Using model-based evidence in the governance of pandemics

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.

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.

Efficiency in the governance of the Covid-19 pandemic: political and territorial factors

BACKGROUND

The pandemic generated by Covid-19 has changed the way of life of citizens around the world in a short time, affecting all areas of society directly or indirectly, which is facing a global health crisis with different national responses implemented by governments. Several months into the pandemic, the first after-effects of Covid-19 are beginning to be felt by citizens, who are questioning the management carried out so far. In order to improve the performance of governmental decisions to reduce the impact of the pandemic during the coming months, we calculated the levels of efficiency in the management of health resources. In addition, we identify some country characteristics that may condition efficient management.

RESULTS

We obtained significant differences according to the geographical location of the country, with European and American countries being less efficient than Asian and African countries. Likewise, we can affirm that greater freedom of expression, a higher median age and an unstable economy and labor market reduce efficiency. However, female leadership of the government and greater compliance with the rule of law offer more efficient management, as do countries that derive more revenues from tourism.

CONCLUSIONS

These results provide an opportunity for political leaders to reflect on their management during these months of the pandemic in order to identify mistakes and improve the implementation of effective measures. It has been shown that using more resources does not mean managing better; therefore, policymakers need to pay special attention to the use of resources, taking into account the budgetary constraints of the public sector.

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

In efforts to control disease, mathematical models and numerical targets play a key role. We take the elimination of a viral infection as a case for exploring mathematical models as 'evidence-making interventions'. Using interviews with mathematical modellers and implementation scientists, and focusing on the emergence of models of 'treatment-as-prevention' in hepatitis C control, we trace how projections detach from their calculative origins as social and policy practices. Drawing on the work of Michel Callon and others, we show that modelled projections of viral elimination circulate as 'qualculations', taking flight via their affects, including as anticipation. Modelled numerical targets do not need 'actual numbers' or precise measurements to perform their authority as evidence of viral elimination or as situated matters-of-concern. Modellers grapple with the ways that their models transform in policy and social practices, apparently beyond reasonable calculus. We highlight how practices of 'holding-on' to projections in relation to imaginaries of 'evidence-based' science entangle with the 'letting-go' of models beyond calculus. We conclude that the 'virtual precision' of models affords them fluid evidence-making potential. We imagine a different mode of modelling science in health, one more attuned to treating projections as qualculative, affective and relational, as excitable matter.

Coping with the Challenges of COVID-19 Using the Sociotype Framework: A Rehearsal for the Next Pandemic

The world, as a global village, is currently taking part in a real-time public health, medical, socio-cultural, and economic experiment on how best to combat the COVID-19 pandemic. Extraordinary times demand extraordinary measures. Depending on the time from the outbreak, strategies have ranged from minimal intervention to mitigation by quarantine for high-risk groups (elderly with chronic illnesses) to containment and lockdown. Adherence to such restrictions have depended on the individual and national psyche and culture. One can understand and forgive governments for being over-cautious, but not for being ill-prepared. It seems that Singapore after SARS (2003) and South Korea after MERS (2015) learnt from their experiences and have fared relatively well with minimal disruption to daily routines. Coping with the challenge of COVID-19 is an urgent global task. We use the Sociotype ecological framework to analyze different coping responses at three levels: Context (government and leadership, social context, health services, and media); Relationships; and the Individual. We describe the many negative outcomes (e.g. mortality [obviously], unemployment, economic damage, food insecurity, threat to democracy, claustrophobia) and the positive ones (e.g. new, remote teaching, working, and medical routines; social bonding and solidarity; redefining existential values and priorities) of this surreal situation, which is still evolving. We highlight the importance of humor in stress reduction. Regular and reliable communication to the public has to be improved, acknowledging incomplete data, and learning to deal with fake news, misinformation, and conspiracy theories. Excess mortality is the preferred statistic to follow and compare outcomes. When the health risks are over, the economic recovery responses will vary according to the financial state of countries. If world order is to be reshaped, then a massive economic aid plan should be launched by the rich countries-akin to the Marshall plan after the Second World War. It should be led preferably by the USA and China. The results of the tradeoffs between health and economic lockdowns will only become apparent in the months to come. The experiences and lessons learned from this emergency should be used as a rehearsal for the next epi-/pandemic, which will surely take place in the foreseeable future.

Modelling the COVID-19 pandemic in context: an international participatory approach

The SARS-CoV-2 pandemic has had an unprecedented impact on multiple levels of society. Not only has the pandemic completely overwhelmed some health systems but it has also changed how scientific evidence is shared and increased the pace at which such evidence is published and consumed, by scientists, policymakers and the wider public. More significantly, the pandemic has created tremendous challenges for decision-makers, who have had to implement highly disruptive containment measures with very little empirical scientific evidence to support their decision-making process. Given this lack of data, predictive mathematical models have played an increasingly prominent role. In high-income countries, there is a long-standing history of established research groups advising policymakers, whereas a general lack of translational capacity has meant that mathematical models frequently remain inaccessible to policymakers in low-income and middle-income countries. Here, we describe a participatory approach to modelling that aims to circumvent this gap. Our approach involved the creation of an international group of infectious disease modellers and other public health experts, which culminated in the establishment of the COVID-19 Modelling (CoMo) Consortium. Here, we describe how the consortium was formed, the way it functions, the mathematical model used and, crucially, the high degree of engagement fostered between CoMo Consortium members and their respective local policymakers and ministries of health.

The Governance of UK Dairy Antibiotic Use: Industry-Led Policy in Action

This article analyses the progress made in the UK with regard to tackling antibiotic "misuse and overuse" in food-producing animals. Moving beyond statistical realities, the paper examines how the UK's industry-led policy approach is shaping practice. Using a multi-sited ethnography situated in Actor Network Theory and Callon's sociology of markets, the UK dairy supply chain policies and practices were studied. Findings reveal that dairy industry policies only partially address the complex network of people, animals, and the environment in which dairy antibiotics circulate. Antibiotic "misuse and overuse" in agriculture is far from a behavioural matter, with solely farmers and veterinarians to blame. Instead, antibiotic use in food animals is embedded in complex economic networks that constrain radical changes in dairy husbandry management and antibiotic use on farms. More attention toward the needs of the dairy supply chain actors and wider environmental considerations is essential to reduce the dairy sector's dependency on antibiotics and support transition toward responsible farming in the UK.

Understanding and managing uncertainty in health care: revisiting and advancing sociological contributions

In this collection we revisit the enduring phenomenon of uncertainty in health care, and demonstrate how it still offers coherence and significance as an analytic concept. Through empirical studies of contemporary examples of health care related uncertainties and their management, our collection explores the different ways in which uncertainty may be articulated, enacted and experienced. The papers address a diverse range of healthcare contexts - Alzheimer's disease, neonatal surgery, cardiovascular disease prevention, cancer, addiction (use of alcohol and other drugs during pregnancy), mental health/disorders and medical education - and many tackle issues of contemporary relevance, such as an ageing population, and novel medical interventions and their sequelae. These empirical papers are complemented by a further theoretical contribution, which considers the role of 'implicit normativity' in masking and containing potential ethical uncertainty. By mapping themes across the collection, in this introduction we present a number of core analytical strands: (1) conceptualising uncertainty; (2) intersections of uncertainty with aspects of care; (3) managing uncertainty; and (4) structural constraints, economic austerity and uncertainty work. We reflect on the methodological and theoretical stances used to think sociologically about uncertainty in health care, and the strengths, silences and gaps we observe in the collection. We conclude by considering the implications of the insights gained for 'synthesising certainty' in practice and for future research in this area.

Modelling the pandemic: attuning models to their contexts

The evidence produced in mathematical models plays a key role in shaping policy decisions in pandemics. A key question is therefore how well pandemic models relate to their implementation contexts. Drawing on the cases of Ebola and influenza, we map how sociological and anthropological research contributes in the modelling of pandemics to consider lessons for COVID-19. We show how models detach from their implementation contexts through their connections with global narratives of pandemic response, and how sociological and anthropological research can help to locate models differently. This potentiates multiple models of pandemic response attuned to their emerging situations in an iterative and adaptive science. We propose a more open approach to the modelling of pandemics which envisages the model as an intervention of deliberation in situations of evolving uncertainty. This challenges the 'business-as-usual' of evidence-based approaches in global health by accentuating all science, within and beyond pandemics, as 'emergent' and 'adaptive'.

Moving interdisciplinary science forward: integrating participatory modelling with mathematical modelling of zoonotic disease in Africa

This review outlines the benefits of using multiple approaches to improve model design and facilitate multidisciplinary research into infectious diseases, as well as showing and proposing practical examples of effective integration. It looks particularly at the benefits of using participatory research in conjunction with traditional modelling methods to potentially improve disease research, control and management. Integrated approaches can lead to more realistic mathematical models which in turn can assist with making policy decisions that reduce disease and benefit local people. The emergence, risk, spread and control of diseases are affected by many complex bio-physical, environmental and socio-economic factors. These include climate and environmental change, land-use variation, changes in population and people’s behaviour. The evidence base for this scoping review comes from the work of a consortium, with the aim of integrating modelling approaches traditionally used in epidemiological, ecological and development research. A total of five examples of the impacts of participatory research on the choice of model structure are presented. Example 1 focused on using participatory research as a tool to structure a model. Example 2 looks at identifying the most relevant parameters of the system. Example 3 concentrates on identifying the most relevant regime of the system (e.g., temporal stability or otherwise), Example 4 examines the feedbacks from mathematical models to guide participatory research and Example 5 goes beyond the so-far described two-way interplay between participatory and mathematical approaches to look at the integration of multiple methods and frameworks. This scoping review describes examples of best practice in the use of participatory methods, illustrating their potential to overcome disciplinary hurdles and promote multidisciplinary collaboration, with the aim of making models and their predictions more useful for decision-making and policy formulation.

Perspectives on econometric modelling to inform policy: a UK qualitative case study of minimum unit pricing of alcohol

Background: Novel policy interventions may lack evaluation-based evidence. Considerations to introduce minimum unit pricing (MUP) of alcohol in the UK were informed by econometric modelling (the ‘Sheffield model’). We aim to investigate policy stakeholders’ views of the utility of modelling studies for public health policy. Methods: In-depth qualitative interviews with 36 individuals involved in MUP policy debates (purposively sampled to include civil servants, politicians, academics, advocates and industry-related actors) were conducted and thematically analysed. Results: Interviewees felt familiar with modelling studies and often displayed detailed understandings of the Sheffield model. Despite this, many were uneasy about the extent to which the Sheffield model could be relied on for informing policymaking and preferred traditional evaluations. A tension was identified between this preference for post hoc evaluations and a desire for evidence derived from local data, with modelling seen to offer high external validity. MUP critics expressed concern that the Sheffield model did not adequately capture the ‘real life’ world of the alcohol market, which was conceptualized as a complex and, to some extent, inherently unpredictable system. Communication of modelling results was considered intrinsically difficult but presenting an appropriate picture of the uncertainties inherent in modelling was viewed as desirable. There was general enthusiasm for increased use of econometric modelling to inform future policymaking but an appreciation that such evidence should only form one input into the process. Conclusion: Modelling studies are valued by policymakers as they provide contextually relevant evidence for novel policies, but tensions exist with views of traditional evaluation-based evidence.