It is even worse. The CDC instructs doctors to add COVID-19 even in the absence of positive testing if there is even a circumstantial possibility of exposure to COVID-19. It does make reported number of cases and deaths suspect.

See page 6 of the CDC instructions here:
https://www.cdc.gov/nchs/data/nvss/vsrg/vsrg03-508.pdf

Hi smayer97, perhaps you could provide some further information where in the world social distancing has had little impact on the spread of Covid-19. New Zealand has had strict lock downs, closed borders and social distancing. With a population of 4.9 million has had only 1474 cases and 19 deaths.
Australia also closed borders and businesses and imposed a strict lock down, has a population of 25 million and only 6,738 cases and 88 deaths. America’s population is 13.2 times that of Australia so if America adhered to similar rules it should have only 89,000 cases and 1,162 deaths at this point in time. The first Covid-19 cases were determined in each country about the same time.

Hi smayer97, perhaps you could provide some further information where in the world social distancing has had little impact on the spread of Covid-19. New Zealand has had strict lock downs, closed borders and social distancing. With a population of 4.9 million has had only 1474 cases and 19 deaths.
Australia also closed borders and businesses and imposed a strict lock down, has a population of 25 million and only 6,738 cases and 88 deaths. America’s population is 13.2 times that of Australia so if America adhered to similar rules it should have only 89,000 cases and 1,162 deaths at this point in time. The first Covid-19 cases were determined in each country about the same time.

Especially when you consider how flawed some of the advice and projections coming from official channels has been.

The CDC as late as March was advising the public against using masks – they said they were not effective. Only later did they say they should be prioritized toward healthcare workers, but the fact remains that was their advice. How many people were harmed as a result of that advice? Have Google, Facebook and Twitter shut them down?

The original Ferguson model projected a worst case scenario figure of 2.2 million fatalities in the US – which we know now will be so far off the mark as to be unhinged. Where are the pitchforks and torches coming after the creators of that model?

Makes me crazy. (as everybody can see)

I fully agree that there are so many other factors to consider! So much commentary smacks of the “damned lies and statistics” school of data massage. Getting to the bottom of it is critical, and we should be looking at all angles.

And of course taking down a reasonably-argued video is unacceptable.

Lately I’m confused about what gets counted as a COVID-19 death, there’s the clip circulating from Dr. Ngozi Ezike (Illinois) saying that anybody testing positive at the time of death gets counted as a Covid death

full quote is:

“If you were in hospice and had already been given a few weeks to live, and then you also were found to have COVID, that would be counted as a COVID death. It means technically even if you died of a clear alternate cause, but you had COVID at the same time, it’s still listed as a COVID death. So, everyone who’s listed as a COVID death doesn’t mean that that was the cause of the death, but they had COVID at the time of the death.”

Is that standard across states and locales? Wouldn’t that inflate the death count? Here’s a WebMD article

https://www.webmd.com/lung/news/20200417/how-accurate-are-coronavirus-death-counts

citing that in multiple states fatalities are counted as covid if there is just a suspicion, but without a confirmed diagnosis.

The two doctors also said in their video that there is pressure to add covid to the c.o.d in multiple locales.

New York City is the only place I’ve been tracking the numbers on that lists “probable deaths” separately from “confirmed”, so there I stick to the confirmed only. But again, are the confirmed really confirmed given the co-morbidity factors?

Italy’s numbers look particularly affected by this, one study finding 99% of the fatalities had co-morbidity factors. In that study only three people had no previous pathology (which penciled out to 0.8%, of the fatalities).
(https://www.bloomberg.com/news/articles/2020-03-18/99-of-those-who-died-from-virus-had-other-illness-italy-says)

It’s just seems really hard to get decent, uniform data. Here in Toronto over 50% of the fatalities are in long term care facilities (old age homes), it would be worth knowing what that ratio is everywhere else.

Anyway, you may be right along with some of the other commenters in this thread looking at the math. The biggest bone of contention being how they extrapolated from a biased sample. But also consider: it is possible that COVID-19 left China in November or December, not January. Given the asymptomatic transmission, maybe they aren’t that far off in that extrapolation. 96% of prisoners who tested positive across four prisons had no symptoms:

https://www.reuters.com/article/us-health-coronavirus-prisons-testing-in-idUSKCN2270RX

that is pretty huge and could mean COVID-19 became a lot more widespread and earlier than we think.

The larger point being, this is the right way to deal with things when somebody puts out their take on things. Dive into the numbers and what’s out there and try to make sense of it in a civil manner.

Deplatforming these guys (somebody else pointed out their facebook page was taken down) just smacks of “witchunt”, which is why I went totally ballistic over it. There may be something in what they said that is on point and relevant but we’d never know if we just shut down all dissenting opinion.

NYC data: https://www1.nyc.gov/site/doh/covid/covid-19-data.page

As of this writing, cases: 159865, confirmed deaths: 12287. That’s a case fatality rate (CFR) of 7.7%. Assume the infected fatality rate (IFR) is 10x higher, that gives us an IFR of 0.77%.

Now, let’s look at the flu. Looking at numbers from https://www.cdc.gov/flu/about/burden/index.html and picking the 2014-2015 season more-or-less at random, we have estimated deaths: 51000, estimated symptomatic illnesses (not medical visits!): 30000000. That gives an IFR of 0.17%.

This makes covid-19 approximately 4.5x deadlier than the flu. Perhaps a less-than-an-order-of-magnitude difference should be negligible in terms of public policy. That’s a matter of debate. But to suggest “it’s the same as the flu” is disingenuous at best.

Now, let’s adjust the numbers. The same NYC data has additional “probable” deaths (there is reason to believe many covid deaths are not included in the counts, see https://www.nytimes.com/interactive/2020/04/21/world/coronavirus-missing-deaths.html). Let’s include those for New York. Cases, still 159865. Deaths now become 12287+5302=17582. This bumps the CFR to 11%, and the IFR to 1.1%.

So now, we _are_ talking about a disease which is a full order of magnitude deadlier than the flu. That makes these doctors’ whole, oft-repeated number of “many infections, few deaths” total bullshit. 1% is a whole lot of corpses.

I am intentionally omitting discussing the comorbidity story. It’s an important factor. So is the fact that covid-19 lasts longer than the flu, and has left many people who recover with lasting and possibly permanent damage. So let’s focus on the basic prevalence statistics here, and these two dudes’ numbers simply do not add up. They encouraged viewers to check their numbers, and I did.

I found the extrapolation of data to be quite suspect too. I was wondering if the numbers from the flu are calculated in the same manner or not. If we have 51K deaths from the flu, is that because we had 30,000,000 flu tests, symptomatic patients to the doctor, or x number of people coming in times population. I did look at one paper in the website link I provided (https://www.ncbi.nlm.nih.gov/pubmed/25738736) that indicates calculated illness is hospitalization rate times a factor. Here is an excerpt:

” Five sites of the Influenza Hospitalization Surveillance Network (FluSurv-NET) collected data on the frequency and sensitivity of influenza testing during two seasons to estimate under-detection. Population-based rates of influenza-associated hospitalization and Intensive Care Unit admission from 2010-2013 were extrapolated to the U.S. population from FluSurv-NET and corrected for under-detection. Influenza deaths were calculated using a ratio of deaths to hospitalizations. “