Containment efforts for the COVID-19 Coronavirus are facing significant challenges, with experts suggesting that it may no longer be feasible to prevent its global spread. The virus has a mortality rate of approximately 2-3%, which could lead to a substantial increase in deaths if it becomes as widespread as the flu. Current data indicates around 6,000 cases, with low mortality rates in areas with good healthcare. Vaccine development is underway, but it is unlikely to be ready in time for the current outbreak, highlighting the urgency of the situation. As the outbreak evolves, the healthcare system may face considerable strain, underscoring the need for continued monitoring and response efforts.
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Vanadium 50 said:
If you look state-by-state I don't think one can draw conclusions. Wave 1 hit blue states harder. Waves 1 and Waves 3 are anticorrelated. Wave 3 hit red states harder. Which is cause and which is effect?
I'm not sure about that. Based on the second source I provided above the top 11 states with fewest cases per million are democratic (one neutral) based on the partisanship metric used, and the top 9 states with most cases per million are republican (one neutral). I'm referring to the most recent numbers, not the trend over time.
Same source different chart: top 13 states for death rate are republican and 13 out of 15 of the states with the lowest death rate are democratic.
I'm not making any claim about cause and effect because I could only speculate. Just an observation.
I'm not sure about that. Based on the second source I provided above the top 11 states with fewest cases per million are democratic (one neutral) based on the partisanship metric used, and the top 9 states with most cases per million are republican (one neutral). I'm referring to the most recent numbers, not the trend over time.
V50 said first wave; that link's graphic starts in June. The first wave hammered liberal states, in particular in the New England region. It gets fuzzier when you look at pandemic totals, though, since the testing rates are vastly different for the different waves/stages.
In my view, the media getting to add a partisan zinger is just a bonus; the main motivation I expect, is the media needing stories to drive clicks and sell ad space. And then people post those stores here, as if they are meaningful, when they aren't. PF members should know better.
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russ_watters said:
V50 said first wave; that link's graphic starts in June. The first wave hammered liberal states, in particular in the New England region.
I understand that. But the overall rates of infection and deaths right now are distributed differently. My point was that, though it may be misleading for news sources to pick individual towns/cities/counties when they say "Hey look at this case/death rate and the fact that they are democratic/republican" there is a definite correlation between partisanship and case/death rates at the state level.
Maybe that doesn't mean much. Maybe the trend will flip and in a few months we'll see USA Today headlines about how democratic towns have the nations highest death rates and this will reflect a shifted nationwide trend where democratic states are back at the top.
I understand that. But the overall rates of infection and deaths right now are distributed differently. My point was that, though it may be misleading for news sources to pick individual towns/cities/counties when they say "Hey look at this case/death rate and the fact that they are democratic/republican" there is a definite correlation between partisanship and case/death rates at the state level.
Maybe that doesn't mean much. Maybe the trend will flip and in a few months we'll see USA Today headlines about how democratic towns have the nations highest death rates and this will reflect a shifted nationwide trend where democratic states are back at the top.
I think it's unlikely to flip back, but no, I don't recall the news media taking the opportunity to point out the partisan disparities in the other direction when they existed/had the opportunity. What I remember from the media analysis in the spring was a heavy focus on racial disparities...which is related, but a different spin.
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At least in the spring, such stories had statistical relevance so they were worthy of discussion. But the political spin is, again, just an excuse to politicize what is primarily an issue of demographics (who lives in cities?).
There's certainly some value in doing a real analysis of things like mask mandate effects, but care needs to be taken to focus on the issue and analyze it properly to avoid the political bias, rather than crafting the analysis to support the bias.
#4,455
OmCheeto
Gold Member
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Regardless of how this current argument turns out, I would just like to say that I've found these Dan Goodspeed graphics simply fascinating.
The correlation between deaths per million and the Cook Political Index is -0.10. Republican defined as positive, so the fatality rate is incrementally lower in red states. But 0.1 is very low - it means 1% of the effect could possibly be ascribed to this factor. I think this makes a pretty strong case that the partisan effect is chasing ghosts.
I am, of course, prepared to believe that if you remove the period of time when blue states did worse you find they do better. However, I don't think that tells us anything - it's pretty close to a tautology.
If one insists that surely this is real, I feel compelled to point out that this correlation is weaker than death rate and alphabetical order (0.11) and much weaker than death rate and state name length (0.36). And my favorite - the death rate is positively correlated (0.2) with the number of negative tests.
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Vanadium 50 said:
The correlation between deaths per million and the Cook Political Index is -0.10. Republican defined as positive, so the fatality rate is incrementally lower in red states. But 0.1 is very low - it means 1% of the effect could possibly be ascribed to this factor. I think this makes a pretty strong case that the partisan effect is chasing ghosts.
That's a good point. I had overlooked that Goodspeed's charts are only accounting for numbers since July so they tell a different story than if we were counting from January. My mistake.
It's expected that Moderna's vaccine will get emergency use authorization (EUA) in the US in two days, first vaccinations with it could start early next week.
It would become the fifth vaccine with such an approval somewhere, and the second in Western countries.
Tozinameran (BioNTech, Pfizer), is quickly accumulating approvals internationally.
BBIBP-CorV got an EUA in China in July (!) and a full approval in UAE and Bahrain in the last week.
CoronaVac got an EUA in China in July (!)
Sputnik V got an EUA in Russia in August
GOVE COUNTY, Kan. (KWCH) - With numbers showing that about one out of every 132 of its people has died from COVID-19, a report published last week in USA Today calls Gove County “The deadliest place in America.” The distinction comes because, as of last Thursday (Dec. 10) COVID-19 has killed a higher percentage of Gove County residents than any other county in the U.S. As of late last week, there were 20 COVID-19-related deaths in the county with a population of about 2,600 residents.
While COVID-19 has hit the rural community hard, Eyewitness News spoke with Gove County Medical Center CEO David Caudill who said recent numbers tell a different story, showing improvements from the past couple of months. In October, Caudill was among 50 employees at the hospital in Quinter who tested positive for COVID-19. In response to a spike in cases, the Gove County Commission on Nov. 23 passed an emergency resolution that requires masks in public.
Gove County Sheriff Alan Weber has been hospitalized since mid-October and has been on a ventilator for weeks at a hospital in Denver (about 280 miles W from Quinter).
I couldn't readily find out about Lansdale (Pa, I presume), but I found the Montgomery county (PA) dashboard, and the state dashboard, which oddly, don't agree (with the state showing higher numbers).
https://data-montcopa.opendata.arcgis.com/pages/covid-19 (Dec 15, 2020)
Montgomery County, Pa (pop. 830,915 (2019)), Lansdale, Pa (pop. 16,707 (2018))
Positive cases 27394 cumulative (3.30% of population)
Hospitalizations 536 current
Active Cases ??
Deaths 941 (since March 7, 2020) 0.113% of population
Alternatively, from the Pa state dashboard with slightly higher number (??). https://www.health.pa.gov/topics/disease/coronavirus/Pages/Cases.aspx
Montgomery County
Total Cases: 29,234
Confirmed: 28,116
Probable: 1,118
Negative: 242,335
Cases per 100,000: 3,528.1
Deaths: 1,018
Deaths per 100,000: 122.9
For comparison, some counties with which I am familiar. Both Wa and NY have some kind of mask mandate, but certainly there are folks who do not wear masks. From observation, the non-compliance to the mandate seems somewhat higher in Benton-Franklin than in Dutchess Co.
Dutchess County, NY (pop. 294,218 (2019)) = 0.354 * Montgomery Co, Pa
Positive cases 9146 cumulative (0.334 * Montgomery Co, Pa) (3.11% of population), so similar to Montgomery.
Active Cases 1366
Deaths 206 (0.218 * Montgomery Co, Pa), 0.070% of population, slightly lower compared to Montgomery https://www.dutchessny.gov/Departments/DBCH/covid-19-dashboard.htm
Benton County, WA (pop. 204,390 (2019)) + Franklin County, WA (95,222 (2019)) https://www.doh.wa.gov/Emergencies/COVID19/DataDashboard
Positive cases 10052 (Benton), 7789 (Franklin) 17850 total cases (5.96% of pop)
Hospitalizations 576 (Benton), 430 (Franklin) cumulative
Deaths 150 (Benton), 77 (Franklin) 227 total deaths, 0.076 % of population
Two very rural counties in WA with low incidence, and not deaths, so far.
Garfield County, WA (pop. 2,247 (2018)) - last county to have 0 cases
Positive cases 61
Hospitalizations 3
Deaths 0
Wahkiakum County, WA (pop. 4,426 (2018))
Positive cases 44
Hospitalizations 0
Deaths 0
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Top 15 Counties and least populous county in NY, by population, Pos. Cov and deaths
County Pop. 2019 Pos. Covid % of pop Deaths % of pop
1 Kings 2,559,903 102995 4.02% 5266 0.206%
2 Queens 2,253,858 104668 4.64% 5251 0.233%
3 New York 1,628,706 53345 3.28% 2180 0.134%
4 Suffolk 1,476,601 76625 5.19% 2132 0.144%
5 Bronx 1,418,207 72409 5.11% 3470 0.245%
6 Nassau 1,356,924 72122 5.32% 2304 0.170%
7 Westchester 967,506 59991 6.20% 1575 0.163%
8 Erie 918,702 33793 3.68% 983 0.107%
9 Monroe 741,770 24293 3.28% 375 0.051%
10 Richmond 476,143 28810 6.05% 831 0.175%
11 Onondaga 460,528 15722 3.41% 288 0.063%
12 Orange 384,940 19892 5.17% 469 0.122%
13 Rockland 325,789 24334 7.47% 554 0.170%
14 Albany 305,506 8214 2.69% 173 0.057%
15 Dutchess 294,218 9482 3.22% 208 0.071%
62 Hamilton 4,416 70 1.59% 1 0.023%
Now interestingly, the population of NY State is ~19.45 million (2019) and number of tests as of today for Coronavirus is 22,316,327. It's possible health care workers have been tested more than once, although I thought the cumulative number was supposed to be one individual.
I don't understand this fascination with small communities (except for the obvious provocation value).
My interest in rural counties, or small communities, relates to several interests. The first five years of my life, I lived in two small towns, then my family moved to the suburbs of a large city. I started university in a large city then transferred to a state university in a moderate size city. In my professional life, I've lived in two semi-rural areas outside of two small cities. When I leave my current job, I'll be looking for opportunities in small rural areas, and I believe that rural areas are underserved, particularly regarding healthcare.
I was interested to see if Gove County actually had a high rate as claimed. I suppose on some per capita level, it may, but I haven't satisfied myself yet. I'm also curious about several areas where I have lived, or had some experience. Some are listed above.
Clearly, some places thought that they were not vulnerable and wearing masks was not a strong local practice. I have limited first hand experience, but having a high proportion of the population wearing masks and maintaining social distancing to the extent possible does mitigate the spread of infection and does save lives. Until one receives a vaccine, the best defense is wearing a mask and limiting social contact outside the immediate family.
One does have to be careful with raw data and statistics, and numbers reported by others.
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Selected small counties in Montana
Rank County Pop (2019) Pos Cov % of pop Deaths % of pop
37 Mineral 4,397 111 2.52% 0 0%
43 Liberty 2,337 97 4.15% 1 0.043%
44 Wheatland 2,126 119 5.60% 6 0.282%
I passed through Mineral and Wheatland Counties several months ago. Unfortunately, I didn't have time to stay. Liberty County is on the border with Canada and is pretty remote. Mineral County has I-90 running through it (but probably not a lot of folks stopping), while Wheatland County has US12 running through it (mostly local traffic).
As of 2020, there are currently 3,143 counties and county-equivalents in the 50 states and the District of Columbia. If the 100 county equivalents in the U.S. territories are counted, then the total is 3,243 counties and county-equivalents in the United States. Ideally, someone would do some big data analysis of the pandemic and determined why some counties had low incidence of infection/mortality and others higher.
Caveat: some data may have been updated while I was composing this post.
The USA Today article contains many anecdotes about controversy over mask wearing, but exactly zero statistics about it. Maybe that was a factor, maybe it wasn't; the article doesn't present a data-backed case. That's how the media works; primarily what gets clicks and views is compelling stories. Data is boring and is best avoided. Facts are nice to have, but aren't critical when crafting a compelling story. The narrative is what matters.
I couldn't readily find out about Lansdale (Pa, I presume), but I found the Montgomery county (PA) dashboard, and the state dashboard, which oddly, don't agree (with the state showing higher numbers).
I track Montgomery County data daily, with my own spreadsheet, so I'm fully aware of the status and evolution of the pandemic here. My point is that data on small towns is not very useful for insightful analysis or broad conclusions. Turns out, Pennsylvania doesn't consider small towns even relevant enough to report data for towns like Lansdale. And why should they? In the half hour drive to my parents' place in Lansdale, I drive through at least 5 towns of a few thousand residents, while remaining in Montgomery County.
Montgomery County, on the other hand, is larger than North Dakota by population. But it's not a state, so it doesn't get listed on charts of states. As if the title "state" means anything when analyzing data.
As of 2020, there are currently 3,143 counties and county-equivalents in the 50 states and the District of Columbia. If the 100 county equivalents in the U.S. territories are counted, then the total is 3,243 counties and county-equivalents in the United States. Ideally, someone would do some big data analysis of the pandemic and determined why some counties had low incidence of infection/mortality and others higher.
I'm certain people will. And I'm certain much of it will be spun to fit agendas, and even when people try to do honest analysis it will be difficult to keep biases at bay and draw salient conclusions. There are a lot of variables to account for and the data is very noisy.
The attached image linked above shows the infection rate by county.
The first wave was not anticipated nor was anyone ready. Also, it hit the coastal areas first. You can't compare the first wave to this one.
The very first case in the US was a few miles from my home, in January! And I think I had it all through February and into March. Comparing that to people getting infected today is illogical. No one was pushing the use of masks until March or April. In fact the recommendation for use of masks was delayed due to shortages for medical workers.
Yes, that's my point. And at the next level, you also can't really compare pandemic totals either, because of the differences between the waves:
1. For case rates, most of the cases in the first wave went undiagnosed.
2. The severity of the first wave impacts the severity of subsequent waves.
There's going to have to be analysis done - and I'm sure it will be - but making sense of the [spread of the] pandemic will not be an easy task.
One thing I'm noticing for the current wave that I find interesting is that while google tells me there are some, I'm seeing a lot fewer reports of temporary field hospital deployment during the current wave than in the first one.
I can't believe people are still arguing "My political tribe is doing better! You just have to look at the data the right way!"
OK, so my numbers proved unconvincing. Here's the scatter plot. Cook Political index is on the x-axis and deaths per million is on the y-axis.
Do you see a trend there? I sure don't. People who are arguing that their political tribe is doing best are starting with the conclusion and fiddling with the data to support it. They sure aren't looking at the above data and drawing conclusions from it.
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Vanadium 50 said:
Do you see a trend there? I sure don't. People who are arguing that their political tribe is doing best are starting with the conclusion and fiddling with the data to support it. They sure aren't looking at the above data and drawing conclusions from it.
Perhaps part of the problem is that people are considering different time periods. When you look at the data from January there is (currently) no correlation as you point out and all states are doing poorly. If folks want to slice up the time intervals trends may be noticed. For example, taking data from July 1 there is a positive correlation (r = 0.5) with the partisan index and death rate (again letting Republican partisan index being greater than zero).
Whether or not the partisan divide plays a cause/effect relationship with any of these sub intervals probably can't be determined with certainty.
I consider this an example of starting with the conclusion, and figuring out what cuts are needed to get the data to show it.
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Vanadium 50 said:
I consider this an example of starting with the conclusion, and figuring out what cuts are needed to get the data to show it.
Maybe. Maybe not. I don't think it is unreasonable to look at different time intervals and I can imagine that some folks looked into this without being motivated by partisanship.
Whether or not a person has a partisan motivation behind looking at sub-intervals might be irrelevant if we communicate more clearly about the specific intervals being observed and follow basic statistics 101: don't draw cause/effect conclusions from observational data.
Maybe. Maybe not. I don't think it is unreasonable to look at different time intervals and I can imagine that some folks looked into this without being motivated by partisanship.
I agree. My complaint is fairly specific to the types of stories posted in this thread recently. They are basically saying; "Red states/counties/towns/households/people are managing the pandemic poorly because they are red." Period. Little or no context and no or cherry-picked data, with heavy emphasis on anecdotal narratives. These are not scientific reports.
If a statement is made that reds are doing poorly "today", then there needs to be a broader context provided to avoid the implication that it's just a political cheap-shot. My first reaction on seeing these stories was; who cares about Bumblefrick, Kansas? Does this really tell us anything useful about the pandemic? Why is this newsworthy? Then I realize I know the answer to that.
I'm seeing a lot fewer reports of temporary field hospital deployment during the current wave than in the first one.
Well, they weren't exactly needed then. And beside, who cares about people dying in Bumblefrick, Kansas. Their plight is only important in that it generates clicks. (OK, I'm feeling cynical)
It's not so clear what can or should reasonably done. Gove County - "Bumblefrick" had 20 fatalities, so they must have had ~40 hospitalizations, even though Kansas says 7. They have 21 beds in that county, so it sure looks like a mismatch. Kansas as a whole has about the same number of hospital beds per person as the US as a whole, but the majority is in the eastern half of the state.
Is plopping down a field hospital with maybe 100 beds really a better solution than sending people to an existing facility if it's 50 miles away? Pre-Covid, people would have to do this (travel to a hospital) all the time for anything non-routine, and nobody seemed to care. Because Bumblefrick.
Oddly, Kansas reports only 7 hospitalizations (Sheridan reports 25) which seems low for 20 deaths.
I believe the 7 hospitalizations reflects 'active' hospitalizations as opposed to cumulative.
I searched and found Gove County Health Department data and made a timeline. Between March 01 and October 04, there were a total of 101 positive cases and no deaths. The first (2) deaths occurred between October 4 and 7. As of November 25, there were 20 deaths, but then none since. In the timeline below, the second active refers to active hospitalizations.
Oct 04, 2020 - 101 positive cases cumulative, 67 active cases; 12 hospitalizations cum., 4 active, and 0 deaths
Oct 07, 2020 - 115 positive cases cumulative, 77 active cases; 16 hospitalizations cum., 3 active, and 2 deaths
Oct 12, 2020 - 136 positive cases cumulative, 63 active cases; 21 hospitalizations cum., 3 active, and 2 deaths
Oct 16, 2020 - 148 positive cases cumulative, 39 active cases; 26 hospitalizations cum., 5 active, and 9 deaths
On October 18, Gove County Sheriff Allan Weber was flown to the Swedish Medical Center in Denver Colorado in respiratory distress due to Covid 19 complications. Upon arrival, he was intubated, put on a ventilator and placed in the Intensive Care Unit.
Oct 21, 2020 - 162 positive cases cumulative, 34 active cases; 28 hospitalizations cum., 6 active, and 10 deaths
Oct 23, 2020 - 171 positive cases cumulative, 29 active cases; 29 hospitalizations cum., 5 active, and 11 deaths
Oct 28, 2020 - 182 positive cases cumulative, 28 active cases; 30 hospitalizations cum., 2 active, and 12 deaths
Oct 30, 2020 - 185 positive cases cumulative, 23 active cases; 31 hospitalizations cum., 2 active, and 12 deaths
Nov 02, 2020 - 202 positive cases cumulative, 25 active cases; 35 hospitalizations cum., 3 active, and 16 deaths
Nov 04, 2020 - 213 positive cases cumulative, 33 active cases; 37 hospitalizations cum., 4 active, and 18 deaths
Nov 06, 2020 - 220 positive cases cumulative, 39 active cases; 41 hospitalizations cum., 3 active, and 19 deaths
Nov 16, 2020 - 245 positive cases cumulative, 29 active cases; 59 hospitalizations cum., 9 active, and 19 deaths
Nov 20, 2020 - 263 positive cases cumulative, 40 active cases; 61 hospitalizations cum., 12 active, and 19 deaths
Nov 23, 2020 - 272 positive cases cumulative, 40 active cases; 62 hospitalizations cum., 8 active, and 19 deaths
Nov 25, 2020 - 283 positive cases cumulative, 49 active cases; 62 hospitalizations cum., 8 active, and 20 deaths
Dec 02, 2020 - 294 positive cases cumulative, 35 active cases; 69 hospitalizations cum., 6 active, and 20 deaths
Dec 07, 2020 - 297 positive cases cumulative, 17 active cases; 73 hospitalizations cum., 7 active, and 20 deaths
Dec 09, 2020 - 299 positive cases cumulative, 17 active cases; 75 hospitalizations cum., 5 active, and 20 deaths
12/14: "On 12/13/20 Allan went into cardiac arrest while he was at PAM Speciatly Hospital. They do not know the exact amount of time he was without oxygen. His code lasted 15 mins after they found him unresponsive, after three rounds of epinephrine and shocking him they were able to get him back. He was transferred to Denver Health and the care at Denver Health has been beyond amazing."
Here's Covid mortality vs. the number of negative tests, state by state:
Wouldn't it make more sense to divide negative tests by population, too?
Number of negative tests is very similar to the total number of tests now. Early in the pandemic that was different.
The mortality rate is correlated with population. As are the number of positive tests (0.96) and negative tests (0.90) However, it turns out the population is only correlated with mortality rate at 0.08, so I suspect that about 2/3 of the effect is random noise.
And random noise is clearly a factor, since the correlation with nonsensical quantities like state names is even larger than the "signal" being bandied about.
The mortality rate is correlated with population. As are the number of positive tests (0.96) and negative tests (0.90) However, it turns out the population is only correlated with mortality rate at 0.08, so I suspect that about 2/3 of the effect is random noise.
And random noise is clearly a factor, since the correlation with nonsensical quantities like state names is even larger than the "signal" being bandied about.
Geography correlates with behavior. There is nothing silly about it.
People who are infected and don't wear masks, spread the virus. Right?
Moderna studied how many people tested positive at the time of the second dose (28 days after the first one). The vaccinated group had ~1/3 the rate of positive tests of the placebo group, suggesting that the vaccine is very effective against asymptomatic infections as well (even after the first dose). That's good news for other mRNA vaccines, too. Source
Vaccination trackers have started. Worldwide - at the moment only the UK has data at 0.2% of the population By US state - they seem to use doses and people interchangeably which confuses me. There should be a factor 2. All data there are plans. Generally the expectation seems to be that states get enough doses by the end of the year to give ~5% of their population the first dose. Most of these will be healthcare workers.
Geography correlates with behavior. There is nothing silly about it.
People who are infected and don't wear masks, spread the virus. Right?
The problem is that the data is complex enough that it may be genuinely difficult to extract conclusive evidence for that; and, someone with a particular poltical agenda can look at the data differently to reject the conclusion.
The best/worst example I can remember of this was when the Scottish government began paying undergraduate university fees. The English Conservative government was/is opposed to this - and in England students have to take out a student loan.
One English Conservative looked at the data and concluded that students from poorer backgrounds were less likely to go to university in Scotland because their fees were being paid!
I've always remembered that because that's about as mad as politics can get.
I might say that, for an unknown reason, Scotland was still struggling to get more students from poorer backgrounds to go to university despite their fees being paid. Not because of it.
I had heard about it but didn't check out the audio until this morning. He does sound a bit upset.
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Vanadium 50 said:
I can't believe people are still arguing "My political tribe is doing better! You just have to look at the data the right way!"
OK, so my numbers proved unconvincing. Here's the scatter plot. Cook Political index is on the x-axis and deaths per million is on the y-axis.
View attachment 274435Do you see a trend there? I sure don't. People who are arguing that their political tribe is doing best are starting with the conclusion and fiddling with the data to support it. They sure aren't looking at the above data and drawing conclusions from it.
I guess, due to the many confounding factors, it is difficult to compare political influences. Rural areas are usually more republican leaning. Rural areas are also less densely populated. And it seems that for the most part, they are getting hit later, with right now being the beginning of a surge. There is also a difference in attitude once it starts hitting. At my hometown, even staff at the clinic weren't taking it seriously and even believed it was a hoax. But as soon as someone close to them was hospitalized, they started freaking out.
I propose instead that you should be comparing based on the measures and adherence to them. If there are compelling correlations with mask use, distancing, etc, and there are correlations with political affiliation and those measures, then you might have something fairly solid to talk about.
In my personal opinion, I think that preventative measures work, but are limited in effectiveness when not taken far enough and/or not adhered to strongly enough. We see clearly from the success of many countries (particularly in Asia) that measures can work very well. But perhaps due to culture differences, many countries tend to have populations which act more or less responsibly overall. And also, which have worse or better guidance and resources.
In places like the US, we see a somewhat half baked response, coupled with half baked adherence. There is no doubt also some political correlation, but the entire county is not doing so good. We have public officials (poor response, messaging, etc) and the public (poor adherence) both to blame I guess. It makes sense to talk about the political influence, but yes, lying with statistics is pretty easy (e.g. cherry picking, not correcting for population density, accounting for time, etc, etc. etc.).
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Borg said:
I had heard about it but didn't check out the audio until this morning. He does sound a bit upset.
It's funny because the day before, there was a lot of media buzz about Tom's cake gifting. He sends cakes to a whole bunch of people for Christmas. I guess that was his PR team doing some preemptive damage control. Even though I doubt it will hurt him anyways.
Same source different chart: top 13 states for death rate are republican and 13 out of 15 of the states with the lowest death rate are democratic.
You've seen the full data. It doesn't show what you want, so you can cut it until it does. "We're going to make it political if it kills us! Literally."
Top and bottom states? NJ and VT. Both Deomcratic. That doesn't work. Top two? Add ME and MA. Both Democratic. That doesn't work either.
OK, let's go to 3. HI and CT. No, that doesn't work. Both Democratic. Number 4? AK and ND. Both Republican. Fifteen states? Still even, although you can make the difference a state depending on what you do with NH (Cook index is zero) and whether you include DC or not.
The full dataset shows no effect. It's possible to get any effect I want by a posteriori deciding my cuts and metric. I can pick my starting date. If June doesn't show what I want, I can try May. Or July. Ten states don't? Try fifteen. Maybe eight. Whatever it takes.
We wouldn't accept that in "real science" and we shouldn't accept it now. (I know, "...but politics is important!")
What's with those Vermonters, not wanting to join the party?
I'm a Vermonter. I'm in Vermont June-October and in Florida November-May.
All summer in Vermont I saw nearly 100% compliance with social distancing and masks. Instantly when I returned to Florida on November 1 I was shocked to see maybe 2/3 of the people in public with no masks. I take that as a clue to how much people changed their behavior.
By the end of November, Vermont (population 500K) had only 2 people hospitalized with COVID.
Odd. It's dated two days ago. Seems like we would have heard about this immediately.
Why? There is no indication that this particular mutation is different from the thousands of others people have seen.
Looks like new daily cases have stopped increasing in the US in December, stable at ~200,000. That means new deaths could stabilize at ~2500/day soon, too (as weekly average).
#4,488
OmCheeto
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mfb said:
Why? There is no indication that this particular mutation is different from the thousands of others people have seen.
...
Why? There is no indication that this particular mutation is different from the thousands of others people have seen.
I posted about that somewhere, either in this thread, or in one of the threads in Biology and Medical forum. The significance is that "The new variant, which UK scientists have named “VUI – 202012/01” includes a mutation in the viral genome region encoding the spike protein, which - in theory - could result in COVID-19 spreading more easily between people."
You've seen the full data. It doesn't show what you want, so you can cut it until it does. "We're going to make it political if it kills us! Literally."
Top and bottom states? NJ and VT. Both Deomcratic. That doesn't work. Top two? Add ME and MA. Both Democratic. That doesn't work either.
OK, let's go to 3. HI and CT. No, that doesn't work. Both Democratic. Number 4? AK and ND. Both Republican. Fifteen states? Still even, although you can make the difference a state depending on what you do with NH (Cook index is zero) and whether you include DC or not.
The full dataset shows no effect. It's possible to get any effect I want by a posteriori deciding my cuts and metric. I can pick my starting date. If June doesn't show what I want, I can try May. Or July. Ten states don't? Try fifteen. Maybe eight. Whatever it takes.
We wouldn't accept that in "real science" and we shouldn't accept it now. (I know, "...but politics is important!")
Are you arguing that masks don't work or that party has no bearing on behavior? Are you denying that events like political rallies and Sturgis were likely superspreader events? I don't understand what you are trying to deny here.
Clearly you can't look at the first wave as you suggested given that no best practices were in place yet. How is that logically consistent? And infections are still spreading wildly, showing by far the highest density in areas that focused on rallies and public events.
Did I say any of that? Tell ya' what. You make your points and I'll make mine. That way you don't have to
A posteriori argument. Feel free to make it, but don't pretend it's anything else.
I posted a map showing the CURRENT infection rates. You are using data going back to the beginning before best practices. Use the data I posted and your argument is moot.
I can't believe people are still arguing "My political tribe is doing better! You just have to look at the data the right way!"
OK, so my numbers proved unconvincing. Here's the scatter plot. Cook Political index is on the x-axis and deaths per million is on the y-axis.
View attachment 274435Do you see a trend there? I sure don't. People who are arguing that their political tribe is doing best are starting with the conclusion and fiddling with the data to support it. They sure aren't looking at the above data and drawing conclusions from it.
There is a striking correlation (at the moment) in the US between political trends and Covid-19 outbreak severity. But you won't find it that way for a couple of reasons:
Trying to find correlation between US states is too broad of strokes. A given state can have large metropolitan areas with a given political leaning while having large swaths of counties in other areas with the opposite leanings; all within the same state. It's better to get a bit more granular, like comparing counties.
It's not useful to include data early on in the pandemic. Very early on, the pandemic was spread by travel hubs, people were unaware of the proper precautions, and none of it was politicized.
Do you remember what it was like for those couple of weeks back in March? The whole country banded together. We were going to beat this virus, or at least pound it into submission for awhile to gain some time. We were all going to do it together. As a team. (Well, everybody except Trump who was spewing out some sort of unsubstantiated nonsense about how we should figuratively stick our heads in the sand because the virus is going to magically go away by Easter, "like a miracle.") Oh, I haven't forgotten those times.
I took the liberty of gathering some information summarizing the virus' spread and mortality rate by date and county, and comparing that to the 2020 presidential election results, also by county. I focused on a narrower timeframe, starting at Oct. 1st, up to the present. That let's us focus on the latest surge. The results mostly speak for themselves.
Figure 1. Scatter plot showing correlation between recent Covid-19 cases and voting trends, per county. Each dot represents a single US county/parish/municipality. (Note: Data for individual counties in Alaska were not available, thus all of Alaska is represented by a single dot.)
The correlation becomes pretty obvious when you look at Fig. 1. Each dot represents a single county. Counties on the right are those that showed larger support for Trump in the 2020 general election. Those on the left showed more support for Biden. A dot at 80 means that in that county, Trump had 80% of the vote. A dot at 20 means Trump only had 20% of the vote for that county.
Dots that are higher on the graph are counties that are getting hit hard by the pandemic right now.
The green, dashed line is a linear trendline confirming the correlation. If the pandemic was independent of voting trends, that line would be level with the horizontal. It's not even close to level.
You might be wondering why most of the dots are to the right of the 50% mark since Biden won the election. That's merely because there are more numerous, sparsely populated counties that leaned politically to Trump. Those to the left of the 50% mark, supporting Biden, while fewer in number, are more heavily populated on average.
Fig. 2 shows a similar plot but for Covid-19 deaths.
Figure 2. Scatter plot showing correlation between recent Covid-19 deaths and voting trends, per county (Note: Data for individual counties in Alaska were not available, thus all of Alaska is represented by a single dot.)
I've also attached a spreadsheet where one can analyze the data, if one wishes.
So what about causation? Well, that's complicated. I mean we all might be tempted to say that the unity the country embraced at the beginning of the pandemic fell apart when the red and tinfoil hat, conspiracy theorists started shouting bloody murder and mouth breathing about how a small piece of cloth on their nose is the most intense hardship they've ever experienced since they lost Civil War I. But I might caution against that since the relationship between correlation and causation can be a tricky one. (But, yeah.)
But since this thread was questioning the correlation aspect of it, I can and will say this: Yes, the correlation is there. It is real and undeniable according to my data.
It's not useful to include data early on in the pandemic. Very early on... and none of it was politicized.
Hehe.
Even if that were true, which is isn't, there's an underlying assumption that the first wave has no impact on subsequent waves. But each wave necessarily lessens the severity of subsequent waves by reducing the pool of potential victims. The more severe the prior waves, the less severe subsequent waves will be.
I suggest to end the Democrat/Republican discussion here. It's questionable if it is within the forum rules, and it's getting really repetitive and it seems to take over the thread. Posting largely the same arguments yet another time isn't helping anyone.
The significance is that "The new variant, which UK scientists have named “VUI – 202012/01” includes a mutation in the viral genome region encoding the spike protein, which - in theory - could result in COVID-19 spreading more easily between people."
... is true, then this ...
mfb said:
"In theory" every mutation can do that, but without any evidence that this particular one does anything it's just one of many.
... is unfortunately wrong, simply because the currently hyped vaccines other than other vaccines primarily attack named spike protein, i.e. it is not clear whether currently planned vaccinations protect against that new strain. However, I saw this on a news channel and the professor who was interviewed said, that it is unlikely that a mutation changes the spike. Maybe he had been caught on the wrong foot and he was not prepared. But he expressed his concerns that it won't remain in UK over the holidays. "It's just one of many" is an a priori assessment. Let's hope it will become an a posteriori assessment, too.
... is unfortunately wrong, simply because the currently hyped vaccines other than other vaccines primarily attack named spike protein, i.e. it is not clear whether currently planned vaccinations protect against that new strain. However, I saw this on a news channel and the professor who was interviewed said, that it is unlikely that a mutation changes the spike. Maybe he had been caught on the wrong foot and he was not prepared. But he expressed his concerns that it won't remain in UK over the holidays. "It's just one of many" is an a priori assessment. Let's hope it will become an a posteriori assessment, too.
There are already mutations in the spike protein. Currently, they are thought not to affect the spike protein enough to affect vaccine effectiveness. These mutations do affect the behaviour of the virus in cell culture, but at the clinical level these are thought to be of minimal significance.
In principle, spike protein mutations can cause a problem for vaccines, however, back of the envelope estimates by Trevor Bedford suggest that this is likely to take a few years:
"If I had to guess, I would predict that #SARSCoV2 will behave similarly to existing seasonal coronaviruses in its ability to mutate to avoid vaccines and immunity.
...
So, my prediction is that we should see occasional mutations to the spike protein of #SARSCoV2 that allow the virus to partially escape from vaccines or existing "herd" immunity, but that this process will most likely take years rather than months."
United (I believe other airlines require the same) requires all its passengers to complete a "Ready-to-Fly" checklist before checking into a flight, which prompts them to acknowledge they have not tested positive for COVID-19 nor have they had any related symptoms in the past two weeks.
Yet, a man with symptoms of Covid-19 died on a flight.
United Flight 591 (Orlando (MCO) to Los Angeles (LAX)) was diverted to New Orleans on Monday after a male passenger became ill. Paramedics transported the individual to a local hospital where he was pronounced deceased, the carrier said.
If he had symptoms, he shouldn't have been flying.
If he had symptoms, he shouldn't have been flying.
This virus is an excellent measure on how far a society is altruistic or selfish. All individual measures are mainly to protect others rather than oneself. E.g. people who refuse to wear a mask and protest against it justify that with their personal freedom. A freedom to the expense of others. One should not complain about it because it is in accordance to the general commitment on individualism versus social responsibility.
