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  • A lot of people have been asking why we designed and built

  • the FT's coronavirus trajectories tracker.

  • And some of these questions come up time and time again,

  • so we just wanted to take five of these

  • and go through the explanations with you today.

  • Question number one is, why are we

  • using a log scale, a logarithmic scale,

  • on the y-axis, the vertical axis?

  • Viruses spread exponentially.

  • So by that we mean it doesn't go from one person infected today,

  • then two, then three, then four.

  • It's more like one, then two, then four, then eight.

  • It rises at an ever-increasing, ever-accelerating rate.

  • And so the great thing about log scales

  • is that they naturally take that into account.

  • So instead of a line that looks a bit like a hockey stick

  • and shoots up into the sky, you get a nice straight line.

  • And now, some people will counter and say, well,

  • doesn't that mean that people are going to be less concerned?

  • They're going to think this is only

  • going up at a steady pace, rather than an exponential one.

  • But I'd say a couple of things to that.

  • The first is that what we want to do with these charts

  • is to inform people and make people

  • aware of the severity of the issue, but not to panic people.

  • And so by showing this on a straight line,

  • we're emphasising that there's an inevitability about how

  • coronavirus spreads.

  • So most countries we're seeing are

  • on this line of cases doubling every two, three, four days.

  • And we want to emphasise that even if there

  • are only a few cases in your country

  • today, based on all the data we have you will end up

  • going along that path, the same path

  • that the likes of Italy and Spain

  • have been on so tragically.

  • So yeah, with the log scale, we're

  • not trying to play down the rate at which it increases.

  • We're trying to emphasise that the exponential nature

  • of this spread is something that we see everywhere,

  • and we're trying to make it easier

  • to see here's where you are today,

  • here's where you might be in five, six, seven days,

  • and how does that compare to other countries whose cases

  • you'll be familiar with, where they were at the same stage.

  • The second question I often get asked

  • is, why aren't we adjusting for countries' population sizes

  • in this chart?

  • So this one is a bit more of a judgement call.

  • What we have with this virus is something

  • which spreads at a fairly consistent rate regardless

  • of the situation on the ground.

  • We tend to see over a certain number of days

  • the same number of cases, after day one, day two, day 10,

  • et cetera.

  • And that's because this virus it does spread fast,

  • but it doesn't, you know, ripple through a country's

  • entire population in a matter of days.

  • So the overall population of a country

  • is not any sort of limiting factor on how fast it spreads.

  • It will tend to spread as the people in those cities,

  • in those areas mix at similar rates at the same rate.

  • Now, we could, of course, still adjust for population,

  • and give you sort of per capita or per million people numbers

  • of cases or deaths.

  • What that would do is essentially just

  • make larger countries look like their outbreaks aren't quite as

  • bad, and smaller countries look like theirs are much worse.

  • With this chart, we're focusing on trajectory.

  • We're focusing on saying where are things right now,

  • where are they going to be in a few days,

  • and how does this compare to other countries

  • that you're already familiar with from following the news?

  • So if we changed to per capita the slopes

  • wouldn't actually change.

  • All that would change is the vertical positioning

  • of different countries' lines.

  • And they would change in such a way that, for example,

  • the American outbreak would look less alarming than it is,

  • and the Danish or Swiss outbreaks would look worse.

  • The numbers that come up in the news, and the numbers

  • that we as humans instinctively react to

  • are numbers of people, numbers of deaths.

  • I think if we start moving into per capita, per million people

  • rates, first of all, you lose a bit of the immediacy,

  • a bit of the sort of visceral nature of these numbers.

  • We would lose that connection with the numbers

  • that we're seeing in the news.

  • We're hearing about hundreds and thousands of people

  • being infected and dying tragically in countries

  • like Italy and Spain.

  • And I want people to be able to see on that y-axis

  • where they are in relation to that, not where they

  • are in relation to some more abstract number, which loses,

  • as I say, some of the sort of emotional power

  • that I hope this chart has.

  • So another of the questions we get asked

  • is, isn't there an issue when we talk about numbers of cases,

  • where the number of confirmed cases in a country

  • is more a function of its testing

  • regime than of the actual number of people infected?

  • We need to be very clear about referring to confirmed test

  • cases, and not just the number of cases,

  • because of course, much as our governments are all

  • trying to test as many people as they can,

  • there are going to be hundreds, thousands

  • of people in countries all over the world who do

  • have coronavirus.

  • They may be completely symptom-free,

  • but they've not yet been tested.

  • And so in the earlier versions of our chart,

  • showing the case trajectories, the y-axis title

  • talked about the cumulative number of cases.

  • Now, a few days ago we actually changed that

  • to the cumulative number of confirmed positive tests,

  • and not ground truth in terms of number of cases.

  • So another thing people have asked

  • is should we not be showing some indication of when countries

  • actually started imposing their various measures to get

  • the virus under control?

  • We immediately felt, yes, we should be doing something here.

  • We need to emphasise when Italy, France, Spain,

  • and so on asked their citizens to be confined to their homes,

  • partly, you know, just to add that important context

  • to the chart, but also because it's then

  • going to be very interesting for us to be able to look at when

  • do those curves hopefully start flattening down, bending down

  • after lockdowns have been put in place.

  • Because of course, the number of deaths, for example,

  • in a country isn't going to start flattening overnight

  • after a lockdown has been instituted, because it takes

  • two weeks or more for someone to go from being infected

  • with the virus to dying, if they unfortunately reach that stage.

  • Another thing we've been asked over the last few days

  • is, can we, as well as showing these national numbers of cases

  • and deaths, go down into countries

  • and look at specific regions?

  • The virus tends to spread in small,

  • but gradually growing pockets.

  • This tends to come from one outbreak and spread from there.

  • So initially, of course, this all

  • started in the city of Wuhan in China.

  • We then saw a nasty outbreak in Daegu in Korea,

  • and then Lombardia was the worst affected region in Italy.

  • So we wanted to get into that with this chart,

  • and look at how different regions have been affected,

  • rather than countries as a whole.

  • When you think about the impacts of the lockdowns that are now

  • being put in place, we're really talking

  • about cities, which are usually these vibrant, dynamic, busy,

  • noisy hubs, falling silent.

  • We're going to be updating these charts, and any charts that we

  • add, daily.

  • So if you have any more ideas for features

  • that we should consider adding or changing,

  • then please give us an email or a tweet.

  • And for the latest versions on the charts,

  • you can go to ft.com/coronavirus-latest.

A lot of people have been asking why we designed and built

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