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

• Sexual activity and sexually active populations

• Transmission Dynamics
• Contact rates and activity groups
• Risk of Transmission

• Sexual mixing
• Demographic Stratification

6
Who do we model?

• We model the individuals who are
• currently sexually active

and able to contract the disease
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Sexually Active
We define the sexually active population age
range as

• The range in years in which an individual changes
  sexual partners more than once per year on average

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Sexually Active Ages

• Given this concept of sexual activity the age
  ranges for each model are

HPV 15-30
0
20
40
Age (years)
9
Transmission Dynamics
Contact Rates

• Modeling sexually transmitted diseases is similar
  to modeling other infectious diseases, they
  depend on

Population Mixing
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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
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Sexual Activity Groups
partner changes/year
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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
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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

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Demographic Stratification

• To accurately model geographic regions, we
  categorize the population further

Demographics
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Demographic Stratification
Low

• We have our three activity groups

Moderate
High
And we have our demographic parameters

• Now we combine
• a demographic trait
• the sexual activity classes
• to represent the

demographically stratified population
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Example Stratification

• HPV
• Age range 15-30 years
• Stratify at 5 year intervals
• Different contact rates can be assigned to each
  group

17
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

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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

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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

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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

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Application

• Our goal is to bridge the gap between the
  mathematical epidemiologists and professionals in
  industry and public health officials

We have developed a computer application
interface to this model, which simulates endemic
prevalence of a disease
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Application Interface

• Input parameters
• Disease
• Population
• Vaccine

Output Populations in each state over length
of simulation
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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

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Application Start Page
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Input Parameters
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Population Parameters
Denton County, 2000 U.S. Census Data
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
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Vaccine Parameters
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Application Output
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Population Graph Output
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Population Ratio Graph Output
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HPV Experiments
Vaccination Policy Male (M) Female (F)
Hughes, Garnett and Anderson Model Hughes, Garnett and Anderson Model 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 0.020 0.030 0.035 0.037 0.033 0.036 0.039 0.020 0.027 0.037 0.038 0.035 0.036
Temporal Model Temporal Model Temporal Model
None M F F Ages 15-19 M F Ages 15-19 F Ages 20-24 M F Ages 20-24 F Ages 25-29 M F Ages 25-29 F 0.047 0.014 0.033 0.025 0.038 0.029 0.040 0.038 0.044 0.050 0.015 0.025 0.026 0.033 0.031 0.036 0.040 0.043
Proportion of population with sustained
infection
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Results

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

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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

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Conclusion

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

36
References
Thank You!

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• 2002.
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