Infected hosts differ in their responses to pathogens; some hosts are resilient and recover their original health, whereas others follow a divergent path and die. To quantitate these differences, we propose mapping the routes infected individuals take through “disease space.” We find that when plotting physiological parameters against each other, many pairs have hysteretic relationships that identify the current location of the host and predict the future route of the infection. These maps can readily be constructed from experimental longitudinal data, and we provide two methods to generate the maps from the cross-sectional data that is commonly gathered in field trials. We hypothesize that resilient hosts tend to take small loops through disease space, whereas nonresilient individuals take large loops. We support this hypothesis with experimental data in mice infected with Plasmodium chabaudi, finding that dying mice trace a large arc in red blood cells (RBCs) by reticulocyte space as compared to surviving mice. We find that human malaria patients who are heterozygous for sickle cell hemoglobin occupy a small area of RBCs by reticulocyte space, suggesting this approach can be used to distinguish resilience in human populations. This technique should be broadly useful in describing the in-host dynamics of infections in both model hosts and patients at both population and individual levels.

If the path to sickness differs from the path to recovery, we should treat each route differently. Current treatments that inhibit pathogen growth might not be useful when pathogens are already being cleared. To determine the trajectory of both disease and recovery we created a multidimensional map of a malaria model infection in mice, tracing the route from initial infection through recovery. We did this using physiological parameters like red blood cell counts, temperature, weight and blood sugar and included molecular markers from blood transcriptome that was serially collected daily. We then plotted transcript levels versus parasite load and anemia to create a map for the full course of the infection. We can now use this map, constructed from longitudinal data, to scaffold data obtained in human cross sectional studies where we do not know the point of the patient in the infection timeline. This reveals previously unseen structure to these data and should provoke a re-examination of past conclusions. As we add data from non-recovering mice we will build maps that predict outcomes. Our goal is to not simply use these maps as a way of identifying biomarkers but to measure how treatments warp this multidimensional disease space.