Skip to main content

Social Networks in Environmental Epidemiology

  • Chapter
  • First Online:
Virtual Communities, Social Networks and Collaboration

Part of the book series: Annals of Information Systems ((AOIS,volume 15))

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Anderson, C., Wasserman, S., & Crouch, B. (1999). A p* primer: Logit models for social networks. Social Networks, 21, 37–66.

    Article  Google Scholar 

  2. 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.

    Article  MathSciNet  MATH  Google Scholar 

  3. 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.

    Google Scholar 

  4. Bollobás, B. (2000). Modern graph theory. New York: Springer.

    Google Scholar 

  5. Bollobás, B. (2001). Random graphs (2nd ed.). Cambridge, UK: Cambridge University Press.

    Book  MATH  Google Scholar 

  6. Bradford-Hill, A. (1965). The environment and disease: Association or causation? Proceedings of the Royal Society of Medicine, 58, 295–300.

    Google Scholar 

  7. Dale, M. R. T. (1999). Spatial pattern analysis in plant ecology. Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  8. Frank, O., & Strauss, D. (1986). Markov graphs. Journal of the American Statistical Association, 81, 832–842.

    Article  MathSciNet  MATH  Google Scholar 

  9. Greenland, S., & Brumback, B. (2002). An overview of relations among causal modelling methods. International Journal of Epidemiology, 31, 1030–1037.

    Article  Google Scholar 

  10. Höfler, M. (2005). The Bradford Hill considerations on causality: A counterfactual perspective? Emerging Themes in Epidemiology, 2, 11.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  12. 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.

    Article  Google Scholar 

  13. Jansen, M. J. W. (1998). Prediction error through modeling concepts and uncertainty from basic data. Nutrient Cycling in Agroecosystems, 50, 247–253.

    Article  Google Scholar 

  14. 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.

    Article  Google Scholar 

  15. Klepper, O. (1997). Multivariate aspects of model uncertainty analysis: Tools for sensitivity analysis and calibration. Ecological Modelling, 101, 1–13.

    Article  Google Scholar 

  16. 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.

    Google Scholar 

  17. 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.

    Google Scholar 

  18. 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.

    Article  Google Scholar 

  19. 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.

    Google Scholar 

  20. 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.

    Google Scholar 

  21. 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.

    Article  Google Scholar 

  22. 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.

    Google Scholar 

  23. Ward, A. C. (2009a). The role of causal criteria in causal inferences: Bradford Hill’s “aspects of association”. Epidemiological Perspectives and Innovations, 6, 2.

    Article  Google Scholar 

  24. Ward, A. C. (2009b). The environment and disease: Association or causation? Medicine, Health Care and Philosophy, 12, 333–343.

    Article  Google Scholar 

  25. 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.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Stelios Zimeras .

Editor information

Editors and Affiliations

Rights and permissions

Reprints 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)

Publish with us

Policies and ethics