The chart below caught my eye in a recent Quanta Magazine article. Rendered by Lucy Reading-Ikkanda for “A New Way to Predict Infection's Toll” by Emily Singer, it provides a clear and intuitive look at how disease-resilient and high-risk individuals can differ in their travels through so-called “disease space.”

The chart in Quanta appears to be an idealized connected scatter plot. Perhaps tipping over into the realm of concept art, as I'm not convinced we’re looking at actual values plotted against the axis tick marks. But that doesn’t bother me, as the graphic’s value lies in its power to explain a concept with a visual model, not in representing a specific data set.
It's strongly rooted in the work it references, “Tracking Resilience to Infections by Mapping Disease Space” by Brenda Torres, Jose Henrique Oliveira and colleagues, published in PLOS Biology on April 18, 2016. The Stanford and University of Houston researchers traced paths through disease space as represented by variables such as red blood cell count and malaria parasite density. Resilient individuals make small loops, non-resilient individuals forge larger loops (longer infection times and more severe symptoms) and are more prone to swinging out over the threshold, into death.
The cyclical infection pattern of malaria—as well as the offset nature of some of the variables involved (different immune cell concentration levels don't peak or bottom out at the same time)—make it a particularly good disease to examine with this model, as shown in the original paper (below).

(A) This figure imagines a map tracing the path that a resilient infected individual might travel through disease space. The host starts at a position of comfort, but as pathogen load increases and health decreases, it enters a state of sickness. Once pathogen load decreases, the host starts to actively repair its health, resulting in an open loop in which every position along the path is unique. (B) For a system in which two parameters oscillate through a single cycle, they will trace three basic shapes through phase space. If the parameters do not overlap with each other in time, they will trace an L-shaped curve (i, iv). Complete overlap produces a line (ii, v), and partial overlap produces a loop (iii, vi). The color gradients show increasing concentrations of each parameter. See also S1 Fig." CREDIT: Torres BY, Oliveira JHM, Thomas Tate A, Rath P, Cumnock K, et al. (2016) Tracking Resilience to Infections by Mapping Disease Space. PLoS Biol 14(4): e1002436. doi:10.1371/journal.pbio.1002436
Indeed, paths through disease space for individuals inflicted with malaria are roughly circular for certain key indicators, as opposed to boomeranging back and forth along a straight line over time.


The Quanta chart is an idealized form that reflects information presented in the original research paper's figure 2C and figure 4E, all of which plot red blood cell concentration against immature red blood cell (reticulocyte) concentration. The result; an elegant summary figure for a non-specialist audience.