But you're overlooking the most likely outcome - that nobody is qualified in this field. However software developers can admit this and epidemiologists can't.

But you're overlooking the most likely outcome - that nobody is qualified in this field. However software developers can admit this and epidemiologists can't. Bearing in mind for a moment that epidemiologists are amateur programmers, their work output is literally papers+code, they have a major handicap that they can't stop and say "maybe this doesn't work". What else would they do? I've found the comments by Caswell Bligh very insightful on this topic. Bligh is one of the awful "men in tech" programmers the article laments, who has been writing his own models based on epidemiological papers. Perhaps even from an armchair. Everything he's concluded is worth reading but I'll quote some extracts: https://lockdownsceptics.org/2020/04/26/latest-news-12/#comment-935 https://lockdownsceptics.org/2020/04/26/latest-news-12/#comm... Do ‘R0’ or the other ‘R’ values even mean anything when ‘infection’ versus ‘exposure’ can’t even be defined? If social distancing reduces the level of exposures but not the number, does this mean the ‘R’ number is the same, or reducing? Maybe the lockdown means that the population is being quietly, in the background, inoculated to a certain level of resistance (which is what I think is probably happening) but that it doesn’t show up in antibody tests. Many of these things will never be known. They are probably not even predictable with viruses that are not ‘novel’. Virologists and immunologists may know some of it, and they also know what they don’t know. The modellers don’t know any of it. As such, models will never be able to give you absolute values for the number of infections, deaths and so on. We may as well not bother with the units on the axes and simply use the model to illustrate the different shapes that are possible In this interview there's a useful quote: https://fivethirtyeight.com/videos/how-one-modeler-is-trying-to-forecast-the-toll-of-covid-19/ https://fivethirtyeight.com/videos/how-one-modeler-is-trying... At about 8.00: “The nature of SEIR models is they do tend to always show this huge increase up to the point of saturation where most people get infected and very often that has not historically happened”. https://lockdownsceptics.org/how-reliable-is-imperial-colleges-modelling/#comment-1070 https://lockdownsceptics.org/how-reliable-is-imperial-colleg... @Hugh Osmond. What you’ve articulated – and it’s something that’s been bothering me – is that ‘R0’ and the other ‘R’ variants are not a real characteristic of the virus. Snapshots during the epidemic may give you observed values for them, but that doesn’t mean that they represent something fundamental. Attempting to both derive, and then use values for ‘R’ in models, has led to this disaster. It needs to be shouted from the rooftops. ‘R0’ is not a characteristic of the virus. And saying “‘R0’ depends on the virus but also age/population density/pollution etc. etc.” is just another way of saying the same thing. If you know ‘R0’ in advance, you’ve already anticipated what the simple model is going to tell you – there’s almost no point running the model. In the more sophisticated, more realistic model we shouldn’t even try to accommodate an ‘R0’ input at all, and nor should we try to modify an ‘R’ as time goes on. That’s the wrong way round. Really, I think you’ve articulated something extremely important here. It’s been driving me mad for weeks: the illness is being defined in terms of a standard, simplistic, mathematical model that was easy to compute before the invention of computers. But even now, if you want to make a more sophisticated model, you are still forced to talk about its results in terms of the simple model. When I told a biologist friend of mine I was creating a model, the first thing she asked me was “What value of R0 are you using?”. This is how we have arrived at this disaster.