SACRAMENTO, Calif. (AP) — California Gov. Gavin Newsom has from the start said his coronavirus policy decisions would be driven by data shared with the public to provide maximum transparency.
But with the state starting to emerge from its worst surge, his administration won’t disclose key information that will help determine when his latest stay-at-home order is lifted.
State health officials said they rely on a very complex set of measurements that would confuse and potentially mislead the public if they were made public.
They refuse to share their data with other public health officers.
Oh, those poor little stupid members of the public can’t be trusted. Leave it to the technocrat experts to know what’s best for you – because they use “models”
If you wonder why conspiracy theories gain traction, well, there you go …
The real world data has deviated from California’s model. Here are the charts from Los Angeles Public Health on January 22, 2021:
There was no holiday surge
You can see from the charts there was no “holiday surge”. That is a fictional explanation offered to explain what they do not understand.
The slopes of the curves remain roughly constant through the holidays. There are perturbations in the curves because public health shut offices for holidays and then caught up with the data p over the next few days. But there was never any holiday surge.
They made up the surge claim because the situation in California kinda sorta correlates with the holidays (they have since acknowledged there was no such surge in the Sacramento metro area). But look at other states – they peaked well before the holidays. It was nothing more than a random correlation in time in California.
Because public health is unable to have a coherent explanation – or that California’s problem may have been caused by public health guidance – they will stick with this unsupportable assertion to the point they are just lying.
Use of Computer Models
I’ve taught university courses in the development and use of computer models for decision making. The pandemic nonsense has caused me to lose confidence in model output. Models are badly misused.
A model is a guess or hypothesis about how we think something works. We throw data and assumptions into it and out pops a result – which is itself an hypothesis that will be supported or disproved by real world data. The output is not reality.
Today, those using models treat output as “reality” to the point that when real world data disagrees with the model, we now assume our real world data is wrong, not that the model is wrong.
This is not science. This is bull shit.