Research Initiation Award: Mathematical model of the impact of social behavior in the transmission dynamics of HIV epidemics

点击翻译按钮获取中文摘要

Research Initiation Awards provide support for junior and mid-career faculty at Historically Black Colleges and Universities who are building new research programs or redirecting and rebuilding existing research programs. It is expected that the award helps to further the faculty member's research capability and effectiveness, improves research and teaching at her home institution, and involves undergraduate students in research experiences. The award to Norfolk State University has potential broader impact in a number of areas. The project will focus on constructing a mathematical model to investigate the role of social behavior in the transmission dynamics of HIV epidemics. The outcomes of this work can inform public health officials when analyzing and determining the impact of social behavior in the transmission of HIV epidemics. This project will also enhance the research experience and training of undergraduate students at Norfolk State University.This project will focus on developing mathematical models, in the form of deterministic systems of nonlinear differential equations, to qualitatively and quantitatively analyze the role of risky behavior on the transmission dynamics of HIV/AIDS in a population. Studies have established a strong correlation between risky behavior and the acquisition of HIV infection. Some of these behaviors are associated with substance abuse. Although it is understood that there are different degrees or stages of substance abuse, the impact of such heterogeneity on the overall disease transmission process has not been rigorously studied. This project will investigate these dynamics and their role in the transmission dynamics of HIV/AIDS in a population. To achieve this objective, new realistic mathematical models, which incorporate the essential features of HIV disease as well as the dynamics of relevant risky behaviors, will be developed. These models, which will be rigorously analyzed using techniques from nonlinear dynamical systems, such as asymptotic stability and bifurcation theory, to gain insights into their dynamical features, will be parametrized using available relevant public health and demographic data. Detailed uncertainty and sensitivity analysis, using suitable sampling techniques such as Latin Hypercube Sampling and Partial Rank Correlation Coefficients, will be carried out on the parameters of the models to assess the impact of uncertainty in the estimates of the parameter values used in the simulations on the overall simulation results obtained, and determine the most important parameters that drive the disease transmission process.