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  • Hey there, everyone.

  • We're about to show you an interview with a mathematical biologist called Kit Yates.

  • It's about Corona virus.

  • If you'd like to see a longer version of full 28 minute podcast, I'll put that on the number filed to YouTube channel, as I usually do.

  • And you can also listen to the podcast in all the usual ways.

  • Just search for number file, and it's the episode Cold Gondor calls for aid.

  • My name's Kate Yates.

  • I'm mathematical biologist.

  • I'm also a senior lecturer at the University of Bath, and so by mathematical biologist.

  • I think people find out about the strange one to deal with it.

  • I think people think that typically matters quite pure and abstract, and biology is pretty messy in real world and never the twain for me.

  • But really, what I do day today is take biological systems that I think are interesting.

  • So maybe anything from a swarm of Locusts to the way the eggs get pardoning, or the way that embryos develop and try and write down the system equations or computer code, which described that to try and make predictions about those systems kicked.

  • You say that people don't often see the link between biological systems and mathematics.

  • I feel like that's changed pretty rapidly in the last few weeks.

  • Everyone is talking about exponential growth, about epidemic epidemiological models, about modeling and basic reproduction numbers that Boris Johnson was talking about.

  • The fast opera tech of the curve on dhe.

  • Yeah, even Colin McGregor I saw the other day was talking about implementing half measures to help exponential growth of the disease in Ireland.

  • So, yeah, everyone is.

  • Everyone is talking about mathematical biology at the moment, so it's a good time to be a mathematical biologist.

  • Is there a degree of that?

  • I know, I know.

  • I'm sure if you had a magic wand, you would make this old girl way.

  • And what a terrible thing this is for the world.

  • But mathematicians kind of thinking at last.

  • People get it.

  • I think that's true.

  • I think people like there's there's a good mean going around where it's just a guy standing a chalkboard during an exponential curve and students saying crowd, when are we ever gonna need this right on it, Sort of the case.

  • It sort of feels like to some extent, a little bit of vindication, although, yeah, of course.

  • Ideally, you would wish it away if you could do, but yeah, I think Mahmut mathematicians tryingto make the most of this opportunity And And to try and show people why must really can be important.

  • It can be a matter of life and death sometimes.

  • Do you feel like this has shown up Sort of a problem with mathematical literacy?

  • Or do you think people have kind of risen to the occasion and they're getting it like, how have you felt about how well the public are understanding some of these things before we do start talking about the matter?

  • Yeah, I think I think people doing an okay job.

  • Actually, I think the fact that people even talking about it and caring about you know how good these models are asking these questions is a really good sign on, but I think it's it's a little bit difficult to explain all the really complex models that are out there at the moment.

  • But I think that actually the basic mathematical models that underlie epidemiological spread, they're actually not too difficult to understand in fairly, fairly straightforward terms.

  • And I think it's our job is must communicate is to try and make those things is is understandable.

  • It's possible.

  • I think there's a lot of good people out there working really hard to do that Corona virus.

  • Let's deal with that for a second.

  • That's exponential growth in the moment.

  • Yeah, I like the way these the simplest mathematical epidemiological models workers you bake, you break the population down to three different compartments.

  • They're called susceptible.

  • So people who haven't had the disease infective through people who have the disease and confected other people on.

  • Then we have this removed category.

  • It's sort of a euphemistic term for people who have recovered.

  • But also people have died as well.

  • This is the sir model we hear talked about, right?

  • This is called S I R.

  • But yes, whatever you wanna call it, really On dhe, we have the very early stages.

  • You have a whole bunch of people who were susceptible to the disease and very few people who are infected, and so each individual can go on.

  • In fact, a certain number of people who were susceptible.

  • We call this the basic reproduction number of disease.

  • And if that number of people that they, in fact, over the course of their infections period is greater than one.

  • Then they will.

  • It'll average, in fact, that many people who will then go in effect, that many people again for covert at the moment on estimates of the basic reproduction number, but between 1.5 and four.

  • So it's quite a big window.

  • The 2.5 is generally sort of acceptable number to the first person goes in effects.

  • 2.5 of the people they're gonna affect 2.5 more on 2.5 more on the on the the exponential growth then occurs you get.

  • You start to see the numbers growing exponentially.

  • What kind of jobs mathematicians doing at the moment like I can see what I can see the job of doctors.

  • And I can see that the job of researchers trying to come up with vaccines I mean the these graphs and models you're talking about being known long before covert came along, and that obviously they're being applied now.

  • But other day to day things, mathematicians can be doing like new work they could be doing to help in this battle.

  • Yeah, absolutely could.

  • Every disease is different, right?

  • And these these simple models that I've described, this s I r model.

  • They can be made infinitely more complex, Right?

  • So s I r is fine for some very simple diseases.

  • But actually, there's extensions that we can make to those SL models to include things like a carrier class of people who have the disease, but on unnecessarily showing symptoms so they're not necessarily you wouldn't cost them is infected.

  • You don't know they're infected yet, But they've had this asymptomatic period, which is what's happening with Cove it and so they could be spreading it without you even knowing that they're infected.

  • And that could be it could be a real problem.

  • That has been a real problem with Kobe because we can't just isolate people as soon as they show symptoms.

  • We have to be isolating everyone in case they've got symptoms.

  • So that's just one really simple extension you can make adding in things like noise, so stochastic city or randomness in the way that people bump into each other.

  • Taking account of the networks with which people interact with each other can make a huge difference as well.

  • You get these people who are social hubs and therefore will spread the disease to lots of people compared to people who were stuck at home on Don't go out so much so you can make these things arbitrarily complex.

  • And there's actually been a call by the Royal Society for Rapid Assistance and Modeling Pandemics, where all mathematicians, especially applied mathematician to have familiarity of modeling, can sign up on dhe.

  • Help out in this course of the others loads to be done in the moment.

  • And we're keeping really busy kit hair or someone building something like these.

  • These, you know, these hubs super spreader type people, as opposed to people who are locked away like I see that they exist.

  • But how would you put them into a model?

  • These sort of unmeasurable zor like I say, you could guess at what they are.

  • But then how do you know they're not necessarily unmeasurable is we actually know a lot about our social structure, in part from using social networking data, but also just there's been a number of studies done, and you can actually classify the way that people interact using the network So people are notes in that network and their connections that other people are edges.

  • And so you can classify what's called the degree distribution of that network which tells you how many people are there who have 100 contacts.

  • How many people?

  • 99 98 so on.

  • How don't, er, how many people are there with no contacts?

  • And that distribution could tell you a great deal about the way that people interact So you can incorporate that network modeling into these s i R model.

  • So you you start the disease off in a particular node with particular connectivity.

  • How many people did they actually interact with?

  • And you see how it spreads around the network, so they may seem almost intangible.

  • But if we have good enough data and this is what it all comes down to really have good enough data, then we can actually capture capture these behaviors.

  • But data at the moment on this emerging pandemic is really difficult to come by.

  • I mean, it seems pretty obvious that this pandemic is gonna leave a scar on the world and have some kind of some kind of long lasting effect.

  • What about mathematically.

  • Do you think it's gonna have a mathematical legacy?

  • Isn't gonna like cause meth, because I've never seen mathematics being talked about, so I'm actually the global news of it.

  • Yeah, exactly.

  • I think I hope it will draw people into mathematics and make people realize that you if you become a mathematician, you can have a real impact on the world.

  • You're not.

  • Just sat behind the desk right in the tax book for, you know, for a subject which is dead and gone, you're actually living in the real world, and you're doing things which can have a really important impact on people's lives.

  • So I hope that people, future generations of kids who were studying must in school.

  • The moment will say, Well, this is actually this living, breathing, exciting subject on dhe.

  • I want to be part of that.

  • And so I hope we'll get more people into mathematics.

  • Andi.

  • I also hope that people will take months a little bit more seriously themselves, so must generally only gets in the news when it's, you know, the fields medal's times and and you know, people struggled to understand the very complicated math field medals have won the muscle, but actually, you know the moment we can tell people about relatively straightforward mathematical models which are having a huge impact on their everyday lives, of suggesting how we have come to this unprecedented lock down that we've never seen.

  • We've never seen before, so yeah, hopefully it will have a really big impact on people's lives.

  • Just a reminder.

  • The full 28 minute podcast number file to also number five dot com or search number file on your podcast player of choice.

  • I'll also put some links in the video description to some useful videos and information, including the number five video with Ben Sparks, about the Corona virus curve and great ones from some other creators as well, and just a quick number.

  • File update.

  • Before everything went into lock down, we actually did manage to film lots and lots of number five videos.

  • Normal ones, not Scott calls where, editing them at the moment on, they'll be appearing in your feeds very soon.

Hey there, everyone.

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B1 中級 新型コロナウイルス 新型肺炎 COVID-19

数学とコロナウイルス - Numberphile (Mathematics and Coronavirus - Numberphile)

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    林宜悉 に公開 2021 年 01 月 14 日
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