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Jason Wood

Hyperparasitism and the evolution of parasite virulence

01.10.23

Hyperparasites (species which parasitise other parasites) are common in natural populations and affect many parasitic taxa, including: eukaryotic parasites; bacterial and fungal pathogens; and insect parasitoids. Hyperparasitism is therefore likely to shape the ecology and evolution of many host-parasite systems, and represents a promising method for biocontrol (e.g. treating antimicrobial resistant infections). However, the eco-evolutionary consequences of hyperparasitism have received little attention. We use a host-parasite-hyperparasite model to explore how introducing a hyperparasite drives the evolution of parasite virulence, and how this affects host population dynamics. We show when the introduction of a hyperparasite selects for higher or lower parasite virulence, and how this changes the disease burden for the host population. Crucially, we show that variation in the virulence and infectivity of hyperparasites, along with the probability of co-transmission, can lead to a previously unseen hysteresis effect, whereby small shifts in hyperparasite characteristics can lead to sudden shifts in parasite virulence. We show that hyperparasites can induce diversification in parasite virulence, leading to the coexistence of high and low virulence strains. Our results show hyperparasites can have dramatic effects on the evolution of parasite virulence, and that the use of hyperparasites in biocontrol should therefore be approached with caution.

Diagnostic testing and the evolution of detection avoidance by pathogens

18.09.23

Diagnostic testing is a key tool in the fight against many infectious diseases. The emergence of pathogen variants that are able to avoid detection by diagnostic testing therefore represents a key challenge for public health. In recent years, variants for multiple pathogens have emerged which escape diagnostic testing, including mutations in Plasmodium falciparum (malaria), Chlamydia trachomatis (chlamydia) and SARS-CoV-2 (COVID-19). However, little is currently known about when and the extent to which diagnostic test escape will evolve. Here we use a mathematical model to explore how the frequency of diagnostic testing, combined with variation in compliance and efficacy of quarantining, together drive the evolution of detection avoidance. We derive key thresholds under which a testing regime will (i) select for diagnostic test avoidance, or (ii) drive the pathogen extinct. Crucially, we show that imperfect compliance with diagnostic testing regimes can have marked effects on selection for detection avoidance, and consequently, for disease control. Yet somewhat counterintuitively, we find that an intermediate level of testing can select for the highest level of detection avoidance. Our results, combined with evidence from various pathogens, demonstrate that the evolution of diagnostic testing avoidance should be carefully considered when designing diagnostic testing regimes.