Plot cumulative deaths averted across models and runs

This function takes simulated model output and produces a line plot of cumulative deaths averted over time. Deaths averted are calculated as the difference between the number of drug-related deaths under a counterfactual scenario with no PSS and the number of deaths under the PSS intervention. The results are grouped by model and simulation run, and cumulative sums are plotted as semi-transparent lines to visualize uncertainty across runs.

Usage

plot_deaths_averted_lines(model_data)

Arguments

model_data

A data frame containing model simulation output with the following required columns:

• date — time variable (Date or numeric).

• no PSS drug_deaths — number of deaths in the counterfactual scenario with no PSS intervention.

• PSS drug_deaths — number of deaths in the PSS intervention scenario.

• model — model identifier (factor or character).

• run — simulation run identifier (numeric or character).

Value

A ggplot object showing cumulative deaths averted over time, stratified by model with multiple simulation runs shown as faint lines.

Examples

if (FALSE) { # \dontrun{
# Example model data (toy example)
df <- data.frame(
  date = rep(seq.Date(as.Date("2020-01-01"), by = "month", length.out = 12), 2),
  `no PSS drug_deaths` = rpois(24, 10),
  `PSS drug_deaths` = rpois(24, 7),
  model = rep(c("Model A", "Model B"), each = 12),
  run = rep(1:2, each = 12)
)

plot_deaths_averted_lines(df)
} # }