One reason is that daily increases are very volatile/noisy. The standard way to handle this is to use n-day moving averages, but they have a time lag and are harder to interpret.

Financial Times has the best graphical trackers I've seen. They used to show cumulative counts, but switched to 7-day moving averages of daily increases" recently.

https://www.ft.com/coronavirus-latest

I agree that displaying increases/growth rates are more informative overall, but it does take more time to understand the metrics. (Just compare the descriptions: "Total Deaths" vs "7-day moving average of daily increase in Total Deaths")

Not really. Data for Spain can be perfectly used and fit without averages on daily increases => https://media-exp1.licdn.com/dms/image/C5622AQH1JmaVMs3mqQ/f...

It's unclear what that graph is supposed to show. It can't be cumulative cases (because there are days with decreases), but the scales are completely off if they're supposed to show daily increases/diffs (Spain certainly did not see daily increases anywhere near 100k in the past few days).

On the other hand I don't understand how bad it is from looking at the graph with the easy to understand metric.

Yeah I came by to say the same thing. I understand that "cumulative" and "dead" are super sensationalist, but what really matters isn't how many people have had it, but how many people have it right now and how that number's changing day by day. It's impossible to figure out and it makes this chart very misleading.

For instance, China's hovering around 10e5 cases on the chart, but they have 2e3. That's three orders of magnitude misleading, especially when 95-100% of people recover completely and are quite unlikely to get it again.

tl;dr: China features prominently on those graphs but their outbreak is contained and finished, they've re-opened internally and pollution (hence factory output) is returning to normal. The graph doesn't reflect that in a meaningful way. [1]

[1] https://www.forbes.com/sites/jeffmcmahon/2020/03/22/video-wa...

I agree! Cumulative charts are less useful. For my charts, I represent new cases or new deaths per day. It shows such a clear picture of the trend of the growth:

https://mobile.twitter.com/zorinaq/status/124578701743622144...

Do you have these charts for the main countries? From your chart it looks like Italy and Spain have reached the peak, US is still growing, Germany may be just about to peak. But it is hard to read.

The FT graphs show the increases/growths/1st-derivative. Italy and Spain are likely over peak-growth, Germany and France nearly, UK and US not yet. These are 7-day rolling averages, so there's roughly 3.5 days delay in the estimated signal.

https://www.ft.com/coronavirus-latest

Daily numbers have problems too, they can get quite noisy. Both have their uses, particularly seen together. I also think linear scales have their place alongside log (which can be hard for laypeople to grasp). Here are daily numbers for France for example:

https://coronavirus.projectpage.app/france?period=28

The jump recently is due to a change in reporting to include deaths outside hospital (something most countries have yet to do), and these are reported deaths not actual, and subject to all kinds of caveats. Reported cases is even worse. Recovered cases is worst of all and not being properly tracked in most areas.

A moving average of daily cases might be best. I think the ft (who have nice graphs of this along with commentary) are moving toward this.

https://www.ft.com/coronavirus-latest

There's a nice commentary on what they're doing here:

https://mobile.twitter.com/jburnmurdoch/status/1246184639540...

Just in case you haven't seen it, I'm a big fan of this [0] chart, which plots new weekly cases against cumulative total, both axes on a log scale. It has a whole video explanation [1] if you're interested in the logic behind it.

[0]: https://aatishb.com/covidtrends/

[1]: https://www.youtube.com/watch?v=54XLXg4fYsc

I'm not too fond of that chart because all you can really see clearly on it is when the increase stops being exponential. It is pretty hard to see the base of the exponential (in most countries the increase in confirmed infections has been slowing down, relatively speaking, but that is incredibly hard to spot from that graph, which makes the situations seem worse than it already is).

Apparently the right way to do it is to show Daily Total deaths (ALL deaths not just covid) per local region Vs daily historic total deaths of that region - so some kind of Excess Death indicator.

And that shows pandemic effect rising and falling, because many cases either don't reach a hospital, get misclassified etc

But getting a daily ticker of total deaths (all deaths not just covid) in most parts of the world gets shutdown quick by politicians of all stripes.

Log cumulative graphs make it hard to see whether the daily rates are increasing or decreasing. Here are non-log, non-cumulative, new daily deaths per 1M people to compare different countries:

https://colab.research.google.com/drive/1dNAVpgRGjEViK-9ULhE...

New cases may be calculated as a difference between cummulative prev and next day. This is how daily cases are shown here by JHU dataset: http://covid-19.seektable.com/report/fe66549d73c64773bd6712e...

For a while there, it made no difference. When cases are growing exponentially, the two graphs are proportional to one another. The cumulative graph made it easy to spot when a particular region or locality had gone exponential, which I took to indicate the onset of community spread.

The FT switched from cumulative to new. While taking that first derivative has some advantages, I for one was clamouring for the original plots, and am grateful that the author has brought them back. Thanks!

I'm not sure if the equation is right but I've done a daily case plot for Indian cases.

https://gist.github.com/theSage21/29608c666a378dcb276a9dd9e4...