A computational model of the dynamics of diversity among T-cell receptors and MHC: peptide complex molecules is presented. We propose a method by which individual immune systems may evolve effcient or ineffcient states as a result of T-cell receptor crossreactivity as well as genetic variation among pathogens. By combining shape space and physical space models, valuable insight is obtained into how immune system-wide state is, in large part, determined by localised space dynamics. In the model, system-wide state also informs local dynamics, especially in the lymphatic system during primary immune response. The process by which similar initial infection conditions across individuals may result in highly variable end states (a phenomenon observed in the clinical context) is modelled. Our results show that activity alone is not a good indicator of infection suppression or removal. In this work, we postulate that successful viral clearance is characterised by broad T-cell receptor activation (in shape space), and results in low average concentration levels of activated cytotoxic lymphocyte cells.