The posted curve proved my point. It's not the same curve at all, is it? The trend appears the same but overlaying the data over the same time period shows it's different. If it's exactly the same, the data should be on top of one another but we're 10 to 11 days apart... so far. Extrapolating the analysis to include total population shows the rate of infectivity is far less in the U.S.

ensignmatic, appreciate your self portrait.

quote:

Originally posted by maladat:
Same curve, 10-11 days behind.

If we have the same number of cases as Italy, that's a win, considering we are 5.5 times larger.

quote:

Originally posted by Dakor:
Now correct that graph based on total population for each country.

Total population is irrelevant to the rate of spread until/unless a sizeable percentage of the total population has been infected.

quote:

Originally posted by Dakor:

quote:

Originally posted by jimmy123x:

quote:

Originally posted by smithnsig:

quote:

Originally posted by Dakor:

quote:

Italy is not the U.S., not even close.

And, again: It's been demonstrated numerous times the shape of the curve reflecting the advance of the disease in the U.S. is the same as what was seen in Italy.

Ensigmatic,

Care to share your data analysis?

World o meter

Plenty of statistics and graphs to compare.

He is correct. U.S. is on the same curve as Italy, but 3 weeks or so behind Italy.

Wrong, the data is over the same time period and says otherwise (look at the X and Y axes).

Here are the REAL numbers day by day of TOTAL infected (each site shows day by day numbers):

Italy total infected
March 1 1128
March 8 5883

U.S. total infected
March 11 1208
March 18 8054

Scroll down for the graph of U.S.
https://en.wikipedia.org/wiki/..._medical_cases_chart

Italy
https://en.wikipedia.org/wiki/..._medical_cases_chart

quote:

Originally posted by RHINOWSO:

quote:

Originally posted by maladat:
Same curve, 10-11 days behind.

If we have the same number of cases as Italy, that's a win, considering we are 5.5 times larger.

Totally irrelevant. When the number of cases is small, the growth in number of cases depends on the number of cases, it doesn't have anything to do with the total population.

The graph I posted shows growth in cases from the day each country reached 100 confirmed cases. It shows the exponential growth of cases in Italy and the US is essentially the same, COVID-19 just got loose in Italy a little earlier.

quote:

Originally posted by RHINOWSO:
Yes, the yellow virus of pollen.

The Yellow Peril.

quote:

Originally posted by maladat:

quote:

Originally posted by RHINOWSO:

quote:

Originally posted by maladat:
Same curve, 10-11 days behind.

If we have the same number of cases as Italy, that's a win, considering we are 5.5 times larger.

Totally irrelevant. When the number of cases is small, the growth in number of cases depends on the number of cases, it doesn't have anything to do with the total population.

The graph I posted shows growth in cases from the day each country reached 100 confirmed cases. It shows the exponential growth of cases in Italy and the US is essentially the same, COVID-19 just got loose in Italy a little earlier.

Unfortunately the numbers are difficult to compare b/c of exponential growth. Go back to 2/21 and there is only a 7 patient difference between the countries. Yet look at the difference between the infected population now between the U.S. and Italy. Do you concur? Also, under what circumstances did the infection spread? The latter is the biggest issue that goes into such an analysis and use of stats is flawed if one doesn't understand the actual drivers to the data.

quote:

Originally posted by Balzé Halzé:
Got my test results back.

Phew! That's a relief. Now I can report back to the ship.
...

Well, no its not “totally irrelevant”. If our total end case equal Italy, its a huge difference.

I get your point about rate of increase curve and current location of the virus, but in the end of it all, if we contain it to Italian totals, we have won big.

Dismiss it if you want, go on with the hysteria, please.

In the end the data will tell the truth.

quote:

Originally posted by RHINOWSO:
Well, no its not “totally irrelevant”. If our total end case equal Italy, its a huge difference.

It's irrelevant from the perspective of gauging the progress of the disease.

quote:

Originally posted by RHINOWSO:
... but in the end of it all, if we contain it to Italian totals, we have won big.

That "if" in there is the key word. As long as the rate-of-increase mirrors what occurred in Italy, we can not be confident that will obtain.

We won't know until and unless the curve begins to flatten. It hasn't... yet.

quote:

Originally posted by Dakor:

quote:

Originally posted by maladat:

quote:

Originally posted by RHINOWSO:

quote:

Originally posted by maladat:
Same curve, 10-11 days behind.

If we have the same number of cases as Italy, that's a win, considering we are 5.5 times larger.

Totally irrelevant. When the number of cases is small, the growth in number of cases depends on the number of cases, it doesn't have anything to do with the total population.

The graph I posted shows growth in cases from the day each country reached 100 confirmed cases. It shows the exponential growth of cases in Italy and the US is essentially the same, COVID-19 just got loose in Italy a little earlier.

Unfortunately the numbers are difficult to compare b/c of exponential growth. Go back to 2/21 and there is only a 7 patient difference between the countries. Yet look at the difference between the infected population now between the U.S. and Italy. Do you concur? Also, under what circumstances did the infection spread? The latter is the biggest issue that goes into such an analysis and use of stats is flawed if one doesn't understand the actual drivers to the data.

The comparison of cases on 2/21 is not particularly useful for a variety of reasons.

From a statistical standpoint, measurements of such small populations are prone to larger proportional errors, and small populations are more likely to deviate from average statistical behavior.

That's why all the comparisons either start from the dates at which different countries reached a given threshold of cases (in this instance, 100) or simply lay the curves on top of each other.

A fiasco in the making? As the coronavirus pandemic takes hold, we are making decisions without reliable data
By JOHN P.A. IOANNIDIS

MARCH 17, 2020

The current coronavirus disease, Covid-19, has been called a once-in-a-century pandemic. But it may also be a once-in-a-century evidence fiasco.

At a time when everyone needs better information, from disease modelers and governments to people quarantined or just social distancing, we lack reliable evidence on how many people have been infected with SARS-CoV-2 or who continue to become infected. Better information is needed to guide decisions and actions of monumental significance and to monitor their impact.

Draconian countermeasures have been adopted in many countries. If the pandemic dissipates — either on its own or because of these measures — short-term extreme social distancing and lockdowns may be bearable. How long, though, should measures like these be continued if the pandemic churns across the globe unabated? How can policymakers tell if they are doing more good than harm?

Vaccines or affordable treatments take many months (or even years) to develop and test properly. Given such timelines, the consequences of long-term lockdowns are entirely unknown.

The data collected so far on how many people are infected and how the epidemic is evolving are utterly unreliable. Given the limited testing to date, some deaths and probably the vast majority of infections due to SARS-CoV-2 are being missed. We don’t know if we are failing to capture infections by a factor of three or 300. Three months after the outbreak emerged, most countries, including the U.S., lack the ability to test a large number of people and no countries have reliable data on the prevalence of the virus in a representative random sample of the general population.

This evidence fiasco creates tremendous uncertainty about the risk of dying from Covid-19. Reported case fatality rates, like the official 3.4% rate from the World Health Organization, cause horror — and are meaningless. Patients who have been tested for SARS-CoV-2 are disproportionately those with severe symptoms and bad outcomes. As most health systems have limited testing capacity, selection bias may even worsen in the near future.

The one situation where an entire, closed population was tested was the Diamond Princess cruise ship and its quarantine passengers. The case fatality rate there was 1.0%, but this was a largely elderly population, in which the death rate from Covid-19 is much higher.

Projecting the Diamond Princess mortality rate onto the age structure of the U.S. population, the death rate among people infected with Covid-19 would be 0.125%. But since this estimate is based on extremely thin data — there were just seven deaths among the 700 infected passengers and crew — the real death rate could stretch from five times lower (0.025%) to five times higher (0.625%). It is also possible that some of the passengers who were infected might die later, and that tourists may have different frequencies of chronic diseases — a risk factor for worse outcomes with SARS-CoV-2 infection — than the general population. Adding these extra sources of uncertainty, reasonable estimates for the case fatality ratio in the general U.S. population vary from 0.05% to 1%.

That huge range markedly affects how severe the pandemic is and what should be done. A population-wide case fatality rate of 0.05% is lower than seasonal influenza. If that is the true rate, locking down the world with potentially tremendous social and financial consequences may be totally irrational. It’s like an elephant being attacked by a house cat. Frustrated and trying to avoid the cat, the elephant accidentally jumps off a cliff and dies.

Could the Covid-19 case fatality rate be that low? No, some say, pointing to the high rate in elderly people. However, even some so-called mild or common-cold-type coronaviruses that have been known for decades can have case fatality rates as high as 8% when they infect elderly people in nursing homes. In fact, such “mild” coronaviruses infect tens of millions of people every year, and account for 3% to 11% of those hospitalized in the U.S. with lower respiratory infections each winter.

These “mild” coronaviruses may be implicated in several thousands of deaths every year worldwide, though the vast majority of them are not documented with precise testing. Instead, they are lost as noise among 60 million deaths from various causes every year.

Although successful surveillance systems have long existed for influenza, the disease is confirmed by a laboratory in a tiny minority of cases. In the U.S., for example, so far this season 1,073,976 specimens have been tested and 222,552 (20.7%) have tested positive for influenza. In the same period, the estimated number of influenza-like illnesses is between 36,000,000 and 51,000,000, with an estimated 22,000 to 55,000 flu deaths.

Note the uncertainty about influenza-like illness deaths: a 2.5-fold range, corresponding to tens of thousands of deaths. Every year, some of these deaths are due to influenza and some to other viruses, like common-cold coronaviruses.

In an autopsy series that tested for respiratory viruses in specimens from 57 elderly persons who died during the 2016 to 2017 influenza season, influenza viruses were detected in 18% of the specimens, while any kind of respiratory virus was found in 47%. In some people who die from viral respiratory pathogens, more than one virus is found upon autopsy and bacteria are often superimposed. A positive test for coronavirus does not mean necessarily that this virus is always primarily responsible for a patient’s demise.

If we assume that case fatality rate among individuals infected by SARS-CoV-2 is 0.3% in the general population — a mid-range guess from my Diamond Princess analysis — and that 1% of the U.S. population gets infected (about 3.3 million people), this would translate to about 10,000 deaths. This sounds like a huge number, but it is buried within the noise of the estimate of deaths from “influenza-like illness.” If we had not known about a new virus out there, and had not checked individuals with PCR tests, the number of total deaths due to “influenza-like illness” would not seem unusual this year. At most, we might have casually noted that flu this season seems to be a bit worse than average. The media coverage would have been less than for an NBA game between the two most indifferent teams.

Some worry that the 68 deaths from Covid-19 in the U.S. as of March 16 will increase exponentially to 680, 6,800, 68,000, 680,000 … along with similar catastrophic patterns around the globe. Is that a realistic scenario, or bad science fiction? How can we tell at what point such a curve might stop?

The most valuable piece of information for answering those questions would be to know the current prevalence of the infection in a random sample of a population and to repeat this exercise at regular time intervals to estimate the incidence of new infections. Sadly, that’s information we don’t have.

In the absence of data, prepare-for-the-worst reasoning leads to extreme measures of social distancing and lockdowns. Unfortunately, we do not know if these measures work. School closures, for example, may reduce transmission rates. But they may also backfire if children socialize anyhow, if school closure leads children to spend more time with susceptible elderly family members, if children at home disrupt their parents ability to work, and more. School closures may also diminish the chances of developing herd immunity in an age group that is spared serious disease.

This has been the perspective behind the different stance of the United Kingdom keeping schools open, at least until as I write this. In the absence of data on the real course of the epidemic, we don’t know whether this perspective was brilliant or catastrophic.

Flattening the curve to avoid overwhelming the health system is conceptually sound — in theory. A visual that has become viral in media and social media shows how flattening the curve reduces the volume of the epidemic that is above the threshold of what the health system can handle at any moment.

Yet if the health system does become overwhelmed, the majority of the extra deaths may not be due to coronavirus but to other common diseases and conditions such as heart attacks, strokes, trauma, bleeding, and the like that are not adequately treated. If the level of the epidemic does overwhelm the health system and extreme measures have only modest effectiveness, then flattening the curve may make things worse: Instead of being overwhelmed during a short, acute phase, the health system will remain overwhelmed for a more protracted period. That’s another reason we need data about the exact level of the epidemic activity.

One of the bottom lines is that we don’t know how long social distancing measures and lockdowns can be maintained without major consequences to the economy, society, and mental health. Unpredictable evolutions may ensue, including financial crisis, unrest, civil strife, war, and a meltdown of the social fabric. At a minimum, we need unbiased prevalence and incidence data for the evolving infectious load to guide decision-making.

In the most pessimistic scenario, which I do not espouse, if the new coronavirus infects 60% of the global population and 1% of the infected people die, that will translate into more than 40 million deaths globally, matching the 1918 influenza pandemic.

The vast majority of this hecatomb would be people with limited life expectancies. That’s in contrast to 1918, when many young people died.

One can only hope that, much like in 1918, life will continue. Conversely, with lockdowns of months, if not years, life largely stops, short-term and long-term consequences are entirely unknown, and billions, not just millions, of lives may be eventually at stake.

If we decide to jump off the cliff, we need some data to inform us about the rationale of such an action and the chances of landing somewhere safe.

John P.A. Ioannidis is professor of medicine, of epidemiology and population health, of biomedical data science, and of statistics at Stanford University and co-director of Stanford’s Meta-Research Innovation Center.

Link

Maladat,

Correct but where does one arbitrarily start such an analysis? Is 100 infected sufficient? 1000? What about the causative factors, how much influence do they have? What about total population differences or population density metrics?

I appreciate your contributions.

quote:

Originally posted by maladat:
The comparison of cases on 2/21 is not particularly useful for a variety of reasons.

From a statistical standpoint, measurements of such small populations are prone to larger proportional errors, and small populations are more likely to deviate from average statistical behavior.

Yup.

I almost got into populating sizes in my comments on statistical processing, earlier, but I'd figured I'd given the non-stats-geeks enough already (And I'm really only a middlin' stats geek, anyway. A layman, really.)

quote:

Originally posted by Dakor:

Too little information to go by to simply say “well Italy and the US are exactly the same” when it comes to this virus.

Indeed. See the article I just posted.

Why, yes. Unfortunately, this naughty virus has refused to cooperate with statisticians, healthcare providers or governments in its progress. It should be punished.

quote:

Originally posted by Balzé Halzé:

quote:

Originally posted by Dakor:
Too little information to go by to simply say “well Italy and the US are exactly the same” when it comes to this virus.

Indeed. See the article I just posted.

Except nobody's claiming Italy and the US are "exactly the same." It is more accurate to say the progress of the spread of the disease in the U.S. appears to be similar to, if not the same as, it was in Italy.

May seem like mincing words, but there are distinct differences between the two statements.

And, yes: There is a dearth of reliable data. As Mr. Trump has stated several times: "We've never dealt with anything quite like this before." So we're obliged to go by the best data we have available, and refine it as we're able.

I like graphs, especially the ones in color. Let's think about those pretty graphs for a second. They are proporting to show a trend in the number of people infected. That seems simple. How do we find out which people are infected? Well, we test people. Still simple. What happens if we test more people? We'll find more infected people. Simple again.

What happens if we test people faster than the Italians did because we had more time to ramp up our testing capacity than they did?

Lies, damn Lies, and then there's statistics.

quote:

Originally posted by RHINOWSO:
Well, no its not “totally irrelevant”. If our total end case equal Italy, its a huge difference.

I get your point about rate of increase curve and current location of the virus, but in the end of it all, if we contain it to Italian totals, we have won big.

I see what you're saying, and I can agree in principle that at the end of all this, if we end up with the same number of cases Italy ends up having, we will almost certainly be in a better place.

There are two problems. First, "better" does not mean "good." Second, the curves in the US and Italy only demonstrate exponential growth thus far and don't say anything about where the curves will stop.

Under epidemiological models, the curves stop either with a large percentage of the population of the country having been infected, or several weeks after sufficient containment measures have been implemented to halt the spread of the disease. The first obviously hasn't happened, the standing question is whether the current measures in effect in the US and Italy are enough, which we will find out over the next several weeks. Of course, as measures are relaxed, spread is likely to start up again.

quote:

Originally posted by RHINOWSO:
Dismiss it if you want, go on with the hysteria, please.

In the end the data will tell the truth.

From Merriam Webster, "hysteria: behavior exhibiting overwhelming or unmanageable fear or emotional excess."

A calm, rational discussion of data and statistics is not hysteria, but by all means, continue using personal attacks to dismiss viewpoints you don't agree with rather than actually trying to justify your own.