Read on Twitter
It has been requested that I do a simpler version of this thread, explaining why the modellers advising the Irish government are so amazingly wrong in their forecasts and in their understanding of the risk from Covid-19.
Here goes.
https://twitter.com/GrahamNeary/status/1318262666322993152
Here goes.
https://twitter.com/GrahamNeary/status/1318262666322993152
1. The death count is wrong. We know this because it has been admitted to be wrong by the government (including by Leo Varadkar) and the number itself makes no sense.
All explained here: https://twitter.com/GrahamNeary/status/1317880298554744836
All explained here: https://twitter.com/GrahamNeary/status/1317880298554744836
2. The modellers think very few people have been infected so far. Wrong.
They say less than 2% of the population had antibodies 10-14 weeks after the epidemic peak.
But research suggests many people already lose their antibodies 8 weeks after infection. https://www.nature.com/articles/s41591-020-0965-6
They say less than 2% of the population had antibodies 10-14 weeks after the epidemic peak.
But research suggests many people already lose their antibodies 8 weeks after infection. https://www.nature.com/articles/s41591-020-0965-6
3. The modellers think everyone is susceptible to Covid-19. Wrong.
Research from Sweden suggests at least 28% of people had pre-existing immunity against Covid-19 last year, long before the epidemic. This natural immunity can make antibodies redundant. https://www.cell.com/cell/fulltext/S0092-8674(20)31008-4
Research from Sweden suggests at least 28% of people had pre-existing immunity against Covid-19 last year, long before the epidemic. This natural immunity can make antibodies redundant. https://www.cell.com/cell/fulltext/S0092-8674(20)31008-4
What the modellers do is they take a vastly overestimated death count, divide it by a vastly underestimated infection count, and get an infection fatality rate that is wrong probably by at least one order of magnitude.
They then naively apply this to the entire population.
They then naively apply this to the entire population.
This false view of the fatality risk is combined with false case data, false hospitalisation data, false admissions data, and false ICU data, whereby the label "Covid" is applied to anything it can be applied to, regardless of cause and effect: https://twitter.com/GrahamNeary/status/1317243273052913665?s=20
So we end up with estimates derived from meaningless data and forecasts built on baseless and false assumptions.
From a personal point of view, I take consolation from the fact that these messages will be here for posterity, and I played my part in trying to wake people up to what is happening. If you'd like to help me, please share this with your friends. Thanks.
