In the world of science, biases lurk beneath the surface, tainting the work of even the most revered experts. In her new book, Expired – Covid the untold story, Dr. Clare Craig reveals how beliefs, fear and selective evidence have shaped the Covid debate and beyond. Here’s an excerpt.
How can science be so muddled up with beliefs? Surely, at the heart of science there is neutrality with evidence leading decision making? Unfortunately, in reality, scientists are subject to all the flaws of human decision making. All scientists. No-one owns the truth. Given enough time, everyone will be proved wrong about some of their beliefs. Some people find that very easy to forget. Some scientists seem to believe that no future generation will look back at us and laugh about what we got wrong, despite that happening for every previous generation. Being open to accepting error requires adopting contrary views and handling the inevitable cognitive dissonance that comes with doing that, which is unpleasant and takes work.
Science progresses, not when more evidence is found that supports what is already believed, but when evidence proves the conventional narrative wrong. It is hard to prove certain things are right just through experience. If you believe that all swans are white then finding more and more white swans adds weight to that belief but it does not prove it. In contrast, finding a single black swan disproves your belief. Science progresses by proving theories wrong rather than collecting more and more evidence that fits the existing theory.
People hate being wrong (including me). Nevertheless, the way science is approached is to take a belief, a hypothesis, and set out to see if it can be proved wrong. First you think of an explanation of the world that would mean you can explain something, that is your hypothesis. Then you set out to prove that the interpretation is wrong. If you were wrong you have progressed. The foundation of science is the exploration of being wrong. Every scientific breakthrough has proven those in powerful positions to have been wrong. Each one has had to be fought for and many have taken years to be accepted.
The obvious counter to scientists who believe they are omniscient is that the complexity of the world far exceeds the ability of one person to understand. Even within a niche specialty, there will be plenty of areas which are poorly understood. Therefore, if we want to improve our collective understanding of the world, it must be done through listening to every voice and not letting those who believe they are omniscient drown out those with interesting questions.
Any debate about Covid was heard only by select audiences willing to engage. There were very few scientific breakthroughs. Instead there was a strange inversion. Normally, old established assumptions believed by the majority are challenged by a minority with new evidence. With Covid the majority beliefs were based on new assumptions and when they were challenged it was done so on the basis of old, established evidence. For example, the idea that natural immunity would not be protective, despite decades of experience around immune memory, was ignored. Why was this old, established evidence being ignored?
It was not simply that some scientists were being fooled into believing something outside of their discipline. The belief that catastrophe was around the corner meant that reassuring evidence was considered dangerous. The focus was on preventing catastrophe, so anything that distracted people from that goal must surely be, not only wrong, but immoral. From that foundation arose the ill-founded fears that everyone was susceptible to each variant, that the healthy or asymptomatic were spreading disease, that viral spread could only be stopped by intervention and that every intervention would work.
In order to maintain these incorrect assumptions there needed to be distortion of the truth. Aside from plain old errors, there are three key ways in which the evidence can be exaggerated, misrepresented and distorted to allow scientists to defend their preconceived ideas. I have called this the Evidence Manipulation Triad and both sides of the Covid debates are guilty of using some of these techniques. The three elements are:
Extrapolating happens when weak evidence is given overly significant weight or when evidence is synthesised. Synthesised evidence comes in three flavours:
a.) Modelled results based on assumptions only
b.) Weak evidence that is adjusted to produce more impressive results
c.) Evidence that addresses only part of the question at hand
The close contact transmission model was based on a closed-minded understanding of the mechanisms through which germs can spread persisting as a hangover from debates taking place over a hundred years ago. Extrapolating from evidence that bad smells were not the source of disease led to an inability to conceive of the notion that viruses could transmit through the air. Worse still, the mistaken idea that infectious particles would almost all fall straight to the ground was based on a model of droplet dispersion with a mistake at its very heart, yet was extrapolated and used in public health guidance for many years. Additionally, some, myself included, extrapolated from the mathematics of the first wave, showing most the population were not susceptible, and assumed the epidemic was over in summer 2020.
Excusing happens when evidence is presented that contradicts the scientist’s current belief. A scientist should think openly about all implications of the new evidence and devise several new hypotheses to test. Instead, our reaction is often to find reasons why this evidence might be wrong. Perhaps it was measured incorrectly, or the sample was biased or there was some factor that was not controlled for that meant things did not progress as expected. For example, the public health authorities were so wedded to close contact being the only route for transmission that evidence of spread from unknown sources was excused as being due to asymptomatic spread. Similarly, Von Pettenkofer, the miasma proponent, who had seen the germ theory evidence discounted it because of years of expert knowledge about bad air and must have made excuses for that evidence in his mind before drinking the cholera culture and catching the disease.
Excluding happens when there are no excuses to be made and instead the evidence is totally ignored. The paradoxes of outbreaks in remote locations like Antarctica and on boats at sea or the genetically identical variants of influenza appearing simultaneously across the northern hemisphere even in eras before international travel are totally ignored. By excluding such evidence, scientists may overlook crucial information that could enhance their understanding of the phenomenon being studied.
The way science has historically handled these biases is through open debate. In theory, if all voices are heard and the evidence is presented from every angle then the conflicting evidence becomes apparent. Further experiments or measurements can then be carried out to clarify the point of contention. In practice, further work is only carried out if it can attract funding. Funding is almost all secured through powerful institutions which are often either linked to government or, directly or indirectly to industry. There was no funding for work that challenged the official Covid narrative.
It was a struggle to be heard, especially for those working free of charge and without a job title or institutional backing and it was worrying to see preprint servers, where submissions can be made prior to peer review, rejecting papers from established professors. The editors of the journal Science commented on how they had discussed whether it was “in the public interest to publish the findings” before printing a peer reviewed paper. The danger is clear. Though this is just one example – inspired by a superficially benign motive – the question arises as to whether other editors filter publications, not because they are unreliable, but because the findings are not politically helpful, creating a marked bias in the published literature.
Breadth and depth of scientific knowledge is immense. Too much for anyone to keep on top of. To keep up, people trust the experts and take things on faith. Because the majority of the population are taking things on faith, a consensus answer takes on immense power. It is hard to weigh up the evidence yourself and admit uncertainty. It is much easier to dismiss someone who is scientifically questioning the consensus by claiming they are mistaken. In many cases, the background knowledge required to be able to carry out an assessment is not within reach anyway, so the consensus will win by default. The shortcut of believing what authorities say and what the majority believed did not favour the truth around Covid.