In 2006 the Better Regulation Commission published a rather good document titled “Risk, Responsibility and Regulation: whose risk is it anyway?”. In the introduction it declared: “We want to challenge the easy assumption that governments can and should manage all risks”.

One of its proposals echoed a more generalised proposal from my own research institute for the institutionalisation in government of fast, initial assessment procedures, labelled QuickScans, aimed at providing some rapid, initial intellectual order and cool headedness into frequently chaotic and panicky first responses.

It is, however, a dangerous thing to challenge the easy assumptions of governments, and about fifteen months later the Commission itself was, in classic Whitehall fashion, abolished: we want to be challenged; you have challenged us, but not in the way we wanted; goodbye.

This past history comes to mind now in the Covid-19 context because, if a policy programme is launched in chaos, with little overall sense of what it is that needs to be done and with what priorities, it can be difficult later to get to an effective, sustainable strategy. Incoherence and arbitrariness can become the order of the day.

An Irish proverb is helpful here: “tús maith leith na hoibre” (“a good start is half the work/journey”). Unfortunately, in facing the Covid-19 challenge the UK’s governance system started very badly.

Vital signals and important inferences were missed or discounted in February and March. One was the uptick in 111 calls starting in late February. Another, more fundamental, concerned the sharpness of the increase in daily death numbers in the first half of March. The daily death figures are lagged responses to triggering, earlier infections and the response to infections that occurred on any specific day in the past is (probabilistically) spread out over time.

This feature of the “response curve” means that the deaths curve is not simply a scaled transposition of the (unknown and not directly measurable) infections curve of the contagion. The deaths curve tends to be flatter and to have a lower peak than the infections curve. Crucially, as a consequence of the flattening effect, it tends to rise less steeply than the infections curve in the early stages of contagion.

An immediate inference is that, if daily deaths are rising very quickly, which they were in early and mid-March, the infections that triggered them were likely increasing at an even faster rate two or three weeks earlier, i.e. the infections curve was already likely vertiginous at the earlier time.

That’s the maths, but it is a maths that is congruent with observable realities: the fast rising tide of hospital admissions and the observation that, ahead of those, millions of Britons had been packing themselves together on public transport for the twice a day participation in super-spreading events called commuting.

Traditional WHO guidance was a contra-indicator for Lockdown in these circumstances, but Lockdown became the policy. Why?

Answers are necessarily conjectural at this stage, but one for the short-list is a familiar risk assessment bias among those in positions of responsibility, a particular variant of politicians’ logic: “Something must be done. Our continental neighbours are doing this. Therefore we will do this.” Go with the gang and, if it all goes pear-shaped, as it very well might, it will be difficult to blame us alone.

Although Lockdown was rationalised in terms of flattening the curve to protect the NHS, wilder thoughts of eradicating the disease in Britain also appeared to emerge (a policy in want of a justification can be a great generator of tall tales).  This was reflected in use of expressions such as “stamping out the virus” and “wrestling it to the ground”.

As the Swedish Health Authority concluded at the outset, eradication would very likely be infeasible in the immediate future and the costs of attempting it would be inordinately costly. In practice, however, the government did not initially walk the walk of “eradication in one country”: for example, constraints on foreign travel were light. Quarantine came much later, puzzlingly at the tail end of the epidemic when new infections had become much fewer.

There is another “why” question here and other conjectural answers. One is attributable to Adam Smith: political leaders can, over time, become the dupes of their own sophistry, even though they may not have believed it when first formulated. Another is that a laggard in joining the tough-boys gang might think it has something to prove to the others when an opportunity arises.

Whatever the conjectures, the ill-conceived notion has persisted in domestic politics and is to be found now in overwrought reactions to local upticks in infections. A more balanced view would see the upticks as predictable consequences of the easing of social distancing in those places where immunity levels acquired (for a period at least) from earlier infections are simply lower than longer-run equilibrium values, as some places almost inevitably are in a rapid contagion that is characterised by significant  randomness. They call for careful monitoring and management, not the “whack-a-mole” treatment, but they can be expected to die back as the random process teases out its equilibrium.

In this context, the notion of “herd immunity” has been an intellectual virus that, at the beginning, was allowed to escape from the Westminster laboratory and has caused significant harm since. It is a soundbite of an early epidemiologist trying to draw the attention of a wider audience to a specific feature of an epidemiological model. Professor Gupta has explained the technical meaning in a previous interview for Reaction, but she is a small voice of reason in a tide of passions and the policy significance of the technical point has been vastly overplayed.

“Acquired immunity” is a much more useful expression and it is not model-dependent (a good thing, because models can, as we have seen, be egregiously wrong). It simply means the immunity (for a period at least) of any individual consequential on having been infected by the virus in the past.

At the community level, acquired immunity is a continuous variable that can take any value from 0% to 100%. The higher it is the more difficult things are for a virus that is programmed to replicate itself in new hosts and the easier it will be for a policy authority to manage outcomes – and it is that relationship that is all that the policy maker needs to know.  The task of the relevant authority is then to take account of this particular trade-off (between levels of acquired immunity and the characteristics of viral spread) – which is only one of several relevant trade-offs – in a wider, balanced judgment.

Once acquired, the immunity is manifestly a good thing, a point that has perhaps only been fully appreciated in Sweden. The issues therefore boil down to the harms potentially caused in acquiring it and those are dependent on policy choices and implementation. The costs are low for those with strong immune systems and high for those without them, so a (targeted) policy emphasising “protect the vulnerable”, not “stay at home everyone”, would have been a natural first base. That at least was the conclusion of my own QuickScan at the relevant time.

Professor George Yarrow is an Emeritus Fellow, Hertford College, Oxford.