Kolek planning to go pro
I think people are working hard to find reasons beyond the obvious. Southern states have less buy in on things like wearing masks. The result, more cases. I think that is reason number 1, particularly as it relates to young people who are a vector of spread.
Now explain California and Washington state.The point I'm making is this is a multi-faceted issue and the reductive nature of the discourse(its cause we opened up, yeah but protests, no it's really young people, etc. Etc etc) is part of the problem.
We just need to learn to live with Covid-19. It is not going away anytime soon and I do not see us going back to lockdown. This is where a unified country would be nice.
Identifying the risk factors is actually really straight forward as you've largely identified them. As you have more risk factors and modified by your behavior that gives you an idea of what your risk is for A) contracting it and B) the likely impact if you do contract it. We have enough data to model that and basically do a risk calculator for people. I'd be very interested in a Google project or WebMD or something along those lines to allow me to go determine my risk model.As to the blood type, if you read the article you linked and the supporting articles the blood type is very likely a correlation but not causation.
Correct - I never said otherwise. But if the correlation proves correct, it would still mean type A people are at higher risk of contracting the virus, and of more severe illness.
I never said there was only 1 reason. But looking for things like AC to explain the south, is a stretch. There is an obvious reason for why the south is seeing increased cases.For states like California and Washington, we need far more data than I have access to. I know some of the hot spots in California are in rural counties, where again buy in on masks is limited. You also have a large number of people that are now returning to work, that must take mass transit. There are a variety of factors at play and one needs very localized data on hot spots (which isn't always available) to discern the effects. Large swaths of the south though have a glaring obvious answer. People refuse to wear masks, the single most effective way to limit the spread of the disease.
I wake up every morning and the sun also rises in the east every morning, the correlation is correct but it don't mean $hit
Wasn't the heat and sun supposed to eradicate this? I'm not saying this to be snarky. I know Trump and others were saying this as a fact when actually it was more hope than anything, but lots of "experts" were saying it, too.And yet, the worst situations right now when I look at my handy-dandy "coronavirus reopening map" seem to be in Arizona, Nevada, Arkansas, Alabama, South Carolina, Florida, North Carolina and Louisiana.I don't think it's a coincidence that, by and large, this generally is "Eff the masks and social distancing" territory, but I allow that I could be wrong.As someone pointed out, Wash and Oregon look to be trending worse, too, though their arrows aren't pointing up as sharply as the others.
Everyone is assuming that because if it isn't true the places where there were protests are unnatural carnal knowledge$D. Honestly, there is no evidence that outdoor transmission is much of a thing (mask protests, only one infection as the result of the Ozarks Memorial Day fiasco, beaches and parks being open for a couple of months, etc).I honestly can't say why these regions are going up because the data we have access to is inconclusive or missing:-The increase is both with early open states(Texas) and late open states (Washington)-I've seen no data on the consistency/usage of masks-I've seen no data on outbreaks from outdoor versus indoor sources-Limited demographic breakdown of the current outbreak (ie who is getting infected and/or hospitalized).etcBut ultimately this is why I'm fighting all the various narratives because we just don't know and the narratives are largely driven along political lines. A couple of hypothesis off the top of my head:1. It takes at least 6 weeks from "paradigm shift" (re-opening, protests, etc) for it to show up in the infection/hospitalization rates2. There are individual/localized hot spots that are triggering overall numbers to go up but not indicative of some paradigm shift3. We've had a series of events that have created a growth funnel for infection that continually builds the infection rate (reopening, then memorial day weekend, then protests, then.....)4. There is a mutated strain lose in these areas that is at least more infectious or resistant to our counter measures than we realize5. Compliance exhaustion.....people are taking less and less precautions and being less diligent in their activities because it's been 4 months and/or they think it's passed/overblown.6. Some combination of all of the above7. Something I haven't thought of.
You obviously don't understand medical risk factors. They are simply identifiable traits, behaviors or other factors that make someone more susceptible than the average person. So it doesn't matter if it's causative or correlative - a risk factor is a risk factor. And thus far, the data seems to show that having type A blood might be a risk factor for covid.And seriously - do you think the person on his or her deathbed from covid really gives a $hit if he is about to die from covid because he was obese vs having type A blood? Seriously?
Eng no one knows for certain — Your hypotheses probably have some truth in them. If there is a group that should have a POV, it’s the CDC. Truthfully the only thing that keeps coming up everywhere as a risk reducer is wear a mask. My guess is the doctors figure out better treatments before we figure out the best public policy to battle this.
In the good news category, I do think doctors are starting to figure out how to treat this. Conversion of infections to hospitalizations (even when adjusting for testing volume) is lower now than it was in March and Conversion of hospitalizations to deaths is lower now than it was in March. I don't think it's because the virus is mutating but what do I know.
Not sure what your last line has anything to do with anything I said, but I would find it unlikely that a dying patient will care.medical risk factors are the same as any other risk factor....statistical probability that having condition A will result in outcome B. My point is that there are so many mechanisms of risk that are encompassed in blood type it may not be blood type that determines the outcome or that a certain blood type has higher risk than another. As an example from the article, Type A people have Type B antigens and vice versa while Type O has both, and perhaps transmission from one person of a blood type to person of another type is less prevalent/severe than transmission between two people of the same blood type. In that scenario the fact that someone has Type A only matters in that if they get infected by someone with Type A blood their viral load or whatever is higher than if they were infected by someone with B or O types.Simply put, I'm highly skeptical, based on the data analysis I've seen, that in the long run it will turn out that the virus attacks people with Type A more than any other blood type. Is there a mechanism based on blood type that impacts transmission in some way, seems logical but I haven't stayed in a Holiday Inn Express recently. This is why correlation versus causation is so important. More people of Type A blood have been infected than others (of confirmed infections) is that because Type A is riskier or is that because more Type As came in contact with Type As or is there no difference is transmission and Type As for some reason experience worst outcomes than any other blood type (your theory). Don't know, but there is definitely not enough evidence to say anything conclusively and add it to the list of risk factors.
Scott Gottlieb has been super vocal that it is treatment based. His opinion is the biggest learning/breakthrough thus far has been the early scanning for blood clots & immediate treatment with blood thinners, etc (of course steroids may make things better if proven effective). .
Here is where you lose me every time.....when the data sets match your hypothesis it's tied to a single root cause (masks) when the data sets don't match your hypothesis it's tied to all sorts of root causes that can't be separated ("variety of factors at play").Maybe you don't realize it, but you can't separate your political narrative from your situational analysis (you have a conclusion in your head and you are fitting the data to match that conclusion)