Intelligent human beings use data to make informed decisions. Numbers give method to madness and is why a scientific narrative has overtaken “feelings” in today’s age. The scary aspect behind numbers are that they can be formed to tell whatever tale you want them to tell. So assuming we’re dealing with accurate numbers (I would doubt we are for many reasons outside of this post), let’s look at how the Corona #’s stack up with the election results.
I compiled a spreadsheet with Corona and Election data using WorldoMeters and 270towin.
Outliers
My main purpose of data mining was to see how Democratic and Republic states handle the Corona Virus. The first part was to identify what I considered outliers with the Corona #’s and then compare that to how that state voted. This would give indicators to if the data we are being told is accurate or manipulated by certain parties.
The states with the highest death rates are New York (4.99%), New Jersey (4.92%), Massachusetts (4.70%), Connecticut (4.24%), and Washington DC (3.16%). Based off of this data it’s clear that the Corona virus has a preference to Democratic states in the Northeast. The margins of these democratic victories by vote counts respectively were 22%, 16%, 34%, and 34%, Not close calls.
How does the Corona Virus handle the Republican states with the greatest margins which are Wyoming (45%), West Virginia (39%), North Dakota (34%), Oklahoma (34%), Idaho (31%), South Dakota (27%), Kentucky (26%), Alabama (26%), and Tennessee (24%). Lucky for the people of those states the death rates are quite lower by a significant margin. .68%, 1.55%, 1.2%, .88%, .93%, 1.17%, 1.06%, 1.44% and 1.22%. Lucky for these states the Corona doesn’t kill at such a high rate.
Let’s look at testing. Which states have done the most? Rhode Island, Alaska, Massachusetts, New York, DC, Connecticut, and Illinois have more tests as a % than any other state. 6 of 7 of these states are Democratic. Rhode Island has done 1.4x more tests than their entire population! Everyone has gotten tested more than once. How do you explain that? Well that’s one way to lower your death rate.
Let’s Cut the Shit
The first question to ask about this entire post regards data quality. Are these the actual numbers? Are hospitals all being told the same reporting procedure and do they abide by it? Do they all classify deaths the same? Do states incentivize different results? If you do 5 tests on 1 person is that 5 or 1? These are all questions that you won’t have answers to because this information is protected. There are people who know the answer to these questions, perhaps the CDC, but I doubt that truth will ever hit the public because it’s so complex. Let’s just assume for the sake of this post that the data is accurate…which it almost can’t be.
I’d make the argument that after 9 months of the Corona Virus in our lives, it is deadlier in Northeast Democratic states than the Republicans’ Midwest. How can the virus kill at a 3-4% clip vs 1%. It’s the same virus right? Is the sample size big enough? Wouldn’t the stereotypical fat, lazy midwestern human be more prone to death than the white privileged Northeast individual? Data doesn’t lie like this so it has to be the people.
Could a non-bias person argue that the Democratic states did a shitty job handling corona and Republican states do a great job based on the death rate? I guarantee Democrats wouldn’t concede this point. Yet 7 out of 8 states with the most cases per population are all Republican. So the Republican states are doing a great job at getting the virus and then not dying from it. I’m curious to hear an explanation for this.
I’m a moron but don’t feed me bullshit. I don’t look at this exercise with a bias towards anything but pointing out that it’s not right. Whatever side you want to see from these #’s is fine, but at least make your arguments with the #’s being given. The simple logic behind these #’s are that democrats wanted this virus to look bad. If the #’s are real, how do you explain the difference in death rates in Republican States vs Democrat? How can Rhode Island test 1.4x the size of their population and our great state of Pennsylvania has only tested 28%. Who is asking these questions? Who is giving the answers? An idiot blogger can’t be responsible for sifting through this to come up with these anomalies.
You should be asking, what should we be seeing if we didn’t have a biased party system? The numbers to have consistency. It’s the same virus. This isn’t opinion based here. My arguments are being deduced from the #’s being fed. If you read what I write and think this is obviously a right learning conspiracy theorist, I don’t know what to tell you other than I’m not going in with that mindset. I’m genuinely asking how these #’s makes sense? Finally, if you read this post and you disregard what I’m pointing out or feel that I’m misleading, I feel bad for you because this is what you’re being sold and you’re buying it. And if I’m completely wrong with this analysis, and if you’ve done more work on this than I have, please help me out.
As you noted, data can “lie” and only take you so far without considering the context for the data. For instance, imagine different heats of a race at the Penn Relays. Heats 1 + 2 run and complete. A torrential downpour starts while heats 3 + 4 run. In the end the times are the times – but they don’t exactly tell the story of why. (I don’t know race rules – maybe they call the heat or something – but it is an analogy).
Things the numbers may not show you:
– No nationalized plan was implemented – leaving it up to states to determine how to proceed.
– Without a nationalized plan, testing was kind of the “wild-west” – as states competed against each other for those resources. One test (positive or negative) does not tell if actions are effective or not. You wouldn’t change a variable and rely on the data from when before you changed the variable to establish to establish the effectiveness (or not) of the change, no? Lack of a long-term testing plan is probably one of the reasons why the numbers have such anomalies.
– Geography + Travel. Certain states/regions have a higher volume of travel (tourism, shipping, cross-state work, etc. The Northeast – or “Acela Corridor” is one of them).
– Population density vs. healthcare resources. While NYC has a larger population than Jackson Hole – and thus greater healthcare resources – it does not mean those resources are proportional to an extreme, sudden demand on the system. You’d have to look at what % capacity each place was operating at before the pandemic. My bet would be that Wyoming was over served in its healthcare resources vs. need compared to where NYC was. So in just this facet, they are potentially not given the same starting line. There would be a greater chance of the healthcare system in NYC being overwhelmed before one in a more rural area – thus a higher death rate.
– I am not sure the Red/Blue analogy works everywhere (as you noted). For instance, PA (Blue) was one of the top 5 performing states in the country for most of the pandemic re: spread, etc. – lumped in with super rural states like Alaska, etc. Now we are not.
“You should be asking, what should we be seeing if we didn’t have a biased party system? The numbers to have consistency. It’s the same virus.”
If we didn’t have a biased party system I would imagine we would still have had the same huge spikes in major urban centers, but it probably would not be still occurring as dramatically as it is. I think the virus is different because of how it is treated/approached in different places. 50 different messages with a singular goal does not usually result in reaching the goal in an effective manner. Without having ever implemented a national protocol for this I fear when we look back from the other side it will still be just as confusing. You can’t conduct 50 simultaneous experiments on the same subject and find any conclusive results about what worked or didn’t with any instructive certainty.
The numbers are the numbers and debatable. Only because the circumstances to arrive at them are so muddled and varied. Regardless, an entire generation of public policy and med students will have a PhD topic to study.
I believe the argument was death rate rather than infection rate. Wake up “Brookes”
This tool allows you to compare up to 6 states.
https://covid.cdc.gov/covid-data-tracker/#compare-trends_totaldeaths