mitey (version 0.2.0)

plot_si_fit: Visualize Serial Interval Distribution Fit to Outbreak Data

Description

Creates a diagnostic plot showing the fitted serial interval mixture distribution overlaid on a histogram of observed index case-to-case (ICC) intervals from outbreak data.

Usage

plot_si_fit(dat, mean, sd, weights, dist = "normal", scaling_factor = 1)

Value

A ggplot2 object that can be further customized or displayed. The plot includes appropriate axis labels, legend, and styling for publication-quality figures

Arguments

dat

numeric vector; the index case-to-case (ICC) intervals in days. These represent the time differences between symptom onset in the index case (case with earliest symptom onset) and each other case in the outbreak

mean

numeric; the estimated mean of the serial interval distribution in days, typically obtained from si_estim()

sd

numeric; the estimated standard deviation of the serial interval distribution in days, typically obtained from si_estim()

weights

numeric vector; the estimated weights for different transmission route components. Length and interpretation depends on distribution:

• Normal distribution: 4 weights corresponding to aggregated transmission routes (co-primary, primary-secondary, primary-tertiary, primary-quaternary)

• Gamma distribution: 3 weights for the reduced component set

dist

character; the distribution family used for serial interval estimation. Must be either "normal" (default) or "gamma". Should match the distribution used in the original si_estim() call

scaling_factor

numeric; multiplicative factor to adjust the height of the fitted density curve relative to the histogram. Values > 1 make the curve higher, values < 1 make it lower. Defaults to 1. Useful when histogram and density have different scales.

Details

The function displays:

• Histogram: Observed ICC intervals binned by day, representing the empirical distribution of time differences between symptom onset in the index case and all other cases in the outbreak

• Fitted curve: The estimated mixture distribution combining different transmission routes (co-primary, primary-secondary, primary-tertiary, and primary-quaternary), weighted according to their estimated probabilities

• Reference line: For normal distributions, a dashed vertical line indicates the estimated mean serial interval

References

Vink MA, Bootsma MCJ, Wallinga J (2014). Serial intervals of respiratory infectious diseases: A systematic review and analysis. American Journal of Epidemiology, 180(9), 865-875.

See Also

si_estim for serial interval estimation, f_norm and f_gam for the underlying mixture distribution functions

Examples

# Example 1: Visualize fit for simulated outbreak data
set.seed(123)
# Simulate ICC intervals from mixed distribution
icc_data <- c(
  rnorm(20, mean = 0, sd = 2),      # Co-primary cases
  rnorm(50, mean = 12, sd = 3),     # Primary-secondary cases
  rnorm(20, mean = 24, sd = 4)      # Primary-tertiary cases
)
icc_data <- round(pmax(icc_data, 0))  # Ensure non-negative

# Plot with estimated parameters
plot_si_fit(
  dat = icc_data,
  mean = 12.5,
  sd = 3.2,
  weights = c(0.2, 0.6, 0.15, 0.05),
  dist = "normal"
)

# Example 2: Using gamma distribution
plot_si_fit(
  dat = icc_data,
  mean = 12.0,
  sd = 3.5,
  weights = c(0.25, 0.65, 0.10),
  dist = "gamma",
  scaling_factor = 0.8
)