I recently asked attendees at the Australian Institute of Professional Intelligence Officer’s conference, if having a strategic intelligence capability embedded in our human biosecurity regulators would have made a difference. On face value, the lack of investment in an integrated intelligence system within Australia’s most powerful regulators could be seen to underscore why there was such a poorly constructed strategic threat and harm picture leading to disingenuous, disconnected, and ill-considered policy and operational responses. But a deeper view, shows that the absence of a strategic intelligence capability was filled by a series of reviews and planning processes that led to the formulation of a sound plan aimed squarely at the expected emergence of SARS2. The Beale Review 2008 is a notable example in the process. There is also the fact that the pandemic response plan was internationally developed and then agreed by all of Australia’s key Ministers/regulators just prior to 2020. All providing evidence of a very timely and targeted strategic intelligence system (absent professional intelligence expertise). (See: Beale et al, One Biosecurity - Working in Partnership, Commonwealth of Australia, 2008, and Australian Government, Health Department, Australian Health Management Plan for Pandemic Influenza, August 2019).
On the one hand, Australia’s Pandemic Plan worked – especially given the early adoption of the key response of closing the international border. This saved Australia from immediate and deep harms from the virus. On the other hand, much of the operationalisation components of the plan were discarded by governments early and response options pursued that generated considerable social and financial harm. The rationale for this abrupt detour from the Plan remains a mystery without a fulsome Royal Commission. Explanations too date of Chinese information influence across Western institutions, or simply key politicians’ opportunism, or even just a lack of regulatory experience and independence in the statutory regulators, all require examination to better contribute to regulatory reform and future harm prevention for SARS3.
The regulatory principles enshrined in the plan (termed the Ethical Framework – see page 18 of the Pandemic Plan) were set to guide planning and responses to a pandemic as follows:
· “Equity - Providing care in an equitable manner, recognising special needs, cultural
values and religious beliefs of different members of the community.
· Individual liberty - Ensuring that the rights of the individual are upheld as much as possible.
· Privacy and confidentiality of individuals - Is important and should be protected. Under extraordinary conditions during a pandemic, it may be necessary for some elements to be overridden to protect others.
· Proportionality - Ensuring that measures taken are proportional to the threat.
· Protection of the public - Ensuring that the protection of the entire population remains a
primary focus.
· Provision of care - Ensuring that health care workers (HCWs) are able to deliver care
appropriate to the situation, commensurate with good practice, and their profession’s code of ethics.
· Reciprocity - Ensuring that when individuals are asked to take measures or perform
duties for the benefit of society as a whole, their acts are appropriately recognised and legitimate need associated with these acts are met where possible.
· Stewardship - That leaders strive to make good decisions based on best available evidence.
· Trust - That health decision makers strive to communicate in a timely and transparent manner to the public and those within the health system”
All regulators are tested against the regulatory principles that define their public value. Against these Ethical principles, one would have to find Australia’s regulatory response to COVID wanting. There are abundant examples of dislocated responses across the governments’ regulators and non-harm related controls, and together provide a tapestry of unnecessary limitations on liberty and poor stewardship without proportionality and reciprocity.
Why? Many reasons. The clear reason being the nature of the regulatory capability with each regulator not structured independently and allocated no capability to enact their regulatory functions. With no capacity Federally (as forewarned by Beale), operational regulation fell to the States who had limited field audit capability that was not culturally aligned to contemporary intelligence-led harm prevention. Hence, the key regulators presented as ‘medical advisors’ to government decision-makers, leaving others to enforce seemingly non intelligence-led, illogical, and non-harm related regulatory controls.
One of the more intriguing questions for the Intelligence Profession is the willingness to ‘give up’ the intelligence advisory space of harm and threat, to the data analyst profession. Much of the harm intelligence used in the ‘medical advice’ to decision-making in COVID was labelled ‘science’. This label was used as a protective shield to deflect scrutiny and to add unwarranted analytical confidence to the advice. However, much of the scientific ‘evidence’ relied upon for regulatory controls and responses was actually futures data-modelling; not evidential-based science of a known outcome. This futures approach takes known data and introduces a number of variables to ascertain how that data can morph over time. The more sophisticated the data modelling, the more the number of variables can be introduced (eg in artificial intelligence). Unfortunately, where the initial data sets are limited or skewed, the outcomes portrayed have limited analytical veracity (such as used by Nostradamus). An intelligence-led decision system, is founded on the intelligence need. A data-led system is founded on what data is available. The great pit-fall for regulators in the modern age is become data-myopic (usually referred to as ‘evidence-led’).
Hence, a great pit-fall for the regulatory intelligence profession is to cede the strategic analytical ground to pseudo-science and ‘trust’ self-interested experts. A key learning is the use of premises in estimative logic where those premises are actually not associated with harm.
· Illogical fallacies and the fascination with COVID testing data. The main data-set available was COVID testing rates. Rather than being a measure of harm, these measured the level of activity of the health regulator. (Like the number of inspections done by a safety regulator). The main source of harm data was then derived from ‘positive COVID’ cases in this testing system. Here, the main indicators of harm were derived from close contact testing as the volunteer testing proved to be a very poor regulatory intelligence tool (akin to fishing in a lake with one fish). The context of harm was lost within incoherent data that did not distinguish the healthy, being not-sick with COVID, being sick with COVID, and being sick from COVID. Leading to a public belief that COVID19 was indiscriminate and deadly to all.
· The worse-case scenario pitched as the most likely. A general model was applied to the positive testing rate never before used in viral reporting. Normally a case requires viral presence and harm (sickness). In COVID, the data was never differentiated by harm. In this way, data reporting – and hence modelling – grossly over-assessed harm, and no independent regulatory system was available to educate otherwise. As each variant had diminishing harm potential the data-modelling went from wrong, to really wrong, to really, really wrong. Data-modelling companies appeared to double-down to protect their reputation rather than readjust to new circumstances. Worse-case modelling dressed up as the most likely future scenario, has become a feature of modern activist-based science. Especially where activists are funded to produce pseudo-scientific modelling for alarmist purposes.
· Cherry picking science. Certain known, proven scientific control benefits were applied beyond the conditions of the established harm reduction benefits, in the mistaken belief there would be broader harm-reduction.
o Mask-wearing is a proven harm-mitigation tool in specific circumstances but were mandated beyond their proven safety use. Hence, before, during and after COVID, masks were reconfirmed to have little general public value – especially for general use by children.
o Similarly social distancing is a major control measure in virus mitigation but proved again not to be useful as a blanket tool; especially in circumstances like outdoor scenarios of minimal risk. One example of such false data analysis is James Glanz and Campbell Robertson’s analysis, published May 20, 2020; and updated May 22, 2020. (https://www-nytimes-com.cdn.ampproject.org/c/s/www.nytimes.com/2020/05/20/us/coronavirus-distancing-deaths.amp.html).
o Travel restrictions (including internal border closures and curfews) controlling the movement of the whole have significant unintended harmful consequences compared to tailored quarantine and protections for the at-risk. Cherry-picking created significant differences in responses, further exacerbating doubt that responses were based on a single scientific view. It also led to rules that could not be enforced or justified.
· The use of false experts. There was significant media and political investment in selling SARS2 COVID-19 as a one-off, ‘black-swan’, life-time event. Hence, many of the research experts who had been studying the iterations of SARS lost air-time early to a new wave of instant experts. This new wave often had a vested interest in shifting the discussion from real harm for specific at-risk groups, to a conflated harm to the medical system they represented. This tended to shift the eye of the response system from protecting the vulnerable to increasing funding for the medical system and those not at-risk (such as children). Unfortunately, this led to greater deaths amongst the vulnerable. The most notable example arising in New York where the Governor shifted infected people from hospitals to nursing homes in order to protect the hospitals. The lack of real expertise was evident in the unwillingness to strategically assess shifts or phases in responses likely as the virus followed a natural course of mutation. Rather than being positioned to adjust, response options appeared to have to wait months for new data to confirm what indicators had already flagged.
· Conspiracy theories. Conspiracy theories were used to silence scientific and analytical views that called-out the lack of harm context in the pseudo-science. For example, the idea that a small percentage of “unvaccinated” would harm “the vaccinated” created divisive and exclusive policies of mandated vaccinations in the later stages of the pandemic – for no prevention benefit. Mandated vaccines may be warranted in the early stages of roll-outs (again for specific circumstances) but are not a cogent harm-reduction tool once the majority is vaccinated. Again, dubious data modelling on small percentages of unvaccinated predicted the collapse of hospitals; feeding on fear about closing maternity wings. Such predictions were unlikely and subsequently proven false. Sections of the public were quick to identify the dubious nature of such measures and a major, global protest movement in the Western world was born. This protest movement was generally labelled as ‘extremist’ by media and political movements that had supported the dubious narrative, but the protests did lead to significant reversals in positions in the Western world and the quietening of false experts. The history of the Reign of Terror - post the French Revolution -shows that the general public’s preference for liberty will eventually trump a regulatory system based solely on conjured fear after 18 months to 2 years.
One interesting highlight of the COVID public/media information tapestry was how often those who provided estimative intelligence that proved wrong, continued to be invited back to provide the same assessment. Normally in professional intelligence circles the analyst gets very few ‘free passes’. Napoleons are not renowned for their forbearance. Hence, there are indications in this history that the intelligence failure was more from the decision-maker (or media group) only buying a stylised assessment rather than seeking objective futures thinking. If so, there are equally problematic issues for the intelligence profession. Either there was no contemporary regulatory intelligence system in place (and the significant estimative effort preceding COVID19 was wasted) or there was a complicit or ineffective intelligence system that mildly gave the field over to the data-profession.
So, for those in the intelligence profession dealing with matters of harm and threat, it is time for a little soul-searching in our own natural bias to believe experts and trust in limited data. As the intelligence profession has learned over many tragedies over many hundreds of years:
· If you wait for all to be known, you fail
· If you trust vested interest, you fail
· If you pitch the most dangerous as the most likely, you better have a rock-solid rationale
· If you lose sight of harm and threat, or are party to conjuring harms and threats, you rarely get invited back to support decisions
· When the situation changes, change the assessment…don’t let your ego force you to double-down on your earlier thinking.