Prediction and Analysis of HIV/AIDS Epidemic in Southeast Asia
Predictive model assessing the severity of the HIV/AIDS epidemic in Southeast Asia. Delve into factors contributing to and controlling the spread, highlighting behavioral and societal influences. Discover how the model bridges gaps in epidemiological data in the developing world, serving as a diagnostic tool for targeted prevention and control strategies. Uncover the main contributing factors and results for countries in the region.
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Presentation Transcript
ANP Model Prediction of Most Severe HIV/AIDS Epidemic in SE Asia
The Model Predictive Cost/Benefits Costs/Contributors: Factors that Contribute to the spread of HIV Benefits/Controllers: Factors that Control the spread of HIV
Countries Burma Cambodia Indonesia Malaysia Thailand Vietnam
Why?? Individual Behavioral studies Empirical findings of societal factors that contribute to HIV No model has attempted to capture the interaction between the two
Why ?? Epidemiological studies in developing world are incomplete or loose estimates ANP can be used to collaborate or contradict these studies Predict environment for future epidemics
Why?? Diagnostic tool to determine what Factors Combination of factors Lead to epidemics Can be targeted for prevention/control
Predictors Criteria Sub-Criteria Local Priorities .249 Sub-sub Criteria Local Priorities Global Priorities .056 Costs (Factors that contribute to the spread of HIV) (.65) Economic (.346) Income Inequality Poverty Geography Smuggling Age of Epidemic Government .751 .297 .169 .539 .169 .019 .011 .034 Other (.098) Political (.209) .547 Corruption Ineffectiveness .667 .333 .050 .025 .031 International Isolation War Cultural Norms/Behavior .23 .026 .020 .189 .75 Social (.346) Drug Use .119 Prostitution Sexual Promiscuity Taboo (sex/drugs) Female literacy gap Illiteracy .365 .281 .061 .047 .235 .040 Education .25 .665 .037 .019 .333 Benefits (Factors that control the spread of HIV) (.35) Economic (.327) .333 .038 Gov t Health Spending International aid Muslim Population Condom Use .667 .667 .076 .096 Social (.413) .333 .048 .091 Government (.259)
Main Contributing Factors Social Prostitution Sexual promiscuity Taboo of speaking of Sex/drugs Female education gap"female education gap Economic Income inequality Poverty Government problems Corruption
Results Alternatives Burma Cambodia Vietnam Thailand Indonesia Malaysia Priority 0.2425 0.1937 0.1674 0.1379 0.1366 0.1217 Ranking 1 2 3 4 5 6
Reality Burma/ Cambodia Thailand Vietnam Indonesia Malaysia
Problems Thailand had early epidemic Many prevention and control measures Effective but still high Vietnam has favorable conditions for epidemic Late (1990s) Prediction?? Cambodia (same)
Problems Hierarchy model Lost some important network interactions interactions
Sensitivity Analysis Contributing factors (Costs) used as control criterion .667 in this study If below .85, Burma remains #1 Above .85 Cambodia #1 Burma remains the most severe throughout the lower range of values for contributing factors. Cambodia is the most sensitive and drops to the 3rdranking if the controlling factor falls below .5.
Sensitivity Analysis If Controlling factors used as control criterion Its value must drop below .15 for Cambodia to overtake Burma If the controlling factors value rises above .5 Cambodia drops to third in the rankings
