The Covid-19 pandemic has proved capable of bringing countries around the world to their knees, upending every aspect of how we live. When it comes to how we respond and rebuild societies and economies there is a clear need and opportunity for us to re-think development more broadly and address structural political-economic conditions alongside the far less ordered, ‘unruly’ processes that reflect our complex and uncertain world. A key challenge – there in past epidemics, and accentuated in the Covid-19 pandemic – is how research, scientific advice and evidence are used in policy.
This week IDS researchers are taking part in Evidence Week, an event designed specifically to bring researchers together with UK parliamentarians and discuss ways of using evidence in policy. The aim is to share knowledge and equip politicians with the skills needed to scrutinise evidence in the future. During the week, we’ll be sharing evidence from IDS on community engagement, trust building, tackling misinformation in a pandemic, and the importance of ‘bottom up’ responses that involve and are led by communities.
It comes at a vital time for science. The past nine months have seen a surge in the prominence of claims about evidence in public daily life. The Prime Minister’s Covid-19 briefings have made people in the UK all too familiar with statistics, models, graphs and scientific advice committees, and the phrase ‘following the science’ has been used consistently by politicians. Yet despite the government’s insistence on a notion of a singular science, there has been much questioning and controversy
Evidence from past disease outbreaks
Scientific models are appealing to politicians – and the media – as they provide a sense of certainty amid uncertainty. Data and models lend something more concrete to base their decisions on. But, as evidence from previous disease outbreaks such as Ebola in West Africa (2013 – 16) and Avian H5N1 in Southeast Asia shows, models are only as good as the assumptions they are based on – and these often miss the mark.
For example, at the start of the 2013–16 Ebola outbreak a Centers for Disease Control model predicted up to 1.4 million deaths based on assumptions that there would be unchecked community transmission. The prediction drove resources to be directed towards providing large numbers of Ebola treatment beds to take cases out of the community. The model did not factor in the possibility of community-led behaviour change, which it turned out was pivotal in bringing infections down before many of the beds were even built, and leaving many that were un-used. In this case we can be thankful that the predictions did not come true, but we should be troubled by the simplistic assumptions which informed the model and resource allocation. These linear assumptions did not align with the region’s embedded, unruly entanglements of kinship, travel and trade and had not taken into account the local adaptions being made, for example in adapting burial practices and locally-led quarantines – which could have been better addressed and supported instead.
Covid-19 has highlighted the problem of relying on a narrow source of evidence and type of expertise. Real world issues, particularly those relating to health and social systems, are messy, diverse and complex. Our approach to the science and evidence needed to help solve them has to acknowledge the inherent uncertainty and allow for a wider set of knowledge, expertise and experiences. This needs to be done in two ways, firstly by considering different forms of knowledge, including experiential – listening to different communities and their experiences, and enabling the responses and adaptations they are making at a local level. Secondly by more interdisciplinarity, drawing on knowledge from a wider breadth of expertise representing different science disciplines, arts and humanities.
Opportunity to share experiences of the pandemic between countries
Beyond grappling with the immediate Covid-19 response, there is an opportunity to use the shared experience of the pandemic to turn the rhetoric of development as a universal issue – applicable to everyone, globally – to reality. From New York to Nigeria, Manchester to Mumbai, countries and cities across the world have been hit badly by the disease and by the economic and social fall-out of attempts to control it. Higher income countries such as the UK, Italy and the US are experiencing some of the worst death rates from this global crisis, linked to their older populations and other factors. Inequalities by class, gender, ethnicity, and place have proved significant everywhere albeit with different manifestations in different countries.
Bringing evidence about inequalities to center stage has proved vital to responses and will be crucial as the world moves to address post-pandemic development. Now is the time for political leaders to gain from mutual learning where knowledge flows equally between low-income countries in the global South and high-income countries in the global North. It requires policymakers in the US and Europe to value and recognise the science and evidence from low-income countries who have been through other recent epidemics and have valuable experience, including learning from their Covid-19 responses, from which all could learn from.
Uncertainty is always present and needs to be acknowledged
A danger with Covid-19 is that it is seen as unprecedented and that improved models could have predicted and stopped it with strong enforcement of ‘top-down’ public health measures. Lessons from multiple disease outbreaks and development initiatives suggest that uncertainty is always present and acknowledging it, in conjunction with using a broad range of evidence including experiential knowledge to generate robustness and reliability, must be at the core of development policy and practice. Delivering on this commitment requires a fundamental rethinking of how expertise of multiple sorts and new forms of professionalism are convened and combined. Now, more than ever, we need to approach global development challenges with an emphasis on learning, adaptation, flexibility and equitable relationships while in turn generating the evidence that policymakers need from diverse sources.