The failure of regulators to set baseline requirements for unblinding leaves a gaping hole in regulatory oversight, raising questions on the commitments to transparency, ethics, and scientific robustness in these and future trials.
The last three years has seen a huge number of publications on all aspects of Covid-19 but has also seen many respected authors suddenly struggling to get work published that does not support the official narrative. So by way of some light relief for August, here are some writing tips for hopeful researchers.
Plausible deniability can provide protection for those in authority who acted in harmful ways. However, things become much less plausible when Freedom of Information (FOI) requests expose just how much people did know.
Imperial College has a terrible track record of presenting modelled data as if it was scientific evidence based on more than just biased assumptions. Imperial was responsible for the fantasy claim that covid would cause 510,000 deaths in the UK without changes to human behaviour.
The statistical jiggery-pokery employed since 2020 is so outrageous it is perhaps best viewed through the lens of (dark) comedy, so we forewarn you in advance that — in a change from our usual (possibly over-sober) tone — the following article has been written in a somewhat jocular manner, despite covering a topic that is very much not a laughing matter.
SARS-CoV-2 is airborne and spreads through aerosols but are there other routes of transmission too? A recent report from the Food Standards Agency suggested that the virus could survive on food and packaging for up to a week.
Despite two and a half years of failure people continue to be paid to publish modelled predictions of the future. The future is not as predictable as some claim. Modellers are not capable of predicting what is going to happen because they do not have measurements for all the variables that contribute to covid waves.
There is good evidence that a minority of individuals produce a disproportionately large number of viral particles. These people are likely the ones responsible for occasions where large numbers of people catch covid at a gathering – a superspreader event – which have certainly occurred.
There has been much talk in recent weeks about excess mortality, but remarkably little interest from UKHSA or ONS (or anyone in the MSM for that matter) in trying to ascertain why this might be….
After watching this pattern play out for over two years there finally seems to be an admission that covid is worse in winter. Fauci has said he expects an autumn and winter wave starting in October and peaking in January.
The weekly tally of deaths above expected levels from non covid causes is finally being started to be reported in a few mainstream media outlets. There has been a wide range of speculation about the cause, with a number of mechanisms postulated including post covid sequelae and lack of access to healthcare during lockdowns in 2020 and early 2021.
The trajectory for hospitalisations and ICU admissions in the UK does appear to show benefits of vaccination since early 2021. The peaks in hospitalisations were comparable with autumn 2020 with the exception of Scotland’s large peak in March 2022. However, the intensive care admission peaks were much smaller than prior peaks.
H0SARS-CoV-2 was nasty, but not unusually so, and certainly not particularly novel. The cure was worse than the disease. It is difficult to bottle up truths forever, and it will be instructive to see whether this hypothesis is still in the running when it comes to writing the definitive history of the Covid epoch.
The disappointment from Covidean doom-mongers about the recent – and entirely expected – downtick in cases of respiratory disease has been palpable, presumably because this has happened without recourse to ‘clever’ public health interventions.
Risk is notoriously difficult to communicate effectively. It is especially hard when referring to an emotive subject like the risk of dying as the emotional response prevents rational interpretation of complex numbers. To simplify understanding of the benefits of interventions the number of people who need to be treated to prevent a death can be measured, the number needed to treat (or “NNT”). The same