Log-log of daily growth vs total cases is a neat graphics hack, one I'd not seen before, let alone thought of.<p>FT have been headlining a semi-log plot of <i>deaths</i> per country, normalised to days after the first ten deaths were reported.<p><a href="https://www.ft.com/coronavirus-latest" rel="nofollow">https://www.ft.com/coronavirus-latest</a><p>Because dead bodies tend to behave characteristically, are inconvenient both directly and via surviving relations, and present a smaller testing target (about 1% of total cases) as well as representing a full course-of-illness endpoint, these data should be generally more reliable and cross-regionally consistent than confirmed cases. Deaths are, however, lagged by about two weeks.<p>FT also provide numerous other graphical representations, including an excellent small-multiples (Tufte fans) matrix of multiple countries' case trajectories.<p>My view is that all serious reporting should lead with similar visualisations.<p>Wikipedia's COVID-19 pages have similarly featured semi-log plots from early on, as does Worldometers.<p><a href="https://en.wikipedia.org/wiki/2019–20_coronavirus_pandemic#Diagrams" rel="nofollow">https://en.wikipedia.org/wiki/2019–20_coronavirus_pandemic#D...</a><p><a href="https://www.worldometers.info/coronavirus/" rel="nofollow">https://www.worldometers.info/coronavirus/</a><p>(Numerous additional pages with regional and specific behavioural characteristics within both sites.)<p>Anoter data visualiser, allowing arbitrary multi-country comparisons:<p><a href="https://rys.io/covid/" rel="nofollow">https://rys.io/covid/</a>
This kind of analysis is also called a phase space plot: the function is plotted against its derivative. And when the function is an exponential growth then the derivative is the same which gives a similar plot for all the different countries. When the function deviates from the exponential like in China you can spot the difference very early in these kind of plots.<p><a href="https://en.wikipedia.org/wiki/Phase_space" rel="nofollow">https://en.wikipedia.org/wiki/Phase_space</a><p>or for an example:<p><a href="https://en.wikipedia.org/wiki/Duffing_equation" rel="nofollow">https://en.wikipedia.org/wiki/Duffing_equation</a><p>or in math notation: the governing equation for exponential growth is :<p>y' = ay<p>which is a linear function where the slope is the growth rate. The plot shows y' vs y. This is the straight line in the plot. Any deviations from exponential growth can be easily spotted now.
There's a nice video about this on the minutephysics channel: <a href="https://www.youtube.com/watch?v=54XLXg4fYsc" rel="nofollow">https://www.youtube.com/watch?v=54XLXg4fYsc</a>
Title should read 'confirmed Covid-19 cases by country', that makes a very large difference. Those figures are not to be trusted to begin with so any kind of processing you apply to them does not result in graphs that output a picture that you can then draw conclusions from.<p>Each country has their own standards in what is a confirmed case and what isn't and some countries actively discourage accurate reporting.
The number of confirmed cases is not really a great value to track, because countries test differently and change tactic after a while.<p>Look at this graph for instance, number of tests per million people vs number of confirmed cases per million people. They're highly correlated, which means the more you test the more confirmed cases you'll have. In some countries it's the opposite, the more cases you'll have seeking medical assistance, the more tests you do.<p><a href="https://ourworldindata.org/grapher/tests-vs-confirmed-cases-covid-19-per-million" rel="nofollow">https://ourworldindata.org/grapher/tests-vs-confirmed-cases-...</a>
Does anyone know of a source for <i>hospitalisation numbers</i> by country? I know some states in the USA provide this (<a href="https://covidtracking.com/data/" rel="nofollow">https://covidtracking.com/data/</a>) and some countries in Europe do the same, but I can't find a site that collects all this data.<p>It seems to me that if you want to eliminate the effect of the totally different testing strategies (which moreover vary substantially over time), then hospitalisation numbers are far more indicative of the spread than positive test results. At least in the sense that you can compare them on different days.
The most transparent number is a direct comparison between deaths last year and deaths this year, in the same timespan (eg. Jan 2019 vs Jan 2020, and so on). But there is a lot of gamesmanship at play among nations, both for internal security and external geopolitics reasons, so these numbers are too much too often false after convenient miscalcultation or plain manipulation.
I’m not sure what to make of this. “Lots of distributions give you straight-ish lines on a log-log plot” (<a href="http://bactra.org/weblog/491.html" rel="nofollow">http://bactra.org/weblog/491.html</a>) so it isn’t surprising that the slopes of the lines are somewhat constant over time.<p>Because taking the logarithm is such an equalizing operator, I also doubt whether it is surprising that lines seem to overlap for each country. Zooming in, there still is a difference of about 20% in new cases/total reported cases between countries, even in the range of 5k-10k total confirmed cases. Taken over the course of multiple days, that can make quite a difference.
I strongly distrust any figures from pretty much any country without a complete and transparent testing regimen - (Hi South Korea, you know what you're doing!). The wide variance of testing protocols, even within countries - is going to kill our ability to really do much with these numbers.<p>I'd be far more interested in _death_ rates. I.e., what was the normal death rate, and what is it now? It's not sexy, it needs to be seasonally adjusted, and it's subject to noise, but it's a much better heuristic than "covid case", because the base number isn't as gameable.
Even within US (Washington - my state) has way lower numbers than New York even though it was hit first. The first reported case and death weren’t far from where I live.<p>So either Washington has flattened the curve or we’re doing a lot fewer tests than New York. I know a couple of friends who have covid-19 symptoms but haven’t been tested since there aren’t enough kits and they are quarantining themselves at home.<p>So my guess is Washington cases number are at-least 2X higher than what’s reported.<p>Overall at this rate US will hit a million reported cases in a couple of weeks. It seems we are the country doing a great job at testing and reporting but the virus is spreading like wild fires in metros.
One plot I was hoping for by now is a back-in-time plot. Something that assumes for each case we find positive today, we assume that person has been polluting the world with covid-19 viruses in an effort to track events and spread based on todays data with the notion that they really caught it 2 weeks ago.<p>The idea is that even if we started social distancing about 7 days ago, we won't see any benefits to that for another 7 days (since the average symptom time is about 2 weeks). And so any spikes you see in these graphs is all of the people that got infected 2 weeks ago, and aren't really sick until right now.
Wouldn't it make more sense to compare new cases vs active cases? At the end of this plot you can see China's new cases increasing again, but the number of total cases is so large that the x axis doesn't move.
If only it was true.<p>All of these articles need to say ‘tested’ and ‘published’ cases. If you aren’t testing randomly, or if you aren’t publishing (China), then the data is really showing the rate of testing of sick people.
There is also explanatory video about that graph on minutephysics Youtube channel:
<a href="https://youtu.be/54XLXg4fYsc" rel="nofollow">https://youtu.be/54XLXg4fYsc</a>
Wait, but if you plot the total number of cases with respect to time on a log-normal scale, you're still going to get a straight line, right? So why is plotting against time a bad idea?
One thing the I don’t see discussed is imho analysis should not rely on a single plot. Multiple different plots describe different thing.<p>Let’s use “all the plots”
Its crazy how correlated the outbreaks are sort of regardless of policy differences outside of China.<p>So correlated that it also makes you wonder if Japan hasn't been sweeping a lot of cases under the rug trying to salvage the Olympics. It will be interesting from here to see what happens with their cases and if they magically "rejoin the line."
Tell me why I'm wrong, usually the more a chart is 'perfect' the more the data is 'averaged' and so the less you can actually see what's going on<p>Also this chart is about number of cases, which is not a good way to compare evolution between countries as all countries have different test method
Did anyone else notice how worldometers.info, which maintains a table listing number of cases, recoveries, deaths, etc. for each country, ordered by number of cases, had China listed first even after USA surpassed it in number of cases. No explanations were given. Then they removed China from the table altogether. What is going on behind the scenes at that website.