The chapter moves between different sites – the modelling community in key medical and public health schools in the UK and two places where the pandemic was being confronted – the UK Midlands and southern Zimbabwe.
The disconnect between the apparent certainties generated by a certain style of epidemiological modelling and the realities on the ground was stark, especially in the UK where predictions initially made use of misleading influenza models and data. The UK science advice system, centred on SAGE (Scientific Advisory Group for Emergencies), was highly centralised and was initially dominated by epidemiologists and modelling expertise. Day-to-day realities were however very different to generic model predictions, influenced by class, ethnicity, location, livelihood, gender, age and so on. In Zimbabwe there were no models – or only very generic ones – and front-line health professionals had to work out what to do, with very few resources. In both settings, the ‘unsung heroes’ of the pandemic were those living with the disease and confronting it in real time. In the UK Midlands it was local general practitioners and nurses, local government officials and care home workers, while in Zimbabwe it was the kind of front-line health professionals such as Joseph Mlambo (not his real name), a nurse in a hospital in the southeast of the country profiled in the book.
Pandemic preparedness for the real world
Through these experiences an argument for a different type of pandemic preparedness approach – less reliant on models and tech interventions – and more on multiple knowledges and direct experiences is developed. The chapter observes that, “In both the UK and Zimbabwe, despite the disparities in science advice, qualified personnel and health infrastructure, and the hugely different political-economic contexts, in the end responses to the pandemic ultimately relied on people on the ground working together, making use of social relationships and practices, developing trust, responding in real-time and adapting flexibility to fast-changing circumstances. While epidemiological models can provide the basis for thinking generally about the future, offering different scenarios, they are poor at providing prescriptive directions for action under conditions of deep uncertainty, even ignorance. As we saw in the UK, a hubristic over-reliance on models and limited sources of technocratic expertise can instead be dangerous, diverting attention from real-world contexts, diverse knowledges and practical experience.” As the UK COVID-19 select committee inquiry argued, “it is the nature of preparing to face future risks that there will be much that must be unknown about them. Perfect foresight, and therefore a perfect response, is not available.”
In rejecting what Sheila Jasanoff calls ‘technologies of hubris’ an approach centred on ‘technologies of humility’ can be developed, the chapter argues. “This requires a different type of professional expertise and new forms of network at the centre of pandemic response. The narrow version of the SAGE-style expert advice system as used in the UK is inadequate and, as experienced through the COVID-19 pandemic, may actually have undermined the capacity to respond effectively. In Zimbabwe, a plural response evolved, with people relying on diverse sources of knowledge and innovation in response to uncertainties. This was partly through force of circumstance as the state health system was unable to deliver, but the types of treatments and forms of support that people made use of helped everyone navigate uncertainties across the phases of the pandemic and so created forms of local resilience (see the blog series and book on our work in southeast Zimbabwe). In this sense, people continuously performed and practised preparedness and generated resilience, even if this was very localised.”
As the chapter explains, “The argument though is not to abandon modelling efforts and just rely on local initiatives, nor to formalise all the impromptu, creative practices of informal responses, but instead to recognise the important limits of science-based prediction in the face of deep uncertainties. Instead of searching in vain for the perfect ‘evidence’ to feed into ‘policy’ in a linear way, a different approach would be to accept, as scientists of course do, that epidemiological modelling is always messy, contested and uncertain, and so requires opening up the debate to greater scrutiny and wider engagement. This would mean not only using the modelling efforts to encourage a plurality of models… but also to encourage challenge of all model framings from different angles. What assumptions are being used? What data are relevant? Is this appropriate to my setting? Rather than seeking ‘evidence-based consensus’ and then ‘communicating’ the results to ‘the public’ with an assumed ‘deficit’ of knowledge, seeing modelling as a space for deliberation (and disagreement and contest) allows for a more robust and inclusive debate about how to respond.”
Pandemics as social and political, as well as biological and medical
But have these lessons been taken on board? The short answer is no. Instead, a plethora of technocratic, centralised solutions are being proposed. As the chapter notes, “We must always remember that pandemics are as much social and political phenomena as they are biological and medical, and so require open, inclusive reflexive spaces for debates about knowledge and action before, during and after a pandemic. Technical solutions are important but are just not enough when thinking about pandemic preparedness. While epidemiological models can certainly offer useful insights, they blind and obscure if too much faith is put in them; models are after all just models. As this book argues across very diverse cases, where uncertainty, ignorance and ambiguity dominate, technocratic risk-based decision-making is inadequate, and a major rethink is required.”
The chapter concludes: “More effective preparedness for future pandemics – which will surely arise, but not necessarily in the form of a re-run of COVID-19 – must instead rely on a number of core principles…. the use of and respect for multiple knowledges, including those outside accredited science; the recognition and support of professionals and their networks – often informal and community-based – who can generate reliability in the face of uncertainty and a decentralised, flexible approach responsive to local contexts and changing circumstance that facilitates responsive, collective action within plural health systems. Building resilience for future pandemics requires all these elements now.”
