COVID-19: Lessons to be learned from confronting a wicked problem
7 August 2020
By Professor Ray Hudson FAcSS (Professor of Geography, Durham University)
There is no doubt that the arrival of the coronavirus has posed unprecedented problems for public policymakers and politicians in and beyond the UK. COVID-19 can be thought of as a classic example of a wicked problem. That is, a complex problem with emergent effects that are difficult or impossible to solve for one or more of the following reasons: incomplete or contradictory knowledge; the number of people and opinions involved; the large economic burden; and the interconnected nature of these problems with other problems.
While by no means sufficient in itself, better knowledge is obviously necessary in seeking to deal with a complex problem. There is a pressing need for better knowledge of the origins of the virus, and a better understanding of how its differing determinants interact and influence one another. Such knowledge is clearly a minimum necessary condition for moving forward by initially containing and subsequently – hopefully – solving the problem, ultimately via developing an effective vaccine. The scientific research community – in its broadest sense, across disciplines and within but extending beyond, universities – must have a central role in producing such knowledge and using it in modelling possible patterns of diffusion of the virus and assessing the predicted impacts of interventions to maximise their effectiveness in combating the virus and ultimately creating a vaccine to give immunity against it.
There has understandably been a great deal of focus on the R (virus reproduction) rate nationally, with important modelling work by staff at UCL and the London School of Tropical Medicine and Hygiene, although government ministers have often been economical with the truth in terms of how their behaviour has followed scientific advice. Modelling such complex processes is demanding, not least of the data input. Other things being equal, the outputs of any model are only as good as the assumptions on which the model is based and the quality of the data used in calibrating the models. Ideally, such data would be valid, reliable, statistically representative of the underlying population from which they are drawn. In practice, it is a question of making do with such data as are available, drawn from outbreaks in different parts of the world on the basis on what has happened there and collected in varying ways. This poses some serious challenges to those carrying out the modelling work.
In the UK, as elsewhere, there has been a strong emphasis on modelling the R rate and spread of the virus at the national level. While the focus on the national R rate is understandable, it is important to remember that the national rate is an average that erases variations below that national scale. Subsequently, there was some recognition of this, as information began to be given about regional rates but these related to very broad regions and R rates could and did vary markedly within these regions, between big cities, small towns and rural areas, within the conurbations and big cities. More recently there has been a growing recognition of the dangers posed by localised ‘second waves’ as at Leicester and former textile towns in Lancashire and Yorkshire , as well as specific sites and workplaces where people unavoidably come into close contact, such as meat processing plants and farms specialised in growing fruit and vegetables. In response, there has been a welcome recognition, albeit belated, of the need to devolve policy to deal with such outbreaks to a more local level.
Rather than respond after the event, however, it could have been, and still is, possible to take a more pro-active approach in recognising the need for more targeted local policies and interventions in particular places. Clearly some people in some places are much more likely to catch the virus, and some are much more likely to die from it if they do catch it. Susceptibility to the virus reflects a complex interaction of personal attributes and environmental influences, the relationships between age, gender, ethnicity, social class, occupation and residential and workplace locations. What is required is better understanding of the interaction of these multi-variate determinants in particular places as a basis for sound policy making.
In the absence of an effective vaccine, an effective multi-level strategy to contain the virus needs to be sensitive to such variations and understanding of their causes. There are a lot of data already existing at small area scale that are readily accessible via online systems that could and arguably should have been used to identify local areas in which people were likely to be most susceptible to the virus. Indeed, it is important to understand why this didn’t happen earlier and why the local knowledge of people employed in local authorities and the NHS wasn’t drawn upon from the outset. Identifying areas where people were particularly at risk could then have been used as a basis for developing effective test, track and trace systems for people living there, and for developing policies to lower the R rates in them, monitoring and containing the spread of the virus and reducing transmission more widely. This in turn would have enabled a more targeted and effective deployment of scarce NHS and social care resources to where they were most needed.
There is general agreement that the only long-term solution to the problem of COVID-19 is an effective vaccine that will give generalised immunity to a population. In the absence of such a vaccine, however, how best to proceed for the foreseeable future? Some 45 years ago Claus Offe wrote of the dilemma facing policymakers in a way that seems particularly apposite in the current context when he wrote of the necessary being impossible, but the impossible as being necessary to deal with a problem. In the absence of an effective vaccine, COVID-19 certainly poses very difficult policy and political choices, not least balancing the risks to public health and well-being against the risks to the health and well-being of the national economy, of balancing the risk to life against the risks to livelihoods.
There are clearly no easy or straightforward answers. But perhaps one lesson to be learned from all this is – emphasised as I watched TV pictures of crowded beaches and urban street parties – that politicians and policymakers need to be clear and unambiguous in the messages that they convey to the wider population. And another is the need to be prepared, to be able tovery quickly bring together knowledge from different sources and academic disciplines in seeking to grapple with the complexity of such wicked problems and provide a basis for rapid and informed policy responses and difficult choices. Social sciences will of necessity have a central role to play in this, both in relation to the provision of data and theories to underpin their interpretation.
Professor Ray Hudson is Professor of Geography at Durham University. Among other roles at Durham, he has been Acting Vice Chancellor and Director of the Wolfson Research Institute for Health and Wellbeing. He is a Fellow of the Academy of Social Sciences and the British Academy and a Member of Academia Europaea. He has published widely. His most recent book is Co-Produced Economies, 2019, Routledge.
The perspectives expressed in these commentary pieces represent the independent views of the authors, and as such they do not represent the views of the Academy or its Campaign for Social Science.
This article may be republished provided you place the following statement and link at the top of the article: This article was originally commissioned and published by the Campaign for Social Science as part of its COVID-19 programme.