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JOHN BURN-MURDOCH: Many people wonder why we designed and built the FT Coronavirus Trajectories Tracker. And some of these questions come up over and over again, so we just wanted to take five and go through the explanations with you today.
The first question is this: why do we use a logarithmic scale, a logarithmic scale, on the y-axis, the vertical axis? Viruses spread exponentially. So, by that, we mean that it doesn’t go from one infected today, then from two, then from three, then from four. It’s more like one, then two, then four, then eight. It is increasing at an ever increasing and always accelerating rate. And so the great thing about logarithmic scales is that they naturally take it into account. So instead of a line that looks a bit like a hockey stick and goes up in the sky, you get a nice straight line.
And now some people will counter and say, well, doesn’t that mean people will be less concerned? They will think that it increases only at a regular rate, rather than exponential. But I would say a couple of things to that. The first is that what we want to do with these tables is inform people and make them aware of the seriousness of the problem, but not panic people.
And so by showing this in a straight line, we emphasize that there is a fatality on the way the coronavirus spreads. Most of the countries we see are therefore in this queue, doubling every two, three, four days. And we want to emphasize that, even if there are only a few cases in your country today, based on all the data we have, you will end up following this path, the same path as that of Italy and Spain. so tragically.
So yes, with the logarithmic scale, we are not trying to minimize the speed at which it increases. We are trying to emphasize that the exponential nature of this spread is something we see everywhere, and we are trying to make it easier to see here where you are today, here is where you could be in five, six, seven days, and how does that compare to other countries, where you know the cases, where they were at the same stage.
The second question that I am often asked is: why do we not adjust the size of the population of the countries in this graph? So this one is a bit more of a judgment. What we have with this virus is something that is spreading at a fairly constant rate regardless of the situation on the ground. We tend to see the same number of cases over a number of days, after the first day, the second day, the tenth day, et cetera.
And that’s because this virus spreads quickly, but it doesn’t spread, you know, across the entire population of a country in a few days. Thus, the overall population of a country is not a kind of factor limiting its speed of propagation. It will tend to spread as residents of these cities in these regions mix at similar rates at the same rate.
Now, of course, we could still adapt to the population and give you some sort of number of cases or deaths per capita or per million people. What that would essentially do is make the big countries seem like their epidemics are not as severe, and the small countries seem like theirs are much worse. With this graph, we focus on the trajectory. We focus on saying, where are things right now, where are they going to be in a few days, and how does that compare to other countries you already know by following the news.
So if we became per capita, the slopes would not actually change. All that would change is the vertical positioning of lines from different countries. And they would change in such a way that, for example, the American epidemic would seem less alarming than it is, and the Danish or Swiss epidemics would look worse. The numbers that appear in the news and the numbers that we humans instinctively respond to are numbers of people, numbers of dead.
I think if we start moving to the per capita rate, per million people, first of all, you lose a little bit of the immediacy, a little bit of the visceral nature of these numbers. We would lose that connection with the numbers we see in the news. We hear of hundreds and thousands of people who have been tragically infected and died in countries like Italy and Spain. And I want people to be able to see on this y axis where they are in relation to that, not where they are in relation to a more abstract number, which loses, as I said, some of the kind of emotional power that I hope this graphic has.
So another of the questions we are asked is this: is there not a problem when we talk about the number of cases, where the number of confirmed cases in a country is more a function of its regime than the actual number of people infected? We have to be very clear about the confirmed test cases, not just the number of cases, because of course, although our governments are all trying to test as many people as possible, there will be hundreds, thousands of people in countries around the world that have a coronavirus.
They may have no symptoms, but they have not yet been tested. And so in previous versions of our graph, showing the trajectories of the cases, the title of the axis there spoke of the cumulative number of cases. A few days ago, we actually changed that to a cumulative number of confirmed positive tests, and we didn’t base the truth in terms of the number of cases.
So another thing that people asked is this: should we not be showing when countries actually started to impose their various measures to control the virus? We immediately thought, yes, we should do something here. We must emphasize when Italy, France, Spain, etc. have asked their citizens to be confined to their homes, in part, you know, just to add this important context to the picture, but also because this is going to be very interesting so we can see when these curves begin, hopefully, to flatten, bend after the locks are put in place. Because, of course, the number of deaths, for example, in a country is not going to start flattening out overnight after a lock is instituted, because it takes two weeks or more for a person to go from virus infection to death, if they unfortunately reach this stage.
Another question that has been asked to us in recent days is the following: can we, in addition to showing this national number of cases and deaths, go down to the countries and examine specific regions? The virus tends to spread in small pockets, but gradually growing. This usually comes from a single outbreak and spreads from there.
So, of course, it all started in the city of Wuhan in China. We then witnessed an unpleasant epidemic in Daegu in Korea, then Lombardy was the most affected region in Italy. So we wanted to go into detail with this table and examine how the different regions were affected, rather than the countries as a whole. When you think about the impacts of the closings that are being put in place, we are really talking about the cities, which are generally these dynamic, dynamic, busy and noisy centers, which are silent.
We will update these charts and all the charts we add daily. So if you have any other feature ideas that we should consider adding or modifying, please email or tweet us. And for the latest versions of the graphics, you can go to ft.com/coronavirus-latest.