Editing 2294: Coronavirus Charts
Warning: You are not logged in. Your IP address will be publicly visible if you make any edits. If you log in or create an account, your edits will be attributed to your username, along with other benefits.
The edit can be undone.
Please check the comparison below to verify that this is what you want to do, and then save the changes below to finish undoing the edit.
Latest revision | Your text | ||
Line 8: | Line 8: | ||
==Explanation== | ==Explanation== | ||
− | This comic is the 19th comic in a row (not counting the [[2288: Collector's Edition|April Fools' | + | {{incomplete|Created by a poorly constructed graph. Please mention here why this explanation isn't complete. Do NOT delete this tag too soon.}} |
+ | This comic is the 19th comic in a row (not counting the [[2288: Collector's Edition|April Fools' comic]]) in a [[:Category:COVID-19|series of comics]] related to the {{w|2019–20 coronavirus outbreak|2020 pandemic}} of the {{w|coronavirus}} {{w|SARS-CoV-2}}, which causes {{w|COVID-19}}. | ||
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 | 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 | + | 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 vs. proximity to NYC]. |
− | This graph, however, 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 | + | This graph, however, 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 mouseover 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) | + | 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). However, a fourth line combines Norway and Sweden -- two countries which are [https://theconversation.com/coronavirus-why-the-nordics-are-our-best-bet-for-comparing-strategies-135344 culturally, economically, and geographically similar 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. |
− | + | '''Metrics used''' | |
− | |||
− | |||
X-axis: | 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. | *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}}. | * 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}}. | ||
− | * | + | * It's not clear what data points would allow you to chart one country over several values of x. Cumulative results at different times? |
Y-axis: | 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/#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. | *[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 | + | *{{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 COVID-19 cases and deaths, but also an higher population than the most other industrialized nations, so using per capita numbers tells a different story. |
− | + | ==Transcript== | |
+ | {{incomplete transcript|Do NOT delete this tag too soon.}} | ||
− | |||
[A graph is drawn.] | [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.] | :[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.] | ||
− | |||
:Y-axis label: Coronavirus deaths today plus total cases one week ago per capita | :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) | :X-axis label: Negative test results per Google search for "COVID" (log scale) |