Which places have done the best job of protecting people from Covid? A thread.

Best at early action: TAIWAN. Quickly halted flights, quarantined travelers, implemented widespread testing, and quadrupled face mask production within a month. The US now has more cases and deaths every 5 minutes than Taiwan has had to date.
Best at learning from recent epidemics: LIBERIA. Hit hard by Ebola in 2014, Liberia was one of the first countries to screen for Covid at airports and implement comprehensive control measures, including rapid testing, complete contact tracing, and effective quarantine.
Best at crushing the curve: NEW ZEALAND, which began preparing hospitals and border control policies in February, then implemented a countrywide lockdown in late March with the goal of eliminating Covid entirely. Clear, empathetic communication has been essential.
Best location in the US: AMERICAN SAMOA. On high alert after a 2019 measles outbreak, health authorities halted all incoming passenger flights and have managed to prevent any Covid cases.
Best at testing: SOUTH KOREA. In the pandemic's first weeks, the country tested aggressively, conducting more than twice as many tests per capita as other countries.
Best at quarantining: HONG KONG. Despite having one of the highest population densities in the world, it kept cases low through strategic testing, mandatory isolation protocols, and quarantine centers for people exposed to Covid.

More from Society

It is simply not correct to point fingers at wind & solar energy as we try to understand the situation in TX. The system (almost) had a plan for weather (almost) like this. 1/x


It relied on very little wind energy - that was the plan. It relied on a lot of natural gas - that was the plan. It relied on all of its nuclear energy - that was the plan. 2/x

There was enough natural gas, coal and nuclear capacity installed to survive this event - it was NOT "forced out" by the wind energy expansion. It was there. 3/x

Wind, natural gas, coal and nuclear plants all failed to deliver on their expectations for long periods of time. The biggest gap was in natural gas! The generators were there, but they were not able to deliver. 4/x

It may be fair to ask why there is so much wind energy in ERCOT if we do NOT expect it to deliver during weather events like this, but that is an entirely different question - and one with a lot of great answers!! 5/x

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Nano Course On Python For Trading
==========================
Module 1

Python makes it very easy to analyze and visualize time series data when you’re a beginner. It's easier when you don't have to install python on your PC (that's why it's a nano course, you'll learn python...

... on the go). You will not be required to install python in your PC but you will be using an amazing python editor, Google Colab Visit
https://t.co/EZt0agsdlV

This course is for anyone out there who is confused, frustrated, and just wants this python/finance thing to work!

In Module 1 of this Nano course, we will learn about :

# Using Google Colab
# Importing libraries
# Making a Random Time Series of Black Field Research Stock (fictional)

# Using Google Colab

Intro link is here on YT: https://t.co/MqMSDBaQri

Create a new Notebook at https://t.co/EZt0agsdlV and name it AnythingOfYourChoice.ipynb

You got your notebook ready and now the game is on!
You can add code in these cells and add as many cells as you want

# Importing Libraries

Imports are pretty standard, with a few exceptions.
For the most part, you can import your libraries by running the import.
Type this in the first cell you see. You need not worry about what each of these does, we will understand it later.