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HIV/AIDS in the Houston Community

HIV/AIDS in the Houston Community

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HIV/AIDS in the Houston Community

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  1. HIV/AIDS in the Houston Community Rukiya Middleton Chelscie Irby Loren Runnels April 28, 2005

  2. AIDS Cases 1989

  3. AIDS Cases 1997

  4. Houston Background • HIV/AIDS has killed more than 58,000 Houstonians • African Americans currently consist of more than 50% of the total AIDS population • African American women consist of 75% of the current female AIDS population

  5. Background Data Awareness has decreased AIDS in 30 year olds however teenagers have a constant number of AIDS cases per year

  6. AIDS Cases by Gender

  7. AIDS Cases by Race

  8. Blacks Dominate the AIDS Population

  9. Prevention Background • Most of the AIDS problem comes from people with many sexual partners • Even married people may contract HIV/AIDS if one spouse continues to have multiple sexual partners. • Black females think that they are not at risk

  10. Goals • 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: • Motivatesocial policy as a valuable method for controlling HIV/AIDS transmission • Bring AWARENESS to the African American community who are disproportionately infected with HIV/AIDS

  11. HIV/AIDS Literature Parameter Estimate Parameters i.e. infection rate Transmission Model (Differential Equations) Loop until match model vs. reported values Predicted Populations Least Squares Fit Of HIV/AIDS Data Solve Using Forward Euler Method • This chart represents steps taken to compose AIDS model

  12. HIV/AIDS Literature • Government websites i.e. CDC, Department of Health, National Health Institute • Peer-review journals i.e. AIDS, JAMA • Pubmed website • Health and Human Services Department in Houston

  13. Components of Our Model • Parameters • Infection rate • Death rate • Birth rate • Migration rate • Poulations • Race • Gender • Sexual Preference

  14. Transmission Model • Differential equations are just rate of change • They can predict future population changes • We solve the equation using Forward Euler

  15. Forward Euler • Uses the slope to determine the next year’s population

  16. Matlab • Forward Euler implemented in Matlab • Matlab is a mathematical programming tool • We wrote a program in Matlab that predicted future AIDS populations

  17. Least Squares Fit • Compare the results of the model for each year to the actual data • Adjust the parameters to better fit the model results to the actual results

  18. Results Figure 1: PREVALENCE of HIV/AIDS. Number of persons living with HIV/AIDS by race in Harris County (1986-1991).

  19. Results, cont. Figure 2: INCIDENCE of HIV/AIDS. Number of cases diagnosed per year of HIV/AIDS in Harris County (1992-2001).

  20. Future Work • Fit our model to prevalence data • Predict future population dynamics • Infer possible policy changes to best deal with HIV/AIDS • Increase education • Increase access to protection • Increase abstinence awareness

  21. Bibliography • HIV/AIDS Surveillance, (March 31, 2005), http://www.cdc.gov/hiv/stats/hasrsupp.htm • AIDS Public Information Data Set, (March 14, 2005), http://www.cdc.gov/hiv/software/apids/apidsman.htm • Modeling 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/

  22. The road to research • Essay application • Weekly instruction-1st Semester • Introduced to Game Theory • Introduced to Matlab • Read chapters from book on Game Theory • Bi-weekly instruction-2nd Semester • Decided on research topic (AIDS) • Found source material online • Wrote computer program to simulate the spread of AIDS in Houston • Made and competed in a poster session with Rice college students

  23. Payoff • The Worthing/Rice experience • College experience • Increased awareness of HIV/AIDS • Learned better research techniques • Unexpected Benefits • Resume building • Potential scholarships • Working with college students

  24. Acknowledgements We would like to give special thanks to : Ms. Golia Howard Dr. Steve Cox Also like to thank: Fernando Acosta Jay Raol Nithin Rajan Zach Kilpatrick Andre T. Mosley Finally, Thanks to Our Parents