re: Running average of 49 worldwide antibody studies is showing a mortality rate of 0.23%

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And like the CFR, the IFR tends to rise since deaths lag infections (e.g., deaths will still occur even when infections stop, some even months later). So I could see it ending somewhere between 0.6 and 0.7 percent in Germany.

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Man I know you have a scientific approach to all of this but your bias is starting to show significantly.

Yea deaths lag, but so does antibody production, sometimes much longer than 2 weeks and some people infected don’t even make antibodies at a detectable level. How are you adjusting for those factors in your analysis?

It seems like you just immediately give credit to anything that makes the virus look worse but seriously scrutinize anything that makes it look better. Not saying you are, it just appears that way.

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Yea deaths lag, but so does antibody production, sometimes much longer than 2 weeks

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Of course. But that doesn’t change the fact that deaths in average, lag infections by 3 weeks.

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How are you adjusting for those factors in your analysis?

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It’s usually accounted for in the estimates of missing infections relative to the confirmed cases when it was believed the infection had occurred. The Stanford study at least did that.

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It seems like you just immediately give credit to anything that makes the virus look worse but seriously scrutinize anything that makes it look better.

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NYC data would indicate IFR closer to 1% and I’m arguing it’s more like 0.8% overall and even lower in Germany (0.6%) and even lower in a small area of Germany:

But I don’t think it’s anywhere near 0.2 percent OVERALL nor do I think it’s anywhere near 2 percent either. Estimates that far off are usually are for off because of there is a lot of issues to scrutinize.

The issues are largely because of the extrapolation widely. I know your estimate is of 0.5%, and while I think it’s higher, I don’t find they unreasonable either.

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I know your estimate is of 0.5%, and while I think it’s higher, I don’t find they unreasonable either.

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I don’t have an estimate but if you forced me to give you a number that’s around where I would be. I’m much more interested in if there is a portion of the population that just doesn’t get infected by this virus. Or has such a mild infection it doesn’t even produce detectable antibodies because of cellular immunity such as T cells.

There has to be an external factor that has cut off the spread of this virus almost exactly the same in every region. There would have been at least one region that went full on exponential growth by now and I haven’t seen it. We already have evidence of this in Sweden. I’m watching Brazil closely, if they level off shortly then I’ll be almost convinced that’s the case. No fricking way those people are locking down or social distancing.

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the Miami study had a test that is so inaccurate, (90.68% specificity), the entire study was pointless and it's likely every single positive was a false positive.

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How does the math on this work out? I've seen talks of specificity and false positives, but I don't understand how the results of something that's 90% effective/correct(?) would equal everything being false.

Not a challenge of the claim, just something I'm not understanding.

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It seems like you just immediately give credit to anything that makes the virus look worse but seriously scrutinize anything that makes it look better. Not saying you are, it just appears that way.

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We are all bias here, but you absolutely hit the nail on the head. He is finding reasons to throw the statistics way off in the direction of Covid being more deadly while all the while there are just as many reasons for it to be thrown off into being less deadly that he is leaving out.

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In the Gangelt study they randomly selected households. They confirmed every positive antibody sample in the lab and the upper conservatives limit of IFR was 0.34%

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What were the lower limits?

They just started giving antibody test at my workplace (plant baw) and everyone getting tested is positive for antibodies and had zero symptoms

So I’d be willing to bet that this percentage is vastly higher than the real percentage if you could include asymptomatic folks that don't even know they were exposed, therefore not tested

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How does the math on this work out? I've seen talks of specificity and false positives, but I don't understand how the results of something that's 90% effective/correct(?) would equal everything being false.

Not a challenge of the claim, just something I'm not understanding.

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Sensitivity avoids false negatives, while specificity avoid false positives.

If the actual prevalence in the population is relatively high, say 30+%, then 90% sensitivity and specificity is pretty decent. But if the actual prevalence is low, maybe 1-2%, then 90% specificity is awful.

If both sensitivity and specificity are 90%, the math works like this for 1% actual prevalence in a population of 1,000.

10 actual infections. The test will accurately find 9 of them (10 * 0.9). 990 are not infected. The test will accurately find ~890 negatives (990 * 0.9). That leaves ~100 false positives. Your test will result in ~110 positives, of which only a handful are true positives.

If the true prevalence is 50%, then you end up with better results at 90% accuracy. 450 out of 500 positives are identified, and 450 out of 500 negatives are identified. You will only get 50 false positives and 50 false negatives.

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Except the Flu kills kids and young adults and Covid does not.

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And they constantly work on vaccines around the clock to be prepared for whichever strain pops up.

So you get a head start and it still kills people of all ages.