Coronavirus, statistical chaos and the news, one year on

Credit: Photo by De an Sun on Unsplash

An Nguyen is an associate professor of journalism at Bournemouth University, with research expertise in science and health journalism, including newsroom approaches to data and statistics and their impact on public opinion. His recent work includes News, Numbers and Public Opinion in a Data-Driven World. The findings from this article were later developed into a full report.

The coronavirus has hit the world at a time when journalism does not seem to be in a terribly good position to deal with its vast uncertainty and complexity.

Decades of immense economic pressures have led the news industry to slash many specialist science, health and data journalists.

Meanwhile, journalists often get a bad press for lacking the ability to handle, scrutinise, communicate or even truly engage with statistics. Quite a few see numeracy as "a kind of virus which, if caught, can damage the literary brain" while others proudly admit they are not good at maths. Many, if not most, would take a laid-back approach to numbers in the naïve belief that they speak for themselves.

Which is why I have been uplifted to hear many positive reflections by senior journalists, statisticians, scientists and media scholars at recent conferences, such as the News Impact Summit on data journalism in November 2020 and the one-day Coronavirus, Statistical Chaos and the News symposium (below) that I chaired in December on behalf of Bournemouth University, the Royal Statistical Society and the Association of British Science Writers.

Even some of the staunchest critics see journalism as a crucial positive force in guiding the public through covid-19 data and science. By August, for instance, 86 per cent of the UK public understood what R0 means and 77 per cent knew what an antibody test is.

There are criticisms and self-criticisms – and rightly so. But it is crucial not to be immersed in the negatives, without being able to see and learn from the positives. Four words can be used to capture journalism's performance in the first year of the pandemic: chaos, resilience, innovation and order.

Chaos in uncertain waters

Nothing has brought statistics to the centre of daily life like the coronavirus: everything we do at an individual, organisational and societal level depends literally on what the numbers tell us.

In March, as virtually every UK adult (99 per cent) accessed news about covid-19 at least once a day, an endless influx of specialist numbers and concepts suddenly permeated into the physical and cultural space of the lockdown family.

"Scary" things – R-naught, infection rate, transmission rate, death rate, excess deaths, false positive, false negative and so on – abounded everywhere, from computer screens to household conversations.

However, the public’s maximal thirst for answers were met with a minimal understanding of scientists about the novel virus. As Ann Hemingway, professor of public health and wellbeing at Bournemouth University, observed, scientists became "experts with no evidence," advising politicians and people primarily on the basis of their knowledge of previous viruses.

The urgency led to an unprecedented number of online preprints (research papers that have not been peer-reviewed), with contradictory conclusions and wide-ranging qualities.

"Armchair epidemiologists" also began to stray far beyond their comfort zone to speculate about the virus. Science was somewhat relegated to the status of opinionated debate in the public gaze.

All this chaos constitutes a very unsettling reporting situation for journalists. The pandemic's progress is characterised by what journalists dislike: abstract and vague figures, with competing concepts and measurements. For instance, should they use the number of new cases, the number of newly reported deaths, the R-number or something else?

And what is a "covid death" anyway? There are at least three considerably different sources of daily death counts by the Department of Health and Social Care (DHSC), NHS England and the Office of National Statistics (ONS). Even the R-number could vary depending on the chosen calculation method. "We didn’t have a United Kingdom of covid-19 data," said Pamela Duncan, data journalist at the Guardian, and that is no fun for a profession that is "allergic to numbers".

Chaos in politicised science world

To make matters worse, covid-19 data has been subject to a rather fierce battle between different frames and narratives in which science had to compete – not always successfully – with religion, culture and, most importantly, politics.

In the midst of confusion, anxieties and fears, the public found mis- and disinformation not only on social media but also, and rather deplorably, in the many press briefings and interviews by ministers and MPs.

Sir David Spiegelhalter, chair of the Winton Centre for Risk and Evidence Communication at Cambridge University, recalled telling an Andrew Marr show in May that the daily press briefings at No 10 were like a completely embarrassing "number theatre".

Radio 4 science presenter and RSS fellow Timandra Harkness was one of the most frustrated, if not infuriated, about the messy and questionable ways in which politicians weaponise covid-19 data, citing the No 10 press briefing to justify the second lockdown as an example.

"That risks eroding the usefulness of data as information, and public trust in statistics as a reliable tool for understanding," she said.

As someone who has many times been baffled by politicians’ interview performance since March, I cannot help linking the above to what Tim Harford calls "statistical bullshit" – or "the casual slinging around of numbers not because they are true, or false, but to sell a message".

Such politicised science got worse as experts continued to contest each other with diverse views on what was happening and what would be happening next. Some with genuine expertise started to be scared.

"With the increasing involvement of political journalists, there was the real danger that some top scientists would turn down interviews for the fear of being pitched against each other at the expense of scientific nuance," said Fiona Lethbridge of the Science Media Centre (SMC).

Order out of chaos

Ross Lydall, the Evening Standard’s Health Editor, described coronavirus as "the most complex story of our career."

Inevitably, mistakes have been made where crucial data has been dangerously – or hilariously – misinterpreted in the news.

In April, for instance, Spiegelhalter published a graph showing that the risk of dying from covid infection is equivalent to the total risk of dying that an average individual is exposed to in an entire year. It was quickly misreported all over the news that covid-19 risk was no greater than the average annual risk. It became a godsend gift for influential anti-lockdown, anti-mask and anti-vaccine figures to share on social media.

All things considered, however, journalism has done well during the pandemic. "There is much more to do, but we have come a long way," said Jane Kirby, health editor at PA Media, who remembered the early days "floundering about the data and what on earth to do with them”.

Sometimes the success goes far beyond what one expects of journalism. The Economist presented three highly complicated, creative covid-19 data projects that have not only benefited audiences and newsrooms around the world but also been applauded, used and expanded by hundreds of academic studies, WHO and tech firms.

Beyond these "blockbuster" cases is so much energy, resilience and innovation in the daily micro handling and communication of data. Here are some fundamental takeaways:

Be sophisticated

Numbers do not simply speak for themselves and should not be taken at face value. Beneath the surface of each daily covid-19 death count, for example, is a very different counting method, each with its own limitations.

As Tom Whipple, science editor of The Times, advised from his own "learning not to trust the daily counts", journalists need to avoid naïve interpretation and look beyond the numbers to tell the story. It might be a quick fix, but it is also misleading.

Respect the audience

Do not assume that the public at large is not capable of understanding data and uncertainty. It can and has backfired during the pandemic. There is research evidence that uncertainty does not reduce the public trust in data and science.

In other words, be transparent in your reporting, admitting and communicating uncertainty where required. If there are different measures for the same thing (e.g., two R-nought figures, three death counts), report all and explain the limitations behind each of them.

Also, tell audiences what the numbers do not tell them. "There is always an extra layer of complexity," said freelance science journalist Tom Chivers, adding that journalists need to bring context to every important number being reported.

Make the data geographically navigable and personally relatable

The pandemic is as global as it is local. Scaling the data down to users’ local settings and making them easy to use have helped immensely.

An excellent example is Reach PLC’s initiatives in updating and localising ONS and government data through interactive maps and postcode checkers. The key aim is to make them "accessible, useful and relevant," said Claire Miller, Reach’s head of data journalism.

Humanise the data

Numbers and graphs are great, but cannot tell the whole story. Data and science reporters need to listen to other modes of pandemic communication and leaves room for emotion, empathy and persuasion. Otherwise, as Whipple said, “one death is a tragedy and one million deaths is just a statistic."

Treat scientists as scientists

In a politicised science debate like covid-19, it is vital to source the right experts (not “armchair epidemiologists”) and let them talk properly. Build trust with them through calmness, humility and transparency in your purposes. Do not lose your healthy enquiring habit and ask hard questions when needed, but let them get to the point and with their evidence. Importantly, do not "force" them, consciously or subconsciously, into taking sides.

Use science journalism intermediaries

SMC has been a controversial model for some in science journalism, but virtually every journalist and statistician at the symposium gave a big shout to its helping hand in connecting them with the right expert in the midst of uncertainty. From January to the end of November, according to Lethbridge, SMC offered 101 briefings and 999 covid-19 round-ups and rapid reactions to journalists, in addition to handling 3,200 journalistic enquiries.

Share your data with other newsrooms

That sounds counter-intuitive in a highly competitive industry. But it can conserve scarce resources and amplify the impact of journalism beyond a single news organisation's reach, all ultimately for the public good.

Time write off the stereotype?

What has emerged from the chaos of covid-19 data and science scene calls on critics, myself included, to rethink the stereotype of journalism as a number-phobic and statistically incompetent profession. Journalists can effectively relay the messiness of frontline science and complicated data to the lay public, provided they are adequately invested to do so. Editors’ attitudes to – and tolerance of – numbers can play a decisive part.

Our symposium was a little skewed to the positive because most participating journalists belong to newsrooms that have maintained more or less strong support for science, health and data reporting expertise. The Economist, in addition to its renowned science journalism, has an established team of 17 data journalists and designers.

It is important to note, things would not have been so positive without the resilient, tireless and selfless dedication of each and every of the not-so-many specialist science, health and data journalists out there wading through a relentless workload.

For scale, Duncan said that of the 338 articles she has produced throughout five years with the Guardian, around a third were covid-related stories written between March and December last year.

That leads me back to the condition above: investment. Journalism as a whole is not in a good position to deal confidently with data and statistics yet. The pandemic highlights rather than belittles the need for more statistical expertise among journalists.

One of covid-19’s good legacies is the growing appetite for data and statistics among both audiences and journalists. That makes a good professional and commercial case for newsroom executives to have a strategic commitment to this crucial but grossly overlooked area of journalism.

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