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