Determination of the processes driving the acquisition of immunity to malaria using a mathematical transmission model

Abstract

Acquisition of partially protective immunity is a dominant feature of the epidemiology of malaria among exposed individuals. The processes that determine the acquisition of immunity to clinical disease and to asymptomatic carriage of malaria parasites are poorly understood, in part because of a lack of validated immunological markers of protection. Using mathematical models, we seek to better understand the processes that determine observed epidemiological patterns. We have developed an age-structured mathematical model of malaria transmission in which acquired immunity can act in three ways (''immunity functions''): reducing the probability of clinical disease, speeding the clearance of parasites, and increasing tolerance to subpatent infections. Each immunity function was allowed to vary in efficacy depending on both age and malaria transmission intensity. The results were compared to age patterns of parasite prevalence and clinical disease in endemic settings in northeastern Tanzania and The Gambia. Two types of immune function were required to reproduce the epidemiological age-prevalence curves seen in the empirical data; a form of clinical immunity that reduces susceptibility to clinical disease and develops with age and exposure (with half-life of the order of five years or more) and a form of anti-parasite immunity which results in more rapid clearance of parasitaemia, is acquired later in life and is longer lasting (half-life of .20 y). The development of anti-parasite immunity better reproduced observed epidemiological patterns if it was dominated by age-dependent physiological processes rather than by the magnitude of exposure (provided some exposure occurs). Tolerance to subpatent infections was not required to explain the empirical data. The model comprising immunity to clinical disease which develops early in life and is exposure-dependent, and anti-parasite immunity which develops later in life and is not dependent on the magnitude of exposure, appears to best reproduce the pattern of parasite prevalence and clinical disease by age in different malaria transmission settings. Understanding the effector mechanisms underlying these two immune functions will assist in the design of transmission-reducing interventions against malaria.

Mathematical modelling of the in-host dynamics of malaria and the effects of treatment

“…Therefore, it is better to suppress the merozoites population which serves as a way of eradicating the disease at blood stage. These results are in agreement with those in [3,23,28,34,35,44] who accessed the impact of chemotherapy and biological control measures on the parasite growth focussing on the erythrocytic cycle of malaria. It is suggested that the effectiveness of chemotherapy and biological measures inversely related to the lifeexpectancy of the targeted stage in the parasite's development cycle.…”

The silent threat: asymptomatic parasitemia and malaria transmission

“…Antidisease immunity is acquired from exposure to malaria infection and develops more quickly with frequent exposure; most children in areas with moderate-to-high levels of malaria transmission gain protection from severe disease by a very young age, usually by 2-5 years of age, followed by a decrease in the rate of symptomatic illness in early adolescence [21]. By contrast, antiparasite immunity increases with maturation of the immune system and appears to be somewhat independent of exposure frequency in areas of moderate-to-high transmission.…”

Dynamical malaria models reveal how immunity buffers effect of climate variability

“…The force of reinfection was considered as a constant proportion, s i , of the force of infection μ S1E (SI Text). Given that the higher the transmission intensity is, the slower the recovery rate (4,22), immunity and recovery were considered as functions of the transmission intensity as explained in SI Text. Drug periods were modeled as constant functions for the corresponding period (see SI Text for details), and treatment success probability was considered through a parameter t s .…”