Predicting Human Papilloma Virus Prevalence and Vaccine Policy Effectiveness

1. Predicting Human Papilloma Virus Prevalence and Vaccine Policy Effectiveness Courtney Corley Department of Computer Science University of North Texas

2. Human Papilloma Virus Sexually Transmitted Virus which can lead to cervical dysplasia (cancer). Found in 99.7% of all cervical cancers Types {16,18,31,45} account for 75% of cervical cancer

3. Human Papilloma Virus 80% of the sexually active adult population will contract HPV U.S. spent over $1.6 billion in treating symptoms of HPV U.S. estimates 13,000 cases of cervical cancer 2004 2005 $5-6 billion spent on screening tests such as pap smears. More than 5,000 will die from cervical cancer

4. HPV Vaccine Exciting news! Several candidate vaccines are in phase III testing with the FDA Drug companies are currently in licensing arbitration

5. Sexually Transmitted Disease Modeling • Transmission Dynamics • Contact rates and activity groups • Risk of Transmission • Sexual activity and sexually active populations • Sexual mixing • Demographic Stratification

6. Who do we model? We model the individuals who are currently sexually active and able to contract the disease

7. Sexually Active The range in years in which an individual changes sexual partners more than onceper year on average We define the sexually active population age range as:

8. 15 30 Sexually Active Ages Given this concept of sexual activitytheage rangesfor each model are: HPV 15-30 0 20 40 Age (years)

9. Transmission Dynamics Modeling sexually transmitted diseases is similar to modeling other infectious diseases, they depend on: Contact Rates Population Mixing

10. Contact Rates The contact-rateis the number of partner changes per year High We define three sexual activity groups by contact-rates: Moderate Low

11. Sexual Activity Groups [partner changes/year]

12. Risk of Transmission The risk of transmission is based on two factors: The risk of transmission in one sexual encounter The average number of sexual encounters with one partner

13. Relative Risk of Transmission The average is taken to determine the relative risk for HPV infection: HPV • Male-to-Female 80% • Female-to-Male 70%

14. Age Race Demographic Stratification To accurately model geographic regions, we categorize the population further: Demographics

15. Age Race Demographic Stratification We have our three activity groups: Low Moderate High And we have our demographic parameters • Now we combine: • a demographic trait • the sexual activity classes • to represent the demographically stratified population

16. 15-19 20-24 25-29 8 9 9.5 2.5 3.5 3 1 1.25 1.5 Example Stratification HPV • Age range 15-30 years • Stratify at 5 year intervals • Different contact rates can be assigned to each group

17. 15-19 20-24 25-29 9 9.5 8 3 2.5 3.5 1.25 1.5 1 Population Interaction A contact can take place between an individual in a subgroup {demographic, sexual activity class} and an individual • In the same subgroup or • In a different subgroup Consider our HPV population example:

18. 15-19 20-24 25-29 9 9.5 8 3 2.5 3.5 1.25 1.5 1 Population Interaction Example A 23 year old male in the moderate activity class will make 3 contacts per year This is an example of where the contacts could occur

19. So far . . . • Sexual Activity Classes • Demographic Stratification • Transmission Dynamics • Contact Rates • Population Interaction

20. Population States Now, we need to keep track of • Who is susceptible to the disease • Who has the disease and is infectious • Who has recovered from the disease Also for HPV • Who has been Vaccinated • Who has the disease and been vaccinated, Vaccinated Infectious

21. Total Sexually Active Population Susceptible Vaccinated Infectious Vaccinated Infectious Recovered HPV Note: A constant population is maintained. Every year/update in the model a proportion of the population • Enters or ages-in as susceptibles • Leaves or ages-out

22. Our goal is to bridge the gap between the mathematical epidemiologists and professionals in industry and public health officials Application We have developed a computer application interface to this model, which simulates endemic prevalence of a disease

23. Application Interface Input parameters: • Disease • Population • Vaccine Output: Populations in each state over length of simulation

24. HPV Application Demo The following parameters are used in this demo: • Age range 15-30, 5 year group interval • Sexual activity classes of low, moderate and high • Denton County, TX population data from the 2000 U.S. Census • 75% vaccine efficacy • 90% vaccine coverage • Vaccine is effective for 10 years

25. Application Start Page

26. Input Parameters

27. Population Parameters Denton County, 2000 U.S. Census Data

28. Vaccine Parameters

29. Application Output

30. Population Graph Output

31. Population Ratio Graph Output

32. HPV Experiments Proportion of population with sustained infection

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

34. 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. Conclusion Modeling these diseases with this application will maximize resource allocation and utilization in the community or population where it is most needed

36. 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. References Thank You!