Disease Transmission Models for Public Health Decision-Making: Designing Intervention Strategies for Schistosoma japonicum

  • Edmund Y. W. Seto
  • Elizabeth J. Carlton
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 673)

Abstract

The purpose of infectious disease transmission modelling is often to understand the factors that are responsible for the persistence of transmission, the dynamics of the infection process and how best to control transmission. As such, there should be great potential to use mathematical models to routinely plan and evaluate disease control programs. In reality, there are many challenges that have precluded the practical use of disease models in this regard. One challenge relates to the mathematical complexity of the models, which has made it difficult for field workers and health officials to understand and use them. Another challenge is that, despite their mathematical complexity, models typically do not have sufficient structural complexity to consider many of the site-specific epidemiologic and disease control details that the practicing health official routinely considers. Moreover, most modelling studies have not been sufficiently explicit or exemplary in explaining how field data may be incorporated into the models to impact public health decision-making. In this chapter, we start with a classic model of schistosomiasis transmission and relate its key properties to the more detailed model of Schistosoma japonicum model presented in chapter by Remais and chapter by Spear and Hubbard. We then discuss how various controls (e.g., chemotherapy, snail control and sanitation) may be evaluated via the detailed model. We then demonstrate in a practical manner, using S. japonicum data from China, how field data may be incorporated to inform the practice of disease control. Finally, we present a new model structure that considers how heterogeneous populations are interconnected, which has particular relevance to understanding disease control and emergence in today’s highly mobile world.

Keywords

Migration Attenuation Trop Schistosomiasis Schistosoma 

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References

  1. 1.
    Woolhouse ME. On the application of mathematical models of schistosome transmission dynamics. I. Natural transmission. Acta Trop 1991; 49(4):241–270.CrossRefPubMedGoogle Scholar
  2. 2.
    Woolhouse ME. On the application of mathematical models of schistosome transmission dynamics. II. Control. Acta Trop 1992; 50(3):189–204.CrossRefPubMedGoogle Scholar
  3. 3.
    Barbour AD. Schistosomiasis. In: Anderson RM, ed. Population dynamics of infectious diseases. London: Chapman and Hall, 1982:180–208.Google Scholar
  4. 4.
    Cohen JE. Mathematical models of Schistosomiasis. Ann Rev Ecol Syst 1977; 8:209–233.CrossRefGoogle Scholar
  5. 5.
    Macdonald G. The dynamics of helminth infections, with special reference to schistosomes. Trans R Soc Trop Med Hyg 1965; 59(5):489–506.CrossRefPubMedGoogle Scholar
  6. 6.
    Chitsulo L, Engels D, Montresor A et al. The global status of Schistosomiasis and its control. Acta Trop 2000; 77(1):41–51.CrossRefPubMedGoogle Scholar
  7. 7.
    Spear RC, Seto E, Liang S et al. Factors influencing the transmission of Schistosoma japonicum in the mountains of Sichuan Province of China. Am J Trop Med Hyg 2004; 70(1):48–56.PubMedGoogle Scholar
  8. 8.
    Anderson RM, May RM. Helminth infections of humans: mathematical models, population dynamics and control. Adv Parasitol 1985; 24:1–101.CrossRefPubMedGoogle Scholar
  9. 9.
    Barbour AD. Macdonald’s model and the transmission of bilharzia. Trans R Soc Trop Med Hyg 1978; 72(1):6–15.CrossRefPubMedGoogle Scholar
  10. 10.
    Barbour AD. Modeling the transmission of schistosomiasis: an introductory view. Am J Trop Med Hyg 1996; 55(5 Suppl):135–143.PubMedGoogle Scholar
  11. 11.
    Pesigan TP, Hairston NG, Jauregui JJ et al. Studies on Schistosoma japonicum infection in the Philippines. 2. The molluscan host. Bull World Health Organ 1958; 18(4):481–578.PubMedGoogle Scholar
  12. 12.
    Seto EYW, Lee YJ, Liang S et al. Individual and village-level study of water contact patterns and Schistosoma japonicum infection in mountainous rural China. Trop Med and Int Health 2007; 12:1199–1209.CrossRefGoogle Scholar
  13. 13.
    Chen MG. Schistosoma japonicum and S. japonicum-like infections: epidemiology, clinical and pathological aspects. In: Jordan P, Webbe G, Sturrock RF, eds. Human Schistosomiasis. Wallingford: CAB International, 1993:237–270.Google Scholar
  14. 14.
    Liang S, Seto EYW, Remais JV et al. Environmental effects on parasitic disease transmission exemplified by schistosomiasis in western China. P Natl Acad Sci USA 2007; 104(17):7110–7115.CrossRefGoogle Scholar
  15. 15.
    Riley S, Carabin H, Marshall C et al. Estimating and modeling the dynamics of the intensity of infection with Schistosoma japonicum in villagers of leyte, Philippines. Part II: Intensity-specific transmission of S. japonicum. The schistosomiasis transmission and ecology project. Am J Trop Med Hyg 2005; 72(6):754–761.PubMedGoogle Scholar
  16. 16.
    Gurarie D, Seto EY. Connectivity sustains disease transmission in environments with low potential for endemicity: modelling schistosomiasis with hydrologic and social connectivities. J R Soc Interface 2008; Epub ahead of print.Google Scholar
  17. 17.
    Williams GM, Sleigh AC, Li Y et al. Mathematical modelling of Schistosomiasis japonica: comparison of control strategies in the People’s Republic of China. Acta Trop 2002; 82(2):253–262.PubMedGoogle Scholar
  18. 18.
    WHO. Preventive chemotherapy in human helminthiasis: coordinated use of anthelminthic drugs in control interventions: a manual for health professionals and programme managers. Geneva: WHO, 2006.Google Scholar
  19. 19.
    WHO. The control of schistosomiasis: Second report of the WHO expert committee. Geneva: WHO, 1993.Google Scholar
  20. 20.
    Zhou XN, Guo JG, Wu XH et al. Epidemiology of Schistosomiasis in the People’s Republic of China, 2004. Emerg Infect Dis 2007; 12(10):1470–1476.Google Scholar
  21. 21.
    Xu B, Gong P, Seto E et al. A spatial-temporal model for assessing the effects of intervillage connectivity in schistosomiasis transmission. Annals of the Association of American Geographers 2006; 96(1):31–46.CrossRefGoogle Scholar
  22. 22.
    Gurarie D, King CH. Heterogeneous model of schistosomiasis transmission and long-term control: the combined influence of spatial variation and age-dependent factors on optimal allocation of drug therapy. Parasitology 2005; 130:49–65.CrossRefPubMedGoogle Scholar

Copyright information

© Landes Bioscience and Springer Science+Business Media 2010

Authors and Affiliations

  • Edmund Y. W. Seto
    • 1
  • Elizabeth J. Carlton
    • 1
  1. 1.School of Public HealthUniversity of CaliforniaberkeleyUSA

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