I'm curious as to whether the consensus is that the observed behaviour of COVID waves was ever fully and satisfactorily explained - the tend to grow exponentially but then seemingly saturate at a much lower point than a naïve look at the curve might suggest?
To those interested in numbers it was explained early - even on TV. Anyone interested saw that it was going like a seasonal flue wave. Numbers were following strict mathematics. My area was early - the numbers peaked right before people started to go crazy - the rest was censorship - There was a lot of fakery going on by using very soft numbers. Very often they used reporting date instead of infection date.. and some numbers were delayed 9 months... So most curves out there were seriously flawed. But if you were really interested you could see real epidemiological curves - but you had to do real work to find the numbers. Strict mathematics of a seasonal virus was something people didn't want to see - and this is still the consensus...
Well, the shapes look very seasonal... Do you know something about epidemiological curves?!
The wave 2020 in Europe was often smaller than 2018. And the data was perfectly seasonal. If you know people working in nursing homes and hospitals, you can ask them what happened later in 2021...
I heard a lot of stories - from first hand... They parked old ladies in the cold in front of open windows for fresh air - until they were blue... They vaccinated old people right into an ongoing wave and of course they had more problems caused from a wrongly trained vulnerable immune system - sane doctors don't vaccinate into an ongoing wave. What was going on in hospitals and nursing homes was a crime for money. Just ask the people that were there. A combat medic I know that now works in a hospital called 2021 a crime.
And still - solid Epidemiological data - wherever you could find it - was still perfectly seasonal. You could see some perfect mathematical curves. Just very high because they actively killed people. Even pupils in school spent all day in front of open windows in the cold... To remain healthy... How stupid is that...
Not all places are equal, but I've taken a look at German all cause mortality. 2020 was not special. In 2021 it started rising synchronous with vaccinations.
This repeatedly confuses correlation and causation. The shape is seasonal - of what relevance is the shape? Why shouldn't we expect there to be a seasonal component of an airborne virus?
Similarly, to say "deaths increased when vaccines happened" is the most clear illustration. Why did the vaccines exist? Could that be related to the mortality increase? You can see charts here for Switzerland, US, UK: https://science.feedback.org/review/misleading-instagram-pos...
The shape is relevant if you want to evaluate measures.
If you can get your hands on some good data you'll find perfect mathematical seasonal functions. This is a serious criterion to exclude any measures from having any influence on the curve. It was just the seasonal thing happening. The data proves that measures were all useless - you could have worn any fancy hat for government measures instead. There are no trend changes in seasonal data you can corrolate to measures. The only trend changes you can find are in the reporting data. There's a decrease in reporting delay before a measure and there's a lot of reporting delay after the measure. Accidentally or intentionally reporting delay tried to make government measure look good.
For vaccines I know 3 cases where people died and 2 who have serious health problems after vaccines. There is a reason, why there's no good official data on vaccine efficency - and why all placebo groups were killed as soon as possible.
Why did vaccines exists? The answer is simpler: Because of Money!
It would probably be hard to do. The really huge factor may be easier to study, since we know where and when every vaccine dose was administered. The behavioral factors are likely to be harder to measure, and would have been masked by the larger effect of vaccination. We don't really know the extent of social isolation over geography, demographics, time, etc..
There's human behavioural factors yes, but I was kinda wondering about the virus itself, the R number seemed to fluctuate quite a bit, with waves peaking fast and early and then receding equally quickly.. I know there were some ideas around asymptomatic spread and superspreaders (both people with highly connected social graphs, and people shedding far more active virus than the median), I just wondered whether anyone had built a model that was considered to have accurately reproduced the observed behaviour of number of positive tests and symptomatic cases, and the way waves would seemingly saturate after infecting a few % of the population.
