Hello fellow Nexians,

In a world too often manipulated by fear and hysteria, I feel a need to share this article with you.

Here

I have seen on the forums here (and in my daily life) a lot of fear and concern for the future. I find that the mainstream media (and social media) are often the manipulators of an incredibly dark fearful narrative. It is often very hard to find the truth in these messages.

The above article is a mathematical approach to understanding infection rate, spread and fatality for the COVID pandemic.

For me, it is encouraging to see some unbiased information which suggests to me that actually, the worst direct losses of life caused by the virus may be over and that an end may not be too far from sight.

How do you know if that link you posted is unbiased?


Kind regards,

The Traveler

I did some quick calculations on the figures here in the netherlands, and the suggested detection rate of the article seems to roughly correlate with the dutch situation.
(Over 90% of the cases go undetected, the article suggests. In the article it is 97% and dutch statistics suggest it is AT LEAST 93%, so i would say that is a rough correlation, especially considering the "at least" part)

Not the lethality rate though. That seems to be way over the 0.06% the article suggests.

Because numbers can't be biased.

Of course the people using them can be. I couldn't readily see any spin in the facts presented. However, I am not educated to degree level, I am sure others here will have valuable insight.

This is not my area of expertise, so just take my words with as much weight as of any random John Doe...

I agree that panicking is not a good response (is it ever?), and that it helps to keep a positive mindset. That being said, being overly optimistic and acting based on a "best case scenario" model can potentially lead to catastrophic results.

Numbers might not be biased, but which numbers are used and how they are presented can certainly be a source of bias. This doesn't mean people are being deceptive or manipulating, but I'm just speaking about the fact that we all have our own ideas and hypothesis and can select information in ways that will tend towards an answer which might or might not be right (myself included).

Here's the two main points i'd like to comment on:

1- The author claims quarantine is too late, with small or no benefits. First, I don't think he presented any data that really backs up such claim. How is he measuring the benefits here? There is plenty of science behind social isolation measures during a pandemic, both from a theoretical side as well as from a practical point, for example when you compare how different levels of social distancing measures effected different states during the spanish flu (Markel, H et al 2007). I think it is dangerous to spread the idea that social distancing is unnecessary given the data is pretty clear. In this case I think we much better err on the side of caution and do as much strict distancing as possible.

2- He uses his mathematics to show CFR(IFR) is, according to his model, 0.06%, with a very high R0. This is NOT what the antibody tests have been showing though, for example recently in New York the governor released preliminary numbers by doing antibody testing in random population to see how many really got infected, and it seems around 20% of the people in NYC got infected, bringing the IFR to 0.63%, 10x higher than the author of the article in the OP said. This is still preliminary data based on 3000 random people but it gives very good clues. Spain is starting next week a large antibody test with 90.000 random people to see how the numbers are here, and more countries are joining suit. Here is another interesting article discussing the calculation of IFR

Lastly, I just want to say that time will tell, we just have to wait and see, but in the meanwhile let's try to keep a positive mind, follow social distancing as much as possible, and just hope that a solution comes soon and that there is some good coming out of this, like changing our unsustainable system and the things we constantly take for granted .

Yeah, that 0.06% is way off.

The point with articles like these, compared to the official numbers from institutes like the CDC, is that official numbers are always the result of a multidisciplinary approach.

Official projections are never just based on virology, or statistic analysis, or epidemiology alone, but always on a combination of all these and other disciplines.

That's why i find the estimates and projections provided by these institutes much more reliable.

Another point is, that the figures that realy matter are not corona deaths or corona related IC admissions itself, but the projected extra deaths or IC admissions as a result of the virus.
That is one of the other reasons why comparing it to the flu doesn't realy makes sense, apart from the lethality. Because ofcourse there will be an overlap between projected annual flu casualties and corona deaths, but that overlap is not going to be 100%.

So you will always end up with a lot of corona deaths, on top of the annual deaths from the flu or cancer, etc, that you would still have with or without corona.

Problem is ofcourse, that there are no official projections for the loss of lives as a result of the economic fallout, with or without the current corona measures.

There simply hasn't been enough time and manpower to make detailed prognoses for the socio economic consequences of different suggested policies.

But the curve has been flattened in most places and that bought us time to think about these other issues. So i suppose that is being done now in various places, or at least it should be.

The article was based on an assumption that surprised me: that covid-19 takes roughly the same course everywhere, regardless of measures. 3 weeks of exponential growth of cases, 3 weeks of stagnant new cases, then decline.

Just a day before the OP, a friend of mine said the same. I didn't want to agree or disagree because i hadn't looked at the numbers in a while.

I now compiled the daily new cases graph for 4 places (source: worldometers.info) into one pic for visual comparison, and i really don't see how one arrives at that conclusion.

Interesting data!

I would assume population density and measures reducing the congregation of individuals inherently slows the spread.

Im really pleased to see that in my own country, new daily cases are consistently falling. I would assume when lockdown measures are lifted cases spike up again, though the article would suggest such shouldn't be the case.