Abstract
Environmental epidemiology is the branch of epidemiology that pertains to the interaction of the disease with certain environmental factors. It studies hazards and conditions putative to pose imbalance to our health (disease, impairs, death), the burden of disease, the extent and limits of contamination, and the preventive steps that need to be taken.
A social network consists of a series of individuals or groups connected by links that represent some kind of relationship or interaction. The investigation of environmental social networks permits an evaluation of the influence that the connections between people have in the transmission of a given disease. The concept of neighborhood applied in social network analysis is illustrated based on graph theory, where adjacent points are nodes connected by a line and all the nodes that one point is connected with nearby nodes construct a neighborhood. In this chapter, a connection between social networks and epidemiology (especially in environmental and social aspect) would be introduced, introducing the spatial variability into the regional areas considering directional graphical models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Anderson, C., Wasserman, S., & Crouch, B. (1999). A p* primer: Logit models for social networks. Social Networks, 21, 37–66.
Berk, R. A., Bickel, P., Campbell, K., Keller-McNutly, S., Kelly, E., & Sacks, J. (2002). Workshop on statistical approaches for the evaluation of complex computer models. Statistical Science, 17, 173–192.
Berkman, L. F., & Kawachi, I. (2000). A historical framework for social epidemiology. In L. F. Berkman & I. Kawachi (Eds.), Social epidemiology (pp. 3–12). New York: Oxford University Press.
Bollobás, B. (2000). Modern graph theory. New York: Springer.
Bollobás, B. (2001). Random graphs (2nd ed.). Cambridge, UK: Cambridge University Press.
Bradford-Hill, A. (1965). The environment and disease: Association or causation? Proceedings of the Royal Society of Medicine, 58, 295–300.
Dale, M. R. T. (1999). Spatial pattern analysis in plant ecology. Cambridge, UK: Cambridge University Press.
Frank, O., & Strauss, D. (1986). Markov graphs. Journal of the American Statistical Association, 81, 832–842.
Greenland, S., & Brumback, B. (2002). An overview of relations among causal modelling methods. International Journal of Epidemiology, 31, 1030–1037.
Höfler, M. (2005). The Bradford Hill considerations on causality: A counterfactual perspective? Emerging Themes in Epidemiology, 2, 11.
Howick, J., Glasziou, P., & Aronson, J. K. (2009). The evolution of evidence hierarchies: What can Bradford Hill’s ‘guidelines for causation’ contribute? Journal of the Royal Society of Medicine, 102(5), 186–194.
Jain, A. K., Kheshgi, H. S., & Wobbles, D. J. (1997). Is there an imbalance in the global budget of bomb-produced radiocarbon? Journal of Geophysical Research, 102, 1327–1333.
Jansen, M. J. W. (1998). Prediction error through modeling concepts and uncertainty from basic data. Nutrient Cycling in Agroecosystems, 50, 247–253.
Jolly, A. M., Muth, S. Q., Wylie, J. L., & Potterat, J. J. (2001). Sexual networks and sexually transmitted infections: A tale of two cities. Journal of Urban Health, 78, 433–445.
Klepper, O. (1997). Multivariate aspects of model uncertainty analysis: Tools for sensitivity analysis and calibration. Ecological Modelling, 101, 1–13.
Morris, M. (2004). Overview of network survey designs. In M. Morris (Ed.), Network epidemiology: A handbook for survey design and data collection (pp. 8–21). Oxford, UK: Oxford University Press.
O’Neill, R. V. (1979). Natural variability as a source of error in model predictions. In G. S. Innis & R. V. O’Neill (Eds.), Systems analysis of ecosystems (pp. 23–32). Fairland, MD: International Co-operative.
Phillips, C. V., & Goodman, K. J. (2006). Causal criteria and counterfactuals; nothing more (or less) than scientific common sense? Emerging Themes in Epidemiology, 3, 5.
Reynolds, J. F., Hilbert, D. W., & Kemp, P. R. (1993). Scaling ecophysiology from the plant to the ecosystem: A conceptual framework. In J. R. Ehleringer & C. B. Field (Eds.), Scaling physiological processes: Leaf to globe (pp. 127–140). San Diego, CA: Academic Press.
Scherm, H., & van Bruggen, A. H. C. (1994). Global warming and nonlinear growth: How important are changes in average temperature? American Phytopathological Society, 84, 1380–1384.
Singh-Manoux, A., Clarke, P., & Marmort, M. (2002). Multiple measures of socioeconomic position and psychosocial health: Proximal and distal effects. International Journal of Epidemiology, 31(6), 1192–1199.
Thacker, S. B., Stroup, D. F., Parrish, R. G., Anderson, H. A. (1996). Surveillance in environmental public health: issues, systems and sources. Am J Public Health 86, 633–638.
Ward, A. C. (2009a). The role of causal criteria in causal inferences: Bradford Hill’s “aspects of association”. Epidemiological Perspectives and Innovations, 6, 2.
Ward, A. C. (2009b). The environment and disease: Association or causation? Medicine, Health Care and Philosophy, 12, 333–343.
Wasserman, S., & Pattison, P. (1996). Logit models and logistic regression for social networks: I. An introduction to Markov graphs and p*. Psychometrika, 61, 401–425.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media New York
About this chapter
Cite this chapter
Zimeras, S., Geronikolou, S. (2012). Social Networks in Environmental Epidemiology. In: Lazakidou, A. (eds) Virtual Communities, Social Networks and Collaboration. Annals of Information Systems, vol 15. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3634-8_13
Download citation
DOI: https://doi.org/10.1007/978-1-4614-3634-8_13
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-3633-1
Online ISBN: 978-1-4614-3634-8
eBook Packages: Computer ScienceComputer Science (R0)