Advertisement

Patterns in Epidemiology of Sexually Transmitted Diseases in Human Populations

  • Masayuki Kakehashi

Abstract

Infectious diseases exhibit a lot of interesting patterns when they spread in host populations. Detailed data are especially available on infectious diseases in human populations. Many typical infectious diseases of childhood, like measles and rubella, show clear seasonality with period of one year [2, 7, 8, 9, 12]. In addition to such annual patterns, they also show periodicity with period more than one year, eg., two years, five years, etc. The period of the same disease may be different in different places and in some places there may be chaotic patterns rather than periodic patterns. Moving focus from such childhood diseases to those of adulthood or adolescents, sexually transmitted diseases (or STDs for short, hereafter) also have interesting patterns although they cannot be clearly observed like childhood diseases. In most studies of pattern formation, some interesting pattern is generated similar to a shadow of some other preceding pattern. In the analysis of STDs, we may find cases where a pattern of more interest exists behind the apparently observed pattern. By the analysis of patterns observed in incidences of STDs, we can find patterns of human sexual behavior, the detail of which is usually hidden although questionnaire surveys can sometimes shed light on it. Some patterns of STDs are common to infectious diseases of childhood. The growth of the number of infected people is exponential at the beginning phase of spread. Another typical pattern is one observed in incidences according to the age of hosts. We first briefly show descriptive epidemiology of STDs in Japan below. The data are collected on the outpatients who visited urological, dermatological, obstetric or gynecological clinics in 1999. After reviewing some observed patterns in epidemiology, we try to explain these patterns by mathematical models of infectious diseases.

Keywords

Infected Individual Interesting Pattern Genital Herpes Childhood Disease Descriptive Epidemiology 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Anderson, R. M. and Garnett, G. P. (2000). Mathematical models of the transmission and control of sexually transmitted diseases. Sexually Transmitted Diseases, 27 (10): 636–643.CrossRefGoogle Scholar
  2. 2.
    Balker, B. M. and Grenfell, B. T. (1993). Chaos and biological complexity in measles dynamics. Proc. R. Soc. Land. B 251 (1330): 75–81.CrossRefGoogle Scholar
  3. 3.
    Dietz, K. (1988). On the transmission dynamics of HIV. Math. Biosci. 90: 397–414.MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Dietz, K. (1988). The dynamics of spread of HIV infection in the heterosexual populations. In: Statistical Analysis and Mathematical Modelling of AIDS. (J. C. Jager and E. J. Ruitenberg, eds. ), Oxford University Press.Google Scholar
  5. 5.
    Garnett, G. P. and Anderson, R. M. (1993). Factors controlling the spread of HIV in heterosexual communities in developing countries: patterns of mixing between different age and sexual activity classes. Phil. Trans. R. Soc. Land. B 342: 137–159.CrossRefGoogle Scholar
  6. 6.
    Garnett, G. P., and Anderson, R. M. (1995). Strategies for limiting the spread of HIV in developing countries: conclusions based on studies of the transmission dynamics of the virus. Journal of Acquired Immune Deficiency Syndrome and Human Retrovirology 9: 500–513.Google Scholar
  7. 7.
    Grenfell, B. T. (1992). Chance and chaos in measles dynamics. Journal of Royal Statistical Society, B54: 383–398.Google Scholar
  8. 8.
    Grenfell, B. T. and Dobson, A. P. (1995). Ecology of Infectious Diseases in Natural Populations. Cambridge University Press.Google Scholar
  9. 9.
    Grenfell, B. T., Kleczkowski, A., Gilligan, C. A. and Bolker, B. M. (1995). Spatial heterogeneity, nonlinear dynamics and chaos in infectious diseases. Statistical Methods in Medical Research, 4 (2): 160–83.CrossRefGoogle Scholar
  10. 10.
    Kakehashi, M. (1998). A mathematical analysis of the spread of H1V/AIDS in Japan. IMA Journal of Mathematics Applied in Medicine and Biology 15: 299–311.MATHCrossRefGoogle Scholar
  11. 11.
    Kakehashi, M. (2000). Validity of simple pair formation model for HIV spread with realistic parameter setting. Mathematical Population. Studies 8 (3): 279–292.MathSciNetMATHCrossRefGoogle Scholar
  12. 12.
    Keeling, M. and Grenfell, B. T. (1999). Stochastic dynamics and a power law for measles variability. Phil. Trans. R. Soc. Land. B 354: 769–776.CrossRefGoogle Scholar
  13. 13.
    Sheldon, B. C. and Read, A. F. (1997). Comparative biology and disease ecology. Trends in Evolution and Ecology 12 (2): 43–44.CrossRefGoogle Scholar
  14. 14.
    Waldstätter, R. (1989). Pair formation in sexually-transmitted disease. In: Mathematical and Statistical Approaches to AIDS Epidemiology. (C. Castillo-Chavez, ed.) Lecture Notes in Biom.athematics 83. Springer-Verlag.Google Scholar
  15. 15.
    Williams, J. R. and Anderson, R. M. (1994). Mathematical models of the transmission dynamics of Human Immunodeficiency Virus in England and Wales: Mixing between different risk groups. Journal of Royal Statististical Society A 157: 69–87.CrossRefGoogle Scholar
  16. 16.
    Yamaguchi, F and Kakehashi, M. (2002). A prefectural index of the spread of sexually transmitted diseases and the related socioeconomic factors. Kosei no Shihyo. 49(10): 24–30 (In Japanese).Google Scholar

Copyright information

© Springer Japan 2003

Authors and Affiliations

  • Masayuki Kakehashi
    • 1
  1. 1.Faculty of MedicineHiroshima UniversityKasumi, Minami-ku, Hiroshima, HiroshimaJapan

Personalised recommendations