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MARCH 26TH, 2020
Epidemiologist Behind Highly-Cited Coronavirus Model Admits He Was Wrong, Drastically Revises Model
By Amanda Prestigiacomo
Epidemiologist Neil Ferguson, who created the highly-cited Imperial College London coronavirus model, which has been cited by organizations like The New York Times and has been instrumental in governmental policy decision-making, offered a massive revision to his model on Wednesday.
Ferguson’s model projected 2.2 million dead people in the United States and 500,000 in the U.K. from COVID-19 if no action were taken to slow the virus and blunt its curve.
However, after just one day of ordered lockdowns in the U.K., Ferguson has changed his tune, revealing that far more people likely have the virus than his team figured. Now, the epidemiologist predicts, hospitals will be just fine taking on COVID-19 patients and estimates 20,000 or far fewer people will die from the virus itself from from its agitation of other ailments.
Ferguson thus dropped his prediction from 500,000 dead to 20,000.....
:tiphat:https://www.dailywire.com/news/epide...revises-model/



If we assume an H5N1 viron has entered the body but not infected a cell but has made first contact with the immune system. Step one is the immune system needs to see it as ‘not self’. Only the external surfaces of the virus are ‘visible’ and in our case that principally means parts of the H & N proteins. Ignoring all the detail of what happens next the salient points are that certain surface features of these proteins’ tertiary structure (antigenic sites) are ‘learnt’ and communicated (antibodies are produced by the B-cells) to the rest of the immune system which then gears up to fight the infection. This is an important step change; the immune system has changed from looking for any thing that is ‘not self’ (generalised xenophobia) to identifying a specific threat and targeting it. A second viron – with identical surface feature – will now have a much lower chance of successfully reaching its target cell type as it is being actively targeted. If the surface features are similar – but not identical - then protection will fall somewhere between the two states. This learnt protection is not based on the whole overall viral surface shape but on lots of small features with an antibody produced for each.