Multiple Infections, Immune Dynamics, and the Evolution of Virulence

Abstract

Understanding the effect of multiple infections is essential for the prediction (and eventual control) of virulence evolution. Some theoretical studies have considered the possibility that several strains coexist in the same host (coinfection), but few have taken their within-host dynamics explicitly into account. Here, we develop a nested approach based on a simple model for the interaction of parasite strains with their host's immune system. We study virulence evolution by linking the within-host dynamics to an epidemiological framework that incorporates multiple infections. Our model suggests that antigenically similar parasite strains cannot coexist in the long term inside a host. We also find that the optimal level of virulence increases with the efficiency of multiple infections. Finally, we notice that coinfections create heterogeneity in the host population (with susceptible hosts and infected hosts), which can lead to evolutionary branching in the parasite population and the emergence of a hypervirulent parasite strategy. We interpret this result as a parasite specialization to the infectious state of the hosts. Our study has experimental and theoretical implications in a virulence management perspective.

Within-host competitive interactions as a mechanism for the maintenance of parasite diversity

“…When public goods allow greater host exploitation [8,86] or virulent immuno-provoking forms of apparent competition [87], mixed infections are predicted to result in lower virulence due to within-host selection favouring cheats. However, incorporating epidemiological feedbacks can change these predictions [88,89]. Similarly, spiteful forms of interference competition have been shown to lower virulence [90], but they can raise virulence depending on the scale of competition and the kin structure within the host [91].…”

Modelling and estimation of infectious diseases in a population with heterogeneous dynamic immunity

“…We see that r(0) < 0 (possibly −∞) and r(θ ) > 0 (again possibly infinite) for any θ bigger than sup ω∈ [0,1] q(ω) =: Q. Therefore any solution of the equation r(θ ) = 0 must lie in the interval (0, Q).…”

“…Then it is obvious that I θ and S θ are both nonnegative, as g(θ ) > 0. Due to our definition of g(θ ), it is easy to see that 1 0 q(ω)I θ (ω) dω = θ . Using this, by a simple differentiation of Equation (10), we obtain that for ω ∈ (0, 1)…”

“…Lemma A.1: There exists a constant C such that for every u 1 (19) and (20) with u = u j , j = 1,2. Then, it is straightforward that…”

From within-host interactions to epidemiological competition: a general model for multiple infections

“…We based our approach on nesting the within-host parasite growth into the between-host epidemiology. Nested models have already been investigated in epidemiology to study multiple infections, either by modelling exploitation competition through host resource use [24], or immune cell infection dynamics by viruses [25,26]. However, existing nested models (reviewed in [27]) often ignore several within-host processes, such as within-host parasite interactions, and force the outcome of infection (superinfection or co-infection).…”