Using a logarithmic scale to represent COVID-19 growth

In summary, the author, John Burn-Murdoch, discusses the benefits of using a logarithmic scale in visualizing data, particularly in the context of the ongoing pandemic. The log scale allows for a better understanding of the data, as it prevents certain countries' data from being squashed and highlights the exponential growth of cases outside of China. While some are interested in seeing the data on a log-log plot, the author notes that it may not provide much additional insight due to the lack of a well-defined zero on the time axis. However, the author does provide a log plot and notes the point where the growth begins to increase. Overall, the use of a logarithmic scale can reveal important patterns and trends in the data.
  • #1
scottdave
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TL;DR Summary
I came across this interesting thread showing the difference between linear and logarithmic scales when visualizing coronavirus infections.
The author, John Burn-Murdoch, shows here ( https://threadreaderapp.com/thread/1237748598051409921.html ) how the logarithmic scale can give a better "sense" of what is happening. In linear scales, some countries' data is squashed to almost nonexistent, while others explode out of control.

I wasn't sure where the best place to post this - I thought it was interesting and wanted to share.
 
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  • #2
I agree that the log scale is more informative. This site has a lot of good information, and allows you to toggle back and forth between linear and log scales. Note that on a log scale the total cases outside of China has been growing exponentially since February, and shows no sign of departing from a straight line (exponential growth).
Cases.png
 
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  • #3
phyzguy said:
I agree that the log scale is more informative. This site has a lot of good information, and allows you to toggle back and forth between linear and log scales. Note that on a log scale the total cases outside of China has been growing exponentially since February, and shows no sign of departing from a straight line (exponential growth).
View attachment 258928
I'd be interested in seeing what this looks like on a log-log scale plot. Any chance you would consider doing that?
 
  • #4
Chestermiller said:
I'd be interested in seeing what this looks like on a log-log scale plot. Any chance you would consider doing that?
I don't actually have easy access to the data to re-plot it. Why are you interested in that? Exponential growth should be a straight line on a log-linear plot, and it looks like that is what we are seeing.
 
  • #5
phyzguy said:
I don't actually have easy access to the data to re-plot it. Why are you interested in that? Exponential growth should be a straight line on a log-linear plot, and it looks like that is what we are seeing.
I don't know why. I just like to look at the data in different ways, and I though that it would even be straighter on a log-log plot.
 
  • #6
A logarithmic date axis? It doesn't even have a well-defined zero to use.

It won't make anything look straight. It will compress the time where the cases explode even more.
 
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  • #7
mfb said:
A logarithmic date axis? It doesn't even have a well-defined zero to use.

It won't make anything look straight. It will compress the time where the cases explode even more.
I’d still like to see what the log-log plot looks like.
 
  • #8
Chestermiller said:
I’d still like to see what the log-log plot looks like.
By putting both axes on a log scale, the general shape should go back (from the log-plot) to the exponential shape. Both axes of the original plot will be distorted similarly. But I think that the log of the time axis will be hard to use or interpret.
 
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  • #9
Chestermiller said:
I’d still like to see what the log-log plot looks like.
I got the numbers from the source code of the webpage.
coronaloglog.png

All logs to base 10. Note that the point where the growth starts to increase at 1.47 or so is at about 20 february
 
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  • #10
willem2 said:
I got the numbers from the source code of the webpage.
View attachment 258972
All logs to base 10. Note that the point where the growth starts to increase at 1.47 or so is at about 20 february
Thanks very much. I was most interested in the part between 1000 and 300000 cases.
 

Related to Using a logarithmic scale to represent COVID-19 growth

1. What is a logarithmic scale?

A logarithmic scale is a type of scale used in graphs and charts that represents data using a logarithmic function. This means that the distance between each value on the scale increases exponentially, rather than linearly.

2. Why is a logarithmic scale used to represent COVID-19 growth?

A logarithmic scale is used to represent COVID-19 growth because it allows for easier visualization and comparison of data over a wide range of values. This is particularly useful for representing exponential growth, which is often seen in the spread of diseases like COVID-19.

3. How does a logarithmic scale differ from a linear scale?

A linear scale represents data using a constant distance between each value, while a logarithmic scale uses a constant ratio between each value. This means that on a logarithmic scale, the distance between 1 and 10 is the same as the distance between 10 and 100, while on a linear scale, the distance between 1 and 10 is the same as the distance between 10 and 11.

4. Can a logarithmic scale be used for any type of data?

No, a logarithmic scale is most commonly used for data that follows an exponential pattern, such as population growth, economic growth, and disease spread. It may not be appropriate for data that follows a linear or other non-exponential pattern.

5. Are there any limitations to using a logarithmic scale to represent COVID-19 growth?

While a logarithmic scale can be useful for visualizing and comparing data, it can also be misleading if not used properly. It is important to carefully label and interpret the scale to avoid misrepresenting the data. Additionally, a logarithmic scale may not accurately represent the actual number of cases or deaths, as it compresses the values on the scale.

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