Predicting Human Papilloma Virus Prevalence and Vaccine Policy Effectiveness Courtney Corley Department of Computer Science University of North Texas June 27, 2005 Human Papilloma Virus Sexually Transmitted Virus which can lead to cervical dysplasia (cancer). Found in 99.7% of all cervical cancers June 27, 2005 Types {16,18,31,45} account for 75% of cervical cancer 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 June 27, 2005 $5-6 billion spent on screening tests such as pap smears. More than 5,000 will die from cervical cancer HPV Vaccine Exciting news! Several candidate vaccines are in phase III testing with the FDA Drug companies are currently in licensing arbitration June 27, 2005 Sexually Transmitted Disease Modeling Sexual activity and sexually active populations Transmission Dynamics • Contact rates and activity groups • Risk of Transmission Sexual mixing • Demographic Stratification June 27, 2005 Who do we model? We model the individuals who are currently sexually active and able to contract the disease June 27, 2005 Sexually Active We define the sexually active population age range as: June 27, 2005 The range in years in which an individual changes sexual partners more than once per year on average Sexually Active Ages Given this concept of sexual activity the age ranges for each model are: HPV 15-30 15 0 30 20 June 27, 2005 40 Age (years) Transmission Dynamics Modeling sexually transmitted diseases is similar to modeling other infectious diseases, they depend on: June 27, 2005 Contact Rates Population Mixing Contact Rates The contact-rate is the number of partner changes per year High We define three sexual activity groups by contact-rates: Moderate Low June 27, 2005 Sexual Activity Groups Population 100 100 80 60 High Moderate Low People 40 20 0 1.5 3 9 Contact Rate June 27, 2005 [partner changes/year] Risk of Transmission The risk of transmission is based on two factors: The risk of transmission in one sexual encounter June 27, 2005 The average number of sexual encounters with one partner Relative Risk of Transmission The average is taken to determine the relative risk for HPV infection: HPV Male-to-Female Female-to-Male June 27, 2005 80% 70% Demographic Stratification To accurately model geographic regions, we categorize the population further: Demographics Age June 27, 2005 Race Demographic Stratification We have our three activity groups: Low Moderate High And we have our demographic parameters Now we combine: Age Race • a demographic trait • the sexual activity classes to represent the June 27, 2005 demographically stratified population Example Stratification HPV Age range 15-30 years Stratify at 5 year intervals Different contact rates can be assigned to each group 15-19 20-24 8 25-29 9 2.5 9.5 3.5 3 June 27, 2005 1 1.25 1.5 Population Interaction A contact can take place between an individual in a subgroup {demographic, sexual activity class} and an individual or In the same subgroup In a different subgroup Consider our HPV population example: 15-19 20-24 9 8 1 9.5 3 2.5 June 27, 2005 25-29 1.25 3.5 1.5 Population Interaction Example A 23 year old male in the moderate activity class will make 3 contacts per year 15-19 20-24 9 8 This is an example of where the contacts could occur June 27, 2005 9.5 3 2.5 1 25-29 1.25 3.5 1.5 So far . . . Sexual Activity Classes Demographic Stratification Transmission Dynamics • Contact Rates • Population Interaction June 27, 2005 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 June 27, 2005 HPV Total Sexually Active Population Susceptible Vaccinated Infectious Vaccinated Infectious Recovered Note: A constant population is maintained. Every year/update in the model a proportion of the population June 27, 2005 Enters or ages-in as susceptibles Leaves or ages-out Application Our goal is to bridge the gap between the mathematical epidemiologists and professionals in industry and public health officials June 27, 2005 We have developed a computer application interface to this model, which simulates endemic prevalence of a disease Application Interface Input parameters: Disease Population Vaccine Output: Populations in each state over length of simulation June 27, 2005 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 June 27, 2005 Application Start Page June 27, 2005 Input Parameters June 27, 2005 Population Parameters Denton County, 2000 U.S. Census Data June 27, 2005 15-19 20-24 25-29 Total Males 15,923 17,106 19,237 52,266 Females 15,579 18,478 19,193 53,250 Vaccine Parameters June 27, 2005 Application Output June 27, 2005 Population Graph Output June 27, 2005 Population Ratio Graph Output June 27, 2005 HPV Experiments Vaccination Policy Male (M) Female (F) Hughes, Garnett and Anderson Model None M&F F High-risk M & F High-risk F Spread targeting M & F Spread targeting F 0.038 0.039 0.020 0.030 0.020 0.027 0.035 0.037 0.037 0.033 0.036 0.038 0.035 0.036 0.047 0.050 0.014 0.033 0.015 0.025 0.025 0.026 0.038 0.029 0.040 0.038 0.044 0.033 0.031 0.036 0.040 0.043 Temporal Model None M&F F Ages 15-19 Ages 15-19 Ages 20-24 Ages 20-24 Ages 25-29 Ages 25-29 June 27, 2005 M&F F M&F F M&F F Proportion of population with sustained infection Results Qualitative assessment: Denton County would have a larger benefit in starting vaccination at age (15-19) than vaccinating high-risk minorities June 27, 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 June 27, 2005 Conclusion Modeling these diseases with this application will maximize resource allocation and utilization in the community or population where it is most needed June 27, 2005 References Thank You! 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. June 27, 2005 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.