Editing 2294: Coronavirus Charts

{{comic
| number    = 2294
| date      = April 15, 2020
| title     = Coronavirus Charts
| image     = coronavirus_charts.png
| titletext = Adding data for South Korea but with their cases scaled to match the population of Japan and the land area of Australia, and vice versa.
}}

==Explanation==
This comic is the 19th comic in a row (not counting the [[2288: Collector's Edition|April Fools' comic]] and [[2293: RIP John Conway|the previous tribute comic]]) in a [[:Category:COVID-19|series of comics]] related to the {{w|COVID-19 pandemic}}.

During the current outbreak of COVID-19, there have been many graphs used by health officials and others to show trends in infection and death rates. Their x-axis is usually time. The curves might represent different countries or different mitigation strategies. But 
health officials and media have struggled to decide what to put on the y-axis. Because testing strategies and reporting are so variable across even small regions, their data does not reflect comparable guesses at the true number of cases. So they produce graphs of confirmed cases, confirmed plus suspected cases, deaths, hospitalizations, any of the above per capita, day-to-day changes in any of the above, and [https://fivethirtyeight.com/features/new-york-coronavirus-curve share of test results that are positive for different areas of New York].
 
However, this graph, while sharing similarities with actual data and graphs, is completely useless. This is due to the bizarre data-points being used, as well as the unhelpful graph axes. The caption of the comic notes as much, perhaps indicating that this comic is intended to satirize the useful, but exceptionally detailed graphs that are currently in use. Some of these graphs have a semilog scale, like this graph - but generally the y-axis is the log scale and the x-axis is not. Sometimes the other graphs compare things of vastly different sizes - as demonstrated by showing both the USA and New York. Sometimes they scale the data to population, as referenced by the title text.

In addition, the selection of geographic areas used here is incomprehensible. Two of the lines represent countries (USA and Italy), and another represents part of one of those countries (New York City area). The New York City area may have been chosen because it has a very large number of cases, more than some countries. However, a fourth line combines Norway and Sweden -- two countries which are culturally, economically, and geographically similar [https://theconversation.com/coronavirus-why-the-nordics-are-our-best-bet-for-comparing-strategies-135344 but have imposed very different strategies] regarding closing businesses and schools. Combining Norway and Sweden obscures any differences attributable to their different policies regarding the virus. A fifth line represents not a geographical area but the ''ratio'' between France and Spain, making an already meaningless graph even less comprehensible.

The title text adds a further ambiguity: Usually, there are only two items being compared in a "vice versa" (e.g. "Would you rather have live in a city with the land size of San Francisco and the population density of Tokyo, or vice versa?" when comparing two other cities with those measurements); here there are ''three'', leading to either ambiguity (''possibly'' two South Korea lines, each based on one of two complementary sets of cross-demographic refactoring), or six lines being embodied in that "vice versa".

'''Other metrics used'''

X-axis:
*Negative test results: Negative [https://covidtracking.com/ test results] would refer to people who were tested for COVID-19, but who do not have the disease (or were not able to confirm having the disease). If there are any places reluctant to test, in order to artificially suppress the unpopular number of positives, this measure would similarly be unreasonably low. It might therefore be an important key measure, used as just one component of a meta-measurement, to regrade or even highlight such practices. At least until the figures are freshly massaged by instead overtesting people with a low probability of being infected.
* per Google search for "COVID": Meanwhile, [https://trends.google.com/trends/explore?date=today%203-m&q=covid Google search results for "COVID"] are search hits for that word. There is no relation between these two, and furthermore, it does not make sense for this to be graphed on a {{w|logarithmic scale}}.
* As mentioned above, the x-axis for most charts is time, as it is valuable to know how the virus or deaths are spreading over time. Negative test results should grow over time, but may not grow uniformly depending on availability of tests, and some may later be invalidated as testing methodologies are refined.  Given that and depending on the trends in Google searches for COVID, it's entirely possible for multiple points in time to map to the same value of ''x'' (although none of the curves shown here do, Scenario 4 from [[2289: Scenario 4]] did).

Y-axis:
*[https://www.worldometers.info/coronavirus/worldwide-graphs/#daily-deaths Coronavirus deaths today]: Deaths from the coronavirus "today" are constantly reported by the media, and could be a helpful metric in seeing whether the virus is spreading or not, if deaths "today" are compared to deaths yesterday and previous days.
*[https://www.worldometers.info/coronavirus/worldwide-graphs/#total-cases Total cases] one week ago: This is a much larger number than deaths and will completely dominate the sum. Cases one week ago might have some predictive value for deaths today or in the near future, but adding them together double-counts many cases.
*{{w|Per capita}}: This is a measure of the amount per person, and is useful for averaging out numbers based on population size. For example, the United States have the most publicly-reported COVID-19 cases and deaths, but also has the third-largest population of all countries, so using per capita numbers tells a different story.

Title text: While adding data for South Korea might be helpful (as it shows an Asian country, compared to just Europe and the US), it is only logical to scale the data to the population of another country (e.g. Japan) if you're actually comparing the two countries (i.e. does Japan have more or fewer cases per capita than South Korea).  Scaling cases based on land area is much less useful; it's true that countries with lots of {{w|land area}}, like Australia, do have lower population densities, which affects the spread of disease, but most of the people in Australia live in higher-density cities on the coast, so the actual change is not that great.

==Transcript==
[A graph is drawn.]
:[A curve labeled "United States" starts about halfway up the vertical axis, rises almost to the top, and then levels off about a third of the way along the horizontal axis.]
:[4 other curves are also shown, labeled "New York City area", "Italy", "Norway + Sweden" and "Ratio between France and Spain".]
:Y-axis label: Coronavirus deaths today plus total cases one week ago per capita
:X-axis label: Negative test results per Google search for "COVID" (log scale)
:Caption: I'm a huge fan of weird graphs, but even I admit some of these coronavirus charts are less than helpful.

{{comic discussion}}
[[Category:COVID-19]]
[[Category:Charts]]
[[Category:Line graphs]]