re: Covid 19: misunderstandings in statistics, ascertainment bias (more testing= more "cases")

The statistics are correct. The issue is that they are only testing sick people therefore they cannot be used as a representative sample for the population. There are significant number of people that get a mild or no illness but are not sampled. Therefore the numbers are correct they just cannot be extrapolated to the general population.

https://www.cbc.ca/news/health/coronavirus-covid-pandemic-response-scientists-1.5502423

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Prominent scientist dares to ask: Has the COVID-19 response gone too far? Social Sharing Facebook Twitter Email Leading epidemiologists publish duelling commentaries, igniting debate on social media

Kelly Crowe · CBC News ·
Posted: Mar 19, 2020 4:00 AM ET | Last Updated: 3 hours ago

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It's a clash of titans — an epic battle between two famous scientists over the world's response to the COVID-19 pandemic. In one corner, influential Stanford University epidemiologist John Ioannidis, who wrote a commentary asking whether taking such drastic action to combat the pandemic without evidence it will work is a "fiasco in the making." Across the mat, prominent Harvard epidemiologist Marc Lipsitch punched back with a defiant response titled: "We know enough now to act decisively against COVID-19."

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Better information is needed to guide decisions and actions of monumental significance and to monitor their impact," Ioannidis wrote. "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." Ioannidis told CBC News he worries about the consequences of those measures. "Put a stall to the entire economy. Tell people to stay at their homes, get depressed, commit suicide, domestic violence. Who knows? Child abuse, children losing their education, companies crashing … unemployment, the stock market already dropping 20 per cent.

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Lipsitch said he talked to Ioannidis beforehand and found that they had more in common than it seemed. "I would say that his article did what contrarian writing should do: started a discussion." Lipsitch insists drastic action is required. "Waiting and hoping for a miracle as health systems are overrun by COVID-19 is not an option," he wrote. "For the short term, there is no choice but to use the time we are buying with social distancing to mobilize a massive political, economic, and societal effort to find new ways to cope with this virus."

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This mindset has played out before in medical science. A notable examples was the action taken by the government against dietary fat and cholesterol. The logic that was used:

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The touchstone is the precautionary principle, clearly articulated in the 2006 investigation into Canada's response to the SARS epidemic, written by Justice Archie Campbell of the Ontario Superior Court.

"Where there is reasonable evidence of an impending threat to public health, it is inappropriate to require proof of causation beyond a reasonable doubt before taking steps to avert the threat," Campbell wrote in a chapter called "Spring of Fear," citing Justice Horace Krever, who presided over Canada's tainted blood inquiry.

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And there it is...
*Although we may realize later that our logic was completely wrong and motivated primarily by fear leading to many unintended future consequences that may be far worse

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Prof. Ross Upshur of the University of Toronto is a public health expert, a physician and a scholar of the ethics and history of global health emergencies. He's also a veteran of the SARS outbreak. He has corresponded with Ioannidis over the years and respects the Stanford professor's expertise: "He is one of the most cited, most highly regarded researchers." But in this particular case, Upshur said, Ioannidis is making an error in his analysis by failing to view the current response through the lens of public health instead of evidence-based medicine. "Of course there's a lack of data," said Upshur. "It's all nice to stand on the sidelines and say, 'Hey, you know we don't have very good data. These are not evidence-based decisions.' Well, of course they're not, because we don't have the evidence."

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We desperately need to know, No. 1, the prevalence of infection, and No. 2, the incidence of new infections," he said. "If we make decisions with such tremendous uncertainty, we can get tremendous harms." The lack of data is one point on which Lipsitch and Ioannidis agree. "The U.S. has done fewer tests per capita so far than almost any rich country in the world," Lipsitch wrote. "And many critical details of the epidemiology — including the absolute number of cases, the role of children in transmission, the role of presymptomatic transmission, and the risk of dying from infection with SARS-CoV-2 — remain uncertain."

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https://reason.com/2020/03/20/if-covid-19-killed-1-4-of-people-with-symptoms-in-wuhan-the-overall-fatality-rate-is-likely-to-be-much-lower-than-people-feared/

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If COVID-19 Killed 1.4% of People With Symptoms in Wuhan, the Overall Fatality Rate Is Likely to Be Much Lower Than People Feared The big unknown is how many people are infected but aren't counted in the official numbers because their symptoms are mild or nonexistent.

JACOB SULLUM | 3.20.2020 2:10 PM

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A new study of COVID-19 cases in Wuhan, China, estimates that the death rate among people who were infected and developed symptoms was 1.4 percent. That is far lower than the crude case fatality rate (CFR) produced by dividing total deaths into total confirmed cases (4.5 percent) and far lower than the global CFR initially calculated by the World Health Organization (3.4 percent). The study, reported yesterday in Nature Medicine, suggests that the overall CFR—including people who are infected but do not develop symptoms—will prove to be much lower in the United States than many people feared.

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That's a very wide range, highlighting the weak empirical basis for aggressive, economically ruinous COVID-19 control measures. If the CFR is as low as 0.05 percent, which is half the estimated CFR for the seasonal flu, the number of deaths in the CDC's worst-case scenario would be 107,000, meaning the projection is off by a factor of 16. If a third of the population is ultimately infected, rather than the 65 percent assumed in the CDC's worst-case scenario, the number of deaths plummets from 1.7 million to about 55,000. It is impossible to assess the cost-effectiveness of mass interventions such as statewide or nationwide "shelter in place" orders in the face of such uncertainty.

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The study's main symptomatic CFR calculation assumes that half of the people infected by the COVID-19 virus develop symptoms. If that's true, the number of infections is at least twice as high as the number of known cases, and that's assuming everyone with symptoms gets counted, which is certainly not the case, since people with mild symptoms may never seek medical attention or testing. But that factor alone means that the overall CFR is dramatically lower than it appears to be based on reported cases and deaths. "One largely unknown factor at present is the number of asymptomatic, undiagnosed infections," the researchers note. "Estimates of both the observed and unobserved infections are essential for informing the development and evaluation of public health strategies, which need to be traded off against economic, social and personal freedom costs. For example, drastic social distancing and mobility restrictions, such as school closures and travel advisories/bans, should only be considered if an accurate estimation of case fatality risk warrants these interventions, which seriously disrupt social and economic stability."

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Please do not question the compassion and actions of your dear scientific and political overlords in D.C.Please trust only the facts provided to you that we have so diligently provided.



Respectfully,
The Government

https://www.latimes.com/science/story/2020-03-22/coronavirus-outbreak-nobel-laureate

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Why this Nobel laureate predicts a quicker coronavirus recovery: ‘We’re going to be fine’

By JOE MOZINGO STAFF WRITER MARCH 22, 2020 2:03 PM Facebook Twitter Show more sharing options Michael

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Michael Levitt, a Nobel laureate and Stanford biophysicist, began analyzing the number of COVID-19 cases worldwide in January and correctly calculated that China would get through the worst of its coronavirus outbreak long before many health experts had predicted. Now he foresees a similar outcome in the United States and the rest of the world. While many epidemiologists are warning of months, or even years, of massive social disruption and millions of deaths, Levitt says the data simply don’t support such a dire scenario — especially in areas where reasonable social distancing measures are in place. “What we need is to control the panic,” he said. In the grand scheme, “we’re going to be fine.”

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Getting vaccinated against the flu is important because a coronavirus outbreak that strikes in the middle of a flu epidemic is much more likely to overwhelm hospitals and increases the odds that the coronavirus goes undetected. This was probably a factor in Italy, a country with a strong anti-vaccine movement, he said. But he also blames the media for causing unnecessary panic by focusing on the relentless increase in the cumulative number of cases and spotlighting celebrities who contract the virus. By contrast, the flu has sickened 36 million Americans since September and killed an estimated 22,000, according to the CDC, but those deaths are largely unreported. He fears the public health measures that have shut down large swaths of the economy could cause their own health catastrophe, as lost jobs lead to poverty and hopelessness. Time and again, researchers have seen that suicide rates go up when the economy spirals down.

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While the COVID-19 fatality rate appears to be significantly higher than that of the flu, Levitt says it is quite simply put, “not the end of the world.” “The real situation is not as nearly as terrible as they make it out to be,” he said.

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The problem really isn’t whether this virus is deadly or not. The problem is it’s contagion, and if we can get healthcare up to speed for the onslaught.

Ebola is widely considered one of the most deadly diseases this side of the turn of the century (wasn’t it like an 80% fatality rate?). But it was easily contained.

COVID-19 is highly contagious, but no where near as deadly. Forget about the hand washing and touching for a sec; hopefully everyone is doing that. Some bad news is beginning to emerge about the aerosol dangers: in other words, it don’t matter if you clean your hands, you may very well be more likely to breathe this in.

And so with the contagion aspect being so widespread and prolific(look how fast the entire world has been touched by this) the problem isn’t that it is untreatable and people can’t be nursed back to health.

The issue is that so many people have been infected all at once, not all of them can be treated effectively, or will receive effective treatment timely.

Here is another way to look at it: You have 500 people infected. Let’s say 80% of them are totally asymptomatic or present with mild symptoms. That still leaves 100 with problems. 20%. Let’s say 80 of those are treated and cared for, and they are mostly older. What happens to the other 20, or roughly 4-5%? They fight for their lives I suppose. Some make it, some dont. But 5 people dying out of 500 (1%) doesn’t seem like a lot I know.

But if half the population (160 million?)as some models predict will be infected within 6-8 months, and 1 percent of them die from this (1.6 million people), that is pretty catastrophic.

The panic is to keep the numbers down as best as we can while the healthcare arm can acquire the capabilities to take care of the amount of sick individuals that will be coming through. Yes some will be turned away. It’s the ones that have a good chance of dying we are trying to prepare to save.

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Have more to say later, reserving my spot in the first pag

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The problem is that most people today form their views based on Instagram, Twitter, and apocalyptic movies.

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Where do you people come from?

https://www.cnbc.com/2020/04/17/santa-clara-covid-19-antibody-study-suggests-broad-asymptomatic-spread.html

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Antibody study suggests Covid-19 could be far more prevalent in the Bay Area than official numbers suggest
PUBLISHED FRI, APR 17 20201:35 PM EDTUPDATED 21 MIN AGO
Christina Farr
@CHRISSYFARR

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In a study published on Friday, the researchers, many of whom hailed from Stanford University, noted that the results suggest that Covid-19 could be far more widespread than the official counts suggest.

Specifically, they estimate that between 2.5% and 4.2% of people in Santa Clara County may have antibodies. (The range is a result of different models used to extrapolate the test results to a representative population.)

“These prevalence estimates represent a range between 48,000 and 81,000 people infected in Santa Clara County by early April, 50 (to) 85-fold more than the number of confirmed cases,” the authors wrote.

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Suprises no one here...

Again, does our current response match the threat being posed...

The folks in Sweden (who are typically held up as models to follow when discussing health policy) are laughing at us...

What is going on here...

The knives are coming out for Dr Ioannidis...

The bully of consensus will not stand for dissent...

Read the comments. Watch how quick the term “conspiracy theory” is hurled...
Glad to see more educated, free thinkers calling out self reinforcing group think...

https://undark.org/2020/04/24/john-ioannidis-covid-19-death-rate-critics/

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On Covid-19, a Respected Science Watchdog Raises Eyebrows For his Covid-19 work, the Stanford scientist John Ioannidis is being accused of the same bad science he has criticized.

Top: John Ioannidis chats with audience members outside an appearance at a TEDx event in Athens, Greece, in 2015. Visual: Kostas Limitsios/Flickr/CC
BY MICHAEL SCHULSON 04.24.2020

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In his email to Undark, Ioannidis also suggested more information on his team’s methodologies was forthcoming — and that it will prove their interpretation of the data to be sound. “We will be posting an updated version of the study preprint soon with far more data, additional analyses, more detail on methods and responses, that show that our data and inferences are robust,” he said, adding: “I hope that scientists will be able to focus calmly on the science and not on a blame game or a clash of political agendas. I clearly do not have any political agenda, and the least plausible characterization would be that I am ‘conservative,’ given my track record,” Ioannidis said. “My key focus remains to try to learn what I don’t know and diminish my ignorance.”

In the meantime, critics of Ioannidis say the evidence remains murky, leaving them to wonder whether a venerated scientist known for his learned and very often justified skepticism of others’ scientific work has become blind to his own methodological flaws. Travis Gerke, an epidemiologist at the Moffitt Cancer Center and a visiting scientist at the Harvard T.H. Chan School of Public Health, said that he read Ioannidis’ work while a graduate student. Recently he has wondered if Ioannidis should consider re-reading his own most famous paper, “Why Most Published Research Findings are False.” “His current study fits most of the high-risk criteria for falsehood that he outlines, such as publishing in a really hot scientific field with few corroborating studies, using a small bias sample, [and] reporting provocative findings in a politically charged arena,” Gerke said. “If you just go through his own work,” Gerke added, “he seems to be breaking all his own rules.”

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