Estimate an uncertain shedding load distribution — shedding_dist

This option models a parametric shedding load distribution. It is useful for representing uncertainty about the mean and variation of the shedding profile.

Usage

shedding_dist_estimate(
  shedding_dist_mean_prior_mean = NULL,
  shedding_dist_mean_prior_sd = NULL,
  shedding_dist_cv_prior_mean = NULL,
  shedding_dist_cv_prior_sd = NULL,
  shedding_dist_type = "gamma",
  shedding_reference = NULL,
  prior_weights = NULL,
  weight_alpha = 1,
  modeldata = modeldata_init()
)

Arguments

shedding_dist_mean_prior_mean: Prior (mean) for the mean of the shedding load distribution. Can also be a vector to represent several priors, that will be mixed using a Dirichlet prior. This is useful when combining several shedding load distributions from different studies or sensitivity analyses.
shedding_dist_mean_prior_sd: Prior (standard deviation) for the mean of the shedding load distribution. Can also be a vector, see shedding_dist_mean_prior_mean.
shedding_dist_cv_prior_mean: Prior (mean) for the coefficient of variation (i.e. standard deviation relative to the mean) of the shedding load distribution. Can also be a vector, see shedding_dist_mean_prior_mean.
shedding_dist_cv_prior_sd: Prior (standard deviation) for the coefficient of variation of the shedding load distribution. Can also be a vector, see shedding_dist_mean_prior_mean.
shedding_dist_type: The parametric distribution that should be modeled. Supported are "gamma", "exponential", and "lognormal".
shedding_reference: Is the shedding load distribution relative to the day of "infection" or the day of "symptom_onset"? This is important because shedding load distributions provided in the literature are sometimes by days since infection and sometimes by days since symptom onset. If shedding_reference="symptom_onset", EpiSewer also needs information about the incubation period distribution (see incubation_dist_assume()).
prior_weights: A numeric vector of the same length as the priors for the mean and cv, with weights for the different priors. If NULL, the priors are given equal weight. Will be normalized to sum to the number of distributions.
weight_alpha: Concentration parameter of the Dirichlet prior for the mixture probabilities. The default is weight_alpha=1, i.e. a uniform distribution over all combinations of weights. This will sample various mixtures of the mean and cv prior distributions provided. If a smaller weight_alpha is chosen, the prior distributions are rather considered separately, i.e. in each posterior sample, one of the prior distributions is given almost all the weight.
modeldata: A modeldata object to which the above model specifications should be added. Default is an empty model given by modeldata_init(). Can also be an already partly specified model returned by other EpiSewer modeling functions.

Value

A modeldata object containing data and specifications of the model to be fitted. Can be passed on to other EpiSewer modeling functions to add further data and model specifications.

The modeldata object also includes information about parameter initialization (.init), meta data (.metainfo), and checks to be performed before model fitting (.checks).