Читать книгу The Case for Democracy in the COVID-19 Pandemic - David Seedhouse Dr. - Страница 15

Here, for example, are edited minutes of a meeting of the Scientific Pandemic Influenza Group on Modelling (SPI-M). According to the government's website the SPI-M gives expert advice to the Department of Health and Social Care and wider UK government on scientific matters relating to the UK's response to an influenza pandemic (or other emerging human infectious disease threats). The advice is based on infectious disease modelling and epidemiology, and the group members have a background in public health science and closely related disciplines (19). The minutes of other SPI group meetings can be found here (20).

On February 19th, the group considered the closure of schools in the UK. They said:

At this stage the magnitude of the impact that school closures would have on a UK epidemic of COVID-19 is very uncertain. There are many uncertainties about the virus including, importantly, the role of children in transmission, and the severity of infections in children … we assumed that children have a role in transmission similar to that of influenza…

Three new (models) were produced to address this… one full spatial individual-based model (IBM) exploring a range of scenarios, one individual-based model that considered also reactive closure strategies and varying infectivity profiles and one compartmental model exploring two different forms of age-structured mixing. The different modelling approaches gave results which were similar in some ways but differed in others…

Any impact from school closures on the total number of cases is likely to be highly limited. … The effectiveness of school closures in reducing peak incidence is sensitive to the reproduction number R0: the higher the R0, the less effective they would be…

The key difference between our approaches so far is the predicted reduction in the peak size of the epidemic that school closures produce. … For R0 around range 1.9–2.3 and school closures of 6–12 weeks: the IBMs suggests an effect of the order of 20–60% reduction, while the compartmental models suggest 7.5%–30%. (19)

(Note: the basic reproduction number (R0) of an infection is the expected number of cases directly generated by one case in a population. In commonly used infection models, when R0 is more than 1, the infection could start spreading in a population, but not if R0 is less than 1. It is assumed that the larger the value of R0 the harder it is to control the epidemic.)

On one model the reduction in ‘peak size’ could be as little as 7.5%. On another it could be as much as 60%. Which raises an obvious question: does it make sense even to discuss policy based on such wildly divergent guesses?

To be fair, given the number of variables and ‘what ifs', the modelling challenge is considerable:

Not all parameters and assumptions have yet been cross calibrated between models. Only national-scale closure policies have been compared so far. The knock-on effects on how contact patterns will change with school closures of different lengths is a fundamental knowledge gap that cannot be determined by modelling. … Detailed forecasts of the likely impact of school closures will be possible once there has been several weeks of sustained transmission of COVID-19 within the UK. (19)

In other words, we simply do not know anything like enough even to make a barely informed estimate of the effect of closing schools on the virus. Nor do we know nearly enough to judge the potentially massive effects on mental health, education and domestic stress that closing schools causes.

There were attempts to acquire firmer evidence, but this was not forthcoming (21):

Emergency school closures are often used as public health interventions during infectious disease outbreaks in an attempt to minimise the spread of infection. However, if children continue to mix with others outside the home during the closures, these measures are unlikely to be effective.

We searched four databases from inception to February 2020 for relevant literature. … Activities and social contacts appeared to decrease during closures but contact was still common. All studies reported children leaving the house or being looked after by non-household members. There was some evidence that older child age and parental disagreement with closure were predictive of children leaving the house, and mixed evidence regarding the relationship between infection status and leaving the home. Parental agreement with closure was generally high, but some parents disagreed due to perceived low risk of infection and practical issues regarding childcare and financial impact.

Evidence suggests that many children continue to leave the house and mix with others during school closures despite public health recommendations to avoid social contact.

Similar discussions in uncertainty continued up to March 17th:

In its list of actions, SAGE also noted that it would consider alternatives to closure. One alternative sometimes discussed is a partial ‘dismissal,’ where-by most children are sent home from school, but the children of certain key workers (scope to be defined, but including NHS workers) are allowed to attend as a form of childcare. … Members of SPI-B have not found a robust academic evidence base relating to the acceptability or social impact of dismissal vs closure. We do not know if the Department for Education might have any evidence. (22)

In fact, no-one seemed to know anything much. However, on March 18th it was summarily announced that UK schools would be closed indefinitely and exams cancelled, though schools would remain open for key workers’ children and ‘the most vulnerable’ (23).

As we shall see below, a similar analysis, again in almost complete uncertainty, based on epidemiological modelling by Imperial College, London, was carried out at the same time, and led directly to a nationwide lockdown (24).

Given the pressures on governments to make firm decisions, such choices are understandable. But at the same time there is so much uncertainty in the science, the data, the modelling and the different predictions that it is far from easy to comprehend how such radical measures can be sensibly justified.

There is a disorienting array of questions. How do we know how many deaths have been caused directly by this virus? Given that the great majority of deaths have been of elderly patients already in hospital for other sicknesses, how do we know that the virus isn't at most a contributory factor? Or simply present when the cause of death is something else?

How do we know that ‘the measures are working'? How do we know that if we had not ‘locked down’ the virus would have spread out of control?

How do we know which scientific claims are fact, which are probable, which are possible, and which are wrong? How do we know which experts to believe when they so often disagree? How do we know the motivations of the policy-makers prepared so quickly to disregard decades of consensus about the ethical importance of inclusion and consent?

Does the typical government response cause more harm than it prevents? How are ‘harms’ defined? Who should define them? How can different harms be compared and assessed?

Given how much is simply not known and at the early stages of any pandemic not knowable, there is logically no difference between doing something and doing nothing. Either tactic is a stab in the dark.

Yet most governments not only decided to ‘do something', they decided to pursue measures that would quite definitely damage other areas of social life and human well-being. The puzzle is this: as far as the virus is concerned, little is certain. As far as shutting down society is concerned, a very great deal is certain. If you stop people trading, stop people travelling, stop people playing and watching sports, and stop people going to pubs and restaurants, it is inevitable that many will lose their livelihoods. No question whatsoever.

How is it that governments can be so confident that action based on vast uncertainty is worth the negative economic outcomes that are unquestionably certain?

The government's advisory body SPI-M-O were rather unconcerned about uncertainty:

SPI-M-O: Consensus view on behavioural and social interventions

Date: 16th March 2020.

1 It was agreed that a combination of case isolation, household isolation and social distancing of vulnerable groups is very unlikely to prevent critical care facilities being overwhelmed.

2 It was agreed that it is unclear whether or not the addition of general social distancing measures to case isolation, household isolation and social distancing of vulnerable groups would curtail the epidemic by reducing the reproduction number to less than 1.

3 It was agreed that the addition of both general social distancing and school closures to case isolation, household isolation and social distancing of vulnerable groups would be likely to control the epidemic when kept in place for a long period. SPI-M-O agreed that this strategy should be followed as soon as practical, at least in the first instance. (25)

Not wishing to be unkind, this reads like ‘everything is unclear, apart from our own decisions'.

As Ed Yong wrote in The Atlantic on April 29th:

In a pandemic characterized by extreme uncertainty, one of the few things experts know for sure is the identity of the pathogen responsible: a virus called SARS-CoV-2 that is closely related to the original SARS virus. Both are members of the coronavirus family, which is entirely distinct from the family that includes influenza viruses. Scientists know the shape of proteins on the new coronavirus's surface down to the position of individual atoms. Give me two hours, and I can do a dramatic reading of its entire genome.

But much else about the pandemic is still maddeningly unclear. Why do some people get really sick, but others do not? Are the models too optimistic or too pessimistic? Exactly how transmissible and deadly is the virus? How many people have actually been infected? How long must social restrictions go on for? Why are so many questions still unanswered?

The confusion partly arises from the pandemic's scale and pace. Worldwide, at least 3.1 million people have been infected in less than four months. Economies have nose-dived. Societies have paused. In most people's living memory, no crisis has caused so much upheaval so broadly and so quickly. ‘We've never faced a pandemic like this before, so we don't know what is likely to happen or what would have happened,’ says Zoë McLaren, a health-policy professor at the University of Maryland at Baltimore County. ‘That makes it even more difficult in terms of the uncertainty.'… (26)

So much is in doubt. Apart from the fact that human beings hate uncertainty. We are hard-wired to resist it:

…when we're facing outcomes imbued with uncertainty, it's the fact that something bad might happen that gets us… (27)

In a recent experiment (28), researchers recruited 45 volunteers to play a computer game designed to maintain a high level of uncertainty. Participants had to guess whether each rock concealed a snake. When a snake appeared, they received a mild but painful electric shock on the hand.

In the background, the researchers were running a sophisticated computational learning model to estimate the volunteers’ amount of uncertainty that any given rock was concealing a snake. At the same time, their stress was being monitored via instruments gauging pupil dilation and perspiration.

The volunteers’ level of uncertainty was directly correlated to their stress level: if someone felt sure he would find a snake their stress was significantly lower than if they felt maybe a snake lived here. The physical pain wasn't the greatest problem. It was not knowing that was most damaging (29, 30, 31).

As David diSalvo, a science and technology writer, notes:

We evolved to respond this way to uncertainty for excellent reasons—namely, the thing that might be lurking behind that rock or bush or up in that tree could harm, kill, or quite possibly eat us. Our brains are adaptively wired to react this way from way, way back in our ancestral history. Just because we've launched ourselves (and our brains) into this techno-socially advanced era doesn't mean our brains are reacting less or even differently; they are just reacting to different threatening possibilities—some physical and many more perceptual.

Those perceptual, intangible uncertainties are arguably worse because they morph into different forms in our heads the more we think about them. The world has its monsters, no doubt, but we create many times more in the boundless space of our minds … all rooted back to the brain's insatiable craving for certainty. (32)

The potent fear of COVID-19 stems essentially from uncertainty. We don't know who will get it. We don't know what will happen if we do get it. We don't know which of our neighbours has it. We don't know how many fellow shoppers in the supermarket might infect us. We don't know whether we will pass it on if we visit elderly relatives. We don't know why some nations in lockdown (Spain, Italy, the UK) end up with more cases and deaths than those with more relaxed measures (Sweden). Each time we read the news we have no idea what the next shock will be.

Psychology does not have all the answers, but it can help us understand how we feel and why we react as we do. And it can partially explain the behaviours of the people currently charged with making decisions on our behalf. It makes sense to start the investigation here.