Each year nearly 200 million people are infected with the malaria parasite, Plasmodium falciparum. One of its most notable features is the variable course and duration of infection experienced by different individuals, ranging from high parasite density, acute and often severe infections to persistent, chronic infections. Levels of acute and chronic infections vary across different transmission settings, and what disturbs the delicate balance between parasite growth and immune control, leading to bursts of parasite growth or clearance of an infection, remains an open question. Mathematical models, despite limited knowledge of mechanistic details of host-parasite interactions, have qualitatively reproduced single parasite dynamics observed in patient data. Here we develop a discrete model of blood-stage parasite dynamics including innate and adaptive immune responses. We analyze simulated output to examine how coinfecting strains, particularly from similar clones that elicit overlapping immune responses, impact infection length and infectiousness. We find that the level of both innate and adaptive immune responses present at the time of coinfection as well as the similarity of the coinfecting strains significantly alters the duration of both the resident and coinfecting strains, particularly during chronic infections. Timing of coinfection also influences the infectivity of the coinfecting strains, likely altering transmission patterns at a population level. Duration of infection and infectivity are critical epidemiological parameters for predicting the efficacy of control strategies, particularly with the looming problem of emerging drug resistance.

Lauren M. Childs graduated from Duke University with a Bachelors of Science in Mathematics and a Bachelors of Arts in Chemistry and was elected a member of Phi Beta Kappa. She obtained her PhD in Applied Mathematics from Cornell University under Steven Strogatz, and is currently a Research Fellow in the Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard University.