Professor Sir John Bell FRS is a very eminent medical scientist, with a distinguished career. He’s much more accomplished than I can ever dream of being. And so when, in early November last year, he told the BBC “with some confidence” that things could return to normal by Spring 2021 following the announcement of interim results on the Pfizer-BioNTech Covid-19 vaccine, people (including myself) listened. Just over a year later, we saw soaring numbers of Covid-19 infections and the pandemic looked far from over; Bell’s confidence was plainly misplaced.

The Covid-19 pandemic, like arguments about climate change, has placed scientists in the glare of the media. Interviewers ask for insight, advice, hope for the future. Predominantly, broadcast media treat “experts” respectfully – even fawningly. They mean well; but in the process, scientists have been led to make speculative pronouncements based upon flimsy evidence, in the absence of rigorous argument or peer-review. The result has been that the fog around central questions has increased, not decreased.

A favourite question of journalists is “when will it all end?” Infectious disease experts and epidemiologists have been tempted to speculate. Many have predicted that the progress of Covid-19 will mirror that of the common cold, which is sometimes caused by a coronavirus; at first it, too, was a more severe disease but it evolved to become a comparatively mild illness that bothers us comparatively little, and over the course of time, it is suggested, this will also be the fate of Covid-19. However, William Hanage, professor of the evolution and epidemiology of infectious disease at Harvard University argued that there is no evolutionary imperative for viruses to evolve in the direction of milder disease:

There is a widespread belief that infectious diseases evolve to become less virulent, leaving many hopeful that Omicron will be less severe for everyone, regardless of age or vaccination status. This is false. Virusesdo not necessarily get selected to be milder or more severe. If virulence (the severity of the disease) is not connected to transmission (the factor that makes a virus successful or not) there’s no real link between the two in most real situations. The great majority of Covid transmission occurs before people become seriously ill, and so the virus has already moved on.

The willingness of scientists to speculate on matters outside their immediate expertise is from one perspective understandable: scientists tend to be curious about the world in which they live. However, when complex decisions need to be made about how to handle a global pandemic, any statements that tend to reduce, rather than enhance, clarity of understanding among the public and policy-makers are dangerous.

Curiosity is not the only explanation for such speculation; hubris plays its part too. Mike Hume, a geographer from Cambridge University who has made important contributions to work on climate change, felt qualified to endorse the controversial Great Barrington Declaration (GBD), which advocated allowing Covid-19 to spread unchecked until “herd immunity” resulted, signing himself as one of a list of “Medical and Public Health Scientists and Medical Practitioners”.

Prov Sunetra Gupta from Oxford University was a leading figure in the GBD. In March 2020, she announced that, based on modelling by her research group, she believed that half of the UK population had already developed antibodies to Covid-19. In May, as the UK made cautious steps out of lockdown, she was candid about the challenges of pandemic modelling, admitting to the limitations and uncertainties involved. Remarkably, she suggested that the available data on Covid-19 transmission were fitted equally well by her model – which assumes that Covid-19 arrived early, and that it had infected most of the population – and Neil Ferguson’s model, which informed Government thinking and led to the UK lockdown in March 2020. Perhaps this statement tells us something important about the state of the art in Covid-19 modelling at that early stage of the pandemic, and underlines the difficulty in establishing well-corroborated explanations: the models were crude and the error bars on their predictions were large.

But Gupta went on to state in an interview with UnHerd that “I believe…that while the jury is still out…it is more likely that the pathogen arrived early and that it had already spread substantially through the population by the time the lockdown was put in place…the epidemic has come and is largely on its way out in this country”.

The transition from a discourse about goodness of fit to data to a statement of belief is striking, and the focus of the second half of the conversation was on the harms of lockdown. Qualitatively, these are widely acknowledged, and the World Health Organisation (WHO) has stated repeatedly that it is not in favour of lockdowns. But how do we quantify these costs? How do we integrate the costs of lockdown and the costs of a health service on the brink of collapse? How do we connect all of this with theoretical epidemiology? By now Gupta was far from the much more narrow focus of her scientific research.

By July 2020, Gupta’s team believed that their modelling suggested “that sufficient herd-immunity may already be in place to substantially mitigate a potential second wave”. However, as increasing quantities of serological data became available, it was clear that the fraction of the population testing positive for Covid antibodies was still quite small. Gupta rationalised this as being due to the limitations of the testing done and also the existence of epidemiological “dark matter” (e.g. T-cell immunity from previous infections by different coronaviruses). But by mid-September, Covid testing data were already showing the beginning of a Covid-19 second wave. The lockdown did its job, by suppressing infection rates, but the virus was not eliminated and as the country returned to normal, it began to spread.

It would be arrogant of me to attempt to make a technical judgement about Prof Gupta’s work, because I am not competent to do so. However, the GBD reached far beyond the bounds of theoretical epidemiology. A transition was made from the established methodology utilised by an expert in a specific field of enquiry to bold conjectures about what might be happening in wider society and about what might happen in the future. Such bold conjectures have their place in science: when scientists write research proposals, they develop new ideas – new hypotheses – and they seek funding to explore them. These new ideas are untested, and they may ultimately be falsified. However, without new hypotheses there will be no new knowledge. In applications for research funding, creative ideas that go beyond current paradigms are often encouraged. But it is important to be honest with the public that such ideas are provisional.

Such thinking is necessary for the advance of science but it is a dangerous and insecure basis for formulating policy during a global pandemic. When the GBD was announced to the world, it was an untested hypothesis. Perhaps it had scientific merits, but there were no data to support its claims, and there was no possibility to test those claims without first imposing its consequences on the world. Without a well-corroborated model for Covid-19 spread, and without a hypothesis to connect such a model to a quantifiable theory of costs and benefits associated with lockdown, it was an act of breathtaking hubris to launch the GBD. Now, in early 2022, after several variants of Covid-19, after re-infections of millions of people and in the light of solid evidence that Covid immunity is short-lived, the hypothesis that the best way to deal with the disease was to allow it to spread unchecked seems untenable. Without vaccines – not a given at the time of the GBD – we would have seen death on a colossal scale.

Scientific experimentation involving human subjects is subject to very strict regulation; ethical approval has to be sought before experiments are conducted. A pandemic that sweeps through a global population of six billion people is not an opportunity for academics to demonstrate their brilliance by gambling with lives; inevitably decision-making must be based on what has been known to work, or what is most likely, based on evidence, to reduce loss of life.

It is not without reason that the UK has appointed a Chief Medical Adviser to the Government, and a Chief Scientific Advisor. Their job, and the job of the many scientists working to support them, is to sift the evidence, to assess its reliability and to arrive at advice on policy that will affect the lives of 60M people. The burden of responsibility resting on Sir Chris Whitty’s shoulders is immense. It is not his job to propose ground-breaking ideas that will change understanding, but to provide guidance that can be used by the Government in managing public health. In Sir Venki Ramakrishnan’s words:

Evidence-based decision making should absolutely be a cornerstone of government, especially in a pandemic for which science is of paramount importance to our response.  However, we must recognise both the potential and the limits of science. In an emergency, data for decisions may be uncertain, incomplete, or even missing. Nevertheless, rapid decisions have to be made.  Science advice is only one aspect of this.  Ministers also need to consider economics, ways of implementation, and broad consequences to society, and they need to be able to take the public with them. 

In looking to the future it is important to be honest about the limitations of current understanding. Before Covid-19, epidemiological modelling was a retrospective endeavour; the course of an epidemic could be fitted with mathematical models and analysed to discover knowledge. During Covid-19, the modellers have been adapting their models to make predictions – taking them into uncharted territory with much more at stake than the opinions of peer-reviewers. Undoubtedly, the predictive power of the epidemiological models has grown during the pandemic as they have been refined in real time, but they still retain significant uncertainties (unsurprisingly given the complexity of the systems being modelled). Unlike physics, biology is largely not a predictive discipline; we can look at what has happened and discover mechanisms, but these are rarely quantitative and they rarely enable us to predict future events reliably.

There are many endemic diseases – malaria, HIV and Smallpox – besides the common cold. Only time will tell what the evolutionary path of Covid-19 will be, because there is no predictive model in biology that can tell us what course the disease will take. Thus, when biologists offer the comforting advice to journalists that Covid will soon be over, they are no longer talking scientifically. At present, the question “when will the pandemic be over?” has no scientific answer.