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Predicting Human Papilloma Virus Prevalence and Vaccine Policy Effectiveness

BIBE 2005 October 21, 2005. Motivation. Why is a computer scientist interested in the spread of diseases?Infection dynamics in complex networks Bridge the gap between the public health domain and mathematical epidemiologists. BIBE 2005 October 21, 2005. Human Papilloma Virus. Sexually Transmitted Virus which can lead to cervical dysplasia (cancer)..

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Predicting Human Papilloma Virus Prevalence and Vaccine Policy Effectiveness

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    1. BIBE 2005 October 21, 2005 Predicting Human Papilloma Virus Prevalence and Vaccine Policy Effectiveness

    2. BIBE 2005 October 21, 2005 Motivation Why is a computer scientist interested in the spread of diseases? Infection dynamics in complex networks Bridge the gap between the public health domain and mathematical epidemiologists

    3. BIBE 2005 October 21, 2005 Human Papilloma Virus Sexually Transmitted Virus which can lead to cervical dysplasia (cancer).

    4. BIBE 2005 October 21, 2005 Human Papilloma Virus Exciting news!

    5. BIBE 2005 October 21, 2005 Modeling Epidemics Let ß be the transmission rate based on contact rate and infectivity Let ? be the rate of infectives becoming non-infectious Susceptibles- can be infected Infectives – are infectious Removed- incapable of transmitting disease

    6. BIBE 2005 October 21, 2005 Sexually Transmitted Disease Modeling Transmission Dynamics Contact rates and activity groups Risk of Transmission

    7. BIBE 2005 October 21, 2005 HPV

    8. BIBE 2005 October 21, 2005 Who do we model? We model the individuals who are currently sexually active (?) and able to contract the disease

    9. BIBE 2005 October 21, 2005 Transmission Dynamics Modeling sexually transmitted diseases is similar to modeling other infectious diseases, they depend on:

    10. BIBE 2005 October 21, 2005 Risk of Transmission The risk of transmission (ß) is based on two factors:

    11. BIBE 2005 October 21, 2005 Demographic Stratification To accurately model geographic regions, we categorize the population further:

    12. BIBE 2005 October 21, 2005 Example Stratification

    13. BIBE 2005 October 21, 2005 Population Interaction Consider our HPV population example:

    14. BIBE 2005 October 21, 2005 Population Interaction Example Consider this sample interaction matrix

    15. BIBE 2005 October 21, 2005 Where ? is the infectivity of gender k and sub-group l More about mixing and infection

    16. BIBE 2005 October 21, 2005 So far . . . Sexual Activity Classes Demographic Stratification Transmission Dynamics Contact Rates Population Interaction

    17. BIBE 2005 October 21, 2005 Population States Now, we need to keep track of

    18. BIBE 2005 October 21, 2005

    19. BIBE 2005 October 21, 2005

    20. BIBE 2005 October 21, 2005

    21. BIBE 2005 October 21, 2005

    22. BIBE 2005 October 21, 2005 Application Our goal is to bridge the gap between the mathematical epidemiologists and professionals in industry and public health officials

    23. BIBE 2005 October 21, 2005 Application Interface

    24. BIBE 2005 October 21, 2005 HPV Application Demo The following parameters are used in this demo:

    25. BIBE 2005 October 21, 2005 Application Start Page

    26. BIBE 2005 October 21, 2005 Input Parameters

    27. BIBE 2005 October 21, 2005 Population Parameters

    28. BIBE 2005 October 21, 2005 Vaccine Parameters

    29. BIBE 2005 October 21, 2005 Application Output

    30. BIBE 2005 October 21, 2005 Population Graph Output

    31. BIBE 2005 October 21, 2005 Population Ratio Graph Output

    32. BIBE 2005 October 21, 2005 HPV Experiments

    33. BIBE 2005 October 21, 2005 Results Qualitative assessment: Denton County would have a larger benefit in starting vaccination at age (15-19) than vaccinating high-risk minorities

    34. BIBE 2005 October 21, 2005 Related Material Our paper currently in review with the model description in the appendix: http://cerl.unt.edu/~corley/pub/corley.ieee.bibe.2005.pdf link to the web-application demo http://cerl.unt.edu/~corley/hpv

    35. BIBE 2005 October 21, 2005 Conclusion Modeling these diseases with this application will maximize resource allocation and utilization in the community or population where it is most needed

    36. BIBE 2005 October 21, 2005 References J. Hughes and G. Garnett and L. Koutsky. The Theoretical Population-Level Impact of a Prophylactic Human Papilloma Virus Vaccine. Epidemiology, 13(6):631–639, November 2002. N. Bailey. The Mathematical Theory of Epidemics. Hafner Publishing Company, NY, USA, 1957. R. Anderson and G. Garnett. Mathematical Models of the Transmission and Control of Sexually Transmitted Diseases. Sexually Transmitted Diseases, 27(10):636–643, November 2000. S. Goldie and M. Kohli and D. Grima. Projected Clinical Benefits and Cost-effectiveness of a Human Papillomavirus 16/18 Vaccine. National Cancer Institute, 96(8):604–615, April 2004. The Youth Risk Behaviour Website, Centers for Disease Control and Prevention, 2005. http://www.cdc.gov/HealthyYouth/yrbs M. Katz and J. Gerberding. Postexposure Treatment of People Exposed to the Human Immunodeficiency Virus through Sexual Contact or Injection-Drug Use. New England Journal of Medicine, 336:1097-1100, April 1997. Youth Risk Behaviour Surveillance: National College Health Risk Behaviour Survey, Centers for Disease Control and Prevention, 1995. D. Heymann and G. Rodier. Global Surveillance, National Surveillance, and SARS. Emerging Infectious Diseases, 10(2), February 2004. E. Allman and J. Rhodes. Mathematical Models in Biology: An Introduction. Cambridge University Press, 2004. G. Garnett and R. Anderson. Contact Tracing and the Estimation of Sexual Mixing Patterns: The Epidemiology of Gonococcal Infections. Sexually Transmitted Diseases, 20(4):181–191, July-August 1993. G. Sanders and A. Taira. Cost Effectiveness of a Potential Vaccine for Human Papillomavirus. Emerging Infectious Diseases, 9(1):37–48, January 2003. J. Aron. Mathematical Modelling: The Dynamics of Infection, chapter 6. Aspen Publishers, Gaithersburg, MD, 2000.

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