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The mitey package

The mitey package is a lightweight package designed originally as a companion to the analyses presented by Ainslie et al. 2025 on scabies transmission. However, these methods are more widely applicable than in the context of scabies, thus the motivation behind creating the mitey package was twofold and also provides flexible, documented code for methods to estimate epidemiological quantities of interest.

Currently, mitey includes methods to estimate a) the mean and standard deviation of the serial interval distribution using a maximum likelihood framework developed by Vink et al. 2014 and b) the time-varying reproduction number using the method developed by Wallinga and Lipsitch 2007.

Estimating the serial interval

The method developed by Vink et al. uses data about the time of symptom onset with no precise information about transmission pairs and an assumed underlying serial interval distribution (either Gaussian or Gamma) to estimate the mean and standard deviation of the serial interval distribution. Briefly, the method involves calculating the index case-to-case (ICC) interval for each person, where the person with the earliest date of symptom onset will be considered the index case. The rest of the individuals will have an ICC interval calculated as the number of days between their symptom onset and the index case.

Installation

  1. Install R

  2. Install the development version of mitey from GitHub:

# install.packages("devtools")
devtools::install_github("kylieainslie/mitey")

Vignettes

  • The manuscript pre-print that motivated the development of this package can be found here.

  • A script that reproduces the results from Ainslie et al. 2025 can be found here.

  • Validation of the method used to estimate the mean and standard deviation of the serial interval proposed by Vink et al. 2014 can be found here.

  • Validation of the method used to estimate the time-varying reproduction number proposed by Wallinga and Lipsitch 2007 can be found here.

Data

Several data files are stored in the repo so that the results presented in Ainslie et al. 2025 are reproducible. Data files are stored in inst/extdata/data/. Below is a brief description of the different files.

  • si_data.rds
    • Description: Data on date of symptom onset for scabies outbreaks described by Kaburi et al., Akunzirwe et al., Tjon-Kon-Fat et al., and Ariza et al. For all outbreaks except Kaburi et al. the raw data was not available, thus the date of symptom onset data had to be reconstructed using the epidemic curve provided in the manuscript. The original data from Kaburi et al. is also available in the data directory (Kaburi_et_al_data_scabies.xlsx).
    • Source:
  • scabies_data_yearly.xlsx
    • Description: Annual scabies incidence per 1000 people in the Netherlands from 2011-2023.
    • Source: Nivel
  • scabies_data_consultation_weekly.xslx
    • Description: Weekly numbers of persons consulting for scabies (per 100,000 people) from 2011 to 2023 in the Neltherlands as diagnosed by general practitioners (GPs). Note: Individuals in institutions (e.g., care homes, prisons) usually have their own health care provider and are generally not taken into account in GP registrations.
    • Source: Nivel

Example

This is a basic example of estimating the mean serial interval from data on time of symptom onset for an infectious disease. We first simulate index case-to-case (ICC) intervals. Here, we directly simulate the ICC intervals based on their route of transmission. The specified mean serial interval hmu is 15 and the specified standard deviation hsigma is 3. The weights for each route of transmission are specified as hw1, hw2, hw3, and hw4, respectively.

set.seed(1234)

N <- 5000; hmu<-15; hsigma<-3; hw1 <- 0.2; hw2 <- 0.5; hw3 <- 0.2; hw4 <- 0.1

CP <- rhalfnorm((hw1*N),theta=sqrt(pi/2)/(sqrt(2)*hsigma))
PS <- rnorm(hw2*N,mean=hmu,sd=hsigma)
PT <- rnorm(hw3*N,mean=2*hmu,sd=sqrt(2)*hsigma)
PQ <- rnorm(hw4*N,mean=3*hmu,sd=sqrt(3)*hsigma)

sim_data <- round(c(CP,PS,PT,PQ))

We can look at our simulated data by plotting them as a histogram.

Figure 1. Histogram of index-case to case intervals (in days) for simulated data.

Figure 1. Histogram of index-case to case intervals (in days) for simulated data.

Then, we estimate the mean and standard deviation of the simulated serial interval using the si_estim function.

results <- si_estim(sim_data)
results
#> $mean
#> [1] 15.16373
#> 
#> $sd
#> [1] 2.823293
#> 
#> $wts
#> [1] 2.113966e-01 4.841131e-01 7.836824e-09 2.021913e-01 3.062666e-15
#> [6] 1.022991e-01 1.368660e-21

The output of si_estim is a named list with elements mean, sd, and wts, which contain the estimated mean, standard deviation, and weights of the serial interval distribution, respectively. We see that using the simulated data and assuming an underlying normal distribution, we obtain estimates very close to the input values: a mean serial interval estimate of 15.16 and a standard deviation of 2.82. We are also able to recapture the input weights: hw1 = 0.21, hw2 = 0.48, hw3 = 0.2, hw4 = 0.1.

Using the plot_si_fit function, we can use the outputs of si_estim to plot the fitted serial interval over the symptom onset data.

plot_si_fit(
    dat = sim_data,
    mean = results$mean[1],
    sd = results$sd[1],
    weights = c(results$wts[1], results$wts[2] + results$wts[3],
                results$wts[4] + results$wts[5], results$wts[6] + results$wts[7]),
    dist = "normal"
  )
Figure 2. Fitted serial interval curves plotted over symptom onset data for simulated symptom onset data. Red line is the fitted serial interval curves assuming an underlying Normal distribution.

Figure 2. Fitted serial interval curves plotted over symptom onset data for simulated symptom onset data. Red line is the fitted serial interval curves assuming an underlying Normal distribution.