BY MARTIN KICH
Despite the considerable efforts to personalize the toll of the COVID-19 pandemic, the politicization of the pandemic has resulted in endless arguments over what all sorts of moving numbers mean.
I think that putting the current numbers in some broader perspective could help to show why much of the other noise about the numbers is moot, even if it is very unlikely to be muted anytime soon.
The first two charts show how the total confirmed cases and deaths would rank among the most populous U.S. cities, and the third chart shows how the total confirmed cases would rank among U.S. state populations.
Imagine if the cases and deaths were concentrated in these ways. What would the national response be then? Who would downplay, never mind dismiss, the scope of the catastrophe–especially when there is clearly no end in sight?
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What are the data sources?
If an undergraduate put tables like these in a paper, at least at UChicago (non-Humanities) and sought to advance conclusions or implications, he would receive an “F” or at least a large, red “?” and sent back to the library, or out in the field. (In the Humanities, he might however get an “A+” if accompanied by the correct moral and ideological positions). Maybe I’m overlooking something?
But even if such tables are referenced, that’s just the beginning. The data has to be “unpacked;” tested, verified, sourced in primary form; it’s collection, recording, sampling and management methods interrogated with exhaustive probity. In this data set, even basic secondary sources invoke its reliability (for example, even beginning with publicly available mass-market reporting, the data is suspect: https://www.usatoday.com/story/news/investigations/2020/04/25/coronavirus-death-toll-hard-track-1-3-death-certificates-wrong/3020778001/).
But I don’t think most college professors want to actually question the data: the worse it seems, or is made to seem, the more it may serve their political partisan motivations. The appear to be “all in” with CDC, WHO and the NYTimes D-base. The most basic sensibilities of scientific skepticism and the obligation of attempted falsifiability, seem to have been abandoned.
This is otherwise “spaghetti” data from undisclosed (unless I’m missing some link) and untested sources, and untreated and unfiltered under standards of professional data investigation and care, including interrogating its chain of custody as evidence.
Perhaps the numbers are worse; perhaps not. In either case, this blog entry does not meet threshold standards in econometric fidelity, and its accompanying language more centrally, reflects a logical fallacy, including ad populum; a post hoc argumentation of temporal association assumed to infer causality (part of the co-morbidity problem) and in other predicate logic (and soon to followed by an ad hominem).
Regards, ’96, University of Chicago