Regency D
A Surveillance Data-Based Model System for Assessing the Effects of HIV Intervention and Prevention Strategies (304029)
*Timothy A Green, Centers for Disease Control and Prevention
*H. Irene Hall, Centers for Disease Control and Prevention
*Ruiguang Song, Centers for Disease Control and Prevention
Keywords: HIV incidence, HIV prevalence, surveillance, dynamic model system, viral suppression, intervention and prevention
To reduce and eventually eliminate HIV, implementing effective intervention and prevention strategies is critical. The effectiveness of a combination of strategies is generally evaluated through mathematical modeling. We developed a dynamic model system to assess the effects of various strategies on future HIV infections, diagnoses, and deaths among persons with HIV. Model parameters are defined to quantify the putative effects of HIV prevention strategies that would increase HIV testing to diagnose infection earlier; increase linkage to care and viral suppression to reduce infectiousness; and increase the use of pre-exposure prophylaxis to protect persons at risk of infection. Surveillance data are used to determine initial values of model system variables and parameters, and the impact on the future outcome measures of achieving specific target values or rate changes on model parameters is examined. Users at any geographic level can use this model system and location-specific epidemiologic characteristics to assess how changes in the initial values and rate change over time of model variables or parameters might affect the future course of HIV in their localities.