In order to achieve HIV viral load monitoring targets, innovative technologies and approaches need to be considered by country governments in low-resource settings. Similarly, increasing access to essential and timely diagnostics in low-resource settings, such as coronavirus disease 19 diagnostic testing, requires both strengthening of diagnostic networks and the adoption of innovative technologies that can facilitate access. This thesis used diagnostic network optimisation, and cost and outcome analysis to identify different diagnostic testing strategies that optimise cost, effectiveness of, and access to, infectious disease diagnostic networks in low-resource settings. In an age where cost savings, particularly in HIV care, are increasingly difficult to achieve, the work in this thesis showcases the use of geospatial modelling to optimise specimen referral networks as a simple way to improve efficiency and access. Further, the work in this thesis would suggest the judicious use of point of care viral load technologies in complementing, not replacing, conventional centralised testing platforms where placement decisions are informed by facility characteristics (such as patient volumes, remoteness, distance to testing laboratories), device characteristics such as polyvalency, and patient characteristics. Lastly, the work in this thesis indicates that the adoption of inferior diagnostic technologies in terms of performance would be warranted when they facilitate increased access. In conclusion, the use of geospatial modelling to optimise infectious disease diagnostic transportation networks reduces costs; however, the strategic adoption of innovative technologies is required to increase access and reduce the costs of reaching the last mile.