Mathematical models of infectious disease transmission typically neglect
within-host dynamics. Yet within-host dynamics - including pathogen
replication, host immune responses, and interactions with microbiota - are
crucial not only for determining the progression of disease at the individual
level, but also for driving within-host evolution and onwards transmission, and
therefore shape dynamics at the population level. Various approaches have been
proposed to model both within- and between-host dynamics, but these typically
require considerable simplifying assumptions to couple processes at contrasting
scales (e.g., the within-host dynamics quickly reach a steady state) or are
computationally intensive. Here we propose a novel, readily adaptable and
broadly applicable method for modelling both within- and between-host processes
which can fully couple dynamics across scales and is both realistic and
computationally efficient. By individually tracking the deterministic
within-host dynamics of infected individuals, and stochastically coupling these
to continuous host state variables at the population-level, we take advantage
of fast numerical methods at both scales while still capturing individual
transient within-host dynamics and stochasticity in transmission between hosts.
Our approach closely agrees with full stochastic individual-based simulations
and is especially useful when the within-host dynamics do not rapidly reach a
steady state or over longer timescales to track pathogen evolution. By applying
our method to different pathogen growth scenarios we show how common
simplifying assumptions fundamentally change epidemiological and evolutionary
dynamics.Comment: 34 pages, 5 figure