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Coronavirus Real Time Map

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Re: Coronavirus Real Time Map

#111
post #93

Earlier quoted context omitted.

China has stated that they think it can be contagious before symptoms. The US has stated as of yesterday (Jan 27) that there is no evidence for that yet.

The German case confirms it is. A woman (from Shanghai, infected by her parents from Wuhan) gave a training seminar in Munich. She transmitted the virus to her German colleage (33 yr), but started to feel ill only on her flight back to China. (The man developed symptoms over the weekend, but his state improved, so he actually went to work on Monday, was hospitalized on Tuesday.)

In case anyone wants a link to a source for the above comment, here you go:

https://www.dw.com/en/germany-confirms-human-transmission-of...

Re: Coronavirus Real Time Map

#112

Earlier quoted context omitted.

I think their point is that with such a high reproduction number, the impact of anyone slipping through is amplified so much that reduction in travel alone will not do much to stop the spread.

> reduction in travel alone will not do much to stop the spread. This is exactly what you can't conclude from the analysis that was done, the numbers matter significantly. If a 99% restriction locally gives a 25% reduction globally, that tells us very little about what happens with a 99.99% restriction, which is probably closer to what has been achieved.

That would have been nice. The real figure is more like -50% effective: https://nypost.com/2020/01/27/half-of-wuhans-population-fled...

Re: Coronavirus Real Time Map

#113

Earlier quoted context omitted.

What about a 99.99% reduction in travel? Wuhan has 11 million and 1% of that is 100k, I'd imagine the quarantine is more successful than preventing 100k people from travelling.

Figure 4 in the images from this tweet[1] (taken from the same paper above, page 8) show the effect of a 99% reduction in travel, over 65% of people in cities across China will become infected. [1] https://twitter.com/DrEricDing/status/1220919589623803905

The guy needs to Chill T F O. R0 of measles is like 12-18.

And with the city-wide quarantine people should be modelling 99.99% reductions in travel not 99%.

Re: Coronavirus Real Time Map

#114
post #112

Earlier quoted context omitted.

> reduction in travel alone will not do much to stop the spread. This is exactly what you can't conclude from the analysis that was done, the numbers matter significantly. If a 99% restriction locally gives a 25% reduction globally, that tells us very little about what happens with a 99.99% restriction, which is probably closer to what has been achieved.

That would have been nice. The real figure is more like -50% effective: https://nypost.com/2020/01/27/half-of-wuhans-population-fled...

It's not. The model used historical figures from Jan 2017 as its input data, so would also be taking into account these New Year's mass-migrations. Also, this year's stopped at a relative early stage.

Sure, if you want to get precise, you can model a 0% reduction in traffic for the first 10 days, then a subsequent reduction to 99.99%. The numbers will be completely different than modelling a reduction to 99% for the whole period.

Quoting numerical estimates without understanding how the underlying model compares to reality, is just stupid.

Re: Coronavirus Real Time Map

#115
post #112

Earlier quoted context omitted.

That would have been nice. The real figure is more like -50% effective: https://nypost.com/2020/01/27/half-of-wuhans-population-fled...

It's not. The model used historical figures from Jan 2017 as its input data, so would also be taking into account these New Year's mass-migrations. Also, this year's stopped at a relative early stage. Sure, if you want to get precise, you can model a 0% reduction in traffic for the first 10 days, then a subsequent reduction to 99.99%. The numbers will be completely different than modelling a reduction to 99% for the…

Are you replying to the wrong comment? The article I posted is about five million having left the city _before lockdown_, which completely invalidates any model.

Re: Coronavirus Real Time Map

#116
post #115

Earlier quoted context omitted.

It's not. The model used historical figures from Jan 2017 as its input data, so would also be taking into account these New Year's mass-migrations. Also, this year's stopped at a relative early stage. Sure, if you want to get precise, you can model a 0% reduction in traffic for the first 10 days, then a subsequent reduction to 99.99%. The numbers will be completely different than modelling a reduction to 99% for the…

Are you replying to the wrong comment? The article I posted is about five million having left the city _before lockdown_, which completely invalidates any model.

I am replying to the correct comment. Like I said, the model uses input data from Jan 2017, which will contain exactly this same migration as what you're currently discussing. This lockdown is strictly an improvement on the modelled situation. Read the paper describing the model, then come back and reply.

Re: Coronavirus Real Time Map

#117
post #35

Found this report circulating on twitter from a junior doctor in Australia. https://www.medrxiv.org/content/10.1101/2020.01.23.20018549v... Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions > Key findings: > ● We estimate the basic reproduction number of the infection (𝑅𝑅0) to be significantly greater than one. We estimate it to be between 3.6 and 4.0, indicating t…

What about a 99.99% reduction in travel? Wuhan has 11 million and 1% of that is 100k, I'd imagine the quarantine is more successful than preventing 100k people from travelling.

According to published reports (see [1]), 5 million people left Wuhan before the quarantine was implemented.

[1] https://www.scmp.com/news/china/society/article/3047720/chin...

Re: Coronavirus Real Time Map

#118
post #94

Earlier quoted context omitted.

Western news media are currently reporting ~100 dead and ~4,500 infected (though that number is surely much higher in reality). This is a ~2% mortality rate. Can we naïvely extrapolate that we expect ~4,000 casualties from ~190,000 infections? I'm not good at understanding numbers, but I'm sure someone here can chime in with a better way to read this.

You can naively extrapolate that, but it will be, well, a naive extrapolation. Not necessarily a bad thing, but it won't necessarily be accurate. If the virus does get into, say, the US, but it happens to only infect 20-40 year-olds through office transmission, it probably won't even be that fatal. If it happens to get into a senior home, it could be a great deal more deadly. And that's before we consider the spoiler…

[deleted]

Re: Coronavirus Real Time Map

#119
post #41

For those who want something a little more mobile-friendly, we just built a similar map over the weekend: https://coronavirus.app/

U.S. map looks off. The case you have near Washington D.C. should be in Snohomish county, Washington. You should have two cases in CA, one in Orange county and the other in Los Angeles county, not in San Joaquin.

It seems both maps have same issue re: CA infections, which suggests that there might be bad data.

Re: Coronavirus Real Time Map

#120
post #64

Earlier quoted context omitted.

> It's 3-4 orders of magnitude more deadly than the normal seasonal flu Citation?

Influenza deaths are 2 per 100,000. source: https://www.cdc.gov/nchs/fastats/flu.htm SARS death rate is around 10% source: https://en.wikipedia.org/wiki/Severe_acute_respiratory_syndr...

You are comparing apples to oranges.

Here's a better set of comparisons:

Deaths per 100,000:

Influenza: 2

SARS: 0.22

https://www.cdc.gov/nchs/fastats/flu.htm, https://en.wikipedia.org/wiki/Severe_acute_respiratory_syndr..., https://en.wikipedia.org/wiki/South_China

% of deaths of hospitalized people:

Influenza: ~10%

SARS: ???

https://www.cdc.gov/flu/about/burden/index.html

% of deaths of diagnosed people (~CFR):

SARS: ~10%

Influenza: 0.1%-10% per strain

https://en.wikipedia.org/wiki/Severe_acute_respiratory_syndr..., https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3809029/

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