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The current epidemic is a classic application of what economists call “radical uncertainty” (most recently explored by John Kay and Mervyn King in their brilliant book of that title, which came out last month): in a world that has inevitably become too complex to be adequately captured in models, a world of both “known unknowns” and “unknown unknowns”, the most sensible response to the question “what should we do?” is “I don’t know”. At the onset of this crisis, we could not put probabilities on which forms of social distancing would best limit its spread because we’d never done it before. We didn’t know how people would alter their behaviour in response to the appeal to “save the NHS”. We didn’t even know whether reducing the spread was desirable: perhaps fewer deaths now would come at the cost of more next winter. And these were just the known unknowns. With a disruption as big as this, unknown unknowns are also lurking. We have no experience of the material and economic repercussions from shutdowns of this nature and their aftermath in a modern economy, and no meaningful way of assigning probabilities; nor of how people’s behaviour will evolve.

What the modellers should have said, right from the beginning, was that it was vital to establish two fundamental parameters: the incidence and the rate of contagion, both of which require mass testing, and without which mortality rates are impossible to decipher and hence sensible policy impossible to implement. It is frankly astounding that four months into this new virus such tests are only now being instigated.

. Shifting responsibilities down the system not only enables rapid scale-up, it has a further huge advantage: the power of decision is closer to the coalface of practitioner experience. We learn not just from accumulating and analysing codifiable knowledge – the domain of the expert. We learn by doing, or by trying to do things that we can’t do and that force us to experiment. A decentralized system learns from a litany of failed experiments running in parallel, and so it learns fast: teams copy other teams that have hit on something that works well enough to get the job done.