Lesion severity scales have been developed for a number of wildlife diseases causing external pathology. Perhaps the best known and most widely used scoring system has been developed for finch mycoplasmosis in which observers measure conjunctival pathology along a four-point scale of increasing severity. We developed novel techniques to characterize variation in host phenotype based on occupancy of multidimensional trait space (disease space). First, we used shape analysis to track distortions of the inner and outer eye rims, defined by 16 anatomical landmarks. Then, we used community analysis to evaluate pathology based on the presence or absence of a unique set of binary descriptors. We applied these techniques to experimental infection data to relate differences in conjunctival pathology to stage of infection. Specifically, by comparing specimens that received the same severity score at different time points in infection, we asked if shape or community analyses could distinguish between individuals in early infection versus those in recovery. We found that individual eyes followed predictable loops through disease space, tracking further from their origin with more severe pathology. Also, certain pathological descriptors were more likely to appear earlier versus later in infection. Our results indicated that leveraging differences in pathology captured in complex trait space could complement severity scores by better resolving the time course of infection from limited data points.