That said, SARS-CoV-2 really does spread faster than influenza; among other reasons, we know this because influenza cases went almost to zero during the pandemic, implying that the same behavior in the same population that clearly resulted in R0 > 1 for SARS-CoV-2 resulted in R0 < 1 for influenza. That's a relatively trivial and obvious claim, but it's falsifiable and it involves R0.
It's pretty common to reduce a time series to a single number. For example, in economics, it's common to look at a compound growth rate per year, averaged over the period of interest. Likewise, in epidemiology, it's common to look at a compound growth rate per estimated serial interval, averaged over the outbreak and corrected for immunity acquired during the outbreak. That's R0, with all the convenience and all the flaws of any other simple aggregate statistic.
> Therefore, my suggestion - meant constructively! - is to rebase the field on top of microbiological theory. Scrap the models for now. Delete "and everything else" from the R0 definition and come up with an algorithm to compute a measure of infectiousness from DNA/RNA or lab experiments only.
I hope you realize that biologists aren't all stupid? If they could somehow define "infectiousness of the pathogen alone, without environmental factors", then that would be incredibly useful, removing all the factors that complicate comparisons of R0. The fact that they've made no attempt to do so should be a clue that the concept that you're wishing for simply doesn't exist.
They do study growth rates in cell culture, or the amount of virus exhaled by a sick lab animal, or the amount of virus that a healthy lab animal must inhale to get infected with some probability. Those are well-defined and somewhat repeatable lab measurements, but they're not very predictive of spread in actual humans. Computational methods are even less predictive; the idea of calculating infectiousness in humans from a viral genome is mostly science fiction for now. They're trying, but this may be harder than you think.
We don't know this no, what you said here is just an assumption. There's a competing hypothesis (viral interference) which seems to explain the data better.
The way epidemiology currently works just cannot tell us which virus is more infectious. I agree that a rebase onto microbiology would be hard and maybe fail but the current approach has already failed. Doing difficult research that might not pan out is how they justify grant funding in the first place.
How would that explain why SARS-CoV-2 suppressed influenza, instead of influenza suppressing SARS-CoV-2? If viral interference occurs (which I agree it may), then the two simultaneous pandemics are coupled; but you'd still expect the virus with higher R0 to win.
The confusion is the other way around. An outsider to the field would think R0 would be defined biologically given it's called the "basic reproduction number" and because epidemiologists regularly make claims like "influenza has an R0 of this and measles has an R0 of that" (the sort of claim that started this thread), but it takes all of five minutes to discover that they don't use it that way, that there's nothing basic about this number and it's not even a well defined concept.
That's why as presently defined it's useless. Epidemiologists routinely compare R0 numbers with each other despite that as the Wikipedia page makes clear, you can't compare them because of how they're calculated. Put another way, claims about R0 numbers aren't falsifiable. What they mean isn't even definable in a non-circular way: because the definition includes anything that might conceivably affect transmission two different teams can "measure" R0 as totally different numbers even for the same country or city during the same time period, and the field has no way to decide who's right. Instead they decide that everyone is right! Even a single team doing a single calculation yields useless results, look at the CI on that paper discussed in this thread. They compute a CI that ranges from 2 to 11. That's not a usefully precise range.
Epidemiology needs to develop far more robust methods that aren't just applying R-the-software to random datasets scraped from the web if it wants to be taken seriously as a field. There are very basic philosophy of science issues here. Argument by textbook gets us nowhere, because the textbooks are themselves written by people engaged in unscientific practices.
Therefore, my suggestion - meant constructively! - is to rebase the field on top of microbiological theory. Scrap the models for now. Delete "and everything else" from the R0 definition and come up with an algorithm to compute a measure of infectiousness from DNA/RNA or lab experiments only. Once you've got a base definition that lets different labs replicate each other's numbers, you can start to incorporate other aspects (under new variable names) like immune system strength, population density grids etc.
Such definitions won't let you give governments predictions of hospital bed demand, and indeed may not let you calculate much of real world value right away, but the field isn't able to do that successfully today anyway: COVID models were all well off reality. What it would do though, is put epidemiology on track to one day deliver accurate results based on a firm theoretical foundation.