Skip to main content

Coronavirus: How are we doing?

Unless you live under a rock, you've heard of COVID-19, colloquially known as Coronavirus (or, if you prefer, Caronavirus). Your state may have multiple confirmed cases, and you may be under quarantine and working from home. You might even have it and passed it to others in your life. In any way, it has undoubtedly affected your life at this point as it has taken the world by storm:

Perhaps the headline of Coronavirus in the United States has been the Federal Government's alleged mishandling of the virus as it reached our shores. From faulty test kits to contradicting statements from our Commander-in-Chief, it's hard to say that the response has been stellar. I wanted to see if we can statistically show that the government's conduct in these trying times has had a measurable impact on outcomes for U.S. residents.

First, I'll start off by listing reasons why this exercise is a fair one and not some political witch-hunt. In the lead-up to the pandemic, the current administration (among other things):
  • Fired their pandemic response team in 2018, declining to hire replacements 
  • Initially placed Vice President Mike Pence in charge of coordinating their Federal Government's response to Coronavirus. Regardless of how you feel about Mike Pence, he has a spotty record of managing disease outbreaks
  • Repeatedly downplayed the risk posed by the virus in what was ostensibly an attempt to calm the stock market, calling it a "hoax" among other things
  • Insisted on creating its own test kits instead of using the ones available from the WHO. These test kits didn't work at first, meaning that states had to send all samples directly to the CDC for testing, which took days. Even now, there are chronic shortages of the (now functional) test kits in many states. That has made it nearly impossible for public health officials to plan a response to the crisis, as they had no clue of its extent.
  • Discontinued a special program called "Predict" that was responsible for monitoring the threat of animal-born diseases (of which COVID-19 is one) to humans. The Predict program successfully predicted a strain of Ebola found in Zaire. 
With that out of the way, let's see how we can measure the performance of the United States. Ostensibly, a rough proxy of the effectiveness of a government's response to a pandemic is how rapidly it is spreading within a country. A slow spread means the government is probably responding effectively, while a rapid spread means the government is probably responding poorly. 

For our measurement, we must first learn about two types of growth: linear growth and exponential growth. Linear growth is characterized by the formula $y=ax$, while exponential growth is characterized by the formula $y=ae^{x}$ (follow the links to see what they look like). As you can see, there is a big difference between these two types of growth. Like, really big. Exponential growth is orders of magnitude more rapid than linear growth. In fact, an unchecked virus is often thought to grow exponentially.

As such, if the virus is spreading linearly in a country, the government is probably doing a pretty good job of containing its spread. Conversely, if it is growing exponentially, the government is almost ineffective (as this is the natural, unchecked growth of the virus). I developed a metric (see accompanying post) that graded whether the spread of Coronavirus in a country was more linear or exponential, with 0 meaning only a linear model effectively explains the growth, and 1 meaning only an exponential model effectively explains the growth. You can think of this metric as a "grade" of a government's response, where 0 is totally effective and 1 is totally ineffective. 

Below are the results for a few key countries:

Our results are not promising for the U.S. Federal Government. Among these key countries, it is tied for worst in the metric that I have created. Moreover, interestingly enough, these countries can be clearly separated into two distinct categories by score: Western nations, with higher (worse) scores, and Asian nations, with lower (better) scores*.

Universally, the Asian nations (municipalities) of SingaporeSouth Korea, and Hong Kong have pursued aggressive containment measures to combat their own outbreaks. In South Korea's case, that has likely prevented an Italy-like situation. Singapore likely is not experiencing a major outbreak because of the drastic measures it took. These nations coincidentally also score well on my metric.

On the other hand, we have the western nations (including the U.S.), with much higher scores than their Asian counterparts. Two nations -- Spain and Germany -- perform just as poorly on my metric as the U.S., and have more cases despite far smaller populations. However, it seems that Italy's total quarantine has yielded some mild dividends. And it turns out that French smurf rally might not have been a big deal after all.

In sum, if the Federal Government's had been more effective, the United States likely would have performed much better on my metric, or at least as well as France, which has much fewer cases than its immediate neighbors despite open borders with them. However, luckily for us, things are starting to change. Action is finally being taken. In my home state of Massachusetts, Governor Charlie Baker has banned gatherings of more than 25 people and restaurants from hosting dine-in service. Similar measures are being taken around the country as I write this post (3/16/20). Let's hope that it will slow the spread of COVID-19 so that life can return to normalcy sometime soon and as few people catch it as possible.


*- Note: China is excluded from this analysis due to insufficient data.

P.S. This virus will perhaps be a chance for us to confront some of the deep-seated biases that we all hold. Lets all remember that as the Coronavirus spreads throughout the U.S., it is not an excuse to be racist


Comments

  1. Thanks for writing this great article! It’s very informative, and you included some great points to the equally great article regarding Rapid Covid Test Kit for Sale.

    ReplyDelete

Post a Comment

Popular posts from this blog

Why isn't Robinhood letting me trade? (hint: there's probably not a conspiracy against you)

Today's been a big day in the stock market . Lots of people have lost a lot of money, and a lot of people are understandably really upset . Here's a quick breakdown of what's happened so far A subreddit called /r/wallstreetbets  (visit at your own peril), which has exploded in popularity recently and has over 5 million subscribers (and counting) got really excited about three stocks: GME (Gamestop), AMC (the movie theater place), and BB (Blackberry). Gamestop was the main stock.  Yes, I know all three companies are doing terribly in the real world. I won't go into why they got excited about the stocks here.  They convinced a lot of other people to buy the stocks and they did well. Really well. Take a look at their Yahoo Finance pages and look at their 1 month price charts (then ignore the past two days). GME , BB , AMC Everyone got in on it, and I mean it. When a lot of people buy a single stock, the price rises. It turns out, this was hurting a lot of Hedge Funds and I

Determining NFL Quarterback Archetypes (with stats!)

We're obsessed with grouping things together. We self-select each other into groups based on which political candidate we support, which sports team we root for, and which arbitrary country we're born in. People also spend hours on the internet arguing over "tiers", or groupings, of their favorite athletes and sports teams. For example, which NBA players are "elite" vs. "great" vs. just "good"? Did Carmelo Anthony belong  on the Banana Boat ? When engaging in these arguments, we typically use statistics like points or rebounds per game to back up our points, but at the end of the day, the groups are more or less kind of arbitrary.  But what if there was a way to algorithmically sort observations into groups based on shared characteristics using machine learning methods? Enter clustering , which is the methodology of grouping similar observations into groups, or "clusters", using a mathematical distance metric derived from a set

Analyzing Hip Hop - Who's Most Lyrical, What Determines Popularity, and More

Have you ever thought about bringing cold, hard statistics to one of life's greatest artistic joys? Well fear not, because in our increasingly data-driven world, our analyst friends are hard at work attempting to statistisize (numerize?) everything you can think of, so we can analyze and therefore optimize it. One of the art realms that is increasingly falling under the purview of data science is music. We all benefit from it in the form of curated daily Spotify playlists and Pandora stations that allow us to find new artists and songs.  I was recently able to get my hands on a Spotify dataset  that contains data on over 160k tracks dating from 1921 through December 2020. Aside from containing some basic features like track name, duration, and release date, it also contains some advanced metrics as calculated by Spotify like "track positivity" (is it a sad, depressed song, or a happy, positive song?), "danceability", "energy", "speechiness" (