🗣 @Ianblackford_MP: “Mr Speaker, this afternoon, millions around the world will breathe a massive sigh of relief when President Joe Biden and Vice President Kamala Harris are sworn into office.” #PMQs

🗣 @Ianblackford_MP: “The democratic removal of Donald Trump gives all of us the hope that the days to come will be that little bit brighter.” #PMQs
🗣 @Ianblackford_MP: “Turning the page on the dark chapter of Trump's presidency, isn't solely the responsibility of President Joe Biden.” #PMQs
🗣 @Ianblackford_MP: “It is also the responsibility of those in the Tory party, including the Prime Minister, who cosied up to Donald Trump and his callous worldview.” #PMQs
🗣 @Ianblackford_MP: “This morning, the former Prime Minister, the member for Maidenhead, accused the current Prime Minister of abandoning moral responsibility on the world stage by slashing international aid.” #PMQs
🗣 @Ianblackford_MP: “So, if today is to be a new chapter, if today is to be a new start, will the Prime Minister begin by reversing his cruel policy of cutting international aid for the world's poorest?” #PMQs

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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!

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