Excitable models: Projections, targets, and the making of futures without disease

In search of a 'good number': knowledge controversy and population estimates in the endgame of hepatitis C elimination

We explore the contentious life of a metric used to assess a country's progress in relation to global disease elimination targets. Our topic is hepatitis C elimination, and our context is Australia. A fundamental metric in the calculation of progress toward hepatitis C elimination targets, as set by the WHO, is the population prevalence of people living with hepatitis C. In Australia, this modelled estimate has generated some controversy, largely through its repeated downsizing as an effect of calculus. The 2015 baseline population estimate in Australia, from which measures of current elimination progress are assessed, has reduced, over time, by around 30%. Informed by a social study of science approach, we used qualitative interviews with 32 experts to explore the knowledge controversy. The controversy is narrated through the core concerns of 'scale' and 'care', with narratives aligning differently to imaginaries of 'science' and 'community'. We trace how constitutions of 'estimate' and 'number' circulate in relation to 'population' and 'people', and as affective values. We show how enactments of estimates and numbers materialise hepatitis elimination in different ways, with policy implications. The event of the knowledge controversy opens up the social and political life of enumerations-for science and community-inviting deliberation on how to make 'good numbers' in the race to eliminate hepatitis C.

'There's too much power in this number. It's freaking the whole response out': The views of key informants on evidence and targets to achieve hepatitis C elimination goals in Australia

Enumeration of disease is a key management tool. Setting of targets, like for hepatitis C elimination, have deep meaning and effect. We use the case of elimination in New South Wales (NSW), Australia to examine key informants' understandings of the use of targets, and the evidence that informs them, to drive action in elimination. Twenty-eight key informants working in NSW, elsewhere in Australia and internationally in high-income countries participated in a semi-structured qualitative interview in 2022. Analysis was informed by scholarship calling for examination of the ways in which science constructs what is thought possible in action. Participants pointed to the power of quantified evidence and targets and their complex effects, and questioned the usefulness and certainty derived from these at the "pointy end" of elimination. Although a range of targets exist in global and local strategies, reaching testing targets was the assumed solution to achieving elimination. Achieving elimination was thought to require "off piste" and experimental approaches that went beyond available evidence. The different types of work that participants felt necessary for late-stage elimination may require additional metrics to explain return on investment ratios. What threshold would be used to reduce efforts in elimination was a major concern. These data indicate that understandings of the evidence underpinning elimination targets and how to achieve them are far from settled. At this point, elimination efforts may need to rely on locally produced and community-driven evidence and shift from evidence-based to evidence-making paradigm.

Conceptualising the value of simulation modelling for public engagement with policy: a critical literature review

As we face complex and dynamically changing public health and environmental challenges, simulation modelling has come to occupy an increasingly central role in public engagements with policy. Shifts are occurring not only in terms of wider public understandings of modelling, but also in how the value of modelling is conceptualised within scientific modelling communities. We undertook a critical literature review to synthesise the underlying epistemic, theoretical and methodological assumptions about the role and value of simulation modelling within the literature across a range of fields (e.g., health, social science and environmental management) that engage with participatory modelling approaches. We identified four cross-cutting narrative conceptualisations of the value of modelling across different research traditions: (1) models simulate and help solve complex problems; (2) models as tools for community engagement; (3) models as tools for consensus building; (4) models as volatile technologies that generate social effects. Exploring how these ideas of 'value' overlap and what they offer one another has implications for how participatory simulation modelling approaches are designed, evaluated and communicated to diverse audiences. Deeper appreciation of the conditions under which simulation modelling can catalyse multiple social effects is recommended.

Domesticating models: On the contingency of Covid-19 modelling in UK media and policy

Our article traces the representation of pandemic modelling in UK print media from the emergence of Covid-19 to the early stages of implementing the first UK-wide lockdown in late March 2020. Covid modelling, it is widely assumed, has shaped policy decisions and public responses to the pandemic in unprecedented ways. We analyse how the UK print media has configured modelling as a significant evidence tool in the representation of the pandemic. Interrogating assumptions about infectious disease modelling, we ask why models became the trusted tool of choice for knowing and responding to the Covid pandemic in the UK. Our analysis has yielded four different periods in the evolution of intersecting policy and media frames. Initially, modellers, policymakers and media alike emphasized uncertainty about available data, and hence the speculative character of modelled projections, thus justifying a 'wait and see' approach to government intervention. With growing public pressure for government action, policy and media frames were adjusted to emphasize the importance of timing interventions for best effect, with modelling evidence mobilized to justify inaction. This gave way to a period of crisis, as the press increasingly questioned the reliability of the existing models and policies, leading modellers and policy makers to dramatically revise their projections. Finally, with the imposition of the first UK lockdown, policy and media frames were brought back into alignment with one another, in a process of domestication through which the language of modelling became a basic resource for the discussion of the epidemic. Our epistemological microhistory thus challenges general accounts of the impacts of pandemic modelling and instead emphasizes contingency and interpretative flexibility.

Undoing elimination: Modelling Australia's way out of the COVID-19 pandemic

In the middle of 2020, with its borders tightly closed to the rest of the world, Australia almost achieved the local elimination of COVID-19 and subsequently maintained 'COVID-zero' in most parts of the country for the following year. Australia has since faced the relatively unique challenge of deliberately 'undoing' these achievements by progressively easing restrictions and reopening. Exploring the role of mathematical modelling in navigating a course through the pandemic through qualitative interviews with modellers and others working closely with modelling, we argue that each of these two significant phases of Australia's COVID-19 experience can be understood as distinct forms of 'model society'. This refers at once to the society enacted through the governance of risk, and to the visions of societal outcomes - whether to be sought or to be avoided - that are offered up by models. Each of the two model societies came about through a reflexive engagement with risk facilitated by models, and the iterative relationship between the representations of society enacted within models and the possibilities that these representations generate in the material world beyond them.

Uncomfortable science: How mathematical models, and consensus, come to be in public policy

We explore messy translations of evidence in policy as a site of 'uncomfortable science'. Drawing on the work of John Law, we follow evidence as a 'fluid object' of its situation, also enacted in relation to a hinterland of practices. Working with the qualitative interview accounts of mathematical modellers and other scientists engaged in the UK COVID-19 response, we trace how models perform as evidence. Our point of departure is a moment of controversy in the public announcement of second national lockdown in the UK, and specifically, the projected daily deaths from COVID-19 presented in support of this policy decision. We reflect on this event to trace the messy translations of "scientific consensus" in the face of uncertainty. Efforts among scientists to realise evidence-based expectation and to manage the troubled translations of models in policy, including via "scientific consensus", can extend the dis-ease of uncomfortable science rather than clean it up or close it down. We argue that the project of evidence-based policy is not so much in need of technical management or repair, but that we need to be thinking altogether differently.

Making pandemics big: On the situational performance of Covid-19 mathematical models

In this paper, we trace how mathematical models are made 'evidence enough' and 'useful for policy'. Working with the interview accounts of mathematical modellers and other scientists engaged in the UK Covid-19 response, we focus on two weeks in March 2020 prior to the announcement of an unprecedented national lockdown. A key thread in our analysis is how pandemics are made 'big'. We follow the work of one particular device, that of modelled 'doubling-time'. By following how modelled doubling-time entangles in its assemblage of evidence-making, we draw attention to multiple actors, including beyond models and metrics, which affect how evidence is performed in relation to the scale of epidemic and its policy response. We draw attention to: policy; Government scientific advice infrastructure; time; uncertainty; and leaps of faith. The 'bigness' of the pandemic, and its evidencing, is situated in social and affective practices, in which uncertainty and dis-ease are inseparable from calculus. This materialises modelling in policy as an 'uncomfortable science'. We argue that situational fit in-the-moment is at least as important as empirical fit when attending to what models perform in policy.

New directions for participatory modelling in health: Redistributing expertise in relation to localised matters of concern

Participatory modelling seeks to foster stakeholder engagement to better attune models to their decision-making and policy contexts. Such approaches are increasingly advocated for use in the field of health. We review the instrumental and epistemological claims made in support of participatory modelling approaches. These accentuate participatory models as offering a better evidence-base for health policy decisions. By drawing attention to recent modelling experiments in a sector outside of health, that of water management, we outline a different way of thinking about participation and modelling. Here, the participatory model is configured in relation to matters of 'knowledge controversy', with modelling constituted as an 'evidence-making intervention' in relation to the making of science and expertise. Rather than presenting participatory models as an improved technical solution to addressing given policy problems within an evidence-based intervention approach, models are alternatively potentiated as sites for the redistribution of expertise among actor networks as they seek to engage politically in a matter of concern. This leads us to consider possible new directions for participatory modelling in the field of health.

Futures-oriented drugs policy research: Events, trends, and speculating on what might become

One concern in the field of drugs policy is how to make research more futures-oriented. Tracing trends and events with the potential to alter drug futures are seen as ways of becoming more prepared. This challenge is made complex in fast evolving drug markets which entangle with shifting social and material relations at global scale. In this analysis, we argue that drugs policy research orientates to detection and discovery based on the recent past. This narrows future-oriented analyses to the predictable and probable, imagined as extensions of the immediate and local present. We call for a more speculative approach; one which extends beyond the proximal, and one which orientates to possibilities rather than probabilities. Drawing on ideas on speculation from science and technology and futures studies, we argue that speculative research holds potential for more radical alterations in drugs policy. We encourage research approaches which not only valorise knowing in relation to what might happen but which conduct experiments on what could be. Accordingly, we trace how speculative research makes a difference by altering the present through making deliberative interventions on alternative policy options, including policy scenarios which make a radical break with the present. We look specifically at the 'Big Event' and 'Mega Trend' as devices of speculative intervention in futures-oriented drugs policy research. We illustrate how the device of Mega Trend helps to trace as well as to speculate on some of the entangling elements affecting drug futures, including in relation to climate, environment, development, population, drug production, digitalisation, biotechnology, policy and discourse.