On “Science” and Controversy
Infectious disease outbreaks require constant re-evaluation of accepted truth
Predictive infectious disease outbreak interpretation and management is a wickedly problematic endeavor. Walking along a high wire over quicksand during a howling wind with no safety net starts to approximate the reality. But it is not just your life that is at stake, but those of friends, family, the public and society at large. The only thing that seems constant is change. Having done this for decades now, through far too many outbreaks, one conclusion that I am confident in is that (at a minimum) it requires humility and flexibility.
Bear with me as I set this essay up with an example. As the South African data began to come in on Omicron, one of the fundamental “truths” of SARS-CoV-2 started to shift. Until recently, the data were quite strong that natural immunity
(immunity obtained after infection and recovery) is quite effective in preventing infection, disease and death. As previously reviewed on this substack, the many mutations which characterize the Omicron variant result in a viral genotype with remarkable ability to circumvent both genetic spike protein vaccine-induced immunity AND (apparently to a lesser extent) natural immunity.
Although this preliminary analysis was based on early emerging data, subsequent data sets continue to demonstrate that Omicron reinfection of both the vaccinated and naturally immune are common, and detailed analyses suggest that vaccination may be associated with NEGATIVE vaccine efficacy against Omicron (ie: make it more likely that a vaccinated person becomes infected) as discussed here and here
Now these analyses are not official “peer reviewed” academic journal publications, and real time peer reviewers (of the old school type – fellow scientists and statisticians actually publicly critiquing the work) note that all potential confounding variables such as social behavioral changes in vaccinated vs unvaccinated (for example, the “I am vaccinated and therefore bulletproof” paradox) have not been addressed and in many cases cannot be corrected for. But in outbreaks, we never have perfect data, and it often seems that as soon as we get enough data to perform the necessary corrections the pathogen or some other variable changes and we start all over. One is left with choosing between devil and deep blue sea, imperfect current data or more (historically) accurate but outdated information.
Getting to the point, if you seek to be an honest broker of current truth and reasonably accurate predictor of the future, as one travels along the stream of data and disruptive events which comprise that high wire, there come times when one has to adapt to unexpected changes. To those with the benefit of looking through a retrospectoscope, these adaptations can seem like “flip flops”; inconsistencies or paradoxes. And within a community of fellow travelers all moving along the same data stream, different individuals will set their own criteria for when it becomes necessary to shift and adapt to new data. Which can lead to conflict, misalignment, or public disagreements within the community. Early adopters risk reacting based on preliminary data which later are demonstrated to have been consequent to a million possible artifacts (often due to data biases and undetected confounding variables). Late adopters risk drawing conclusions on data which are outdated and no longer predictive.
But the data are relentless, and somehow must be confronted and accommodated. If policy adjustments are made promptly, then social and financial disruption is minimized. If delayed, the gap between current policy and the data will grow to the point where only a major disruption can reconcile policy which is no longer adapted and aligned with “ground truth” reality.
Selecting from my own rich library of past mistakes to illustrate the point, my personal decision to accept Moderna SARS-CoV-2 vaccination is often raised by detractors and trolls. At the time, when data on the adverse event profile was still emerging, the decision made sense for me. I took my chances and paid the price (Stage 3 hypertension peaking at 230mmHg systolic in my case, among other things). Fortunately, thanks to an astute and skilled cardiologist, I survived. In that case, I would have been better served by delaying that decision until more data were available. Hopefully, this example helps to demonstrate the point. Even the highly informed make mistakes. Fairly frequently. Biology is a complex, harsh and unforgiving professor, and a wise student is always careful to recognize one’s profound ignorance in confronting that underlying complexity. Hence the importance of always maintaining balance on the high wire by recognizing that most “understanding” is best expressed as a working hypothesis.
Based on my experience with infectious disease outbreaks, which is still a few years shy of that of Dr. Anthony Fauci but a bit longer than that of Dr. Rochelle Walensky (to choose two current examples), I prefer to take a bit more risk, and operate out at the front edge of the data. Sometimes this can result in mistakes. Often it results in drawing conclusions and making proactive public statements which derive from a blend of experience, emerging incomplete data and subconscious intuition. Because this idiosyncratic combination of objective and subjective variables feed into a given assessment and conclusion, it is often useful to express inferences as working hypotheses. I have done this so often, for so long, and have had enough of these predictions and inferences validated by past experience, that I have become comfortable with publicly disclosing, discussing and acting on these inferred hypotheses. But sometimes I get it wrong, or the data shift in ways that I did not predict. This is a fundamental characteristic of what happens when an infectious disease pathogen crosses over into a new species (humans, for example) or moves into a new niche (for example a sporadic rural threat agent that gets introduced into a densely populated urban environment). An intellectual journey along a high wire over quicksand during a howling wind with no safety net.
In contrast, in my experience, the position typically taken by most public health leadership is reactive, not proactive. One based on more cautious position which relies on mature data rather than forward looking based on emerging data. The problem with this stance is that the resulting policy is almost always outdated, particularly during an outbreak where “ground truth” is a hazy, fluid, rapidly shifting reality. In other words, the fog of war. This paradox is compounded when the leadership positions and decision making require development of bureaucratic group consensus.
The logical consequence of the interaction of these two strategies is intellectual and scientific conflict and controversy. Those who are out at the front edge of the data will draw conclusions and recommend policy shifts which are inconsistent with the current consensus. Those wedded to reactive positions based on well-established data and group consensus will reject more proactive policy recommendations. Even assuming good faith, professional competence, and absence of conflicts of interest this will always be the case because the two approaches rely on different sets of data, different versions of “truth” and reality. Continuing the warfare metaphor, this is akin to the natural tension between the commander of a forward operating tank battlegroup and a senior general operating well behind battle lines.
This must be the essential dialectic of science (and medicine) as an intellectual endeavor and discipline. Good science (and good public health policy) requires conflict. There is no one “Science”. No single “truth”. Quality science is like a song sung by a choir. A solo scientist is usually better described as a philosopher or priest. Any individual claiming to embody “Science” or scientific truth is (by definition) no longer a practitioner of the intellectual discipline of modern science.
The scientific process requires constant external challenge and criticism to reach a working approximation of truth. Such (typically autocratic, paternalistic) people lose the ability to maintain objectivity and typically transition to functioning as an enforcer of their version of reality. These people often resort to a form of crude binary thought – their version of the truth versus all alternatives. In contrast, the modern scientific practitioner approaches the effort to draw truth out of the unknown as something closer to mathematical calculus, a process of serial approximation which gradually approaches an asymptote hypothesized to be scientific truth.
Enter the trusted news initiative. Here is the official BBC justification for this intrinsically anti-science, pro-censorship insult to the free exchange of ideas, and an alternative interpretation of this Orwellian bureaucracy. This intellectual obscenity purports to be able to discern and enforce scientific “truth” by defining truth as that which established public health bureaucracies (and singularly autocratic public health “leaders”) say it is. The trusted news initiative aggressively employs both globally coordinated media and the tools of modern big technology to censor, demean, de-platform, delegitimize and de-license all others who seek to document, advance or discuss alternative versions of officially endorsed reality. The trusted news initiative has functionally morphed into Orwell’s predicted ministry of truth. Backed by the combined power of national and international governmental structures, massive transnational investment funds the likes of which the world has never seen before, and the commercial assets (Big Pharma, Big Media, Big Tech) over which the funds exert horizontally integrated control through access to investment capital and structural leadership ties.
This is the most intrinsically anti-science global organization ever implemented in the history of modern man. The closest historical approximation to this monstrosity is the Catholic Church during the Spanish inquisition.
When this history of this pandemic is written, the combined effect of the Trusted News Initiative and autocratic national and international public health leaders will be documented as being responsible for massive excess human suffering and loss of life due to suppression of the discussion and dissent which is critical for the modern scientific process to accurately discern evolving truth and inform effective public policy decisions. This must stop, before yet more avoidable, unnecessary suffering and loss of life accrues.