Thursday, March 26, 2020

Now He Tells Us: The Latest From Nutjob Ferguson

Neil Ferguson

The nutjob medical doctor, Neil Ferguson, who scared the world by claiming a million Americans would die from COVID-19 and 250,000 in England, only to pull that forecast, now tells us that up to two thirds of coronavirus victims who have died may have died this year anyway from other complications.

Here is the latest from the nutjob that government officials seem to think is stable:
It might be as much as half or two thirds of the deaths we see, because these are people at the end of their lives or have underlying conditions so these are considerations.
Fatalities are probably unlikely to exceed 20,000 [in Endland] with social distancing strategies but it could be substantially lower than that and that’s where real time analysis will be needed.
 Remarkably, this man sits on the UK government’s Scientific Advisory Group for Emergencies (Sage) when in reality he needs to be locked up for the duration.



  1. I am not sure what to make of this. His down grading to 20,000 was, I think, based on the forecast in the model under intervention. The upper limit was the "if nobody changes behavior" condition. The 20,000 was the prediciton based on what the UK and USA have at this point done as interventons. I don't think this is as bad as saying that he himself retracted the the original prediction, but the whole methodology seems to be borderline pseudoscience to me. For example, if he now has to retract some of the empirical inupts, what good is the model from the very beginning? If he doesn't know the basic facts of the situation then the parameters for the "do nothing" condition are useless, and, thus, also the rest of the conditions. The other possibility is that the model is just an list of every statistical calculation and that he can retroactively put new inputs in as he goes along collecting more data, and say in the end, "Look how great our model is!" It is a bit like the worst parts of psychoanalysis, neural network models of the mind, or Ptolemaic (epicycles) astronomy. Or, at least, that is my intuition. Perhaps people that are more familiar with complex mathematical modelling can enlighten me on this.

    1. They used a SEIR (Susceptible -> Exposed -> Infected -> Recovered) model, where the changes over time in each of S, E, I and R are governed by a set of very basic differential equations.

      About a month ago I built a discrete-time version of a SEIR model, because it seemed to me that there were far too few deaths for covid19 to be worth worrying about.

      I picked what seemed to be sensible parameters - and lo, when ICL released their latest model, it turned out I had picked very nearly the same values as they had (I had a longer 'infectious' period).

      The model is horribly unrealistic on a bunch of levels; first, there's no genuine constraint on the number of infected - such constraint as there is, happens as a result of a stupidly unrealistic mechanism for the transition equations for 'Susceptible'.

      I replaced the stupid mechanism with one that was based on behaviour (i.e., the number of interactions that infected people have per day, and the probability that an interaction infects someone else).

      Anyhow... suffice it to say that if a single asymptomatic individual was introduced into the US population in December 2019, by mid-Feb 2020 there would have been millions of asymptomatic-infected wandering around infecting others. Well before the 'lockdowns' and what-not. By the time governments started to respond, SEIR says it was already too late.

      I'm mounting the model on a webserver this weekend, so that people can see for themselves how the model behaves when they 'tweak' the parameters - including allowing them to change the date for 'Patient Zero', and then see how the modelled infection happens over time.

      As it happens, my opinion of the SEIR model is that it is amateurish garbage... that's irrelevant: it's the model whose results are being bandied about as if it produces 'hard numbers'.

      My background is in large-scale economic and econometric modelling - specifically in performing systematic sensitivity analysis to get some idea about the statistical propertties of forecast 'manifolds' (as opposed to 'lines' or 'points').

      A lot of people are very hostile towards economic and econometric modelling - mostly because it tells them what they don't want to hear (e.g., that the policy they're infatuated with, won't do what they calim). By contrast, medical statisticians (including epidemiologists) get a 'halo effect' by being part of an authoritarian profession with government imprimatur... despite the fact that medical error is the 3r or 4th highest cause of deaths every year.

      Lastly: Consider these two things together, and ask yourself how it affects the model.

      ① There is a very high probability that Patient Zero exhibited no symptoms, and was simply waved through border controls: prior to March 15th, nobody was bothering to test (or quarantine) travellers who had no symptoms. Furthermore, 30-50% of people infected downstream of Patient Zero will have been asymptomatic.

      ② There won't have been a single 'Patient Zero' for the whole US, either: there will have been several on the same day, at every port of entry into the US. Many of them will never have developed symptoms, and will now be clear of the virus.

      If there were 3, or 5, or 20 original 'Patient Zero' in late December, then there would be the same number of SEIR processes happening simultaneously.

      (This is what makes me certain it's a nothingburger: otherwise there should be ~1000 people a day dying in the US at this stage).

  2. The questions people should be asking is: Who is this Neil Ferguson? Who, do his political sympathies lie with? Does he have a strong, reliance upon or take orders from, a particular group, nation or organisation that has a particular ideology or economic regime to enforce? When someone gives you information you must look at that information but you must also look at the individual who is giving the information, especially when that information is coming from a projected reality or analytical data based upon guesses. Interpretation matters as we are seeing.