Honest question: if you and your kids use YouTube to the degree you describe, why not pay for the ad free experience? Why go through so many hoops? Is it the amount of money, is it the principle, or something else?
People are mentioning this is due to lack of testing, but if that were the case wouldn’t hospitals be overwhelmed like they have been in Italy and elsewhere?
Testing or no testing if hospitals run out of ventilators for people in need, or out of masks, that would still happen if the disease was spreading like it does elsewhere
Note: that article also lists number of ICU beds per 100000 population, and it seems that it's not that highly correlated. Turkey has the most ICU beds.
I think due to the nature of (generally) exponential growth, no country can be expected to be able to "cover" a true outbreak. However, it's also true that the spike in cases will only happen in a very short period of time so it's not very informative to look at hospital beds if the peak is not there yet.
That said, I would expect Japan to be further on the growth curve compared to other Western countries, so not sure what's going on especially given the limited information.
I have no real data to back this up, but it could also be that there's a lower number of comorbidity across the average population. Or it could be that Japanese have perfected 'social distancing'.
I don’t think there will ever be a way to make a “big decision” with thorough analysis that will make you certain you made the right choice. There are too many variables to ever know for sure the “right” choice
A very poignant if cynical poem by the Greek poet Kavafis on this topic:
You said: “I’ll go to another country, go to another shore,
find another city better than this one.
Whatever I try to do is fated to turn out wrong
and my heart lies buried as though it were something dead.
How long can I let my mind moulder in this place?
Wherever I turn, wherever I happen to look,
I see the black ruins of my life, here,
where I’ve spent so many years, wasted them, destroyed them totally.”
You won’t find a new country, won’t find another shore.
This city will always pursue you. You will walk
the same streets, grow old in the same neighborhoods,
will turn gray in these same houses.
You will always end up in this city. Don’t hope for things elsewhere:
there is no ship for you, there is no road.
As you’ve wasted your life here, in this small corner,
you’ve destroyed it everywhere else in the world.
I can tell from personal experience that if you change your environment and have new experiences, you will also change. Changing a big part of your life (job, location, relationships) makes it easier and sometimes necessary to also change your behaviour. Changing your behaviour in a big way also means to change your personality. However, if you're the kind of person who moves a lot, you're not really changing anything when you move. It's about doing scary things you've never done before.
This is a library that provides access to Matlab-like plotting functions for easily creating javascript plots from within Python.
No need to deal with HTML, Javascript, or CSS to get some plots with multiple linestyles, colors, markers, and labels. Just plain Python code and a simple plotting syntax. The currently supported Javascript plotting libraries are Flot and NVD3 (which is a wrapper around D3).
One use case for this library is to have Python scripts running automatically and updating websites that serve Javascript plots to visitors.
For example, we used this library to create and update the plots on prespredict.com. During US presidential election seasons, a cron job calls the plotting script daily, which automatically updates the plots on the website with data from the latest polls.
The resulting scripts may not be "Pythonic" enough, but I was aiming for the simplicity of creating plots that Matlab has.
I did look at some empirical data to derive the shape of the curve, though the derivation is not as thorough as it can be. If I get time to make the derivation more formal, I'll post in in the methodology section.
One difference I have with 538 is that, I believe, they have some model of the state results, maybe with some correlation, and then simulate the election many times to come up with the PDF of results.
On the other hand, I assume the state results are independent (conditioned on the latest polling) and so I am able to derive a closed form calculation of the PDF, with no simulations. So the independence assumption makes my calculation easier, though may not be as accurate as a model with correlation between states. However, the model has done quite well for the past three elections, so maybe the independence assumption is not that bad.
I sorted the data by which occupation marries within the same occupation the most:
13.9% Physicians and Surgeons
10.7% Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders
10.1% none
9.7% Farmers, Ranchers, and Other Agricultural Managers
9.7% Lawyers, and judges, magistrates, and other judicial workers
9.5% Miscellaneous Personal Appearance Workers
9.3% Veterinarians
8.8% Dentists
8.5% Miscellaneous agricultural workers including animal breeders
8.4% Postsecondary Teachers
7.6% Software Developers, Applications and Systems Software
7.6% Health Diagnosing and Treating Practitioners, All Other
7.5% Optometrists
7.3% Chiropractors
7.2% Pharmacists
6.7% Elementary and Middle School Teachers
6.4% Food Service Managers
6.2% Agricultural and Food Scientists
6.1% Physical Therapists
6.1% Gaming Services Workers
6.0% Upholsterers
5.9% Communications Equipment Operators, All Other
5.8% Air Traffic Controllers and Airfield Operations Specialists
5.8% Physical Scientists, All Other
5.8% Nurse Anesthetists
5.5% Chief executives and legislators
5.5% Real Estate Brokers and Sales Agents
5.4% Clergy
5.2% Marine Engineers and Naval Architects
5.0% Psychologists
4.9% Lodging Managers
4.8% First-Line Supervisors of Retail Sales Workers
4.7% Miscellaneous Managers, Including Funeral Service Managers and Postmasters and Mail Superintendents
4.7% Medical Scientists, and Life Scientists, All Other
4.6% Secondary School Teachers
4.0% Textile Knitting and Weaving Machine Setters, Operators, and Tenders
4.0% Podiatrists
4.0% News Analysts, Reporters and Correspondents
4.0% Sewing Machine Operators
3.9% Bailiffs, Correctional Officers, and Jailers
3.9% First-Line Supervisors of Personal Service Workers
3.8% Tailors, Dressmakers, and Sewers
3.8% Economists
3.8% Musicians, Singers, and Related Workers
3.8% Environmental Scientists and Geoscientists
3.8% Property, Real Estate, and Community Association Managers
3.7% Insurance Sales Agents
3.5% Agricultural Inspectors
3.3% Butchers and Other Meat, Poultry, and Fish Processing Workers
3.2% Morticians, Undertakers, and Funeral Directors
Looks like there's a correlation with long hours and isolated work environments away from other peer groups.
Doctors for instance are well known for pairing up during the residency grind since it dramatically drops their interactions with anyone outside of their residency program and also occurs during their late 20's. A perfect storm.
I've lived with two medical doctors, both of whom referred to me, with a doctorate in particle physics but now a software engineer, as the 'real doctor'.
Which for me really highlights the point that, despite using the same title, the word doctor implies two completely different but equally important types of experience.
