Hessen (https://www.rki.de/DE/Content/InfAZ...chte/2020-05-13-de.pdf?__blob=publicationFile ) and 32.5/100,000 for Sweden (https://www.ecdc.europa.eu/en/cases-2019-ncov-eueea ).

The data shows clearly: Covid-19 kills far more people than the NPIs (non-pharmaceutical interventions) do.

P.S.: Germany now has 58/401 counties with 0 (zero) new Covid-19 cases in the past 7 days. We're containing this epidemic with distancing, contact tracing, and testing.
 
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Leifer

Senior Member.
Aren't you comparing micro-apples to apples here, i.e. 90.000-100.000 deaths per ~330 million in 6 months vs. 375 deaths per 100 thousand in 6 months?
I see what you are saying......... But no..... I'm comparing 100k people to the covid amount, verified..... and how many might be mis-diagnosed and ....is that even an issue ?
 
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Leak

Member
I see what you are saying......... But no..... I'm comparing 100k people to the covid amount, verified..... and how many might be mis-diagnosed and ....is that even an issue ?
Well, if you juxtapose 90.000 to 100.000 deaths with 375 deaths the 375 look miniscule.

But if you normalize both values to "deaths per 100.000 per 6 months" for a valid apples to apples comparison those 90.000 - 100.000 deaths per 330 million per 6 months actually turn into 27 to 30 per 6 months per 100.000... that's my point, and that's definitely an issue of orders of magnitude.
 

deirdre

Senior Member.
The data shows clearly: Covid-19 kills far more people than the NPIs (non-pharmaceutical interventions) do.

Do you mean: "lax social distancing rules kills far more people [per 100,000] than stricter social distancing rules do" ?
 

Mendel

Senior Member.
Do you mean: "lax social distancing rules kills far more people [per 100,000] than stricter social distancing rules do" ?
We have two proposed causes of death:

a) Covid-19
Covid-19 kills through ARDS, zytokine storm, thrombosis/stroke/embolism, and perhaps in other ways as well. We have indications that Covid-19 can leave long-term damage. NPIs reduce this by reducing infection rates. They need to be timely and effective, and people need to buy in and participate. We have evidence for that.

b) NPIs
There is speculation that NPIs harm through loneliness, suicide, deferred operations, domestic violence, psychoses, reduction in standard of care, resulting in a lowered life expectancy. There is no evidence to support that.

The claim is that the damage caused by NPIs outweighs the damage caused by the virus, and therefore it is medically irresponsible to not lift the restrictions ASAP. It's bunk, though, because we can see people die from the virus in Sweden, but we can't see people die from social distancing in Germany, in the overall all-causes weekly mortality.
The death spike in New York mortality and elsewhere is really only the virus, not mislabeled other deaths, because the data shows that these other deaths don't spike.
And that means the "mislabeled deaths" argument is bunk. Elon Musk spreads bunk.
 
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deirdre

Senior Member.
The claim is that the damage caused by NPIs outweighs the damage caused by the virus,
People are making that claim? based on the NYTimes article?

It's bunk, though, because there is no evidence for it, just speculation.
well obviously it's bunk because 2,000-4,000 -non-covid deaths above normal- is obviously less than 16,500 covid deaths. If by 'damage' you mean only actual deaths [that occur in this current time frame].


(although recently Cuomo said
66% of new coronavirus patients in New York stayed home
Content from External Source
so that will come into play, i'm sure, with people arguing against closing everything down. NPI (non-pharmaceutical intervention) though is a bit different than closing the economy or stay-at-home orders, as we can still implement NPIs (masks, hand washing, disinfecting atm machines & handles, 6 foot social distancing) with the economy open. Although i cant believe the amount of people i see on TV or in news articles that are wearing their homemade masks completely wrong! or not at all)
 

Oystein

Senior Member
I am spotting something ... strange, to say the least:
https://www.worldometers.info/coronavirus/

Arabic countries have lots of cases, but claim extremely few deaths.

Take for example Saudi Arabia, which currently has the most cases in the Arabic world. They are currently in 15th place in terms of total cases. Compare to #14 and #16:
Countrytotal casesCases/millionDeath/milliondeaths/1000 cases
Canada80,1022,12516075
Saudi Arabia62,5451,800105.4
Belgium55,9834833790163

Even more strikingly, places 20 through 22:
Netherlands44,4472,595336129
Qatar37,09712,90260.43
Ecuador34,8541,97916483

The UAE has 9.0 deaths/1000 cases
Kuwait has 7.1 deaths/1000 cases
Bahrain has 1.5 deaths/1000 cases

Several other countries in western Europe range between 46 (Germany) and 155 (France), the USA has 60.
The only other country I see with lots of cases but under 10 deaths/1000 cases is Russia at 9.5.

It's not (just) the testing: While Qatar has done 59,000 tests per of 1 million people (about as many as Belgium), Saudi Arabia did only 18K (about as many as Canada).
UAE has done 162K tests/million, Kuwait 60K.

Luxembourg has testet 102K/million, and suffered 27 deaths/1000 cases.

So very systematically, the Arabic countries have very few deaths per 1000 cases, compared to Europe or many other places.
Do they have a much lower percentage of people with specific risks - >80 years, obese, heart- and lung conditions?
Do they count deaths differently, or not at all?
Do they lie?
 

deirdre

Senior Member.
Do they have a much lower percentage of people with specific risks
maybe it has some to do with which populations within their country population are getting the virus. ??

Similarly, many of the cases in the Middle East are within the younger, migrant workforce. The majority of the population in U.A.E. and Qatar are younger expatriates, who also go through health checks before entering the country, and are required to leave once their employment is over.
Content from External Source
https://www.bloomberg.com/news/arti...row-two-small-nations-keep-fatality-below-0-1
 
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