Boris Johnson’s political brand is in deep trouble


Christopher Pich, Nottingham Trent University

Boris Johnson has had a tricky time as UK prime minister of late. He faces criticism that he has mishandled the national response to the coronavirus crisis, leading to public confusion and a very high death toll.

I would argue that part of Johnson’s struggle stems from his political brand. He has been successful as a politician by projecting a certain image to the public. But now, in a moment of extreme pressure, that image does not provide the reassurances the public needs. Johnson has spent recent months attempting to pivot towards a new political brand, but he hasn’t made it all the way there. Now, what is left is a confusing mixture of brands – leaving the British public uncertain of what to expect from the prime minister, and perhaps even the prime minister himself uncertain of how to act.

Every politician has a political brand identity. They may not care to accept this proposition or agree with the terminology, but they do. For centuries, they have attempted to create, develop and manage a desired position that represents “what they stand for”. The hope is that this will then resonate with the electorate and win them office.

The Boris brand

The prime minister actually seems to have two “Boris” brands. Before taking the top job, “Boris” was positioned as “Boris the comic” – confident, humorous, entertaining, admirable. He was a maverick who often strayed from the party line and was, most importantly, relatable to a wide spectrum of voters. Dishevelled blonde hair, theatrical one-liners and optimistic energy were tools that set him apart from his rivals as a non-traditional politician. In many ways, “Boris the comic” was an example of a politician who built an identity around style over substance, focusing more on soundbites, photo opportunities, stunts and imagery.

The second Boris emerged just prior to Johnson’s elevation to Downing Street. He seemed to recognise that extra characteristics needed to be added to his brand at this point in his career. Johnson and his team attempted to position him as “Boris the commander” – a strong, decisive leader, eye-for-detail, in-touch, prime ministerial, honest and accountable.

However, these characteristics were, arguably, contradictory to the original “Boris the comic” brand. Indeed, “Boris the commander” seems paradoxical to “Boris the comic”. The aim now seems to be to build identity around substance over style.

But for the public, that leaves the crucial question: which is the real Boris? Johnson needs to be careful and take stock. Successful political brands need to be clear, consistent, authentic and believable otherwise they can alienate, confuse and disengage the voting public. If people lose trust, faith and respect with political brands, then loyalty can diminish and support can fade away.

While a general election in the UK is unlikely before 2024, it can be difficult to recover and regain trust and support once lost.

A different world

On the first day as prime minister, Boris proclaimed on the steps of Downing Street that the British people “have had enough of waiting” and his job was to was “get Brexit Done” – a slogan that defined his premiership campaign and the election that followed. Back then, it was clear that the team behind Johnson was in firm control of the brand.

Fast-forward six months and the world is very different. The public are losing confidence and trust in Johnson’s ability to manage the coronavirus crisis. Voters are questioning his credibility, trustworthiness, decisiveness and leadership.

Johnson has faced calls to sack his chief political adviser Dominic Cummings after he broke UK lockdown rules during the pandemic. Johnson’s government was also forced into a series of u-turns, such as the decision to end funding for free school meals for England’s poorest families over the summer holidays. And above all, people want to know why so many have died from COVID-19.

All this switching around, changes of mind, carelessness for the rules is more in line with the brand of Boris the joker. Johnson has become attached to this brand over the years – and it has been successful for him, so perhaps it’s no surprise to see the old brand seeping in. Unfortunately, though, this brand is wholly unsuited to this moment of global crisis, when the public is looking for a steady hand.

The last six months may have given the public first-hand experience of “Boris the commander” and it seems to have delivered him lower levels of approval and confidence. And yet the original Boris brand won’t work now either.

Team Johnson could still have time to reposition his political brand, address the confusion of two distinct identifies and could create a new identity for voters to fall in love with again. After all, Johnson is still perceived as “likeable” and “best placed to get things done”. This suggests the brand can be salvaged.

But a hybrid approach, blending joker with commander, would merely add to the confusion and chaos. Johnson needs to reflect on his current brand and vision for the nation. Johnson needs to commit to either the “comic”, “commander” or a completely new brand identity rather than flit between the two. And he must do this sooner rather than later. The longer the confusion festers, the longer it will damage his electablity.The Conversation

Christopher Pich, Senior Lecturer, Nottingham Business School, Nottingham Trent University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Coronavirus: our study suggests more people have had it than previously estimated



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Norman Fenton, Queen Mary University of London; Magda Osman, Queen Mary University of London; Martin Neil, Queen Mary University of London, and Scott McLachlan, Queen Mary University of London

Many people suspect they’ve been infected with COVID-19 by now, despite the fact that only 0.5% of the UK’s population has actually been diagnosed with it. Similar numbers have been reported in other countries. Exactly how many people have actually had it, however, is unclear. There is also uncertainty around what proportion of people who get COVID-19 die as a result, though many models assume it is around 1%.

We believe there has been over-confidence in the reporting of infection prevalence and fatality rate statistics when it comes to COVID-19. Such statistics fail to take account of uncertainties in the data and explanations for these. In our new paper, which has been peer-reviewed and accepted for publication in the Journal of Risk Research, we developed a computer model that took these uncertainties into account when estimating COVID-19 fatality rates. And we see a very different picture.

Our model, called a Bayesian Network, allows us to combine multiple data sources and assess how sensitive the infection prevalence and fatality rates are to two dominating sources of uncertainty.

One is the accuracy of serological (antibody) testing, which is crucially dependant on our ability to accurately measure whether an individual has antibodies. We account for factors such as false positives or negative rates for manufacturer test kits.

We also take account of the reliability of fatality data. This is important because the fatality rate, the probability of death for a Covid-19 infected patient, is defined as the death count divided by the number of infected people in the community. If either of these variables is uncertain, any policy decisions based on the resulting fatality rate will themselves be unreliable, or potentially dangerous.

Both of these factors are much more uncertain than is being reported. When we account for them in our model, we discovered high community infection rates in many regions across the world. For Kobe, Japan, our model suggested that over 800 times more people have had COVID-19 than has been reported. For England and Wales, this figure is 28 times more.

As for the fatality rate, the team from Imperial College in the UK, which is advising the UK government, has previously estimated this number to be 1%. But this is uncertain. The team states that its model “relies on fixed estimates of some epidemiological parameters such as the infection fatality rate…”, while also acknowledging that “amidst the ongoing pandemic, we rely on death data that is incomplete, with systematic biases in reporting, and subject to future consolidation”.

When we adjusted for these uncertainties, we discovered that the fatality rate estimates are most likely to be in the range 0.3%-0.5% for the countries/regions we considered.

Illustration of the new coronavirus.
Andrii Vodolazhskyi/Shutterstock

Although not covered in our study, we also applied our model to New York City data. Here the “actual” NYC fatality count is stated as 23,430, with an estimated fatality rate of 1.4%. But, when the data is input into our model, the estimate for the fatality rate can be adjusted down to range between 0.6% to 1.3% – potentially half of the official figure.

Uncertainties in death numbers

So how could we account for these uncertainties? Each country calculates deaths differently – which is a problem to begin with. And, in many countries, the “actual” fatality count is estimated by adding confirmed deaths, where COVID-19 appears on the death certificate alongside a positive COVID-19 test result, deaths where COVID-19 is on the death certificate but where no test took place, and a statistical estimate of “excess deaths” (how many more deaths it is believed there were than normal).

For example, in New York City the “actual” fatality count is the sum of confirmed 13,156 deaths, where COVID-19 appears on the death certificate alongside a positive COVID-19 test result, 5,126 deaths where COVID-19 is on the death certificate but where no test took place and 5,148 excess deaths. But we don’t actually know whether some of these people died “of” or “with” COVID-19. Many of these deaths are labelled “actual” when they are actually highly uncertain.

What’s more, excess deaths are often calculated by comparing against the preceding five years, excluding years with “bad” influenza seasons – which is a problem. Also, COVID-19 may be accelerating deaths that were imminent. And if the effects of lockdown are preventing people with serious conditions such as strokes and heart attacks from accessing healthcare and dying as a result, there is a risk that including them as “excess deaths” due to COVID-19 has contributed to serious overestimation.

Herd immunity?

This sort of research is worth considering when debating if we are close to herd immunity, or whether a “second wave” of the virus is likely. Taking Sweden as an example, antibody studies show COVID-19 was much more prevalent, at 7% a few weeks ago, than confirmed cases suggested at that time. However, this is still far from the 65% assumed to guarantee herd immunity. If Sweden has not reached herd immunity and not mandated lockdown, why are their death numbers not increasing?

One controversial explanation that we didn’t account for in our study is the existence of “antibody dark matter” that does not show up in antibody testing but nevertheless offers some protection against the virus.

The immune system involves two types of white blood cell: T cells and B cells. But only B cells produce antibodies. Studies show immunity might more rapidly develop from previous infections “similar” to COVID-19, such as SARS-v1, via immunity “T-cells” rather than the B-cells. This means many people may have had coronavirus but not developed antibodies – leading to an underestimation of the number of infections, including in our model.

So while one recent study claimed that about 10% of the population of England and Wales may in fact have been infected, the real number could in fact be even higher.

Clearly, we cannot fully trust statistics on death and infection rates before we get more accurate data and include it into a model such as ours.The Conversation

Norman Fenton, Professor of Risk and Information Management, Queen Mary University of London; Magda Osman, Reader in Experimental Psychology, Queen Mary University of London; Martin Neil, Professor in Computer Science and Statistics, Queen Mary University of London, and Scott McLachlan, Postdoctoral Researcher in Computer Science, Queen Mary University of London

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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