An application of system dynamics to monitor HIV disease progression in a resource limited setting

Abstract

Use of system dydanics methodology in modelling HIV/AIDS disease progression is not very few ,however use of triangulation of methodologies in form of the dynamic synthesis methodology (dsm) to model the relationship between surrogate markers for monitoring HIV/AIDS disease progression is entirely new. A triangulation of methodologies called DSM combining system dynamics and case study methodologies was used to establish the relationships of HIV/AIDS surrogate markers to arrive at an appropriate model for monitoring the HIV/AIDS disease progression in a resource limited setting.The problem was initially analysed in its natural form to arrive at an appropriate reference modes followed by simulation experiments that were used to map values of plasma CD4 cell count onto those of total lymphocyte count and haemoglobin and HIV viral load onto CD8CD38 density representing the same disease rate.The paper makes useful contribution in suggesting surrogate HIV/AIDS makers in a resource limited setting.Values of the two standard HIV disease progression surrogate markers namely CD4 cell count and HIV viral load were continuously mapped onto the alternative surrogate markers namely Total Lymphocyte count, Heamoglobin concentration and CD8CD38 cell density representing the same disease state. DSM was used to capture the feedback effect and time delays between the variables. Data for surrogate markers taken from adult patients suffering from HIV/AIDS and on antiretroviral therapy was used the case study to formulate the problem and later for verification model. Reference modes were used to develop a dynamic hypothesis which led to a model that was using simulation experiments with STELLA 8.1 and validated by experts in the field of HIV/AIDS. The simulation experiments’ values for plasma CD4 cell count were mapped onto corresponding values of TLC and Heamoglobin representing the same disease state, while those of the HIV viral load were mapped onto CD8CD38 density. However, the research was not able to distinguish variability of the surrogate markers between sexes and age groups which were outside the system boundary.