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‘Faked figures’ that drove the world’s reaction to covid


THOMAS Verduyn has just reported a series of astonishing revelations about the case and mortality data dashboard, ‘a user-friendly tool to track the outbreak as it unfolds’ rolled out by Johns Hopkins University (JHU), Baltimore, at the start of the Covid-19 pandemic.

In an intensively researched paper for PANDA, the independent covid response research group, he reports that JHU created and launched their online ‘dashboard’ at a time when the disease was not even named, when there were only four cases – and no deaths – outside China. Though it was created on the ‘spur of the moment’ and ‘took only a few hours’ to make, it rapidly became the premier global Covid-19 data resource, used by media outlets, medical researchers, health authorities and the public. All on a shoestring budget with part-time student resources.

It is notable that it was only three months earlier, in October 2019, that the JHU had hosted the now infamous Event 201, a table-top training exercise based on a fictional pandemic scenario, organised by the World Economic Forum and the Bill & Melinda Gates Foundation, with JHU epidemiologist Professor Thomas Ingelsby in attendance. On January 22 2020 that same institution was rolling out one of the world’s most influential data repositories for a pandemic which had yet to be declared.

Verduyn reports that the dashboard was quickly able to aggregate death and case statistics from virtually every public health authority in the world, and from Twitter and online news services, including data from unofficial sources and obscure websites in China. This achievement stands in sharp contrast to the more recent inability of officialdom to report accurate statistics on excess mortality – they could accurately track mortality in 2020 but have apparently lost this ability since.

How did the JHU’s three creators achieve this feat? Verduyn raises the possibility that the data they uploaded to the dashboard was partly based on epidemiological models rather than actual cases or deaths. Or to put it another way, they used the expected deaths and cases as surrogates for the actual deaths (which they had no way of accurately knowing). The lead author of the JHU dashboard, Professor Lauren Gardner, is a specialist in mathematical modelling of infectious diseases and in 2019 she developed a novel mathematical modelling framework for estimating a viral outbreak, a model which was meant to be calibrated using historical outbreak data.

So, might a computer simulation have been used to estimate the scale of the pandemic and observational data used to adjust and ‘correct’ the modelled output? If so, how trustworthy would the data be in the JHU dashboard, and in official statistics?

Verduyn says that many countries would not have been able to obtain their own data in real time and would therefore rely on the JHU dashboard as an authoritative source, thus replacing their own empirical data with modelled data. This would then find its way back into national official Covid-19 data repositories, to be used in future by JHU in their ongoing modelling efforts, thus setting up a vicious circle of fabricated data reinforcing the pandemic narrative.

He illustrates this vicious circle using mortality data from New York City (NYC). By comparing the sharp rise in NYC spring 2020 mortality with the JHU mortality data he found that week by week there were significantly more Covid-19 deaths listed in the official NYC mortality statistics than in those published by JHU, and that this difference was maintained throughout the Spring 2020 Covid-19 pandemic peak period. This significant difference has never been reconciled or explained by either JHU or the NYC authorities. The fact they are constantly out of sync is signal enough. Verduyn claims this difference provides irrefutable proof that the ‘probable death’ numbers were artificially generated on a computer, using an epidemiological model similar to that applied by Neil Ferguson in his now infamous, and grossly inaccurate, model for predicting covid-19 deaths in the UK and elsewhere. 

Tracking any illness in real time was and remains an impossibility. Given this and the fact that nothing particularly alarming was unfolding at the time of its creation, Verduyn’s analysis suggests that JHU succeeded in creating a Covid-19 dashboard for the purposes of communicating the impression of a pandemic or exaggerating what would, given the mortality burden, otherwise have been perceived as a routine influenza season. Notably, the rollout of the dashboard also fulfilled one of the objectives of Event 201 – to simulate a pandemic so realistic it was utterly believable.

Written by Professor Martin Neil based on the substack article The Dashboard that Ruled the World by Thomas Verdyun

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Professor Martin Neil
Professor Martin Neil
Martin Neil is Professor of computer science and statistics at Queen Mary, University of London

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