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Über dieses Buch

This book analyses the development and use of mathematical models in public health research and policy. By introducing a life cycle metaphor, the author provides a unique perspective on how mathematical modelling techniques have increased our understanding of the governance of infectious risks in society.



1. Introduction: Life-Cycles of Models

The use of mathematical models has increased and become more common in public health, recently. Along with monitoring activities, public health officials work with modellers in order to organise and analyse surveillance data, for example. In order to learn of the severity and spread of a pandemic outbreak, we can study different predictive scenarios. Mansnerus introduces us to the lives of models in both public health and other fields. She discusses how modelling has gained a dominance in current scientific research and policy-making.

Erika Mansnerus

2. Models and the Stories They Tell Us

Models tell stories that can answer vital questions. Models have become essential tools for epidemiological research. How well they address policy-driven questions in public health is explained. In epidemiological research, the mathematical predecessors for current computer-based modelling techniques have a long history. They aimed at policy advice by providing mathematical representations of infectious patterns in populations.Mansnerus analyses the early development of probability theory, showing why modelling became beneficial in governing infectious risks. How models are tailored to meet policy needs is revealed through an analysis of interdisciplinary collaboration. Models and mathematical techniques become accessible for us when we see them through the metaphor of storytelling: the transmission of an infection is presented to exemplify this.

Erika Mansnerus

3. Kinship Relations of Models

In order to show how models are interconnected, Mansnerus focuses on a family of models, built in Helsinki in the 1990s. This interconnectedness has a broader reach, which can be conceptualised as ‘kinship relations’, a notion initially used by Hoover (1991) in relation to economic models. Mansnerus argues that the interconnectedness of models shows how modelling methods, parameter values and estimates, and model-based evidence are stored and disseminated within and across research communities. By understanding the evolving relations of models, the nature of model-based evidence on its journey through public health research networks is made clear.

Erika Mansnerus

4. Working Lives of Models

When models live their lives, they grow up and enter the working life. They leave behind the sheltered world of research where they serve as scientific instruments and measuring devices. They enter a new domain of use, and are no longer close to the modellers, researchers or instrument makers; rather, they stand on their own to give evidence for policy.Mansnerus uses this metaphor of the working life to show how modelling played a pioneering role in public health research in the United Kingdom through a case study on predicting a measles outbreak in the 1994. She analyses the wise use of modelling methods and how they can support a preventive vaccination campaign.

Erika Mansnerus

5. Encounters with Risks

Models can be built upon the available data from previous pandemics; thus, the past is modelled in order to predict the future. Modelling and simulation techniques were widely used during and prior to the 2009 ‘Swine flu’ outbreak. Pre-pandemic modelling became a way to encounter possible pandemic risks and to assess effective mitigation strategies. Mansnerus defines two types of model-based predictions: explanation-based and scenario-building. By discussing their reliability, she highlights what kinds of limitations modelling techniques face.

Erika Mansnerus

6. When Evidence Is Silent

During the 2009 pandemic outbreak, modelling took place in a time-pressured reality. A sense of urgency to make decisions was challenged by the lack of data. Evidence was silent and weak, and modellers did their best to bridge the gaps. In this chapter we analyse these ‘known unknowns’, factors of which we have very limited understanding at the beginning of modelling. These factors can be related to the microbiology of the pathogen or to the safety of the pharmaceutical interventions, for example. Mansnerus looks at how modelling methods alleviate unknowing in the context of pandemic risk assessment.

Erika Mansnerus

7. Governing by Numbers

How do numbers govern the world? How is the authority of computational techniques shaped? Model-based evidence is an important part of the whole body of evidence upon which pandemic predictions or vaccination strategies are based. Models, when functioning as an evidence-base, turn into instruments of governance. Their authority is likely to make us believe in the numerical representations they produce. They act as senior experts that guide and govern public health.Mansnerus argues that models, along with other measurement and assessment tools form a techne of governance (the technical rationality of governance). Models, in this sense, are recontextualised, and their use in policy-making processes is brought to the centre of the analysis. A case study on the use of modelling techniques to improve animal health illustrates this.

Erika Mansnerus

8. Lives of Models in the World of Policy

When models gain seniority as experts in public health, their lives become intertwined with ours. They provide estimates and predictions, turning them into action. Mansnerus argues that this can lead to the renewal of vaccination policies or improvement in animal health. Policy needs are simplified, and the model-based evidence faces a heterogeneity in which their recommendations may be resisted and disputed. Mansnerus concludes by discussing the critical voices that warn us about overreliance on model-based evidence, and the supportive ones that remind us of their beneficial use. We use models to overcome the ethical and financial restrictions we face when making sense of infectious risks that affect us all.

Erika Mansnerus


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