The evolutionary emergence of new pathogens via mutation poses a considerable
risk to human and animal populations. Here, I present work from
two models investigating the emergence of new strains in non-homogeneous
populations.
At the epidemiological level, previous studies have investigated cases where
a potentially pandemic strain emerges from a maladapted strain (that is, its
basic reproductive ratio R0 < 1). However, an alternative cause of pathogen
emergence is where a weakly-adapted strain (with R0 1) mutates into a
strongly-adapted strain (with R0 1). In this case, a proportion of the
susceptible population is removed as the rst strain spreads, but the impact
this feedback has on mutated strain emergence has yet to be quantied. We
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produce a model that takes into account changes in the susceptible population
over time, and nd that the ongoing depletion of susceptible individuals
by the rst strain has a drastic eect on the emergence probability
of the mutated strain, above that assumed by just scaling the reproductive
ratio. We subsequently apply our model to the case of Chikungunya virus
on La Reunion island, and demonstrate that the emergence probability of
the mutated strain was reduced approximately 10-fold, compared to models
assuming that susceptible depletion would not aect outbreak probability.
We next show how these models can be extended to emergence at the withinhost
level, where a pathogen needs to escape immune-cell proliferation. Using
this extended model, we show that contrary to previous results, it is more
benecial for a pathogen to increase its growth rate, rather than tolerating
immunity.
These results highlight the importance of taking population feedbacks into
account when predicting disease emergence.