Mutant variations and the danger of lockdowns
By Jemma Morris:
At the beginning of 2020 we embarked upon a nationwide epidemiological experiment in an attempt to reduce the mortality burden of the novel SARS-CoV-2 virus. The premise of the experiment, though never formally defined, was to trial the efficacy of non-pharmaceutical interventions with respect to the infection rate and subsequent death toll of an airborne respiratory virus.
The hypothesis was treated as a foregone conclusion and presented with little doubt. A significant reduction in person-to-person interactions within a population will lead to a decreased infection rate and reduce the number of deaths associated with the virus. The scientific community were so confident in this hypothesis that they did not present it as a hypothesis at all. The experiment was not defined as an experiment. The resulting data was subsequently ignored.
No matter how certain we are of the outcome, good science is about asking questions
It’s easy to see why. Given our most basic understanding of how viruses spread from one person to another, any measures that suppress the transmission of viruses should inevitably lead to a reduction in associated mortality. But given that we have never actually investigated this correlation in a real-world setting, perhaps assumptions based on our “most basic understanding” are not sufficient. No matter how certain we are of the outcome, good science is about asking questions. If the answers contradict your assumptions then those answers should bring about a shift in your understanding.
One year into the great experiment, we have a wealth of global data to inform our conclusions. This data largely contradicts the confident hypothesis with which we embarked upon this journey and has therefore been ignored. Scientists and politicians have clutched at straws, manipulated data or simply ignored the evidence in an attempt to safeguard the integrity of the original idea.
The hypothesis was treated as a foregone conclusion and presented with little doubt. A significant reduction in person-to-person interactions within a population will lead to a decreased infection rate and reduce the number of deaths associated with the virus. The scientific community were so confident in this hypothesis that they did not present it as a hypothesis at all. The experiment was not defined as an experiment. The resulting data was subsequently ignored.
No matter how certain we are of the outcome, good science is about asking questions
It’s easy to see why. Given our most basic understanding of how viruses spread from one person to another, any measures that suppress the transmission of viruses should inevitably lead to a reduction in associated mortality. But given that we have never actually investigated this correlation in a real-world setting, perhaps assumptions based on our “most basic understanding” are not sufficient. No matter how certain we are of the outcome, good science is about asking questions. If the answers contradict your assumptions then those answers should bring about a shift in your understanding.
One year into the great experiment, we have a wealth of global data to inform our conclusions. This data largely contradicts the confident hypothesis with which we embarked upon this journey and has therefore been ignored. Scientists and politicians have clutched at straws, manipulated data or simply ignored the evidence in an attempt to safeguard the integrity of the original idea.
Related:
Spacing COVID-19 vaccine doses has epidemiological benefits, but longer-term outcomes depend on immunity robustness
