1. Fri. 15 Jan. Zoom call with retired Generals, Colonels & Lin Wood: https://t.co/YGaFI5adpm
- 25,000 troops are now in DC, under the guise of riot control for the inauguration.
- More troops across the country-all major Democrat cities-are on standby which is 1hr recall
More from 🇺🇸I Support Our Military & Law Enforcement🇺🇸
More from Government
The Government is making the same mistakes as it did in the first wave. Except with knowledge.
A thread.
The Government's strategy at the beginning of the pandemic was to 'cocoon' the vulnerable (e.g. those in care homes). This was a 'herd immunity' strategy. This interview is from
This strategy failed. It is impossible to 'cocoon' the vulnerable, as Covid is passed from younger people to older, more vulnerable people.
We can see this playing out through heatmaps. e.g. these heatmaps from the second
The Government then decided to change its strategy to 'preventing a second wave that overwhelms the NHS'. This was announced on 8 June in Parliament.
This is not the same as 'preventing a second wave'.
https://t.co/DPWiJbCKRm
The Academy of Medical Scientists published a report on 14 July 'Preparing for a Challenging Winter' commissioned by the Chief Scientific Adviser that set out what needed to be done in order to prevent a catastrophe over the winter
A thread.
The Government's strategy at the beginning of the pandemic was to 'cocoon' the vulnerable (e.g. those in care homes). This was a 'herd immunity' strategy. This interview is from
Government #coronavirus science advisor Dr David Halpern tells me of plans to \u2018cocoon\u2019 vulnerable groups. pic.twitter.com/dhECJNbmnI
— Mark Easton (@BBCMarkEaston) March 11, 2020
This strategy failed. It is impossible to 'cocoon' the vulnerable, as Covid is passed from younger people to older, more vulnerable people.
We can see this playing out through heatmaps. e.g. these heatmaps from the second
Here are the heatmaps for Covid detected cases, positivity, hospitalizations, and ICU admissions. This is for the week to 3 January 2021.
— Dr Duncan Robertson (@Dr_D_Robertson) January 7, 2021
I have marked a line on 21 September, when SAGE recommended a circuit breaker, so you can see how the situation has deteriorated since then. pic.twitter.com/SEEVgUVK4j
The Government then decided to change its strategy to 'preventing a second wave that overwhelms the NHS'. This was announced on 8 June in Parliament.
This is not the same as 'preventing a second wave'.
https://t.co/DPWiJbCKRm
The Academy of Medical Scientists published a report on 14 July 'Preparing for a Challenging Winter' commissioned by the Chief Scientific Adviser that set out what needed to be done in order to prevent a catastrophe over the winter
One thing civil servants learn is to write things down. Here is @acadmedsci's 14 July report commissioned by @uksciencechief. For the record.
— Dr Duncan Robertson (@Dr_D_Robertson) September 17, 2020
Which metric is a better predictor of the severity of the fall surge in US states?
1) Margin of Democrat victory in Nov 2020 election
or
2) % infected through Sep 1, 2020
Can you guess which plot is which?
The left plot is based on the % infected through Sep 1, 2020. You can see that there is very little correlation with the % infected since Sep 1.
However, there is a *strong* correlation when using the margin of Biden's victory (right).
Infections % from https://t.co/WcXlfxv3Ah.
This is the strongest single variable I've seen in being able to explain the severity of this most recent wave in each state.
Not past infections / existing immunity, population density, racial makeup, latitude / weather / humidity, etc.
But political lean.
One can argue that states that lean Democrat are more likely to implement restrictions/mandates.
This is valid, so we test this by using the Government Stringency Index made by @UniofOxford.
We also see a correlation, but it's weaker (R^2=0.36 vs 0.50).
https://t.co/BxBBKwW6ta
To avoid look-ahead bias/confounding variables, here is the same analysis but using 2016 margin of victory as the predictor. Similar results.
This basically says that 2016 election results is a better predictor of the severity of the fall wave than intervention levels in 2020!
1) Margin of Democrat victory in Nov 2020 election
or
2) % infected through Sep 1, 2020
Can you guess which plot is which?
The left plot is based on the % infected through Sep 1, 2020. You can see that there is very little correlation with the % infected since Sep 1.
However, there is a *strong* correlation when using the margin of Biden's victory (right).
Infections % from https://t.co/WcXlfxv3Ah.
This is the strongest single variable I've seen in being able to explain the severity of this most recent wave in each state.
Not past infections / existing immunity, population density, racial makeup, latitude / weather / humidity, etc.
But political lean.
One can argue that states that lean Democrat are more likely to implement restrictions/mandates.
This is valid, so we test this by using the Government Stringency Index made by @UniofOxford.
We also see a correlation, but it's weaker (R^2=0.36 vs 0.50).
https://t.co/BxBBKwW6ta
To avoid look-ahead bias/confounding variables, here is the same analysis but using 2016 margin of victory as the predictor. Similar results.
This basically says that 2016 election results is a better predictor of the severity of the fall wave than intervention levels in 2020!
