HIV/AIDS in the Houston Community
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Transcript of HIV/AIDS in the Houston Community
HIV/AIDS in the Houston HIV/AIDS in the Houston CommunityCommunity
Rukiya MiddletonRukiya MiddletonChelscie IrbyChelscie IrbyLoren RunnelsLoren RunnelsApril 28, 2005April 28, 2005
AIDS Cases 1989AIDS Cases 1989
AIDS Cases 1997AIDS Cases 1997
Houston BackgroundHouston Background
HIV/AIDS has killed more than 58,000 HIV/AIDS has killed more than 58,000 HoustoniansHoustoniansAfrican Americans currently consist of more African Americans currently consist of more than 50% of the total AIDS populationthan 50% of the total AIDS populationAfrican American women consist of 75% of African American women consist of 75% of the current female AIDS populationthe current female AIDS population
Background DataBackground DataAwareness has decreased AIDS in 30 year olds however Awareness has decreased AIDS in 30 year olds however
teenagers have a constant number of AIDS cases per yearteenagers have a constant number of AIDS cases per year
AIDS Cases by Gender AIDS Cases by Gender
AIDS Cases by RaceAIDS Cases by Race
Blacks Dominate the AIDS Blacks Dominate the AIDS PopulationPopulation
Prevention BackgroundPrevention Background
Most of the AIDS problem comes from Most of the AIDS problem comes from people with many sexual partnerspeople with many sexual partnersEven married people may contract Even married people may contract HIV/AIDS if one spouse continues to have HIV/AIDS if one spouse continues to have multiple sexual partners.multiple sexual partners.Black females think that they are not at Black females think that they are not at riskrisk
GoalsGoals
Our specific aims are two fold: Fit the model data to HIV/AIDS Surveillance Data Use this deterministic model fit with a backtracking
method to predict future HIV/AIDS dynamics
Also we will: Motivate social policy as a valuable method for
controlling HIV/AIDS transmission Bring AWARENESS to the African American
community who are disproportionately infected with HIV/AIDS
HIV/AIDS LiteratureParameter Estimate
Parametersi.e. infection rate
Transmission Model(Differential Equations)
Solve UsingForward EulerPredicted Populations Least Squares Fit
Of HIV/AIDS Data
Loop until match model vs. reported values
MethodMethod
This chart represents steps taken to This chart represents steps taken to compose AIDS modelcompose AIDS model
HIV/AIDS LiteratureHIV/AIDS Literature
Government websites i.e. CDC, Government websites i.e. CDC, Department of Health, National Health Department of Health, National Health InstituteInstitutePeer-review journals i.e. AIDS, JAMAPeer-review journals i.e. AIDS, JAMA Pubmed websitePubmed website
Health and Human Services Department Health and Human Services Department in Houstonin Houston
Components of Our ModelComponents of Our Model
ParametersParameters Infection rateInfection rate Death rateDeath rate Birth rateBirth rate Migration rateMigration rate
PoulationsPoulations RaceRace GenderGender Sexual PreferenceSexual Preference
Transmission ModelTransmission Model
Differential equations are just rate of changeDifferential equations are just rate of changeThey can predict future population changesThey can predict future population changesWe solve the equation using Forward EulerWe solve the equation using Forward Euler
]pop[*deathrate
[infected]*infected]non[*ateinfectionr]move_in[]infected[]infected[*]infectednon[*ateinfectionr]pop[*deathrate
]pop[*birthratedtinfected]-d[non
dtd
movein
Forward EulerForward Euler
Uses the slope to determine the next Uses the slope to determine the next year’s populationyear’s population
MatlabMatlab
Forward Euler implemented in MatlabForward Euler implemented in MatlabMatlab is a mathematical programming Matlab is a mathematical programming tool tool We wrote a program in Matlab that We wrote a program in Matlab that predicted future AIDS populationspredicted future AIDS populations
Least Squares FitLeast Squares Fit
Compare the results of the model for Compare the results of the model for each year to the actual dataeach year to the actual dataAdjust the parameters to better fit the Adjust the parameters to better fit the model results to the actual results model results to the actual results
ResultsResultsFigure 1: PREVALENCE of HIV/AIDS. Number of persons
living with HIV/AIDS by race in Harris County (1986-1991).
Results, cont.Results, cont.Figure 2: INCIDENCE of HIV/AIDS. Number of cases diagnosed
per year of HIV/AIDS in Harris County (1992-2001).
Future WorkFuture Work
Fit our model to prevalence data Predict future population dynamics Infer possible policy changes to best deal with HIV/AIDS Increase educationIncrease education Increase access to protectionIncrease access to protection Increase abstinence awarenessIncrease abstinence awareness
BibliographyBibliography
HIV/AIDS Surveillance, (March 31, 2005), http://www.cdc.gov/hiv/stats/hasrsupp.htmAIDS Public Information Data Set, (March 14, 2005), http://www.cdc.gov/hiv/software/apids/apidsman.htmModeling HIV Transmission and AIDS in the United States, By Herbert W. Hethcote and James W. Van Ark http://biotech.law.lsu.edu/cphl/Models/aids/
The road to researchThe road to researchEssay applicationEssay applicationWeekly instruction-1Weekly instruction-1stst Semester Semester Introduced to Game TheoryIntroduced to Game Theory Introduced to MatlabIntroduced to Matlab Read chapters from book on Game Theory Read chapters from book on Game Theory Bi-weekly instruction-2Bi-weekly instruction-2ndnd Semester Semester Decided on research topic (AIDS) Decided on research topic (AIDS) Found source material onlineFound source material online Wrote computer program to simulate the Wrote computer program to simulate the
spread of AIDS in Houstonspread of AIDS in Houston Made and competed in a poster session with Made and competed in a poster session with
Rice college studentsRice college students
PayoffPayoff
The Worthing/Rice experienceThe Worthing/Rice experience College experience College experience Increased awareness of HIV/AIDSIncreased awareness of HIV/AIDS Learned better research techniquesLearned better research techniques
Unexpected BenefitsUnexpected Benefits Resume buildingResume building Potential scholarshipsPotential scholarships Working with college studentsWorking with college students
AcknowledgementsAcknowledgements
We would like to give special thanks to :We would like to give special thanks to :Ms. Golia HowardMs. Golia HowardDr. Steve CoxDr. Steve CoxAlso like to thank:Also like to thank:Fernando AcostaFernando AcostaJay RaolJay RaolNithin RajanNithin RajanZach KilpatrickZach KilpatrickAndre T. MosleyAndre T. MosleyFinally, Thanks to Our ParentsFinally, Thanks to Our Parents