Understanding and Governing Public Health Risks by Modeling

Abstract

Increase in the use and development of computational tools to govern public health risks invites us to study their benefits and limitations. To analyze how risk is perceived and expressed through these tools is relevant to risk theory. This chapter clarifies the different concepts of risk, contrasting especially the mathematically expressed ones with culturally informed notions, which address a broader view on risk. I will suggest that a fruitful way to contextualize computational tools, such mathematical models in risk assessment is “analytics of risk,” which ties together the technological, epistemological, and political dimensions of the process of governance of risk. I will clarify the development of mathematical modeling techniques through their use in infectious disease epidemiology. Epidemiological modeling functions as a form of “risk calculation,” which provides predictions of the infectious outbreak in question. These calculations help direct and design preventive actions toward the health outcomes of populations. This chapter analyzes two cases in which modeling methods are used for explanation-based and scenario-building predictions in order to anticipate the risks of infections caused by Haemophilus influenzae type b bacteria and A(H1N1) pandemic influenza virus. I will address an interesting tension that arises when model-based estimates exemplify the population-level reasoning of public health risks but has restricted capacity to address risks on individual level. Analyzing this tension will lead to a fuller account to understand the benefits and limitations of computational tools in the governance of public health risks.

Keywords

Public Health Risk Risk Governance Haemophilus Influenzae Type Preparedness Planning Infectious Risk 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

I thank the British Academy for funding my research through Post Doctoral Fellowship at LSE Health, London School of Economics and Political Science. My research on risk and regulation was supported by an ESRC Post Doctoral Fellowship at the Centre for Analysis of Risk and Regulation (CARR), 2009. I was affiliated with Mellon Sawyer Seminar on Modelling Futures: Understanding Risk and Uncertainties at the Centre for Research in Arts, Social Sciences and Humanities, CRASSH, University of Cambridge, and a Visiting Fellow at Wolfson College, 2009–2010. I thank CRASSH for providing a dynamic research environment for me and Wolfson for hosting me.

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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.LSE Health and Social CareLondon School of EconomicsLondonUK

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