Improving HIV early infant diagnosis (EID) supply chains in sub-Saharan Africa
An instrumental part of the battle against the human immunodeficiency virus (HIV) epidemic in developing countries is early infant diagnosis (EID) aimed to identify HIV infected infants as early as possible. Timing is important as roughly 50% of infants infected early die before they reach the age of two years. In resource limited settings the EID system is a intricate network of clinics,
where samples are taken, and labs, where samples are processed. Each lab in the system can be described as a
(sum_i GI^[X_i])\G^[b,b]\c queue. In this paper an approximation of waiting time in such queues is derived as a univariate function of utilization. This approximation is utilized as a mechanism to translate operational decisions into public health outcomes when the problem of improving or designing an optimal operational structure for a general EID system is formulated. The operational aspects examined include the total capacity of system, location of labs, the assignment of clinics to labs, potential segmentation of samples into a normal track and a fast track and the allocation of transport opportunities between the clinics and the labs. Furthermore, a general simulation model is developed and validated using available data for a representative case. This model is used to quantify the effect of various inputs on health outcomes and as a robustness check for the improvements suggested by the optimization model.