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Files to fit model in Konzen et al. (2023)

Installation

You will need to install the BIID package from source. The package depends on the Rcpp and RcppArmadillo packages, which require the installation of the correct C++ compilers. The guidance below is taken from Sections 2.1.1, 2.1.2 or 2.1.3 here.

Windows

Install Rtools.

(Make sure you tick the option to add Rtools to the PATH whilst installing if requested.)

(Tested on R 4.3.1 on Windows 10.)

Mac

Install Xcode command line tools. Execute the command xcode-select --install in a Terminal.

You might also need to install the gfortran libraries from:

https://cran.r-project.org/bin/macosx/tools/gfortran-6.1.pkg

(Not tested.)

Linux

Install gcc and related packages (you might also need gcc-fortran for some of the dependencies).

In Ubuntu Linux, execute the command sudo apt-get install r-base-dev in a Terminal.

(Tested on R 4.3.1 on Ubuntu 22.04.)

Install package

Once the compilers have been installed, then the version in this repository can be installed from source using the devtools package in R. That is, install and/or load the devtools package and then set your working directory in R to correspond to the parent directory that contains the BIID folder contained in the repository. Then run:

library(devtools)
install("BIID")

Once installed, the package can be loaded as usual using e.g.

library(BIID)

Additional packages

Additional packages required can be installed from within R using:

install.packages(c("tidyverse", "reshape2", "MCMCpack"))

Model and data

Notation:

  • $G$: number of social groups.
  • $T$: number of quarters in the whole study, which is on $t = 1, \dots, T$.
  • $m$: number of individuals.
  • $N$: number of iterations.
  • $p$: number of parameters.
  • $J$: number of diagnostic tests.

Data

Processed data required to reproduce results:

  • birthTimes.rds: integer vector (of length $m$) with the birth times.

  • CaptEffort.rds: ($G \times T$) matrix indicating if social group $g$ was being monitored at time $t$ (1 if yes, 0 otherwise).

  • CaptHist.rds: ($m \times T$) matrix indicating if individual $i$ was captured at time $t$ (1 if yes, 0 otherwise).

  • capturesAfterMonit.rds: matrix of two columns showing the last capture times (second column) for individuals (first column) captured after the monitoring period. This information is used for the inference of mortality rates.

  • dfTimes.rds: ($T \times 2$) look-up matrix showing the times in calendar years, e.g.

    > dfTimes
    idx    time
1     1 1980.00
2     2 1980.25
3     3 1980.50
4     4 1980.75
5     5 1981.00

  • endSamplingPeriod.rds: integer vector (of length $m$) showing when the individuals stopped being monitored.

  • groupNames.rds: character vector (of length $G$) with the social group names.

  • startSamplingPeriod.rds: integer vector (of length $m$) showing when the individuals started being monitored.

  • TestMat.rds: matrix where each row represents a capture event. The first three columns are time, individual number, and social group. The remaining columns are diagnostic test results (1 if positive, 0 if negative, NA if not conclusive or not taken).

  • timeVec.rds: vector (of length $T$) showing the dates in calendar years e.g.

    [1] 1980.00 1980.25 1980.50 1980.75 1981.00

Fitting the model

File runmodel.R is used for reading processed data and for choosing priors, initial conditions and details about HMC/RWMH parameter updates.

Outputs of the fitted model

Outputs (posterior samples for parameters and hidden states) are saved in the directory resultsDirectory which is chosen by the user in runmodel.R.

Output files are [X].Rds, where X is:

  • postPars: ($N \times p$) matrix with the posterior samples for all model parameters.
  • logLik and logPost: ($N \times 1$) vectors with the log-likelihood and log-posterior, respectively, for all iterations.
  • nSus, nExp and nInf: ($T \times N$) matrices showing the total number of individuals in each compartment ($S$, $E$, and $I$, respectively) in the whole population, at each time point, for each iteration.

Other outputs are split into blocks of iterations, with each block stored in a different folder e.g. Iters_from1to1000. In the bullet points below $N$ refers to the number of iterations in each block, and then within each folder there are:

  • infTimes, infectivityTimes, deathTimes: ($m \times N$) matrices with the infection times, times of infectiousness, and death times, respectively, for each iteration. The value -10L denotes the absence of the event.
  • nSusTested, nExpTested and nInfTested: ($T \times N \times J$) arrays showing the total number of individuals in each compartment which were tested by each diagnostic test.
  • nSusByGroup, nExpByGroup, nInfByGroup: ($G \times T \times N$) arrays showing the number of individuals in each compartment ($S$, $E$, and $I$, respectively) in each social group, at each time point, for each iteration.
  • nSusTestedByGroup, nExpTestedByGroup and nInfTestedByGroup: lists of $G$ elements (one for each social group), where each element is a ($T \times N \times J$) array showing the number of individuals, in each compartment, which were tested by each diagnostic test.
  • AcontribPop: ($N \times 1$) vector with the relative contribution of the background transmission rates for each iteration in the whole population.
  • AcontribGroup: ($G \times N$) matrix with the relative contribution of the background transmission rates for each iteration in each social group.
  • AcontribPopTime: ($T \times N$) matrix with the relative contribution of the background transmission rates for each iteration at each time point in the whole population.
  • AcontribGroupTime: ($G \times T \times N$) matrix with the relative contribution of the background transmission rates for each iteration at each time point in each social group.