Name: コロナウイルス曲線 - Numberphile (The Coronavirus Curve - Numberphile)
Uploaded: 2021-01-14T10:17:15.000Z
Duration: 22 min 18 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：新型コロナウイルス,新型肺炎,COVID-19

everyone, not a typical number file video for obvious reasons.

様態の副詞

Ben Sparks, who, ironically, is the person who lives closest to me.

But we're gonna have a talk via technology.

I think there's a lot of videos and check out there about how mathematicians are modeling this spread off the covert 19 virus.

And it's kind of like this sort of house in days for mathematical model is, despite it being a horrible circumstance, everybody is curing upto.

Ask the mathematicians opinion on what's gonna happen next, and I guess it's a shower.

Mathematical modeling is what helps us predict what happens in the world in general.

What I wanted to show you was a way of using one of the standard models of disease spread on free piece of software that people could just try themselves and see that the modeling that people are doing to predict this is is kind of accessible.

I see you've got the honoree brown paper needs must we've got to do something properly.

Number five actually don't have any physical brown paper I could find.

So I had to, you know, get a screen grab of brown paper and put it in joji brah, which I'm gonna use to show you a model off a disease spread.

Is that an image taken from an actual number five for actual noble fell brown people.

So I'm gonna try and build you a mathematical file in this program called Joe Dobro, which anyone can try and follow along with the programs.

I'm gonna use a written on the screen already.

You could see that right s for susceptible.

Meaning a bunch of people that are possibly able to get Aziz I for infected people who have got the disease are for recovered by which I mean are not infected anymore.

To be honest, that doesn't necessarily mean recovered right that they may be dead, which means they're not gonna be infectious anymore.

And that's the sad truth of modeling diseases like this is that there were two ways out.

But either way, you stop infecting people.

So I'm gonna just He's recovered, as long as you know what I mean.

We're gonna build up like organ mathematical models, some sort of simple, almost naive assumptions of how diseases spread and follow the mathematical consequences of those simple things.

Just to make a prediction on DATs the judge, we will do our butt lifting the heavy lifting for us.

Okay, I'm gonna set up a population of size one.

This sounds a little bit confusing, and I'm typing into the top of the thing here and equals one, which is mean I'm using, like, my entire population to me, like 100% 0 to 1 measures some fraction of the population on the whole population.

You can do these models with a large number, but it everything to scale.

So I'm just gonna go with between zero and one.

Which is why my graph is zero and one about here on the screen.

I'm also going to set up some initial set up.

So presumably, there are some infected people in my population of the ways you gotta be asking why we're doing the model.

So I'm gonna start some values off infected and susceptible and recovered in the infected.

I mean, the number of infected people have the star of this.

I'm gonna call 1% which is 0.1 on get there.

So they're susceptible, people start is gonna be end.

I start because that's not 9% susceptible.

That's because no one of this stage has recovered.

So our started zero on those, like my initial conditions for a model here looks a bit cryptic.

I'm gonna set up 23 more variables, in fact, which were used in a minute.

And you'll feel a bit arcane until you realize why.

This slider is gonna represent How quickly does this disease gets transmitted?

The infection rate s so I'm making a slider.

Won't do anything yet, being c I can change this number.

It's gonna be something to do with how quickly infects people and then another slider for how quickly people recover from this disease, which is gonna be smaller because it takes a while to recover from diseases other ways they usually don't really register anymore.

What are the units of time here back that it doesn't actually matter.

Were so we could pick a unit and run with it.

Let's say days, but I'm not necessarily gonna have the numbers right to model this in days.

But then if you change the union time, you just have to change these parameters to reflect that we can do some because they're on sliders.

We can do the playground later on to see if we can change the units.

And at the moment they're called A and bay A and B.

If you like to really help me, help me remember what they are.

This transmission was quite trans or coding has to have a horrible names, right?

but I still typing really long names all the time.

So I've got transmission rate and a recovery rate, and I'm gonna use them to build some night.

What's one more parameter I need, Which is how long I'm gonna let the model run for.

What I'm gonna do is set up three what they call differential equations, which sounds scarier than it should.

But these are equations which tell us the sort of naive assumptions were gonna make about these three numbers.

What I want to know in the end, is what these numbers are over time.

How many susceptible, How many are infected and how many have recovered.

But what I can tell you is straight away what those numbers are.

I can guess that how quickly they're gonna change depending on various things.

字幕リスト動画再生

コロナウイルス曲線 - Numberphile (The Coronavirus Curve - Numberphile)

stuff

sort

susceptible

pandemic