Thread by @danielgoyal

The Covid Con!

How many governments have been conned into thinking Protections against Covid are no longer required?

🧵

When Covid first reared its head in East Asia, the surrounding countries went on high alert. Perhaps it was their experience with SARS1 and MERS that kicked them into action.

Or the initial reports suggesting a 10% mortality rate.

Testing ramped up quickly, alongside significant deployment of medical personnel and field hospitals to affected areas.

Contact and Trace - honed by previous experience - was exceptional. Quickly the test positivity rate fell.

It soon became clear that mortality was <3%.

The East Asian countries continued to admit all positive cases and aggressively contact and trace. They did this to a) limit death and therefore disruption/fear to society b) to get to know the virus c) contain the virus d) delay spread until treatments were ready or elimination.

A number of western countries looked at the emerging data and decided SARS2 was not as dangerous as our East Asian counterparts thought it was. Several countries took a more lax approach, with some scientists advising a “herd immunity” through mass infection approach.

In part, this decision came from the reported infection fatality rate (how deadly the virus is on an individual basis) and in part, perhaps the sense “only” the elderly were affected. Whatever the decision was based on, it has since been proven to be wrong…

1) there was a failure to consider the effects of aggressive testing on the mortality rate (the more mild infections you detect, the less deadly the virus appears).
2) there was an unsubstantiated assumption that immunity would last long enough for herd immunity.

3) there was an unsubstantiated assumption the virus wouldn’t mutate in any meaningful way.
4) there was a failure to properly consider the impact on other healthcare services.
5) a failure to appreciate all adults likely have an equal increase in risk of death from Covid.

The carnage that ensued, we have all witnessed. But now, as many countries enter the post-vaccine, post-treatment era and with a significantly diminished mortality rate, has our basis for removing protections been based on the same thinking which led to so many avoidable deaths?

I fear it probably has. Here’s why…

Firstly, the Omicron is mild, it’s like the flu narrative continues to circulate widely. It has not been rejected by leaders, media, and sadly by some scientists (we will get to why later)

I suspect the Covid deniers have used the ONS style

survey data and compared it to sentinel flu data to convince leaders Covid isn’t dangerous. I can visualise the presentations. And to a non-scientific audience the comparability of the datasets are unlikely to be questioned - the job of the scientific advisers, of course.

But why would scientific advisers not challenge such data or caution strongly against conclusions based on it? Maybe they did. I have heard few advisers compare Covid to flu.“We might get there one day” the common response. Instead, some have used the endemic malaria comparison

In part, this lack of questioning may relate to the Achilles heel of scientists - funding and reputation. “Good” scientists are willing to be wrong…must be wrong at times to make progress. But some non-scientists don’t get that. They think the scientist is not so good.

Imagine your boss thought that way.

Scientist: the new data suggests herd immunity won’t work, so we have to change tact.

Boss (Trump/Johnson): so you were wrong?

Arguing against data that proves your initial opinion right or at least not wrong within a political climate…

… could be asking a lot. Such bias need not be conscious.

More likely equivocations over data would prevail and this would be lost in translation to politics.

The age-based risk of death would also be a compelling graph to wrongly convince policy makers that Covid is vanilla

Average age to death (life expectancy) continues to circulate - even amongst MSM - as evidence that “only” the elderly are affected, even though it demonstrates the exact opposite.

Forget the seeming callousness of thinking older more vulnerable people are not worth the effort,

it is the selection of datasets that can be used in combinations to make the argument, even though it isn’t the actual reality.

Secondly, the self-fulfilling prophecy that is Group Think is a major challenge.

Many politicians, particularly those failing to manage Covid well, just want it to be over and “not a big deal”. It is then easy to select scientists and advisers who feed you that narrative. Not the best. Not the scientist with the most integrity. But the most pliable one.

With only 17% of the U.K. public in favour of scrapping self-isolation, secret white-house meetings, Covid minimising think tanks, powerful media agendas, and declarations, the scenario of selective data sharing to convince governments about what they already want to hear…

It is of course a con.
Risk is based on IFR and probability of being infected. And risk also applies to non-Covid disease and displacement of care. This applies too, for the burden of long term effects - some of which remain unknown. By any calculation, honest calculation…

..easing protective measures can only logically occur (assuming public interest), when:
1) risk of transmission is low enough
2) longer term consequences are accounted for, and
3) the local healthcare can sustain the additional pressures without compromising other services.

I suspect politicians (and others) have been duped into thinking Covid is not so bad. Which of course is exactly what they and others wanted to hear. It’s like telling a kid the dentist is not going to hurt - gets them in the door but when it does hurt, they won’t trust you again