What is the relevance of comparing the number of positive Covid cases at the same date in 2020 and 2021? I am following the news in a country (which I will leave unnamed) where I am currently on holiday.
Same as in a related question I asked last year, I find myself completely baffled and dumbfounded by the way Covid data are reported.
Every single day, the daily number of cases (= people who tested positive out of $N$ tested, where $N$ changes every day, and the sampled population is biased and also varies every day) is reported and portrayed as the relevant parameter that gauges the severity of the situation.
Much worse, a while ago they started reporting this number in comparison with the same number at the same date last year, e.g. number of positive cases on 1st Dec 2020 vs 1st Dec 2021 (with absolutely no regard to either the percentage of positives the number meant, or the position in the curve).
Initially, I believe, this was done as a (rather disingenuous, or perhaps just very misguided) attempt to show 'how much better' the country was doing this year compared to last year, thanks to the vaccines; but more recently with the numbers rising and more tests being administered, the plan obviously backfired.
And indeed, sure enough, after this inconvenient truth emerged, they started mentioning percentages of positives and the fact that the number of tests is much larger.
However, as far as I can tell, even the percentage is still largely irrelevant if compared just based on calendar dates, as there is nothing forcing the infection curve to have the same shape or to start and end on the same dates every year, is there?
I don't know, I may be wrong, as I am neither a professional statistician nor an epidemiologist. Hence my question to this community: what, if any, is the relevance of comparing absolute numbers of positive cases between identical calendar dates?.
And if this is not relevant, what would be the correct/recommended method to compare the severity of an epidemiological event at two separate times?.
Frankly I find rather disappointing and depressing that 2 years into this pandemic there should still be so much ignorance and approximation in the official information the public is given about it. Surely this does not reinforce people's trust in the authorities or what they are doing to tackle the situation.

EDIT: addendum after further 'news'
Regrettably enough, my remarks, which may have sounded cynical, were confirmed.
On a particular day, the percentage of positive cases shot up to about 22% (it was 12% the previous day).
'Explanation' given by the newsreader: "BUT this was based on a much smaller number of tests".
Wrong on so many levels... how is this 'informing' the public?
Either they establish that the percentage of positives is an important parameter that determines public health measures, and go with it regardless of whether it's good or bad, or they ignore it and never even mention it. What's the consistency of presenting as valid only those results that 'agree' with a preconceived view of the situation, and instead dismissing by bizarre, statistically wrong arguments any results that don't match what one wants to prove?
Also, strong oscillations in the percentage of positives have been observed since the beginning of these measurements (in many other countries, weekly averages are reported); and indeed, the next day the percentage dropped again from 22% to 15%. But no, somehow this is still stubbornly, obtusely not understood; or at least, the public is still idiotically being fed this 'infections going up' and 'infections going down' nonsense, every single day... :7
 A: Presenting only two numbers from a complex and large data set is always a tricky business.
The reason why it is done here is probably to make the presentation of the figures easier in the media. But you are right that a lot of those presentations are too much simplified to give a decent view of the situation. Indeed all sorts of causes may be underlying the two numbers and one would need an overview of the entire data set, as much as possible and easily done, to get a better grasp of the situation.
This oversimplification is discussed several times in Edward Tufte's work. A typical example is the discussion about oversimplification with PowerPoint and how this leads to the crash of a spaceshuttle. An example that is more specifically about the loss of context is the presentation of the time series of traffic deaths by only two data points as in this presentation of the numbers of fatality due to traffic deaths.
Sidenote: a presentation that makes a comparison with previous years does make sense. Based on many indicators one can say that the upcoming wave (and in some places the peak seems to already be passed) has less volume of infections, sickness and death (at least in average Europe). So, I would see the problem more in the simplistic presentation without much context, rather than the comparison with previous years. The picking out of this single comparison may not need to be so much falicious cherry picking or if it is a false representation it may not need to be intentional.
This occurs a lot in media, that the starting point is the conclusion and one looks for science and data that supports it to present it. It is in some way falicious, the practice is not correct, but that may not make the conclusion false. It may be that the conclusion has been arrived in a correct sense, but that the presentation of the arguments that are used to support it are not correct. One way how this may occur is because the information stream is a bit like the Chinese whispers game, the information gets passed on from scientists to other scientists to government officials to spokespersons to newspaper editorial to journalist to news article. At the end of that chain, what may have sticked is the part of the message that tells that the wave(s) this year might be less bad, and a creative journalist starts collecting data and facts about this that can go into the news article.
