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The Lives of ‘Facts’ in Mathematical Models: A Story of Population-level Disease Transmission of Haemophilus Influenzae Type B Bacteria

Published online by Cambridge University Press:  01 September 2009

Erika Mansnerus
Affiliation:
Centre for Analysis of Risk and Regulation (CARR), London School of Economics and Political Science, Houghton Street, London, WC2A 2AE, UK E-mail: e.mansnerus@lse.ac.uk
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Abstract

This article studies how our understanding of population-level disease transmission has evolved over time. The main question is: what happens to ‘facts’ in the course of their life history? The concept of life history captures the process that shapes the facts of disease transmission, mobilizes them via mathematical and graphical representations, and allows them to evolve and change over time. Hence, this concept provides continuity from knowledge production to utilization. Life history is developed through phases in the ‘lives’ of ‘facts’: birth and youth, adulthood and reproductive years, and old age. The life-history approach consists of a set of ‘facts’ binding together knowledge of a disease, its routes of transmission, and the susceptibility of the exposed population; it thus provides an adequate framework to explore the complex nature of population-level disease transmission. The analytical focus of this article is concerned with how these ‘facts’ are disseminated via model-based or mathematical representations. Just as life histories are stories full of interactions, surprises and struggles, this article highlights the underlying contingencies in the dissemination and accumulation of factual knowledge.

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Articles
Copyright
Copyright © London School of Economics and Political Science 2009

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References

Amsterdamska, O. (2001). Standardizing epidemics: Infection, inheritance and environment in pre-war experimental epidemiology. In Gaudillère, J.-P., & Löwy, I. (Eds), Heredity and infection: The history of disease transmission. London: Routledge.Google Scholar
Auranen, K. (1999). On Bayesian modelling of recurrent infections. Rolf Nevanlinna Research Report 26, University of Helsinki.Google Scholar
Auranen, K., Ranta, J., Takala, A., & Arjas, E. (1996). A statistical model of transmission of Hib bacteria in a family. Statistics in Medicine, 15, 22352252.3.0.CO;2-G>CrossRefGoogle ScholarPubMed
Auranen, K., Eichner, M., Käyhty, H., Takala, A., & Arjas, E. (1999). A hierarchical Bayesian model to predict the duration of immunity to Hib. Biometrics, 55(4), 13061314.CrossRefGoogle Scholar
Auranen, K., Eichner, M., Leino, T., Takala, A., Mäkelä, P. H., & Takala, T. (2004). Modelling transmission, immunity and disease of Haemophilus influenzae type b in a structured population. Epidemiology and Infection, 132, 947957.CrossRefGoogle Scholar
Becker, H.S. (2007). Telling about society. Chicago: U Chicago Press.CrossRefGoogle Scholar
Bynum, W.F. (2006). The Western medical tradition: 1800–2000. Cambridge: Cambridge UP.Google Scholar
Creager, A. (2002). The life of a virus. Chicago: U Chicago Press.Google Scholar
Daston, L. (2000). Introduction: The coming into being of scientific objects. In Daston, L. (Ed.), Biographies of scientific objects. Chicago: U Chicago Press.Google Scholar
Fine, P. (1979). John Brownlee and the measurement of infectiousness: A historical study in epidemic theory. Journal of the Royal Statistical Society. Series A (General), 142, 347362.CrossRefGoogle Scholar
Fine, P. (1993). Herd immunity: History, theory, practice. Epidemiologic Reviews, 15, 265302.CrossRefGoogle ScholarPubMed
Gaudillère, J.-P., & Löwy, I. (2001). Heredity and infection: The history of disease transmission. London: Routledge.Google Scholar
Giesecke, J. (2002). Modern infectious disease epidemiology, 2nd edn. London: Arnold.Google Scholar
Hamer, W.H. (1906). Epidemic disease in England. The Lancet i, 733739.Google Scholar
Kermack, W.O., & McKendrick, A.G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 115(772), 700–721.Google Scholar
Knorr Cetina, K. (1981). The manufacture of knowledge: An essay on the constructivist and contextual nature of science. Oxford: Pergamon Press.Google Scholar
Latour, B., & Woolgar, S. (1986 [1979]). Laboratory life: The social construction of scientific facts. London: SAGE.Google Scholar
Leino, T. (2003). Population immunity to Haemophilus influenzae type b—Before and after conjugate vaccines. Publications of the National Public Health Institute A23/2003. Helsinki: Hakapaino.Google Scholar
Leino, T., Auranen, K., Mäkelä, P.H., & Takala, A. (2000). Dynamics of natural immunity caused by subclinical infections: Case study on Haemophilus influenzae type b (Hib). Epidemiology and Infection, 125, 583591.CrossRefGoogle ScholarPubMed
Leino, T., Auranen, K., Mäkelä, P.H., Käyhty, H., Ramsey, M., Slack, M.et al. (2002). Haemophilus influenzae type b and cross-reactive antigens in natural Hib infection dynamics: Modelling in two populations. Epidemiology and Infection, 129, 7383.CrossRefGoogle ScholarPubMed
Leonelli, S. (forthcoming). Packaging data for re-use: Databases in model organism biology. In Morgan, M., & Howlett, W.P. (Eds), How well do ‘facts’ travel? Cambridge: Cambridge UP.Google Scholar
Mansnerus, E. (forthcoming). Using models to keep us healthy: Productive journeys of facts across public health networks. In Morgan, M., & Howlett, W.P. (Eds), How well do ‘facts’ travel? Cambridge: Cambridge UP.Google Scholar
Mattila, E. (2006a). Interdisciplinarity in the making: Modelling infectious diseases. Perspectives on Science: Historical, Philosophical, Sociological, 13, 531553.CrossRefGoogle Scholar
Mattila, E. (2006b). Questions to artificial nature: A philosophical study of interdisciplinary models and their functions in scientific practice. Philosophical Studies from the University of Helsinki, no. 14. Helsinki: Hakapaino.Google Scholar
Mattila, E. (2006c). Struggle between specificity and generality: How do infectious disease models become a simulation platform? Simulation: Pragmatic Constructions of Reality—Sociology of the Sciences Yearbook 25, 125138.CrossRefGoogle Scholar
Morgan, M. (forthcoming). ‘Introduction’. In Morgan, M., & Howlett, W.P. (Eds), How well do ‘facts’ travel? Cambridge: Cambridge UP.Google Scholar
Mendelsohn, A.J. (2003). Lives of the cell. Journal of the History of Biology, 36, 137.CrossRefGoogle ScholarPubMed
Pittman, M. (1931). Variation and type specificity in the bacterial species Haemophilus influenzae. Journal of Experimental Medicine, 53, 471492.CrossRefGoogle Scholar
Pittman, M. (1933). The action of type-specific Haemophilus influenzae antiserum. Journal of Experimental Medicine, 58, 683706.CrossRefGoogle ScholarPubMed
Rheinberger, H.-J. (2000). Cytoplasmic particles. In Daston, L. (Ed.), Biographies of scientific objects. Chicago: U Chicago Press.Google Scholar
Soper, H.E. (1929). The interpretation of periodicity in disease prevalence. Journal of Royal Statistical Society, 92, 3472.CrossRefGoogle Scholar
WHO (2006). Weekly epidemiological record. 24 November, 47(81), 445–452.Google Scholar
Wollstein, M. (1919). Pfeiffer’s bacillus and influenza: A serological study. Journal of Experimental Medicine, 30, 555568.CrossRefGoogle ScholarPubMed