Transcript Sexually Transmitted Disease Modeling

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
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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
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$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
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Sexually Transmitted Disease Modeling


Sexual activity and sexually
active populations
Transmission Dynamics
• Contact rates and activity groups
• Risk of Transmission

Sexual mixing
• Demographic Stratification
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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:
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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
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40
Age (years)
Transmission Dynamics
Modeling sexually
transmitted diseases
is similar to
modeling other
infectious diseases,
they depend on:
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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
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Sexual Activity Groups
Population 100
100
80
60
High
Moderate
Low
People
40
20
0
1.5
3
9
Contact Rate
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[partner changes/year]
Risk of Transmission
The risk of transmission is based on two
factors:
The risk of
transmission in
one sexual
encounter
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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
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80%
70%
Demographic Stratification
To accurately model geographic regions, we
categorize the population further:
Demographics
Age
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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
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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
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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
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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
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9.5
3
2.5
1
25-29
1.25
3.5
1.5
So far . . .
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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
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

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
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
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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
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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
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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
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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
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
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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
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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
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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.
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N. Bailey. The Mathematical Theory of Epidemics.
Hafner Publishing Company, NY, USA, 1957.
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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.
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The Youth Risk Behaviour Website, Centers for
Disease Control and Prevention, 2005.
http://www.cdc.gov/HealthyYouth/yrbs
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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.
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Youth Risk Behaviour Surveillance: National College
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D. Heymann and G. Rodier. Global Surveillance,
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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
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1993.
G. Sanders and A. Taira. Cost Effectiveness of a
Potential Vaccine for Human Papillomavirus. Emerging
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J. Aron. Mathematical Modelling: The Dynamics of
Infection, chapter 6. Aspen Publishers, Gaithersburg,
MD, 2000.