Timing of Pathogen Adaptation to a Multicomponent Treatment

TL;DR

This paper presents a stochastic model to understand how pathogen populations adapt to multicomponent treatments, revealing key factors influencing the speed of adaptation and offering insights for sustainable management strategies.

Contribution

It introduces a novel stochastic modeling approach that captures mutation and migration processes affecting pathogen adaptation to multicomponent treatments.

Findings

Pathogen adaptation can occur even with small host proportions of treatment.

Stochastic mutation and migration significantly influence adaptation dynamics.

Associating multicomponent treatments with defeated monocomponent treatments can enhance durability.

Abstract

The sustainable use of multicomponent treatments such as combination therapies, combination vaccines/chemicals, and plants carrying multigenic resistance requires an understanding of how their population-wide deployment affects the speed of the pathogen adaptation. Here, we develop a stochastic model describing the emergence of a mutant pathogen and its dynamics in a heterogeneous host population split into various types by the management strategy. Based on a multi-type Markov birth and death process, the model can be used to provide a basic understanding of how the life-cycle parameters of the pathogen population, and the controllable parameters of a management strategy affect the speed at which a pathogen adapts to a multicomponent treatment. Our results reveal the importance of coupling stochastic mutation and migration processes, and illustrate how their stochasticity can alter our view of the principles of managing pathogen adaptive dynamics at the population level. In particular, we identify the growth and migration rates that allow pathogens to adapt to a multicomponent treatment even if it is deployed on only small proportions of the host. In contrast to the accepted view, our model suggests that treatment durability should not systematically be identified with mutation cost. We show also that associating a multicomponent treatment with defeated monocomponent treatments can be more durable than associating it with intermediate treatments including only some of the components. We conclude that the explicit modelling of stochastic processes underlying evolutionary dynamics could help to elucidate the principles of the sustainable use of multicomponent treatments in population-wide management strategies intended to impede the evolution of harmful populations.