idk how these models are done But as a person who has been around data science and forecasting for a while .... I will say that all of these models I’ve seen trying to pinpoint numbers (since January ) ... none of them have any idea wtf is going on. Just today (or yesterday) China added 50% more deaths . Our information that we’ve been using to construct these models from the get go is wrong . What’s their factor for cultural differences? How do they measure following distancing guidelines? I haven’t delved deeply into too many of these models , but I have a feeling they are just AR or some other DGP . And we are only looking at the data and the data isn’t reliable . tldr models are BS .
maybe a month ago I was thinking would it be better to catch it early or later . My thinking at the time was early before hospital capacity runs out . My thinking now is later because better treatment protocols will have been developed .
The point of flattening the curve was never to completely avoid catching it but slow the rate of spread.
They have core models and then look at the actual data - and they shift based on how reality compares to the projections. Places like Texas have outperformed the projections. There's no perfect definition of social distancing or whatnot - but the data from the last few weeks represents the reality of how we're doing social distancing. So regardless of how they define it, if we have fewer deaths than the model projected as of today, then the model shifts downward and better reflects whatever we're currently doing.
My thoughts are that when the data is only a few months old, it's really hard to get a good model going. Maybe I'm thinking like an engineer, but we usually need years worth of data to develop good design standards for the future. Unfortunately, we don't have that kind of time here, so models will always be reflective of whatever data you have. Considering the immense amount of variables that data can have, it's no wonder that **** seems to be all over the place. It is why people are so impatient. Take Houston for example, we appear to have sufficient bed space, the outbreak is nothing compared to many other parts of the world, yet we are taking some drastic measures. Some people are pretending nothing is wrong, but perhaps we took the proper steps early enough to avoid catastrophe, or maybe we didn't have that one case that could of ****ed us all by community spread. It's all just variables that are hard to account for and there isn't a perfect science to it.
Very interesting findings from this early study but I will wait for more studies before jumping to conclusions. I would imagine the results were also skewed due to sample bias as Santa Clara County was a hot spot and perhaps people who were sick who sick recently were more likely to get tested. I look forward to seeing more of such test results in the future.
I cut a bit but you are on my train of thought . For sure . There is no way we know enough or have enough data to make “good” models there is a huge amount of volatility AND the fact we are dealing with time series just makes **** more complicated .
yes, slowing the rate of spread and at the same time trying not to overwhelm the hospital system. As contagious as this thing is, people will inevitably be infected, sooner if not later.
Yeah, running numbers without the data, or running it with inadequate testing/cases counted different ways/protocol etc etc makes this a mess, especially when you look outside of the states it's even worse if you're trying to make data/stats analysis, like your error bars/p value would probably be off the chart. I do like that they're trying, but I'll be interested to see how this looks once we open back up, I mean too early and there's major hotspots and now the date gets pushed back etc. With that said, if they were trying to get case data from some of the earlier countries hit for projection models - it's still rough, China unfortunately didn't do anyone any favors with their lack of reporting/transparency.
they are as “BS” as their input (which has changed for many reasons)... you give me one model that hasn’t changed (adopt to new info) and I would said that model is BS
Let me fix this title for you Of 3,300 people in California county up to 4% found to have been infected by the common cold in the last 1 year. There.
Ultimately, to reduce the death rate. Take a worse case scenario where there is never a vaccine, then flatten the curve doesn’t reduce the number infected but by not have a pile of cases on top of each other, resources can handle it. If it can’t - you get into trouble, like Italy did (CFR is 12% there). Of course, chances are you do get a vaccine and thus you also get less infected.
But there was a pre print study that made the point that the IHME model looked worse the MORE data they put in. In the beginning their projections and predictions were closer but the more time it passed and more data they put in the less it could project reality. I think it is just impossible to have a model for all the states and countries. Each different area needs their own model. This kind of model doesn't take into account many parameters that can affect the Ro and the mortality vastly. For example the societal and cultural parameters. How many people in Houston live with their grandparents? How many people in Houston take public transportation? How many elderly homes there are? How densily built is the city? What is the % of the over 60s? What is the percentage of obesity, diabetes and ethnic minorities? etc etc
Well there are some populations that must completely avoid catching it otherwise there will be a butcher house. Take France and Belgium. More than 1/3 of their deaths are from elderly homes. The army entered elderly homes in Spain and found hundreds of dead bodies. The virus should by all means possible be hindered from entering such environments. It is not the same as a young person catching it. The elderly people and the immuno suppresed and the obese must avoid catching the virus no matter what because it is a matter of life and death and there is little medicine can do to help them survive it.
