  When modelling HIV epidemics, it is important to incorporate set-point viral
load and its heritability. As set-point viral load distributions can differ
significantly amongst epidemics, it is imperative to account for the observed
local variation. This can be done by using a heritability model and fitting it
to a local set-point viral load distribution. However, as the fitting procedure
needs to take into account the actual transmission dynamics (i.e., social
network, sexual behaviour), a complex model is required. Furthermore, in order
to use the estimates in subsequent modelling analyses to inform prevention
policies, it is important to assess parameter robustness.
  In order to fit set-point viral load models without the need to capture
explicitly the transmission dynamics, we present a new protocol. Firstly, we
approximate the transmission network from a phylogeny that was inferred from
sequences collected in the local epidemic. Secondly, as this transmission
network only comprises a single instance of the transmission network space, and
our aim is to assess parameter robustness, we infer the transmission network
distribution. Thirdly, we fit the parameters of the selected set-point viral
load model on multiple samples from the transmission network distribution using
approximate Bayesian inference.
  Our new protocol enables researchers to fit set-point viral load models in
their local context, and diagnose the model parameter's uncertainty. Such
parameter estimates are essential to enable subsequent modelling analyses, and
thus crucial to improve prevention policies.
