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Plot the estimated MDA impact function

plot_mda.Rd

Generate a plot of the estimated impact of mass drug administration (MDA) on infection prevalence, based on a fitted decay-adjusted spatio-temporal (DAST) model. The function simulates draws from the posterior distribution of model parameters, propagates them through the MDA effect function, and produces uncertainty bands around the estimated impact curve.

Usage

plot_mda(
  object,
  mda_history = NULL,
  n_sim = 1000,
  x_min = 1e-06,
  x_max = 10,
  conf_level = 0.95,
  lower_f = NULL,
  upper_f = NULL,
  mc_cores = 1,
  parallel_backend = c("none", "fork", "psock"),
  ...
)

Arguments

object

A fitted DAST model object, returned by dast.

mda_history

Specification of the MDA schedule. This can be either:

  • A numeric vector of event times (integers starting at 0, e.g. c(0,1,2,6)),

  • OR a 0/1 indicator vector on the yearly grid (e.g. c(1,1,1,0,0,0,1)), where position i corresponds to year i-1.

If omitted, the default is a single MDA at time 0.

n_sim

Number of posterior draws used for uncertainty quantification (default: 1000).

x_min

Minimum value for the x-axis (default: 1e-6).

x_max

Maximum value for the x-axis (default: 10).

conf_level

Confidence level for the pointwise uncertainty interval (default: 0.95).

lower_f

Optional lower bound for the y-axis. If not provided, computed from the data.

upper_f

Optional upper bound for the y-axis. If not provided, computed from the data.

mc_cores

Number of CPU cores to use for parallel simulation. Default is 1 (serial).

parallel_backend

Parallelisation backend to use. Options are "none" (default), "fork" (Unix-like systems), or "psock" (cross-platform).

...

Additional arguments (currently unused).

Value

A ggplot2 object showing the median estimated MDA impact function and the pointwise uncertainty band at the chosen confidence level.

Details

The time axis is assumed to start at 0 and increase in integer steps of 1 year. The argument mda_history allows the user to specify when MDAs occurred either by listing the years directly or by giving a binary indicator on the yearly grid. The function then evaluates the cumulative relative reduction \(1 - \mathrm{effect}(t)\) at a dense grid of time points between x_min and x_max, using the fitted parameters from the supplied DAST model.