But after an autumn of absolutely pathetic numbers — for a while we were barely jabbing 40,000 a day, first and second doses combined, down from ten times that number and more in the spring and early summer — the programme does appear to have picked up a little. There were hundreds of thousands of booster jabs alone given over the weekend.

The Government has pinned its entire winter strategy on those boosters. Sajid Javid, the health secretary, has said Plan B, will not be introduced because of them. Plan B is a milquetoasty set of minor interventions, including greater use of vaccine passports, and legally requiring masks in some places. They may ask for people to work from home if things get worse. It’s not exactly Protect and Survive.

And we’re not going there. Yet.

But there is a problem, which is that Covid-19 in the UK has become really hard to predict. Last year, whatever you looked at — cases, hospitalisations, deaths — the numbers followed a simple pattern. They started going up slowly, then they went up quicker, then quicker, then quicker. Then the Government imposed a lockdown, and, a little while and a few thousand more dead people later, the numbers crashed back down again.

But this year, the pandemic is no longer doing nice straightforward exponential curves. If you look at hospitalisations over the last six months or so, you’ll see that they started a familiar-looking rise in about June, and they clambered up to about 1,000 a day in late July and then – they just stopped, and went back down a bit. Then they rose again, and back down. Now they’ve started going up again, but … will that carry on? Will the curve fizzle out again? Unlike last year, it’s not easy to tell what will happen next.

Adam Kucharski, a mathematical epidemiologist, explained why winter 2021/22 is trickier to model than 20/21. First, he says, in any epidemic, “When R is near 1, it’s a headache.” R is the number of people that each infected person goes on to infect, on average. So if your initial 100 cases lead to 200 subsidiary cases, then your R is 2. If R is above 1, your epidemic is growing exponentially; if it’s below 1, it’s dying away.

When R is well above 1, it’s fairly easy to predict what will happen: cases will go up, faster and faster, until it runs out of susceptible people or until policies and behaviours change so that R goes below 1 and the epidemic declines again. That’s how it was last year.

But now, thanks to vaccines and natural immunity, the number of susceptible people around is lower — we’re close to herd immunity — so even with essentially no restrictions, R is close to 1. And when it’s close to 1, smaller things can affect the course of the epidemic much more. “It bounces around,” says Kucharski. “Schools reopening, or closing for half term. The vaccine rollout and boosters. If the epidemic is increasing or decreasing, some small change can knock it the other way.”

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One way to think of it is that if your R is 2.5, and, for instance, schools close for half term, then your R might go down to 2.3. It’s the difference between an epidemic that’s spreading quickly, and one that’s spreading not quite so quickly. But if R is 1.1 and you knock it down to 0.9, then it’s the difference between a pandemic that’s growing and one that’s shrinking.

It also means that individual behaviour becomes important. Around our way, it recently became very obvious that lots of schoolchildren (and their parents) were getting the disease. So when ours got the sniffles we were quicker to take them out of school, and more wary of going to shops. Presumably millions of people have behaved in similar ways. It wouldn’t have made much difference last year — the disease was spreading so rapidly — but it could be enough now to change things from an up-arrow to a down-arrow.

There’s a more interesting dynamic at play as well, says Kucharski. Imagine the first two waves of the pandemic as a forest fire in a virgin forest. Once a tree starts burning, it easily spreads to the trees around it, and, left to its own devices, the fire will carry on until the forest has burnt away. An immunologically naive population is the same. “When you have a fully susceptible population,” says Kucharski, “the simple exponential curve models will give you something sensible.”

But we no longer live in a virgin forest. Vaccinations and acquired immunity means that the large bulk of the population is less susceptible to infection. In our analogy, you could imagine that it’s a far sparser forest, with occasional clumps here and there representing groups with lower vaccination uptake. Now, if a tree catches fire, it might not spread to any others. Or it might be in a clump, and spread around that clump quickly, and then burn out. Or a spark might make it from the clump to a nearby clump. It’s much more dependent on luck and randomness.

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“You have a much patchier network for the virus to spread in,” says Kucharski. “A lot more dead ends, and surges in younger groups and places with lower vaccination cover; you get pockets of susceptibility, and you get large outbreaks, but it has to work its way from one pocket to another. When you’ve got lots of these happening in parallel, you have a much bumpier dynamic, more conducive to a bobbling-along plateau than one that has clear growth or decline.”

All of which makes the epidemic much harder to predict. But there’s another layer of difficulty on top of that, which is that you’re not just predicting an epidemic: you’re predicting human behaviour. More specifically, you’re predicting the behaviour of the current government and parliament. There is not an algorithm which says “At 1,500 hospitalisations a day, we re-enter lockdown” or anything.

Other countries do have something like that — in Taiwan, for instance, there are four levels of restrictions, which are imposed on a specified set of conditions. Level 1 is imposed if there are cases imported from overseas that result in “isolated community transmission”; Level 2 is imposed if there are “domestically transmitted cases from unknown sources”, etc. If you could predict the behaviour of the epidemic in Taiwan, you’d have a pretty good basis for predicting the policy response to it.

But not in the UK. Plan B, or any further restrictions, will be imposed if the Government decides to impose them. I think that’s a bad system, and the Taiwanese method of removing the decision as far as possible from political pressure would be better. But that’s the system we have. So as well as predicting what the virus will do, we have to predict how 361 Tory MPs will respond to the virus, and how Boris Johnson and Sajid Javid will respond to them. So predicting whether we’ll end up in a lockdown is hard.

But perhaps there’s a more interesting question, which is: should we act as though we might have to, and start doing something about it now? Should we impose Plan B?

For all the current doom and gloom, we are in a better place than even the most optimistic of the government’s SPI-M-O models projections from early September. And those models now tend to forecast a decline in cases from later this month, even if we don’t impose Plan B. We’re not facing an immediate crisis.

Oliver Johnson, a mathematician at Bristol, says an autumn decline is plausible: “The key thing is it has been so crazy in schools, with 7% or 8% of schoolkids infected at any one time,” he says. “You can’t have that forever: you’ll run out of people. So it’s a case of working out when you’ll run out of people.” The little clumps of forest will burn down eventually.

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The men who failed Britain

By Tom Chivers

The pandemic, though, has been consistently hard to predict and the great failings of the British Covid response have been trying to be too clever, doing things too late, and not thinking about risk-benefit calculations or worst-case scenarios.

This feels very much like one of those situations. It’s true that, probably, if we leave it and do nothing, things won’t get that much worse, and may soon get better. But there’s a non-trivial chance that they will get worse. Given that the costs of imposing Plan B restrictions are minor, why not take simple, low-cost steps now to avoid the unlikely, but plausible and very bad, future shit show.

We have a suite of tools available to us – not just Plan B, but also things like increased rapid testing – that can avoid future disruption. But, also, if we impose them now, we can undo them later easily if they’re unnecessary, or tweak them if they’re not quite right.

For instance, there are concerns that vaccine passports will damage the entertainment industry and have only a small impact on the virus. If that’s true, we can reduce their use once that’s clear.

And we should be able to avoid stringent restrictions being imposed again, and another catastrophic Christmas lockdown. “Given the tools we now have available,” says Kucharski, “it would be completely absurd if we got to that point.”

I agree. We absolutely should not need to consider a Plan C, one in which we shut stuff down, stop people seeing their loved ones, close the pubs, ruin Christmas. And we can make it all but certain that we don’t have to do that if we take small, sensible precautions now. Hopefully enough elderly people will stand in cold car parks to get their boosters; but let’s not rely on it entirely.

But I do remember very clearly that when I heard that Health Person solemnly say that, vaccine or no vaccine, we would still be wearing masks and social distancing and so on for the next 18 months, my response was not fit to print.

Now, I’m no idiot. I know that a vaccine in a vial is as much use as those gym weights gathering dust under the bed. Vaccines don’t work till people are vaccinated with them. Getting them into the people at serious risk from Covid-19 will take a few months. We’re going to be firefighting till Easter, stoically trudging through the grim, dark, post-Christmas months before normal life can be resurrected with the Spring.

Then it will take more months to vaccinate the rest of us, not everyone perhaps, but enough to provide herd immunity. So no, I am not expecting Covid-19 to vanish overnight, just because a vaccine exists. And I do expect that some of the vulnerable population who can’t be vaccinated will still end up in hospital with it. And that, despite improvements in medical treatments, some of them will die.

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The Government's Covid scaremongering

By Timandra Harkness

But I also expect that the risk of death and serious illness from this novel disease will, by next summer, genuinely become comparable to seasonal influenza.

And that’s why I am worried about the way our relationship with the NHS has been reconfigured during this pandemic.

I regularly hear the voice of NHS Providers, the collective body representing NHS hospitals, mental health, community and ambulance services, in the media. On Sunday, they wrote to the Prime Minister, “urging extreme caution” over relaxing restrictions on everyday life, citing concerns about rising infection rates in some areas, and a potential shortage of hospital beds.

As they point out, “the NHS has been short of beds for each of the last five winters”. I have written before about the inability of our sainted NHS to cope with the annual surge in demand from cold weather and seasonal infections. Compared with our continental neighbours, the NHS is both inadequately funded and ineffective.

In October 2019, NHS Providers reported that 6 out of 10 Trusts were worried about their capacity to meet demand for services over the next 12 months, even before Coronavirus arrived. That report called for substantial investment in both health and social care services, and for ‘informed public debate’ on the future direction of the NHS.

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By Timandra Harkness

If we ever get that informed public debate, it will happen in a very different context from the one that existed in 2019. In previous winters, when routine operations were cancelled, or acutely ill patients died on trolleys or in ambulances, the debate was about why the NHS failed to provide what we, the public who fund it and expect it to be there for us, needed. Now it is more likely to be about what we, the public, should be doing to reduce demand.

Public health bodies have long pointed to our unhealthy habits placing extra demands on the NHS, because our eating, drinking and smoking increased our risk from diabetes, cancer and heart disease. Excessive drinking is also blamed for placing extra strain on emergency services including paramedics and hospitals, mainly from drunken accidents and fights. This has reinforced calls from public health bodies to reduce opening hours, get people to sit down in pubs, and otherwise deter getting drunk.

Trying to change public behaviour to improve health and safety, and thereby reduce demand on health services, is nothing new. But the public has never before embraced such sweeping, compulsory restrictions on how we live.

Though trust in the Government’s Coronavirus measures is falling, along with willingness to adhere to rules that seem detached from either science or common sense, the majority of us still accept them, more or less. Well over half of us have changed our behaviour to reduce the spread of infection, washing our hands more, wearing masks and avoiding crowded places. Two thirds of Britons surveyed in November 2020 said they’d support a third national lockdown after Christmas if cases remained high. And, yesterday, a YouGov poll indicated that 65% of people think face coverings should be made compulsory in busy outdoor areas.

We must take these survey answers with a health-advice-breaking pinch of salt. Another survey said three quarters of us expected that most people would break restrictions to see people over Christmas, though of course only a quarter said they planned to do so themselves. In practice, many people use common sense when applying the rules to their own lives, making their own judgments about acceptable risks. But the principle that society must change around the limitations of the NHS, and not vice versa, is now entrenched in public discourse.

What happens when risks from Coronavirus become comparable to flu, and other seasonal respiratory illnesses? The 2014/5 winter was the worst since 2000 for excess winter deaths, which peaked at 15,000 per week in January 2015. The public was encouraged to take up influenza vaccines, and to wash hands and catch coughs and sneezes. No question of working from home, closing pubs and theatres, let alone sports stadiums or swimming pools.

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Instead of strategies to reduce risk while keeping society running as much as possible, we responded to Coronavirus as if it were an existential threat, in whose teeth any measure was justified. When vaccines beat it back to a nasty disease like so many others that lie in wait for the vulnerable, will we know how to weigh risks to individual lives against the danger to public life?

I fear that, having once been sacrificed on the altar of the NHS and its limited capacity, our freedoms are no longer safe from the utilitarian knife. The same people telling us to shop alone, drink alone, and be in bed by eleven, to save lives from Coronavirus, will continue to make the same arguments over lesser risks.

If we accept pubs serving no alcohol, or alcohol only with a meal, or closing at ten, on the shaky ground that it reduces the spread of a virus, why not accept similar measures to take the strain of drunkenness off A&E departments every weekend? It can’t be coincidence that those rules fit so well with public health campaigners’ longstanding desire to wean us off our boozy nights out.

If we accept mandatory masks for Coronavirus, why not keep the rules for every flu season? And if masks, why not socially distanced theatres, reduced public transport capacity and more working from home? Once we have accepted that it’s our job to reduce demand on the NHS by limiting our lives, where will it end?

So the reason for drunken swearing behind my mask on that train home was not that I mind wearing a mask all that much. I wear a helmet for motorcycling, I take off my shoes for airport security, and if I have the flu I avoid friends on chemotherapy. Masks aren’t that big a deal.

But I want life to go back to normal. I want to meet people indoors, to laugh and drink, and watch performances, and take part in public meetings. I want to go into other people’s homes and have them come into mine. Lord knows, it’s the only thing that makes me get the vacuum cleaner out. And I don’t want to live another 50 years if I’m not allowed to hug anybody.

So once you’ve stuck that vaccine in the arms of the highest risk groups, you can take your 18 more months of social distancing, and stick them somewhere else.

“Many diseases never reach herd immunity – including measles, malaria, AIDS and flu…” he said. “Herd immunity is a flawed goal – even if we could get to it, which we can’t.”

"The Great Barrington declaration is underpinned by two central claims, and both are emphatically false."

Health secretary Matt Hancock tells the House of Commons that "herd immunity is a flawed goal without a vaccine."@MattHancock | @HouseofCommons pic.twitter.com/znCZTOF9jX

— Talk (@TalkTV) October 13, 2020

Let’s have a look at the diseases he mentions. Measles, if it arrives on ‘virgin soil’, can devastate a population. In Tahiti and Moorea and the South-east and North-west Marquesas, between 20% and 70% of the population was lost to the first epidemic. Natural infection with measles provides lifelong immunity, and we now have a vaccine which provides similar solid, durable protection. We have not been able to eliminate the disease, but those who rather selfishly choose not to vaccinate their children are only able make that choice because the risks of infection are kept low by those who are immune — currently, a combination of those, like me, who caught it and recovered and many others for whom it is vaccine induced. The vaccine does not work in babies, which is why you have to wait till they are a year old before they get it. We can do this because herd immunity keeps the risk of infection down, so they are are unlikely to be infected in their first year of life. Without this herd protection, many under ones would die (as they regularly do in sub-Saharan Africa) despite a vaccine being available.

Malaria (a primary focus of my research) is caused by a parasite which carries at least 60 different outfits with which to disguise itself from immune attack. We, nonetheless, typically acquire sufficient immunity upon first infection so as not to suffer severe disease and death upon further attacks. This is a feature it probably has in common with Covid-19. But that is where the similarity ends. It has been difficult to make a malaria vaccine that covers all of its diversity, but fortunately that should not be a problem for Covid-19.

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AIDS is caused by the Human Immunodeficiency Virus which also has an extraordinary capacity to change outfits during the course of infection. This, and other mechanisms of subverting the immune response, allow it to persist indefinitely in an infected person. Even so, rates of infection will slow down as the virus runs out of people to infect.

Fortunately, Covid-19 does not possess such an array of immune evasion mechanisms that could seriously compromise any possibility of vaccine development in the next few years. Like HIV, influenza also has the ability to change its outfit, and periodically a new strain emerges requiring a new vaccine. It is because sufficient immunity accumulates in the population that a radical change of outfit becomes worthwhile. What Mr Hancock means when he says we do not reach herd immunity to flu is that it finds ways around it; it is unlikely, given the nature of coronaviruses that the SARS Cov-2 virus would be capable of doing the same thing.

In a nutshell, the development of immunity through natural infection is a common feature of many pathogens, and it is reasonable to assume that Covid-19 does not have any tricks up its sleeve to prevent this from happening — it would pose a very serious problem for the development of a vaccine if it did. Having said this, the Covid-19 virus belongs to a family of viruses which do not typically give you lifelong immunity against infection. Most of us will never have heard of these other four ‘seasonal’ coronaviruses that are currently circulating in our communities. And yet surveys indicate that at least 3% of the population is infected by any single one of these corona cousins during the winter months. These viruses can cause deaths in high risk groups or require them to receive ICU care or ventilator support, so it is not necessarily true that they are intrinsically milder than the novel Covid-19 virus. And like the Covid-19 virus, they are much less virulent in the healthy elderly and younger people than influenza.

One important reason why these corona cousins do not kill large numbers of people is because even though we lose immunity and can be reinfected, there is still always a decent enough proportion of immune people in the population to keep the risk of infection low to those who might die upon contracting it. Also, all the coronaviruses in circulation — including the Covid-19 virus— have some features in common which means that getting one coronavirus will probably offer some protection against other coronaviruses. This is becoming increasingly clear from work in many labs, including my lab in Oxford. It is against this background of immunity from itself and its close relations that Covid-19 virus has to operate.

So what does Mr Hancock mean by “reaching” herd immunity? Herd immunity is a continuous variable which increases as people become immune and decreases as they lose immunity or die. He is perhaps referring to the threshold of herd immunity at which the rate of new infections starts to decrease. We do not yet have a very clear idea of what this threshold is for Covid-19 as the landscape in which it spreads includes people who are susceptible to it, people who have built up immunity to it, and people who have immunity to other coronaviruses.

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Unfortunately, we do not have a good way of telling how many people have actually been exposed to the new virus, or how many people were resistant to start with. We are able to test for antibodies – and my lab in Oxford has been doing so since early April – but, as with other coronaviruses, Covid-19 antibody levels decline after recovery, and some people do not make them at all, and so antibody levels will not give us the answer. More and more evidence is accumulating that other arms of immunity, like T cells, play an important role.

Indications of the herd immunity threshold having been reached are available from the time signatures of epidemics in various parts of the world where death and infection curves tend to “bend” in the absence of intervention or to stay down when interventions were relaxed (in comparison with other locations where the opposite happened). But we do not know how far we are from it in most parts of the UK. It is important to bear in mind that the attainment of the herd immunity threshold does not lead to disease eradication. Instead it corresponds to an equilibrium state in which the infections lingers at low levels in the community. This is the situation we tolerate for most infectious diseases (like flu which kills 650K people every year globally). The situation can be vastly improved through vaccination, but it is very difficult to eliminate the disease even with a good vaccine.

We are of course also able to test for presence of the virus, and there is much attention on this with ‘test and trace’ strategies. However this test, known as the PCR test, is of limited value as it cannot tell us whether someone is infectious and can pass on the disease, whether they have the virus but cannot pass it on, or indeed whether the virus has been destroyed by the immune system and only fragments remain. This means that we need to make public health decisions based on only partial information, and in a changing environment, and is why assumptions of how many people have been infected and are immune are so important.

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The Great Barrington Declaration proposes a solution for how we may proceed in the face of such uncertainty. It suggests that we exploit the feature of this virus that it does not cause much harm to the large majority of the population to allow them to resume their normal lives, while shielding those who are vulnerable to severe disease and death.

Under these circumstances, immunity will build up in the general population to a level that poses a low enough risk of infection to the vulnerable population that they may resume their normal lives. All of this can happen over a period of six months, and so this Focused Protection plan does not involve the permanent segregation of the vulnerable from the rest of the population.

It is important that any new proposal should receive close scrutiny and constructive criticism. I’ve addressed the short duration of immunity; but another worry is the ‘occult damage‘, or secondary effects sustained by those who are not obviously vulnerable. It is not at all unexpected that some people would suffer post-viral symptoms for extended periods of time (I believe I did!) and that it may be quite debilitating for some. Among the lessons we could learn from this crisis could be a wider recognition of the frequency and intensity of post-viral syndromes and an investment in support (leave of absence from jobs, help with daily activities) of those unfortunate enough to suffer in this way. But it is not a new phenomenon and cannot be a good enough reason to stop the world and potentially let tens of millions of people starve to death.

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How such a plan may be put into place is obviously the next step to detail, and it is natural that some people might view with scepticism that it can be achieved other than in theory. Many components of protecting the vulnerable have already been enacted in the process of locking down so we should be discussing how these can be improved rather than dismissing them. Directing efforts at hospitals and care homes is one obvious priority. Other parts of the problem – such as the protection of vulnerable people within family settings – require careful discussion and thought, but it must always be borne in mind that these are temporary measures and in the long run could save more lives than cycling in and out of destructive lockdowns.

One colleague likened the GBD plan to “putting all your antiques in a room while your house is burning and fanning the flames”. A better analogy for the build up of herd immunity would rather be to douse the rest of the house with water (which could damage the Chippendale commode) — but it is hard to see how these could be the terms of a serious discussion.

Since the declaration last week, the Great Barrington Declaration has come under attack across the media, online (including Wikipedia and Google) from fellow academics as being part of a Libertarian conspiracy (my politics are not remotely libertarian) or being based in “pseudoscience”; others attempting to be less defamatory say that our views are “fringe”. The large number of serious scientists from top institutions taking part suggest otherwise. There are genuine good faith disagreements that must be aired and discussed — the impact on the world is too significant for us to fail to have this discussion in a serious way.

Today, though, the presence of some level of prior resistance and immunity to Covid-19 is fast becoming accepted scientific fact. Results have just been published of a study suggesting that 40%-60% of people who have not been exposed to coronavirus have resistance at the T-cell level from other similar coronaviruses like the common cold.

Now, from the unlikely source of a prominent member of the “Independent SAGE committee”, the group set up by Sir David King to challenge government scientific advice and accused by some of being populated with Left-wing activists, comes a claim that the true portion of people who are not even susceptible to Covid-19 may be as high as 80%.

Professor Karl Friston, like Michael Levitt, is a statistician not a virologist; his expertise is in understanding complex and dynamic biological processes by representing them in mathematical models. Within the neuroscience field he was ranked by Science magazine as the most influential in the world, having invented the now standard “statistical parametric mapping” technique for understanding brain imaging — and for the past months he has been applying his particular method of Bayesian analysis, which he calls “dynamic causal modelling”, to the available Covid-19 data.

Friston referred to some kind of “immunological dark matter” as the only plausible explanation for the huge disparity in results between countries in an interview with the Guardian last weekend. The eye-catching phrase attracted a lot of attention on social media, with some commentators keen to dismiss it as rubbish, but he meant it in a quite precise way: like dark matter, the undetectable substance that makes up approximately 85% of the universe, it is provably there by its effects. We just don’t know anything about it.

His models suggest that the stark difference between outcomes in the UK and Germany, for example, is not primarily an effect of different government actions (such as better testing and earlier lockdowns) but is better explained by intrinsic differences between the populations that make the “susceptible population” in Germany — the group that is vulnerable to Covid-19 — much smaller than in the UK.

As he told me in our interview, even within the UK, the numbers point to the same thing: that the “effective susceptible population” was never 100%, and was at most 50% and probably more like only 20% of the population. He emphasises that the analysis is not yet complete, but “I suspect, once this has been done, it will look like the effective non-susceptible portion of the population will be about 80%. I think that’s what’s going to happen.”

Theories abound as to which factors best explain the huge disparities between countries in the portion of the population that seems resistant or immune — everything from levels of vitamin D to ethnic-genetic and social and geographical differences may come into play — but Professor Friston makes clear that it does not primarily seem to be a function of government coronavirus policy. “Solving that — understanding that source of variation in terms of this non-susceptibility — is going to be the key to understanding the enormous variation between countries,” he said.

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Professor Friston is ultra-cautious in his choice of words, and understandably so: the impact of this realisation, if proven correct, is hard to overstate.

Immediately it would change how we should think about lifting lockdown: a tube carriage in London might in theory have to be restricted to 15% capacity to maintain social distancing of 2 metres, but if, as Professor Friston believes, the susceptible population in London was only ever 26% and 80% or more of that group is now provably immune via antibody testing, you can put a lot more people in a tube carriage without increasing the level of risk. Ditto restaurants, pubs, theatres and most recently, MPs in parliament. It would question the whole idea of social distancing being a feature of any “new normal”.

It would take the heat out of the political argument around the pandemic, and give the lie to the idea that it was ever primarily government actions (however incompetent or incompetently executed) that explain differing death rates. As Professor Friston puts it, once you put into the model sensible behaviours that people do anyway such as staying in bed when they are sick, the effect of legal lockdown “literally goes away”.

His explanation for the remarkably similar mortality outcomes in Sweden (no lockdown) and the UK (lockdown) is that “they weren’t actually any different. Because at the end of the day the actual processes that get into the epidemiological dynamics — the actual behaviours, the distancing, was evolutionarily specified by the way we behave when we have an infection.”

Most significantly, it would mean that the principal underlying assumption behind the global shutdowns, typified by the famous Imperial College forecasts — namely, that left unchecked this disease would rapidly pass through the entire population of every country and kill around 1% of those infected, leading to untold millions of deaths worldwide without draconian action — was wrong, out by a large factor. The largest co-ordinated government action in history, forcibly closing down most of the world’s societies with consequences that may last for generations, would have been based on faulty science.

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When I put this to Professor Friston, he was the model of collegiate discretion. He said that the presumptions of Neil Ferguson’s models were all correct, “under the qualification that the population they were talking about is much smaller than you might imagine”. In other words, Ferguson was right that around 80% of susceptible people would rapidly become infected, and was right that of those between 0.5% and 1% would die — he just missed the fact that the relevant “susceptible population” was only ever a small portion of people in the UK, and an even smaller portion in countries like Germany and elsewhere. Which rather changes everything.

With such elegant formulations are scientific reputations saved. Practically, it makes not much difference whether, as per Sunetra Gupta, the 40,000 officially-counted coronavirus deaths in the UK are 0.1% of 40 million people infected, or, as per Karl Friston’s theory implies, they are more like 0.5% of 8 million people infected with the remaining 32 million shielded from infection by mysterious “immunological dark material”. If you are exposed to the virus and it is destroyed in your body by mucosal antibodies or T-cells or clever genes so that you never become fully infected and don’t even notice it, should that count as an infection? The effect is the same: 40,000 deaths, not 400,000.

This wouldn’t mean that most of the population is technically immune to Covid-19 — scenarios with a very high viral load, such as doctors treating Covid-19 patients in hospitals may still overpower these defences — but it would mean under normal circumstances, most people would never have contracted the disease.

The atmosphere in the UK continues to change irrespective of Government policy, and if people ever were afraid they are becoming less so, having intuited that, for now at least, the coronavirus threat seems to be in retreat. Gradually, the scientists are providing explanations for why that might be.

If Professor Neil Ferguson of Imperial College is the figurehead for the first opinion, then Sunetra Gupta, Professor of Theoretical Epidemiology at the University of Oxford, is the representative of the second. Her group at Oxford produced a rival model to Ferguson’s back in March which speculated that as much as 50% of the population may already have been infected and the true Infection Fatality Rate may be as low as 0.1%.

Since then, we have seen various antibody studies around the world indicating a disappointingly small percentage of seroprevalence — the percentage of the population has the anti-Covid-19 antibody. It was starting to seem like Ferguson’s view was the one closer to the truth.

But, in her first major interview since the Oxford study was published in March, Professor Gupta is only more convinced that her original opinion was correct.

The implications of this are profound – it means that when we hear results from antibody tests (such as a forthcoming official UK Government study) the percentage who test positive for antibodies is not necessarily equal to the percentage who have immunity or resistance to the virus. The true number could be much higher.

Observing the very similar patterns of the epidemic across countries around the world has convinced Professor Gupta that it is this hidden immunity, more than lockdowns or government interventions, that offers the best explanation of the Covid-19 progression:

“In almost every context we’ve seen the epidemic grow, turn around and die away — almost like clockwork. Different countries have had different lockdown policies, and yet what we’ve observed is almost a uniform pattern of behaviour which is highly consistent with the SIR model. To me that suggests that much of the driving force here was due to the build-up of immunity. I think that’s a more parsimonious explanation than one which requires in every country for lockdown (or various degrees of lockdown, including no lockdown) to have had the same effect.”

Asked what her updated estimate for the Infection Fatality Rate is, Professor Gupta says, “I think that the epidemic has largely come and is on its way out in this country so I think it would be definitely less than 1 in 1000 and probably closer to 1 in 10,000.” That would be somewhere between 0.1% and 0.01%.

Professor Gupta also remains openly critical of the Government lockdown policy:

“The Government’s defence is that this [the Imperial College model] was a plausible worst case scenario. I agree it was a plausible — or at least a possible — worst case scenario. The question is, should we act on a possible worst case scenario, given the costs of lockdown? It seems to me that given that the costs of lockdown are mounting, that case is becoming more and more fragile.”

She recommends “a more rapid exit from lockdown based more on certain heuristics, like who is dying and what is happening to the death rates”. She does not believe that the R rate is a useful tool in making decisions about government policies, as an R rate is “principally dependent on how many people are immune” and we don’t have that information.

She believes that deaths are the only reliable measure, and that the number of cases should not even be presented as it is so reliant on the amount of testing being done.

She explains the flare-ups in places like New York, where the IFR seems to have been higher than 0.1%, through a combination of circumstances leading to unusually bad outbreaks, including the infection load and the layout of the population:

“When you have pockets of vulnerable people it might rip through those pockets in a way that it wouldn’t if the vulnerable people were more scattered within the general population.”

She believes that longer-term lockdown-style social distancing makes us more vulnerable, not less vulnerable, to infectious diseases, because it keeps people unprotected from pathogens:

“Remaining in a state of lockdown is extremely dangerous from the point of view of the vulnerability of the entire population to new pathogens. Effectively we used to live in a state approximating lockdown 100 years ago, and that was what created the conditions for the Spanish Flu to come in and kill 50m people.”

Commenting on the Government response to the virus, she suggests it erred on the side of over-reaction not under-reaction:

“I think there’s a chance we might have done better by doing nothing at all, or at least by doing something different, which would have been to pay attention to protecting the vulnerable, to have thought about protecting the vulnerable 30 or 40 years ago when we started cutting hospital beds. The roots of this go a long, long way back.”

And she believes it is a “strong possibility” that if we return to full normal tomorrow — pubs, nightclubs, festivals — we would be fine, but accepts that is hard to prove with the current evidence:

“So what do we do? I think we weigh that strong possibility against the costs of lockdown. I think it is very dangerous to talk about lockdown without recognising the enormous costs that it has on other vulnerable sectors in the population.”

On the politics of the question, Professor Gupta is clear that she believes that lockdowns are an affront to progressive values:

“So I know there is a sort of libertarian argument for the release of lockdown, and I think it is unfortunate that those of us who feel we should think differently about lockdown have had our voices added to that libertarian harangue. But the truth is that lockdown is a luxury, and it’s a luxury that the middle classes are enjoying and higher income countries are enjoying at the expense of the poor, the vulnerable and less developed countries. It’s a very serious crisis.”

She was intimately acquainted with mortality. She’d been a doctor and a farmer, and had by old age outlived her brother, two husbands and three of her four children as well as most of her friends. Even so, confronting her own decline into Alzheimer’s took tremendous courage. While she still had capacity, my mum and I helped her go through the details of a document that listed her medical preferences, for when she no longer had the capacity to advocate for herself.

It was not easy reading: a grim catalogue of scenarios such as seizures and strokes, defibrillation, ventilation, and the circumstances in which she would like medical treatment or palliative care. Despite her tough pragmatism, and her faith in life after death, I remember how tense and miserable she was the day we got the document notarised.

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Some time later, after dementia claimed her once razor-sharp mind, she fell out of bed in the nursing home and broke her finger. Under safeguarding guidelines, the staff were obliged to call an ambulance, and the paramedics were in turn obliged to take her to hospital. These rules could not be sidestepped, despite everyone involved knowing that for someone in their 90s, with advanced dementia and a minor injury, transfer to hospital was likely to cause extreme distress and could even be a death sentence.

Happily, the document detailing her preferences absolved staff and paramedics alike of their reluctant obligation to follow these rules even against their own clinical judgement. They splinted the finger in situ, it mended just fine, and she was spared a confusing and frightening experience.

We have been considerably less clear-sighted than my grandmother about facing the implications of coronavirus. On the surface, much of the response to the government’s measures has treated the situation as a continuation of the Brexit wars. But beneath this displacement activity lurks a consensus that cuts across culture war lines for all but the youngest and most staunchly libertarian: swivel-eyed horror at the realisation that we haven’t, after all, vanquished death.

Of course we know in theory that death comes for us all eventually. But for the most part, our culture treats death as abnormal, even outrageous — not the inevitable fact it still is. While volunteering as a bereavement counsellor, I learned that some GPs will prescribe antidepressants barely days after a loss, as though normal grief at the death of a loved one is a medical condition. And one of the most contentious areas of debate among doctors is how to manage relatives’ pleas for life-prolonging interventions for a patient, even in cases where there is no hope and such measures will only cause pain and distress to a dying person.

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It can't be 'big business as usual' after coronavirus

By Mary Harrington

Or consider the way fatal accidents are often described in tabloid headlines as ‘tragic’. If we read that in the Aristotelian sense, it would imply such fatalities came about due to a flaw in the character of the deceased, and that the pity and fear evoked by their demise should be treated as a moral experience for readers.

Of course, applying Aristotle to tabloid idiom is overkill, and in contemporary usage ‘tragic’ commonly just means ‘very sad’. But the term retains a trace of the classical understanding of a tragedy as more than simply random misfortune. We can see this in the way that random misfortunes described as ‘tragic’ in tabloid newspapers often seem to prompt a search for a moral lesson. ‘Tragic accident’ stories frequently trigger a campaign for new safety regulations, designed to prevent any such misfortune from recurring. Over time, this dynamic has made a substantial contribution to the regulatory environment of daily life, from sensible measures such as compulsory seatbelts to more contested ones such as councils ripping up cobbled streets.

But if we apply it to a pandemic whose mode of transmission is unclear, for which we have no vaccine and no very reliable treatment, and which kills people, it’s clear we’re going to run into trouble. What kind of rules can forestall misfortune on the scale of Covid-19?

This quandary in turn helps explain why the government’s initial response to the pandemic (which arguably it is still following, albeit more circumspectly) was so politically unpalatable. This initial response followed roughly the lines set out in the government’s pandemic flu planning documents. The assumption was that transmission would be impossible to contain, so the aim was to slow the spread, ensure healthcare systems are not overwhelmed, and over time to reach a state where enough people are immune that replication would fade away: ‘herd immunity’.

But from the outrage that erupted when Boris tried to explain this, you’d think it was a calculated plan to kill off the old and frail. Surely we could do more. ‘More’ turned out to be total lockdown — an approach that still commands widespread support with the British public. A recent Opinium survey suggested that even the barest hint that the government might be considering relaxing lockdown has caused public approval of Johnson’s handling of the crisis to dip. Most of us, it seems, want rules that will prevent any further tragic deaths.

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Except lockdown won’t prevent coronavirus deaths, just slow down their rate of occurrence. Unless a vaccine is discovered, any relaxation of lockdown will result in a new spike in infections, followed by further lockdowns, and so on until we reach — yes — herd immunity.

In earlier centuries, everyone experienced the death of loved ones, often of ailments that are preventable today. Funerary practices reflected this familiarity with death, and my grandmother’s wishes belonged in this tradition. When she died, at the age of 96, my mum laid out and washed her body — an ancient practice traditionally carried out by female relatives of the deceased, but barely followed today. Grandma believed that souls stay close to a body for three days after death, and asked for a wake at my mum’s home, with an open coffin. I stayed with my mum for those three days, and it was a surreal but strangely intimate time. Grandma did indeed feel very close to us.

Her death came shortly before my own wedding, and I remember sitting at my mum’s kitchen table, with Grandma in her coffin in the adjacent room, making table favours from tiny packets of wildflower seeds for the wedding breakfast. Joy and sorrow seemed woven together, in a strange in-between time when Grandma wasn’t quite gone, and my new phase of life hadn’t quite started. It was where I said my goodbyes to someone I’d loved very much, and for me the funeral seemed almost an afterthought.

Over my grandmother’s lifetime, hygiene and medicine advanced so dramatically that it must have seemed for a while as though life expectancy would go on rising forever. As recently as 2018, breathless articles were imagining humans living to 1000, while transhumanists continue their quest to defeat death by uploading human consciousness to a computer.

As death has receded, so too has our willingness to look it in the face. Today, barely 10% of families keep a corpse at home between the death and funeral. Funerals themselves have become arms-length affairs, with the deceased mostly whisked away shortly after death is confirmed and only reappearing for a swift ceremony before vanishing again on a conveyor belt to be cremated.

But now, like nothing since the two world wars, the pandemic has brought us into a mass confrontation with the mortality we imagined we’d conquered, or at least indefinitely postponed. And as death has returned to hover at the edge of our peripheral vision, our habitual mass aversion to it has shaped the field of acceptable political response to the virus.

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It's worth taking a risk over Covid-19

By Timandra Harkness

It’s no coincidence that less wealthy nations have in many cases weighed the risk of pandemic against the certainty of starvation for subsistence communities, and decided against imposing lockdowns. When death is already a familiar presence, the risk calculus of virus transmission looks very different.

But in nations where death seemed on the back foot, it’s all very well for scientists to repeat that the only way out is via herd immunity, but it’s not something we seem willing to countenance. Not yet, anyway. Accepting it means allowing risk and death back into the room. So almost without exception we remain crouched in lockdown, a frozen state that addresses our terror of death by banning nearly everything that makes life worth living.

If we’re to find a way out of this state, we’ll need to face not just the likelihood of successive waves of infections, but also the fact that some misfortunes are not tragic in the classical sense, just very sad. Sometimes there isn’t a moral lesson.

No rules can be devised that will banish death altogether. And no amount of prophylactic regulation will stop the coronavirus in its tracks, only slow it and try to ensure medical facilities are not overwhelmed. There’s no guarantee we’ll ever have a vaccine or cure. Until that changes, if we are to begin living again, we may need to re-learn some forgotten lessons about how to walk in the valley of the shadow of death.

But we small islanders are never more at home with our own hypocrisy than when it concerns our sex lives. I’ve known married relationship counsellors warn clients against the perils of conducting love affairs, while having a string of lovers themselves. One tale that best demonstrates our wayward attitude to sexual mores involves my publican mother. When I was editing the Erotic Review magazine in the late 1990s (a job that raised eyebrows) my mum bumped into a church-going neighbour while walking in the local woods. In the course of conversation the woman said to mum, “I’m so sorry about Rowan, that all sounds dreadful”.

My mother replied she wasn’t the least bit embarrassed and was happy I was doing what I loved. At which point the neighbour, without missing a beat, said, “Actually, I was thinking of taking out a subscription for my husband.” Most middle-class Brits main concern when discussing sex is to give the appearance of conforming to societal norms. If the rules are suddenly relaxed within a friendly and discreet setting your average upright person may confide eye-popping misdemeanours.

The contradictory messages about sex are also apparent in people’s reactions to the BBC drama Normal People. It was announced this week that there had been 16 million downloads of the series in its first week on iPlayer. The pre-publicity and reviews all focused on the intense portrayal of sexual intimacy, which presumably helped boost the ratings.

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Yet a number of comment pieces — and myriad threads on social media – expressed discomfort or shock at the unbridled coupling.  One newspaper headline announced, “The sex scenes in Normal people give me the ick.” Which, in the stubbornly enduring spirit of Mary Whitehouse, is a bit like settling down to watch a dramatisation of The Kama Sutra and declaring yourself astonished there’s so much rumpy-pumpy.

If I hear there’s graphic violence in a film or TV series, I tend to opt not to watch it. Yes these sexually-tormented viewers of Normal People nobly struggle on, through every erotic last erotic frame. (Btw the director Lenny Abrahamson has pointed out the two stars were semi-clad in almost every sex scene, so clever cutting and camerawork gives you the sense it’s more explicit than it actually is.)

What Normal People and the Neil Ferguson debacle show is that we Brits are pretty useless at acknowledging our sexual needs. If this is clear under usual circumstances, it’s even truer under lockdown. When New York first locked down the city, NY Health issued a two-page memo detailing how turned-on citizens could stay safe during the epidemic. With endearing understatement the experts said, “This is not a good time to be having orgies.” Instead their top tip was “You are your safest sex partner,” suggesting you wash hands for 20 seconds before and after use. Try as I might, I could find no similar instructions for Londoners, or anyone in the UK. No official recognition that single people, or couples torn apart, would find sexual frustration boiling up like steam in a pressure cooker as lockdown progressed.

As ever, the inconvenient force of desire isn’t something we really want to talk about in this country. The closest the Government came to issuing advice was telling couples who didn’t live together they’d better “choose now” where they were going to stay throughout the period of self-isolation, then stick to that arrangement. Which didn’t begin to cover the full gamut of sexual ‘arrangements’. There were those in tentative new romances who didn’t want to put them under the strain and accelerated familiarity of living together. There were divorced or separated adults in new relationships, who often had children in different abodes to consider. Then there were those, like Ferguson and his married lover, with more unconventional arrangements – licit or illicit lovers outside their own home. On top of that were millions of lonesome souls continuing the search for love on dating apps. As week after week passed some people were bound to find lust overwhelming caution.

At this point readers are almost certainly dividing into two groups. The ones thinking, “Death-wish morons, I have no sympathy”, have a valid point of view, and the Government and tide of public opinion is with them. I’d hazard a guess a significant number are in stable relationships where parking sex for a bit doesn’t seem like a big sweat if other priorities – work, small kids, Covid-19 and the imminent threat of death – assert themselves and if your best beloved shares your house.

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The only problem with that point of view is that it ignores historical precedent and the line of persuasion best used by the metaphysical poets. Writers like Robert Herrick and Andrew Marvell knew the most effective tool of seduction was reminding the beloved of their own fragile mortality. When Marvell wrote his famous couplet, “The grave’s a fine and private place/ But none, I think, do there embrace,” the bubonic plague was still a very real hazard, with the Great Plague of London (1665-6) sweeping off  nearly a quarter of the city’s residents. If you prized sensible chastity over the chance to seize some rapture, there was a good chance you’d die in any case, but without gathering any rosebuds. Similar reasoning was evident in the Blitz when falling bombs led to increased risk-taking and thousands of brief encounters.

Twenty years into the new millennium, nothing much has changed. Persistent thoughts of death lead to insistent thought of sex and, for that second group of people, a heightened risk of infection is a calculation worth living with. Furthermore, learning to calculate risk is something we glean from the earliest days of maths and use in many everyday scenarios, from giving birth, to driving a car and playing contact sports — as Timandra Harkness detailed so compellingly on UnHerd yesterday. Neil Ferguson had already had Coronavirus and been through quarantine, so I have some sympathy for him reasoning the chance of re-infection wasn’t huge. I suppose all this depends on whether or not you acknowledge how overwhelming sexual connection can be and see it as a driver of the human life-force, akin to hunger and thirst. From my reading and viewing of Normal People I’d guess Sally Rooney believes in that kind of erotic imperative.

What’s notable in my own circle of acquaintance, is how many otherwise sensible, intelligent people have followed all the rules – except the implied one about not seeing a sexual partner outside the home. I’m not talking about cavalier sex-jaunts days into lockdown, but the kind of cumulative longing for human touch, sensuality and reassurance that can make you flout rules and risk social censure. One 50-something friend broke at the six-week point and took an empty train to London (“far safer than my local Co-op”) wearing a mask, where her boyfriend picked her up. Another allowed her partner of a year’s standing to stay twice at her house, but said her teenage children gave her, “such a bollocking it almost wasn’t worth it”. A third moved in with a new man half-way through lockdown, after a flirtation fuelled by long Facetime calls and emails became a fully-fledged romance. You may believe your own circle has behaved more impeccably, but as my anecdote about my mother and the Erotic Review shows, when it comes to sex, people tend to tell you what they think you want to hear.

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I’d like to think we could grow up a bit about sex in this country. That we could applaud a drama like Normal People for showing the most realistically tender love scenes in British TV history without anyone calling it icky. That we could draw up some guidelines for any future pandemic that give more leeway to the realities of adult sexual behaviour. If two adults are reasonably sure they’re not infectious, don’t use public transport and wear masks when out, should they be pilloried for having sex – especially if the married one’s spouse had approved the arrangement? Although of course it wasn’t the shagging that did for Neil Ferguson – it was the double standards. And quite right too. But if our experts were prepared to be more honest about the fact there’s little that’s normal when it comes to sex, there’d be no need for such hypocrisy. As Sigmund Freud once observed, “The only unnatural sex behaviour is none at all.”

It makes for an attractive amateur pastime because lots of the things we are trying to know — such as how many people are infected, or how deadly the disease is — are hugely uncertain. But there’s another problem, which is that the things we measure are affected by the simple fact that we’re measuring them — and that the right things to measure change with every passing day.

For instance, we’re all wondering about the “exit strategy” — how, now that we’re all in lockdown, we’re going to get out of it. It’s going to involve some combination of testing, contact tracing, perhaps (eventually) immunity passports, and hopefully in the not-too-distant future vaccines and treatments.

But it’s also going to involve — probably — see-sawing back and forth between tight controls and more relaxed ones, trying to eke out the cases over months to avoid overwhelming the NHS. And those controls will have to be imposed and relaxed, to some degree, on the basis of metrics.

In the UK, it might look a bit like this. The 16 March Imperial model proposed a sequence of automated triggers: specifically, when the number of ICU cases in a week reaches a certain number, say 100, the lockdown (school closures, social distancing, etc) is imposed; when they drop below another certain number, say 60, they are relaxed. The outcome — hopefully — will be a saw-toothed line on the chart: ICU cases jagging up, coming down, jagging up, coming down, but never breaching the line of health service capacity.

But once you start using ICU beds as a metric, you hit a problem. There’s this thing, “Goodhart’s law”. It’s named after the economist Charles Goodhart, and is usually formulated as “When a measure becomes a target, it ceases to be a good measure.” Goodhart proposed it (in more technical language) when discussing Margaret Thatcher’s economic policies, but it applies everywhere. 

Imagine you’re the education secretary of some democracy. Your government has been elected on a platform of education, education, education. You pledged to push lots of money into schools, reduce class sizes and — most of all — improve outcomes.

So you start saying that schools which get more than a certain number of passing grades in their Generic Class Exams will be rewarded, and those which get a lower number will get put into special measures.

Goodhart’s law predicts that once this measure (how many children are passing their exams?) becomes a target (you need 60 children out of every 100 to pass or we’re firing the headteacher), the measure stops being useful. If a school is close to dropping below that magic 60 number, teachers are heavily incentivised to start teaching to the test, or quietly removing children who are likely to fail. 

Essentially, the trouble is that you don’t care about your exam results per se: you care about some complex and ineffable, but real, combination of whether schools make children more productive citizens, whether they make them more well-rounded individuals, whether they make them happier, whether they make them smarter, whether they protect them from abuse at home.

But you, the education secretary, won’t get to see that: all you see is the exam results. And so that’s all you can judge it on. So that’s what people will strive to deliver to you, even if it’s not really connected to the stuff you genuinely care about. “Looking for the perfect summary statistic,” someone once said, “is like trying to write a dust-jacket blurb that replaces the need to read the book.”

Goodhart’s law has obvious implications for governments’ response to Covid-19. I spoke to David Manheim, an Israeli data scientist who’s spent much of his career thinking about both Goodhart’s law and infectious disease, about what problems it might cause and how best to mitigate them.

I expected him to say that the problem would be countries minimising case and death numbers somehow. That is no doubt going on, but it wasn’t his main focus. His chief concern was the exit strategy.

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How likely are you to die of coronavirus?

By Tom Chivers

At some point in the next few months, countries are going to start relaxing their lockdowns. The UK will probably have some mechanism not unlike the one I described at the start of the piece; the US will likely have some more localised version. 

But it is going to be difficult. Lockdown will be unpopular and expensive and will probably cost many elected officials around the world their re-election. Whatever metrics we use, there will be incentives for those carrying out tests and reporting numbers to get and keep those numbers below the magic line.

In the centrally planned UK, with a nationwide strategy and the NHS in charge of testing and reporting, it might be a manageable problem — although you could imagine that when there are 99 people in ICU beds, the next one is put in a hospital bed with a ventilator and 24-hour monitoring that isn’t, for some reason, called “intensive care”. But in the US, with much more latitude for cities to make their own decisions, things could be very different. 

Imagine that US cities with no active cases are allowed out of lockdown, but that when five new cases are discovered, restrictions are back in place. “When there are four cases, will we push really hard to do as many tests as possible?” Manheim says. “The people in charge have a lot of reason to want to minimise how bad things look. There’s a lot of pressure to look at those numbers in ways that are more favourable.”

I don’t want to speculate on the possible impacts, or how much worse it could make any second waves, or anything. But it’s likely to be a real problem, and one we need to deal with.

The exit strategy isn’t the only obvious problem caused by Goodhart’s law: it applies everywhere, for instance with resource allocation. Imagine you’re a local hospital. You have 50 ICU beds. But you know that, in an emergency, you can repurpose another 12 beds to act as ersatz ICUs for a short period, or even improvise further and treat 100 people at a time.

But if the NHS comes around asking each authority how many beds they have, to allocate spare central resources, it’s obviously in each hospital’s interest to report the lowest number — 50 — rather than the at-a-pinch stretch figure, so that they can get access to extra funding. And, of course, all the others will be doing the same thing, in a sort of tragedy of the commons.

It would be easy to say that the problem is that we’re treating people like numbers, or reducing complex systems to a few metrics. But the trouble is that it is impossible to run a massive modern economy, or healthcare system, without metrics and simple, algorithmic rules.

In normal times, you want your system to run efficiently, and to make sure that you’re managing all the day-to-day stuff you want managed, and that means that you need to be able to easily keep tabs on what’s going on. That means metrics, and – Goodhart’s law notwithstanding – they can, if well designed, give you better-than-nothing information about your system, and help you manage the things that need managing.

But that leads us to the second problem: that in these rapidly moving times, the metrics you use quickly become useless. For instance, it’s probably very sensible that palliative-care drugs like morphine are carefully controlled. After Harold Shipman, it was obvious that their use could be easily abused, so it makes sense to ensure that their use is precisely measured and accounted for. Most of the time, their use won’t change dramatically, so you can build a system that allows them to reach the places they’re needed fairly efficiently, while also alerting you if there are any sudden, strange changes that you should be aware of.

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But in a crisis, that falls apart. Suddenly the demand for the drugs skyrockets, but the system can’t cope: it’s designed to be inflexible, because flexible systems are harder to keep track of. So you end up with doctors begging the Department of Health to relax the legislation so they can stop patients from dying in needless pain.

Manheim says that one doctor in the US told him that, while he’s been desperately trying to source PPE for his staff, he’s had a dozen emails demanding that he complete his recertification training, which he has to do every three months. That’s really important — we need to make sure doctors are up-to-date with the latest medical research and techniques. But right now, he doesn’t have the required six hours spare. If the normal-times algorithm is applied unthinkingly, then he could lose his medical licence.

This isn’t quite the same problem as Goodhart’s law — it’s not that people are gaming the system; it’s that the system is measuring the wrong things, trying to operate in a world which no longer exists. But it’s the same root problem, that metrics only give you a partial view of what is really going on.

The question is what to do to avoid these problems. One that would help you minimise Goodhart’s law, but would come at a cost, would be secrecy: hiding the metrics you’re using to trigger lockdowns. If the people doing the testing don’t know what the system is, they can’t game it. 

The obvious problem with that is that transparency is good in democracies. It might, though, in countries like Britain which still have a decent amount of trust in their institutions, be possible to say “we’ll release all the information in six months, but for now we’re keeping it secret”. But perhaps it’s too high a price to pay.

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Another, less problematic one, would be to use more complex multi-factor metrics. Just looking at deaths, or case counts, means that people only have to game that one metric; you should always be looking at several. 

But the main thing is that in a crisis, you need to rely on judgment more. That sounds like a good thing all the time, but — as we’ve discussed — for a system to be governable at all, it will need metrics, hoops to jump through, visible readouts. But when it’s in crisis, and you don’t have time to cobble together a good way of keeping track of the things you care about, you might be best off trusting the people making the decisions locally.

Lots of them will get things wrong, but they’ll be more able to be flexible than if you are forcing them to jump through the same hoops as in peacetime. You don’t want to be forcing doctors to do their recertification training when they’re desperately intubating patient after patient on an unbroken 28-hour shift.

I think this also has implications for us, the public. All we see are the big headline figures: deaths, case numbers. They’re all we have. But they don’t necessarily tell us the whole story: case numbers are about testing, deaths are recorded differently in different countries. And we don’t only care about them, we care about whether the NHS is getting overwhelmed, and whether other people are dying of preventable diseases because doctors can’t get to them, and excess deaths, and a million other things.

So things like direct comparisons of, say, death numbers between the UK and Ireland or Italy or New Zealand or wherever can tell us some things, but if we focus on them exclusively, then we will encourage a Goodhart’s law situation. The Covid-19 death counts will become the only things that matter, and so the incentives will be to bring them down even at the cost of other things.

All the things we normally rely on for governance — accurate metrics, functioning systems — are currently unavailable; statistics are not going to be as informative as they usually are. We’re going to have to rely even more heavily than usual on expert judgment at every level of the pyramid, and that means that we’re going to have to accept that sometimes people are going to get stuff wrong. We might all be amateur epidemiologists now, but when we make mistakes no one notices; when the real ones do, people die.

Then some of the most famous studies from social psychology’s 1970s heyday fell apart on closer scrutiny. The Stanford Prison Experiment, where people were assigned roles as “prisoners” and “guards”, and the guards ended up treating the prisoners abominably? Probably misreported. The study where “pseudopatients” admitted themselves to psychiatric hospitals, acted entirely sane, and were locked up and medicated regardless? Possibly fraudulent.

Now, psychologists are disgracing themselves anew over the coronavirus.

It started with articles relying on psychological “insights” to downplay the severity of the problem. In early February, social psychologist David DeSteno wrote a piece in the New York Times arguing that people get so caught up with their fear of the virus, they fail to understand that they’re unlikely to get it. Referencing some of his own lab experiments, DeSteno wrote that “…quarantine or monitoring policies can make great sense when the threat is real and the policies are based on accurate data. But the facts on the ground, as opposed to the fear in the air, don’t warrant such actions.”

Two days later, the New York Times’s Interpreter column quoted psychologist Paul Slovic, who noted that “[o]ur feelings don’t do arithmetic very well”, and that focusing on the coronavirus fatalities, and not the “98% or so of people who are recovering from it and may have mild cases” is skewing our judgement. The article argued that our fears, triggered by disturbing reports of “city-scale lockdowns and overcrowded hospitals”, overload our critical faculties, making us overreact to the threat the virus poses. The thought that those city-scale lockdowns and overcrowded hospitals might be a mere month away from the United States didn’t seem to occur.

Further psychological insights were provided by Cass Sunstein, co-author of the best-selling book Nudge, which used lessons from behavioural economics (essentially psychology by another name) that could inform attempts to change people’s behaviour. In an article for Bloomberg Opinion on 28 February (by which point there were over 83,000 confirmed coronavirus cases worldwide), Sunstein wrote that anxiety regarding the coronavirus pandemic was mainly due to something called “probability neglect”.

Because the disease is both novel and potentially fatal, Sunstein reasoned, we suffer from “excessive fear” and neglect the fact that our probability of getting it is low. “Unless the disease is contained in the near future,” he continued, “it will induce much more fear, and much more in the way of economic and social dislocation, than is warranted by the actual risk”.

On 12 March, the day after Italy had announced its 827^th death from the virus, the eminent psychologist Gerd Gigerenzer published a piece in Project Syndicate entitled “Why What Does Not Kill Us Makes Us Panic”. It was, to say the least, confused: it opened with an acknowledgement that we don’t know how bad this epidemic could be, but immediately went on to make the case that we’d likely overreact, and failed to consider any opposing arguments.

Gigerenzer’s article discussed the 2009 swine flu pandemic, which affected hundreds of millions but killed relatively few, particularly in Europe, where the fear was nonetheless high. It also likened the coronavirus pandemic to terrorist attacks, which similarly kill small numbers in a relative sense, but — according to Gigerenzer — garner disproportionate attention. If only we could improve our “risk literacy”, Gigerenzer maintained, then we could “approach situations such as the COVID-19 epidemic with a cooler head”.

At the time of writing, Italy’s death toll is now above 10,000, with over 900 fatalties being reported in a single day. The epidemic in the UK has reached 1,000 deaths; in France, 2,000; in Spain, nearly 6,000. New York City is setting up makeshift morgues for the first time since 9/11. The suspiciously uniform daily death figures from Iran might belie a truly devastating epidemic in that country. The world’s economy has largely closed down, as we all anticipate the pandemic getting far worse before it gets better. How could all those psychologists have gotten it so disastrously wrong?

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To back up his points about “probability neglect”, Sunstein had referred to a 2001 paper in the journal Psychological Science. It reported three experiments; Sunstein focused on the third one, which included 156 participants, all of whom were undergraduate students reasoning about how much they’d pay to avoid an imaginary electric shock. It’s not a criticism of the scientists to say that this experiment is only tenuously relevant to a global pandemic.

Indeed, alongside talk of the “replication crisis” there’s been discussion of a “generalisability crisis”, with renewed realisation that results from lab experiments don’t necessary generalise to other contexts. A global pandemic of a completely novel virus is, by its very definition, a context never encountered before. So how can we be sure that the results of behavioural science experiments — even those that are based on bigger or more representative samples than 156 undergrads — are relevant to our current situation?

The answer is that we can’t. Exploring the human capacity for bias and irrationality can make for quirky, thought-provoking articles and books that make readers feel smarter (and can build towards a tentative scientific understanding of how the mind works). But when a truly dangerous disease comes along, relying on small-scale lab experiments and behavioural-economic studies results in dreadful misfires like the articles we encountered above.

Although there are many behavioural phenomena that certainly seem relevant to today’s news — bias, sunk costs, the tragedy of the commons — it’s not at all clear how these concepts would be practically applied to do what needs to be done right now: slowing the spread of the disease.

We can even play the psychologists at their own game: there are also biases in the opposite direction to those discussed above. For instance, “exponential growth bias” might mean that our standard ways of reasoning break down in a situation where a threat accelerates like this virus. As observed in an insightful article on Italy’s ongoing horrific experience, there’s also “confirmation bias”, where we seek out evidence that confirms our previous beliefs or desires.

In this case, many were understandably desperate to believe that the virus wouldn’t be too much of a problem, and it seems to have led them to underreact very badly indeed. None of the psychological writers above seemed to consider these opposing biases — and ironically, it might have been because they were biased against thinking about them.

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That latter idea is just speculation on my part — which is the whole point. As the psychologist Stefan Schubert has noted, “[i]t’s too easy to make up a just-so-story” about us being biased in one direction or another. “The ‘psychology of X’ (e.g. risk, disease) can only tell us so much about X. We should mostly look at X itself”.

Quite so: the critical task for now is not to try and get inside people’s heads, but to learn from the experience of other countries (like Taiwan, South Korea and Singapore) that have been more successful in containing covid-19; and to build realistic mathematical models of the epidemic, and to pour resources into medical advances — like tests, vaccines, and antivirals — that might help us to track and inhibit the transmission of the virus and prevent its symptoms.

Sunstein himself appears to have undergone something of a Damascene corona-conversion. On 26 March, he wrote another column for Bloomberg Opinion, entitled “This Time the Numbers Show We Can’t Be Too Careful”. In an argument that was directly opposed to his own reasoning of a month earlier, Sunstein suggested that the potentially ruinous effects of the pandemic mean that “the benefits of aggressive social distancing greatly exceed the cost”. Remarkably, he made no reference whatsoever to his previous article on this specific crisis — an article which, as the writer Ari Schulman has pointed out, had said that if you held the view Sunstein now holds, you were the sad victim of a cognitive malfunction.

It’s impossible to know how many people changed the way they thought about the virus after reading one of the misconceived articles above. But a consistent set of articles by credentialed scientists telling people they shouldn’t worry so much about the pandemic was the precise opposite of what was required in a situation where governments would eventually, and rightly, be imploring their citizens to stay at home at all costs. At best, the advice from psychology was unhelpful. At worst, it constituted dangerous misinformation.

And what of those governments? Could the “don’t overreact” advice from behavioural science have led politicians to drag their feet in dealing with the crisis, as some clearly have? Again, it’s hard to say — but it’s very possible that the broad message got through to the decision-makers.

Thankfully, the advice given to the UK government by behavioural scientists, which was published just over a week ago, didn’t contain any minimisation of the risks. In fact, it was — in my view at least — mostly banal and common-sensical, noting for instance that using the word “isolation” might have “negative overtones for older adults”, and that there might be unintended consequences if we closed schools.

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Other government behavioural advisors, though, were less circumspect. The Behavioural Insights Team, a consulting company nicknamed the “Nudge Unit”, has been brought in to assist the UK’s response. At first it seemed their focus was on how to encourage handwashing, but there appears to have been mission creep.

For example, the team’s head, David Halpern, was interviewed as a “government coronavirus science advisor” about broad policies of “cocooning” older people. He was also quoted in support of the idea — which might yet seem grievously misguided in hindsight — that social-distancing measures should only be brought in gradually, to avoid people becoming fatigued. After he was reportedly “bollocked” by No.10 a fortnight ago for introducing the unfortunate phrase “herd immunity” into the national conversation, Halpern hasn’t (to my knowledge) been heard from again in public.

And perhaps that’s for the best. As intriguing as many psychological studies are, the vast majority of the insights we’ve gained from our research are simply not ready for primetime — especially in the case of a worldwide emergency where millions of lives are at stake. Much of the useful advice behavioural scientists can give isn’t really based on “science” to any important degree, and is intuitive and obvious.

Where they try to be counter-intuitive — for instance, arguing that people are wrong to find a global pandemic frightening — they simply end up embarrassing themselves, or worse, endangering people by having them make fewer pandemic preparations. This isn’t to say that psychology isn’t useful when it stays in its own lane: it’ll be important to ensure that as many people as possible have access to psychotherapy for the mental-health effects of the pandemic, for instance. But that’s a secondary effect of the virus: my argument here is that psychology can give little reliable counsel about our immediate reaction to the pandemic.

Psychologists should know their limits, and avoid over-stretching results from their small-scale studies to new, dissimilar situations. Decision-makers should, before using psychology research as the basis for policy, know just how weak and contentious so much of it is. And everyone else should stay at home, wash their hands — and beware psychologists bearing advice.

The government also indicated that the amount of testing would be restricted to serious cases and that many showing mild symptoms would no longer be tested. Most notably, the government’s strategy also outlined countermeasures that they had not recommended (school closures or restrictions of mass gatherings), with the idea that the population would become fatigued by social distancing measures. (Yesterday, however, they announced a scaling up of testing.)

The Chief Scientific Adviser, Sir Patrick Vallance, Chief Medical Officer, Professor Chris Whitty, and the government’s science adviser Dr David Halpern indicated that the government’s strategy was to allow the virus to pass through the population, to allow individuals to “acquire herd immunity” at a delayed speed, while vulnerable groups were “cocooned.” This strategy, however, was subsequently contradicted by health secretary Matt Hancock, who insisted that “herd immunity is not our goal or policy”.

The quick reversals did not end there, as a ban was announced on mass gatherings just a day after the government’s initial claims that it was not the right time for such measures. On Saturday, the government briefed select journalists on “wartime measures” to quarantine the elderly at home or in care homes, away from any contact with the rest of the population; earlier than such measures were expected to be announced.

Finally, it was revealed yesterday afternoon that the Prime Minister had decided to dramatically step up countermeasures, and switch entirely to a strategy of containment as a result of advice from an expert response team at Imperial College London, which concluded that the strategy of delay would likely cause “hundreds of thousands” of avoidable deaths.

The initial plans — to establish herd immunity based on research on social fatigue and assumptions that effective vaccines would not be developed — contradicted the guidance from the World Health Organisation (WHO), and the wealth of evidence in the fields of epidemiology, behavioural science and immunology, so it is unsurprising that countless experts have already questioned and criticised the strategy, including epidemiologists, immunologists, and behavioural scientists.

Given reports that President Macron of France and the Netherland’s PM are considering the same strategy, and secret briefings suggesting that the crisis would have lasted until spring next year given the current plan, it remains necessary to explain in detail why the strategy of “herd immunity” proposed by the UK could be dangerously fatalistic and contradicted by the majority of expert opinion and scientific evidence on COVID-19; and why its enormous damage was predictable from the beginning.

A central concept to understand in epidemiology is the basic reproduction number (R0), which is the number of people that are expected to be infected by an individual case, in a population that is susceptible to infection.

This means that if a virus has an R0 of 2, for example, an individual case is expected to infect 2 other people, who are expected to infect a further 2 people each, and so on. In general, a virus with a larger R0 spreads more rapidly in a population. As a consequence, the R0 determines whether a pathogen will remain endemic in a population (if it is >1) or die out (if it is <1). During early stages of the pandemic in Wuhan in January, research from different labs estimated an R0 of 2.54 on average (with a 95% confidence interval between 2.17-2.91).

Crucially, the R0 is not an inherent or fixed property of a pathogen: the expected number of people who will be infected by a case depends on the behaviour of individuals in a population and their environmental context, such as the length of time that cases are infectious, the number of susceptible people they are in contact with, and their general infectiousness.

This is the reason that reducing the number of contacts that individuals have (such as by social distancing) works, as does handwashing and effective treatment — these strategies reduce the R0 of a pathogen.

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Countermeasures in Wuhan and elsewhere have already reduced the local R0 of COVID-19, with research suggesting the R0 was reduced all the way down to 0.32 in Wuhan in early February after extensive testing and containment measures. In Italy, which implemented aggressive countermeasures fairly late into their local epidemic, preliminary analysis suggests the R0 was reduced from 3 in late February to 1.7 in early March and the number of new cases has dramatically slowed down. 

According to an analysis published in the Lancet, approximately 95% of the Wuhan population remained uninfected by the virus at the end of January, after the peak of their crisis, as a result of aggressive countermeasures. These data on their own indicate that herd immunity is not an inevitable outcome, nor is the possibility that up to 80% of the UK population will be infected within the next year, as was claimed by Professor Chris Whitty.

A common rebuttal to social distancing strategies is that the policies to implement them may backfire. For example, on Thursday’s BBC Newsnight, one of the Government’s advisors claimed that shutting down schools or cancelling football matches would simply lead people to gather in other ways — such as by children infecting their parents and grandparents as they return home, or by spectators gathering to watch matches at the pub, respectively.

It is difficult to imagine a scenario in which a child would sustain more contacts while returning to and staying with their family, than if they encountered additional hundreds of children at school every day, or to imagine a scenario whereby spectators would gather in pubs and homes at an equivalent level and density as if a football match was cancelled.

Fortunately, there is substantial literature on this topic from across the world, which the government should have looked towards. It includes historical successes in pandemic control and current countermeasures across the world, such as by South Korea, Hong Kong, and Singapore. Given the way case statistics in the UK are progressing, it will increasingly include recent measures taken in Italy.

Nicholas Christakis, a professor at Yale University who specialises in research on social networks and contagion, calls school closures “one of the most beneficial ‘non-pharmaceutical interventions’ that can be employed, more effective even than reactive quarantines or banning of public gatherings,” in part because “parents also stay home as a result”. He cites evidence from six studies based on data from Japan, Italy, China, the UK and the US which converge on the conclusion that school closures, especially early ones, reduce the total number of cases and delay the peak of epidemics.

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Confusingly, the Government also appeared to admit that social distancing policies would be effective, according to their own models, despite not recommending them. They stated that school closures for at least 13 weeks would reduce the peak of COVID-19 by 10-15%, that self-isolation would reduce it by up to 20%, and that restricting mass gatherings would reduce it by 5%.

To be clear, in any field of research, these would be considered massive effects individually, and if they are insufficient on their own, it was unclear why the government had not considered implementing them all together — as many other European countries had already done – aside from increasing the strain on healthcare workers with children.

In addition, the government initially claimed that the population would encounter “fatigue” if countermeasures are implemented early, and would get tired of self-quarantining, and that “nudges” to encourage people to wash their hands more frequently were sufficient. At the time this piece was published, over 500 behavioural scientists had signed an open letter urging the government to publish their evidence for this claim, stating that they are “not convinced that enough is known about ‘behavioural fatigue’ or to what extent these insights apply to the current exceptional circumstances.”

The evidence that emerged was surprising. On Friday, one of the government’s advisors explained that the idea of social fatigue, which was used as a rationale to delay quarantine, was based on a literature review of the psychological impacts of quarantine.

But crucially, the literature review made no mention or recommendations of how early quarantines should be implemented. It also stated explicitly that only a few of the papers it included directly compared quarantined versus unquarantined patients, which makes it difficult to establish whether quarantine would cause more panic and social fatigue in an epidemic than the absence of quarantine.

The other paper cited as “influential” in the government’s strategy was in fact a working paper that was published a single day before the government’s delays to quarantines were announced. As with the previous paper, it made no recommendations as to when quarantines should be implemented. 

There may be real and substantial psychological, medical and economic side effects of quarantine measures, and of a pandemic more generally, regardless of whether individuals are quarantined or not. But given the exponential nature of the disease’s spread, these effects should be mitigated alongside countermeasures, not traded off for them. If the government believed that individuals would feel fatigued by taking self-isolation procedures and social distancing measures, they should have provided top-down assistance and coordination to alleviate this.

And if these papers represent the basis for the government’s strategy to delay quarantine, they should be seriously questioned. Substantial social distancing measures are recommended by WHO and the CDC to be implemented as soon as possible if there is evidence for local community transmission of COVID-19.

The government’s unsubstantiated claims did not end there, however, as the question remains of whether acquired “herd immunity” from infection was feasible or even desirable. Some further background may help to judge the justification for those claims. 

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Vaccination (the prototypical source of herd immunity) reduces the number of people who are susceptible to a pathogen, which reduces its effective reproduction number. The R0 value can also be used to estimate the proportion of the population that would have to be immunised to prevent further spread of the virus. In general, this proportion is estimated using the formula: 1 – 1/R0. Immunised in this sense refers to being unsusceptible to infection, which can result from acquired immunity (which may develop from a previous infection by a pathogen) or by active immunity (which may develop from a vaccine).

Using this as a guide, the government suggested that its strategy was to allow the virus to spread until “herd immunity” was achieved by the population getting infected, rather than by vaccination. David Halpern, an adviser to the government, indicated that vulnerable groups will be “cocooned” until the population has “acquired immunity” to the virus (comments that were reflected by the Chief Scientific Officer) and the spread of the virus would supposedly be slowed down to reduce the strain on hospitals, as part of a long-term strategy that predicts the epidemic will substantially return several months from now, during the winter. 

What does this entail, practically? Using an R0 estimate of 2.5 and the formula mentioned above, the government has said that this means at least 60% of the population would have to be infected and acquire immunity to the virus. That was an estimated 40 million individuals that government advisors claimed would “inevitably” be required to succumb to the virus, in order to protect the remaining vulnerable groups.

This was perhaps the most egregious claim made by the Government, and it is important to understand the many reasons why. 

One reason is our understanding of the grave severity of the disease: statistics from WHO indicate that the global case fatality rate (CFR) is approximately 3.8%, meaning that 3.8% of globally confirmed cases eventually died from the disease. This rate varies by age group: 0.2% between the ages of 20-39, 0.4% between the ages of 40-49, and 1.3% between the ages of 50-59. The rate increases rapidly with age, reaching 14.8% above the age of 80.

The fatality rates for young to middle-aged cases may sound low at first, but they are over a hundred times greater than those of influenza, and the CFR alone may obscure the seriousness of the illness in the patients who survive. Patient data from China indicates that apart from the 2.3% of cases in China who died, 5% of cases were critical (meaning that they suffered from “respiratory failure, septic shock, and/or multiple organ dysfunction or failure”), 14% of cases were severe (meaning that they suffered from “shortness of breath, respiratory frequency ≥ 30/minute, blood oxygen saturation ≤93%, PaO2/FiO2 ratio <300,30 and/or lung infiltrates >50% within 24–48 hours.”) and 81% of cases were mild. 

What does “mild” mean here, exactly? As a WHO expert explained, it includes anything short of requiring supplemental oxygen: ranging from asymptomatic cases to fever and coughing to mild pneumonia – that is, the definition of a “mild” case of COVID-19 is not equivalent to that of a mild cold. And if you thought that was all, it isn’t: case report data shows that some patients who fully recover from the disease still exhibit weakened lung capacity and organ damage.

Part of the Government’s strategy was to “flatten the curve” of new cases to reduce the strain on the NHS, and relied on an assumption that just 5% of symptomatic cases will require hospitalisation. Given the numbers above and the initial lack of aggressive social distancing measures to achieve such a “flattening,” it was unclear how that figure could be possible. Instead of increasing the amount of testing, the government said that they would restrict testing to serious cases and would turn away mild cases from hospitals for self-quarantining even if they have tested positive for the virus. 

Perhaps most surprisingly, until yesterday the policy was that confirmed cases were required to self-quarantine at home, but household members of these confirmed cases (such as their spouses and flatmates) were still not required to quarantine themselves in hospitals or even at home, despite being at risk of acquiring the virus from their cohabitants and subsequently spreading it to others in society. In addition, government advice was to self-quarantine for 7 days if relevant symptoms arise, even though research shows that individuals are infectious for between 2-14 days and recommends 14 days as the length of quarantine. That, as well, was changed yesterday.

Four epidemiologists from Harvard and Boston University’s schools of public health modelled the number of ICU beds that would be required by the population during the peak of the crisis, assuming the government’s strategy is achieved.

Even while these researchers use highly generous assumptions to the government’s plan (e.g. if full cocooning is achieved), at the peak of the crisis, twice as many ICU beds would be required than are available in the NHS, even if the proportion of detected cases was only 8%, and even if we only considered 20-40 year olds. In total, the degree of failure is expected to be far greater than this, considering individuals of all ages.

The epidemiologists title their paper: “The direction of the UK Government strategy on the COVID-19 pandemic must change immediately to prevent catastrophe.” In an article published in the Observer on Sunday, one of the authors of the same paper, Harvard epidemiologist William Hanage, states plainly that he “assumed that reports of the UK policy were satire” and explains his alarm to the government’s strategy in much greater detail. Note that the report from the response team at Imperial predicted much of the same.

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Meanwhile, reports had arisen on the weekend that hospitals were already being overwhelmed by the crisis, that the virus was spreading far quicker than the government’s models predicted, and that Boris Johnson was already considering an acceleration in aggressive measures.

The media coverage regarding “flattening the curve” (delaying its peak) may be misleading. Hospitals would quickly be overwhelmed by severe cases, which would spiral exponentially out of control, to say nothing of the damage that would have been unleashed when patients with unrelated chronic diseases who require ICU beds and hospital care were displaced by the coronavirus crisis.

As Timothy Gowers, a Royal Society Research Professor, explains: “flattening the peak to the point where the health service can cope is approximately as hard as trying to stop the spread altogether.” The evidence and guidance from WHO is clear – soft measures to flatten the curve and delay its peak are not a viable long-term strategy, containment must be the goal: “Not testing alone. Not contact tracing alone. Not quarantine alone. Not social distancing alone. Do it all.”

One question you may be wondering about at this point is how the Government’s models diverged from the estimates made by epidemiologists and reality so quickly.  

Part of the reason may be that these (as-of-yet unpublished) models were rumoured to be built on assumptions from influenza pandemics: that a second peak would arise due to its seasonal nature and that the population would experience social fatigue from the countermeasures. This, as well, was indicated by reports yesterday.

There are many differences between influenza viruses and coronaviruses that are relevant here. One is that the case fatality rate of COVID-19 is over a hundred and fifty times higher than influenza fatalities in a typical year, as mentioned previously. Another is that the influenza virus mutates rapidly, which is why WHO recommends three vaccine formulations that are produced every year, each containing vaccines for 3-4 strains. In comparison, COVID-19 and coronaviruses in general mutate relatively slowly, explains Trevor Bedford, scientist-developer of the platform Nextstrain, which has tracked mutations in the virus since the first genomes were published. 

Another difference is that the influenza virus exhibits clear evidence of seasonality, while evidence shows only a weak relationship for coronaviruses. An analysis by Harvard epidemiologists uses data from provinces across China and finds “a slight negative relationship” between temperature and the R0 of COVID-19. It concludes that “weather alone (i.e., increase of temperature and humidity as spring and summer months arrive in the North Hemisphere) will not necessarily lead to declines in case counts without the implementation of extensive public health interventions.”

Finally, there is a lack of evidence that lasting herd immunity to COVID-19 was possible in humans when acquired by infection, and that recovered cases would be prevented from reinfection. “Typically coronaviruses don’t make long-lasting antibody responses,” tweeted Brian Ferguson, an immunologist at Cambridge University, adding, “if this is a deliberate approach it’s not scientifically based and irresponsible.”

Research based on the previous SARS virus supports this conclusion: the quantity of SARS-specific antibodies declines substantially after around 6 months of infection, until it is undetectable 2-3 years after disease onset. Several additional things must be considered: we don’t know how similar the antibody responses to this virus will be compared to the SARS virus; there is scant research on whether these antibody responses will prevent re-infection in practice; we simply don’t know how the immune system would respond to a reinfection.

The initial report used by the Imperial team assumed lifetime immunity to re-infection, and deviations from the assumption would mean that an even greater proportion of the population would need to be infected than their strategy assumed (which was already 60%).

The Government’s chief medical advisor claimed that part of the reason he believed cases in China had declined was because 20% of the Wuhan population had been infected by the virus and had acquired herd immunity and because a large proportion of cases were asymptomatic. 

But as mentioned previously, evidence from researchers at the London School of Hygiene & Tropical Medicine estimated that 94.8% of the Wuhan population were still susceptible to infection at the end of January (i.e. had not been infected by the virus) and that “there was evidence that the majority of cases were symptomatic.” Daniel Falush, a statistical geneticist at the University of Bath, tweeted that these claims were contradictory, adding that “unfortunately, tragically, this error is driving UK policy right now.”

Much of the damage could have been avoided if aggressive countermeasures had been taken earlier: the longer the inaction, the greater the spread of the virus and the greater the response required. Given the evidence outlined above, it is perplexing why the government preferred softer and delayed measures, which would have continued to exponentially increase the number of cases, compared to aggressive and immediate ones that would force the new number of cases down.

Aggressive measures would buy time to acquire and scale up diagnostic tests from other countries that could be rolled out more widely, time to continue identifying existing antivirals that may be effective against the virus,” against the virus, and time to test vaccines.

The evidence was not conflicted, it was clear: the Government’s strategy of delaying the peak and inducing herd immunity was unscientific, unfeasible and dangerous. It is hugely unfortunate that the Government delayed aggressive social distancing measures, which will have already caused avoidable deaths and suffering, but it is encouraging that they quickly reconsidered many of their initial plans — for the damage must be mitigated swiftly. Countries around the world  considering the British strategy should seriously reconsider. Containment is possible. Containment is necessary. Containment must be the goal.