> Overall, we report R0 values are likely be between 4.7 and 6.6 with a CI between 2.8 to 11.3

That's an extremely wide CI by any measure. It's a bit unclear how this is meant to be a contradiction. It looks like a good example of the issue. There is no universal theory or method for computing R0. Every single epidemiologist has their own unique approach which is then often discarded in time for the next paper, making the numbers incomparable.

Earlier, you wrote:

> It [R0] was never being established via lab work, as you might expect given its definition.

If you expected that "lab work" could establish R0, then you've grossly misunderstood its meaning and definition. It seems like you're looking for a concept of "R0 but for the pathogen alone, independent of environment and human behavior". That just doesn't exist though, any more than you could define the growth rate of a plant independent of weather and soil fertility.

> the big variation (e.g. R0 from 3.7–203.3 for measles, per my other comment here) is real

Morpheus: "What is real? How do you define, real?"

CIs that wide are just an obfuscated way of saying "we have no idea what's going on or what will happen". Anyone can make predictions that way. For example by the end my life my bank balance will be $4.7 million (CI $5.00-$50M). Those numbers aren't "real" in any meaningful sense. Anyone can express don't-know in sophisticated looking numerical form, and they wouldn't justify me claiming to be a financial expert on the back of them.

> any more than you could define the growth rate of a plant independent of weather and soil fertility.

You can define growth rate of a plant this way: define a standardized lab environment with regularized soil composition and artificial light/watering schedules. Then plant seeds and measure the dry weight at the end of a fixed time period. This will give you a number that's comparable across species. There are other definitions you could use because "growth rate" is slightly ambiguous in English (does a tree grow faster than a weed because the tree achieves bigger mass?), but that's the general idea.

What you shouldn't do is just grab datasets of wildly varying quality off the internet, fit an equation you just invented on the spot to that data, and announce you've discovered something real about the plants in the data. That would indeed yield a growth rate that doesn't tell you anything meaningful.

If the standardized lab environment is dry and sunny, then you'll conclude that a cactus grows faster than a fern, since the ferns will mostly shrivel up and die. If the standardized lab environment is moist and shaded, then you'll conclude that the fern grows faster, since cactuses will mostly die for lack of sun. So which is right?

The concept that you're looking for simply doesn't exist--the growth rate of an organism can't be defined except with reference to its environment, which for a virus that infects humans includes human behavior. (What rate of condom use should the standardized lab environment for HIV correspond to? How will you model the increased popularity of fentanyl?)

You are looking for CS-level rigor and simplicity in biology, but biology doesn't work like that. You are correct that many biological results were thus oversold to the public during the pandemic; but you're once again criticizing those public-facing oversimplifications, not any science as a practitioner would understand it.

Likewise you wouldn't try to measure the infectiousness of HIV in people, clearly. If you want to measure the relative "infectiousness" of viruses in humans using precise numbers then you'd need a controlled experimental environment, presumably something in vitro. That would miss a lot of factors that are important if you're trying to predict epidemics at the society-wide level, but OK, so be it. You need a firm footing of the basics before you can progress to more complex scenarios.

I don't really agree that it's unreasonable to expect CS-level rigor in biology. Microbiologists seem to manage it? It's expected that if two labs sequence the same organism they can in principle get the same DNA sequence, and if they do Xray crystallography on the same protein they'll derive the same structure. So we're not even comparing biology and CS here, we're comparing microbiology with epidemiology. The latter seems to be far closer to a social science in terms of its methods and rigor.

To be clear, it's also fine to do epidemiology using less rigorous methods if it was done in the way it mostly used to be done. When I read papers from the 80s they seemed to be much more appropriate to the actual data quality - largely prose oriented, very limited use of maths, presenting falsifiable hypotheses whilst admitting to the big unknowns. That's fine, science doesn't always have to be precisely quantifiable especially on the margins of what's known. But if scientists do precisely quantify things, then those quantities should be well defined.

That's how EE/CS stuff usually works (at least outside ML), building complex systems hierarchically out of well-understood primitives. The life sciences are different. There's almost nothing there we understand well enough to build like that, so almost all results of practical importance (a novel antibiotic, a vaccine, a cultivar of wheat, etc.) are produced by experiment and iteration on the complete system of interest, guided to some extent by our limited theoretical understanding.

This discrepancy has been noted many times; it's just a completely different way of working and thinking. If you haven't, then you might read "Can a biologist fix a radio?".

> Microbiologists seem to manage it?

A grad student in microbiology can grow millions of test organisms in a few days, at the cost of a few dollars, and get all the usual benefits of the central limit theorem. A grad student in epidemiology absolutely can't, since their test organisms are necessarily people. So you're quite correct that it's basically a social science, since it depends on aggregate human behavior in the same way e.g. that economics does, and is therefore just as dismal. Unfortunately it's also the best and only science capable of answering questions of significant practical importance, like whether the hospitals are about to be overrun. I'd tend to agree that stuff like Imperial College's CovidSim has so many parameters and so little ground truth as to have almost no predictive value. R0 seems fine to me though, and usefully well-defined, in the same way that the CAGR of a country's GDP seems fine.

In the life sciences, it's often possible to design an experiment under artificial conditions that will get a repeatable answer, like the growth rate of a plant in a certain controlled environment. It's much more difficult to use the result of such a repeatable experiment for any practical purpose; consider, for example, the steep falloff in drug candidates as they move from in vitro screens (cheap and repeatable, but only weakly predictive) to human trials (predictive by definition, but expensive and noisy). I'm absolutely not a life scientist myself, in part because I think I'd find that maddening; but essentially all results of practical benefit there came from researchers working in that way.

InB4 "clouds don't exist, nobody can tell exactly where they start and end"

To get meaningfully constant values for infectiousness you'd probably need a theory that let you derive it from RNA sequences.

As others have already noted in this thread, the rate at which a disease spreads inherently depends on both the biology of the pathogen and human behavior. For example, if everyone goes to crowded nightclubs, then R0 for an airborne respiratory disease goes up. If everyone stays home, then it goes down. The biology of the pathogen and human behavior interact in complex, multidimensional ways; promoting condoms will reduce R0 for HIV, but not for influenza. The disease spreads only through human behavior, so the concept of infectiousness independent of that simply doesn't exist.

You are correct that R0 is determined by curve-fitting to actual case counts. It couldn't be otherwise though, since that's the only way to capture the actual human behavior. We expect different R0 in different situations, so the range you see for SARS-CoV-2 isn't a surprise. This is textbook stuff:

> Any factor having the potential to influence the contact rate, including population density (e.g., rural vs. urban), social organization (e.g., integrated vs. segregated), and seasonality (e.g., wet vs. rainy season for vectorborne infections), will ultimately affect R0. Because R0 is a function of the effective contact rate, the value of R0 is a function of human social behavior and organization, as well as the innate biological characteristics of particular pathogens. More than 20 different R0 values (range 5.4–18) were reported for measles in a variety of study areas and periods (22), and a review in 2017 identified feasible measles R0 values of 3.7–203.3 (23). This wide range highlights the potential variability in the value of R0 for an infectious disease event on the basis of local sociobehavioral and environmental circumstances.

https://wwwnc.cdc.gov/eid/article/25/1/17-1901_article

As with your confused understanding of PCR specificity, you may have been misinformed by indefensibly oversimplified public messaging as to the definition of R0. (I had a post deleted from /r/coronavirus in 2020, because they considered my statement that R0 varies with environment and human behavior to be misinformation; the citation didn't help.) The literature is once again available for anyone who wishes to read it, though.

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.

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.

That conclusion doesn't follow from the predicate. The model isn't a single simulation run. To mathematicians, the model is the entire framework. It's the SIR model, or the SIS model, or the SIER model, or my favorite model of immunity decay against pertussis... _those_ are the models. The virulence of the infection being modeled, resilience of the population, morbidity and mortality rates, rate and timing of population quarantine, vaccine adoption rate, vaccine efficacy, whether any of the aforementioned values are themselves functions of time... those are just parameters into your model. We absolutely compare the values between pathogens _within the same model_ when discussing the utility of the model.

But typically, the utility in reproduction ratio is as a point of comparison as you tune other parameters. This is part of why a comparison of reproduction ratio across models isn't recommended. It's not just that it's hard to draw meaning from the comparison, it's that the base assumptions of the models might be too different for the comparison to hold any meaning. The reproductive ratio between a continuous model with uniform population mixing is fundamentally different from the reproductive ratio approximated from discrete simulation. They may both broadly speak to "if everyone they saw was susceptible, how many people do you expect to get sick per sick person?" But what that ratio means is dictated by the context of the model.

A frequent utility in comparing ratios is to discuss intervention impact. One might write "Our model found that overall reproduction was reduced to a factor of 0.XYZ (comprehensive infectivity parameters a=nnn b=mmm c=ppp) when the population undertook vaccination schedule foo. Conversely, reproduction was reduced to a factor of 0.JKL for the same infection under vaccination schedule bar. See Figure 14 for complete population state levels over time."

That's all that is. It's a number that has some meaning in context, and one that is often removed from that context and unreasonably expected to retain its meaning. Averages don't mean as much if you don't know if your distribution is multimodal. It's the same thing - a summary stat that can give you a glimpse of the whole, but still just a summary stat.