Lies, Damn Lies and the Statistics of Denial
Truth is the first casualty in war: and the war against Covid-19 is no different. From the start it has been characterised by lies, misinformation and the suppression of truth; and although this has varied in degree from one place to another, the contagion of deceit has left few places uninfected.
Just before the pandemic broke, I began writing a novel about an alien virus taking over the world. It’s been educational to see that for many of our leaders, the instinctive response would be denial. Some go on to be systematically untruthful; some are selective with the truth for the best of intentions; and some cannot ever bring themselves to admit there is a problem. Each society puts a cultural stamp on its falsehoods: we can tell a lot from the different ways in which we dissemble.
The clearest trait is that totalitarian regimes announce death rates that are unbelievably low. The more authoritarian the regime, the lower the figures. They try to use that as evidence of the need for control, whilst open societies bemoan the vulnerabilities that go with freedom. This is a mistake: anyone who thinks that Russia doesn’t have a runaway epidemic, or that Iran has got things in hand, needs a cold reality bath.
The Chinese government, battered by accusations of cover-up, have recently amended their figures for the early stages of the epidemic in Wuhan. That 50% increase restores a shred of credibility; but the great unknown is the extent to which the severity of the subsequent lockdown and surveillance was effective. We can’t trust their figures, so we don’t know how well it worked.
In the UK, with no systematic testing, government and media cling to the only certainty they can find, which is the number of deaths in hospital. Although forced to admit that this is only part of the picture, they continue to downplay deaths elsewhere, stating them as 10% of the hospital totals despite evidence that 40-50% is a more likely range. This bumbling around the data reflects a British establishment who generally gave up maths so early in their school careers that they never developed a grasp of statistics.
The most open and well educated societies have either combined strong, early measures with excellent campaigns of public information, thus preventing the disease from getting a hold (e.g. New Zealand), or have challenged the whole scientific basis of lockdowns (e.g. Sweden).
To understand the strengths and weaknesses of these approaches, we first need to acknowledge that everyone is confused about what’s really going on. The epidemiologists are almost as lost as the rest of us. Why do death rates vary so wildly? How many people have been infected? Do they then gain immunity? After six months of pandemic, our ignorance still exceeds our understanding.
One mystery of Covid-19 is why its impact varies between ethnic communities. In the USA, 30% of identified cases are Afro-Americans, who form only 14% of the population. In the UK, three quarters of the healthcare workers who have died are from BAME backgrounds. The reason is unlikely to be genetic but a consequence of lifestyle, poverty, associated conditions like obesity – and the fact that they also provide roughly half of our frontline heroes.
Early on, the Chinese thought only 17% of those infected showed no symptoms. Yet when whole communities have been tested, like the crew of the USS Roosevelt, the real figure appears to be almost 60%. Iceland has tested 15% of its entire population and reckons the figure at near 50%. Incidentally, cooping people up in a confined space like an aircraft carrier demonstrates that in similar circumstances, most of us would get infected.
Testing in most developed countries is between 2% and 0.5% of the population and suggests that the total number of infections may be 50 to 70 times the number of identified cases. On this basis, over a hundred million people already have the virus, maybe a third of the population in some countries. Together, these statistics call into question several aspects of the lockdowns.
Big events cause big disasters: Italy and Spain owe most of their pain to a single football match in February. Big egos are worse: super-idiots like Trump and Bolsanaro facilitate contagion more than super-spreaders.
What policy guidance can we draw from all this? Firstly, in countries that have failed to contain the virus it’s likely that most of us are going to get infected whatever we do, so the main purpose of any policy is to manage the flow rate of cases through the health service.
Secondly, viral load is a major factor in determining whether someone lives or dies, so PPE for all frontline workers really is vital.
Thirdly, Sweden was probably right with its approach of focusing on social distancing rather than lockdown, at least once the disease was already present.
Unless Covid-19 mutates into a more deadly form, it is probably going to kill about one of us in every thousand, mostly the old and sick. Balancing this against the human and economic cost of lockdown is a tough call.
In any event we need to become more pragmatic in our policies. Crowding together in a confined space is bad; sunbathing in a park is not.
We also need to recognise that most of us have been very lucky. A flu-like pandemic was top of the risk register for decades: Covid-19 is probably the least deadly disease that could have forced us to make such huge changes.
This will not be the last pandemic. Will we learn the right lessons, before the next one? It would certainly help if we could clarify the truth of what is actually happening. Yet irrespective of the statistics, one lesson is clear. Our chances depend on how we load the dice. Minimising surplus capacity in emergency services is a false economy; but money spent on boosting our disaster preparedness is the wisest investment any politician will ever make.