Abstract

For infectious disease dynamical models to inform policy for containment of infectious diseases the models must be able to predict; however, it is well recognised that such prediction will never be perfect. Nevertheless, the consensus is that although models are uncertain, some may yet inform effective action. This assumes that the quality of a model can be ascertained in order to evaluate sufficiently model uncertainties, and to decide whether or not, or in what ways or under what conditions, the model should be ‘used’. We examined uncertainty in modelling, utilising a range of data: interviews with scientists, policy-makers and advisors, and analysis of policy documents, scientific publications and reports of major inquiries into key livestock epidemics. We show that the discourse of uncertainty in infectious disease models is multi-layered, flexible, contingent, embedded in context and plays a critical role in negotiating model credibility. We argue that usability and stability of a model is an outcome of the negotiation that occurs within the networks and discourses surrounding it. This negotiation employs a range of discursive devices that renders uncertainty in infectious disease modelling a plastic quality that is amenable to ‘interpretive flexibility’. The utility of models in the face of uncertainty is a function of this flexibility, the negotiation this allows, and the contexts in which model outputs are framed and interpreted in the decision making process. We contend that rather than being based predominantly on beliefs about quality, the usefulness and authority of a model may at times be primarily based on its functional status within the broad social and political environment in which it acts.

Highlights

  • Over the last few decades infectious disease dynamical modelling has attained a central role in contingency planning for outbreaks of human and livestock diseases and in guiding policy responses in the face of epidemics [1,2]

  • Study design The analysis presented in this paper draws on a 3-year interdisciplinary research programme addressing the social, technological and natural dynamics of animal disease management across a range of policy scales

  • This is for two reasons: (i) the transmission of infection is a stochastic process, such that no two epidemics are identical; (ii) models will always be an approximation, and rare or unforeseen behavioural events can have a significant impact on the disease dynamics [italics in original] [26]

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Summary

Introduction

Over the last few decades infectious disease dynamical modelling has attained a central role in contingency planning for outbreaks of human and livestock diseases and in guiding policy responses in the face of epidemics [1,2]. Such modelling activities may be commissioned by governments, or may be developed independently by researchers. A key feature of many of these epidemics is that they are emerging or re-emerging [3,4] and have limited or no precedents within the time and place of interest Such situations are simultaneously proposed as being ideally suited to the application of modelling and problematic for modelling. Whilst modelling may be proposed to provide clarity in complex and rapidly emerging situations [5], there is often no ‘‘off the shelf’’ model available, the system may be poorly defined and understood, input data may be limited, there is likely to be limited replicate data for validation and there may be unrealistic pressure for rapid results

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