It was a bit jarring to read such careful analysis and then toward the end read that China was an example of keeping Covid out, I wonder if they fudged things enough to throw this analysis as well?
Covid “Fudge Factor” – A map of Covid data corruption and approach that worked
11–20 of 151 posts
Re: Covid “Fudge Factor” – A map of Covid data corruption and approach that worked
#12Interesting analysis, though he mentions early on a huge issue with the analysis: > It does come with one major caveat: Because it counts ALL deaths, it cannot on its own disentangle deaths caused by Covid itself, impacts from the lockdowns themselves, impacts from vaccines, or unrelated death trends. Especially as time goes on, and we see the effects of missed cancer screenings, economic destruction, increased obesi…
Is it really a "caveat" when it entirely invalidates any attempt at causal analysis with this data? So much for "maximum truth". This is a nice data visualization exercise and descriptive analysis, but that's it.
Re: Covid “Fudge Factor” – A map of Covid data corruption and approach that worked
#13Sweden, which didn't lock down, also in the green.
The green on that map means they didn't fudge the numbers, not that having or not having a lockdown influences the number of excess deaths.
Re: Covid “Fudge Factor” – A map of Covid data corruption and approach that worked
#14Averaging out people dying to "days lost per life"?
Yeah, this is one of those cases where mean is really deceptive, almost to the point of shadowing the veracity of the rest of the paper, I wonder if they chose it because other numbers are just nearly impossible to grasp emotionally. It’s hard to make a cost benefit analysis if you’re convince you are in one camp (or the other, healthy people are probably convinced they’d survive and unhealthy/immunompromised not so)
Re: Covid “Fudge Factor” – A map of Covid data corruption and approach that worked
#15What I know from the Netherlands, at least from the numbers of hospitalisations, is that for the longest time we counted the number of people that had covid while being treated in hospital. This in contrast with most countries that counted the number of people in hospital, because of covid.
I remember reading that they were doing this in the US as well during the very early days of the pandemic. If you happened to have Covid in the hospital when you died, you "died from Covid".
This of course was perfect material for the Covid hoax conspiracy theorists...
Re: Covid “Fudge Factor” – A map of Covid data corruption and approach that worked
#16I'm not at all convinced excess deaths is a good measure of Covid-19 deaths.
What would you use instead?
Re: Covid “Fudge Factor” – A map of Covid data corruption and approach that worked
#17It was a bit jarring to read such careful analysis and then toward the end read that China was an example of keeping Covid out, I wonder if they fudged things enough to throw this analysis as well?
I'm not sure if you can get reliable excess mortality data for China, without that this approach would obviously fail. But I think this specific analysis is also a bit more focused on the earlier parts of the pandemic, and before Omicron at least it looked like China's policy seemed to work well in terms of stopping the spread. With Omicron this seems much more dubious.
Re: Covid “Fudge Factor” – A map of Covid data corruption and approach that worked
#18What I know from the Netherlands, at least from the numbers of hospitalisations, is that for the longest time we counted the number of people that had covid while being treated in hospital. This in contrast with most countries that counted the number of people in hospital, because of covid.
Which is what you should be counting. All the captain hindsight warriors now trying to use numbers now in attempts do discredit the actions taken in response to covid act like we were collecting numbers for their benefits. The reason it was important how many people where in hospital with covid was because we were trying to take measure to prevent overloading the healthcare system. The number of people dead would grow quite rapidly pre vaccine if those affected couldn’t get any medical treatment.
Re: Covid “Fudge Factor” – A map of Covid data corruption and approach that worked
#19Averaging out people dying to "days lost per life"?
Yes. I think that's a bit fishy. A life lost affects multiple people and potentially quite severely. Most people would happily accept their share of aggregate "lifespan reduction" if it stopped someone from suddenly dying. Premature bereavement can ruin lives.
Re: Covid “Fudge Factor” – A map of Covid data corruption and approach that worked
#20The article mentions the young populations of equatorial Africa as a factor in keeping mortality low. Why don't we factor in the age distributions of countries when assessing the effectiveness of their policies?