Title:	Bayesian Transmission Models
Version:	0.1.0
Description:	Provides Bayesian inference methods for infectious disease transmission models in healthcare settings. Implements Markov Chain Monte Carlo (MCMC) algorithms for estimating transmission parameters from patient-level data including admission, discharge, and testing events as described in Thomas 'et al.' (2015) <doi:10.1093/imammb/dqt021>.
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.3
Imports:	assertthat, dplyr, methods, Rcpp (≥ 1.0.12), rlang
Config/testthat/edition:	3
Depends:	R (≥ 4.2.0)
LazyData:	true
LinkingTo:	Rcpp, RcppArmadillo
SystemRequirements:	C++17, BLAS, LAPACK
Suggests:	checkmate, devtools, ggplot2, testthat (≥ 3.0.0), tidyr, knitr, rmarkdown, pillar
RcppModules:	Infect
URL:	https://epiforesite.github.io/bayestransmission/, https://github.com/EpiForeSITE/bayestransmission
VignetteBuilder:	knitr
BugReports:	https://github.com/EpiForeSITE/bayestransmission/issues
NeedsCompilation:	yes
Packaged:	2025-12-07 01:55:57 UTC; root
Author:	Andrew Redd [aut, cre], Alun Thomas [aut], University of Utah [cph], CDC's Center for Forecasting and Outbreak Analytics [fnd] (This project was made possible by cooperative agreement CDC-RFA-FT-23-0069 (grant # NU38FT000009-01-00) from the CDC's Center for Forecasting and Outbreak Analytics. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention.)
Maintainer:	Andrew Redd <andrew.redd@hsc.utah.edu>
Repository:	CRAN
Date/Publication:	2025-12-12 21:10:07 UTC

Antibiotic Parameters

Description

Antibiotic Parameters

Usage

AbxParams(onoff = FALSE, delay = 0, life = 1)

Arguments

Value

list of parameters for antibiotic effect

Examples

AbxParams()

Antibiotic Administration Rate Parameters

Description

Antibiotic Administration Rate Parameters

Usage

AbxRateParams(
  uncolonized = Param(1, 0),
  colonized = Param(1, 0),
  latent = Param(0)
)

Arguments

Value

list of parameters for antibiotic administration.

Examples

AbxRateParams()

Clearance Parameters

Description

Clearance Parameters

Usage

ClearanceParams(rate = Param(0.01), abx = Param(1, 0), ever_abx = Param(1, 0))

Arguments

Value

A list of parameters for clearance.

Examples

ClearanceParams()

Convert coded events to string events

Description

Convert coded events to string events

Usage

CodeToEvent(x)

Arguments

Value

A vector of strings

Examples

CodeToEvent(c(-1:19, 21:23, 31:33, -999))

Convert string events to coded events

Description

Convert string events to coded events

Usage

EventToCode(x)

Arguments

Value

A vector of integers

Examples

EventToCode(c("admission", "discharge", "postest", "negtest"))
EventToCode(c("abxon", "abxoff", "isolon", "isoloff"))

In Unit Parameters

Description

In Unit Parameters

Usage

InUnitParams(
  acquisition = LinearAbxAcquisitionParams(),
  progression = ProgressionParams(),
  clearance = ClearanceParams()
)

ABXInUnitParams(
  acquisition = LinearAbxAcquisitionParams(),
  progression = ProgressionParams(),
  clearance = ClearanceParams()
)

Arguments

Value

A list of parameters for in unit infection.

Functions

• ABXInUnitParams(): In Unit Parameters with Antibiotics.

Examples

InUnitParams(
  acquisition = LinearAbxAcquisitionParams(),
  progression = ProgressionParams(),
  clearance = ClearanceParams()
)
ABXInUnitParams(
  acquisition = LinearAbxAcquisitionParams(),
  progression = ProgressionParams(),
  clearance = ClearanceParams()
)

InSitu Parameters

Description

InSitu Parameters

Usage

InsituParams(probs = NULL, priors = NULL, doit = NULL, nstates = NULL)

Arguments

Value

A list of parameters for in situ testing.

Examples

InsituParams()
InsituParams(nstates = 2)  # c(0.9, 0.0, 0.1)
InsituParams(nstates = 3)  # c(0.98, 0.01, 0.01)

Linear Antibiotic Acquisition Parameters

Description

Acquisition parameters for LinearAbxModel and LinearAbxModel2.

Usage

LinearAbxAcquisitionParams(
  base = Param(0.001),
  time = Param(1, 0),
  mass = Param(1, 1),
  freq = Param(1, 1),
  col_abx = Param(1, 0),
  suss_abx = Param(1, 0),
  suss_ever = Param(1, 0)
)

Arguments

Details

The model for this acquisition model is given by

P(\mathrm{Acq(t)}) = \left[e^{\beta_\mathrm{time}(t-t_0)}\right]\\ \left\{e^{\beta_0} \left[ \left( \frac{\beta_\mathrm{freq}}{P(t)}+(1 - e^{\beta_\mathrm{freq}}) \right) e^{\beta_\mathrm{mass}}\left( (N_c(t) - N_{ca}(t)) + e^{\beta_\mathrm{col\_abx}}N_{ca}(t) \right) + 1 - e^{\beta_\mathrm{mass}} \right] \right\}\\ \left[ N_S(t) - N_E(t) + e^{\beta_\mathrm{suss\_ever}} \left( \left( E_i(t) - A_i(t) \right) + A_i(t)e^{\beta_\mathrm{suss\_abx}} \right) \right]

where P(Acq(t)) is the acquisition probability at time t, with effects from time (beta_time), mass action (beta_mass), frequency dependence (beta_freq), colonized individuals on antibiotics (beta_col_abx), and susceptible individuals currently (beta_suss_abx) or ever (beta_suss_ever) on antibiotics.

Value

A list of parameters for acquisition.

Examples

LinearAbxAcquisitionParams()

Log-Normal Acquisition Parameters

Description

Acquisition parameters for the log-normal model (LogNormalAbxICP). This model has 8 acquisition parameters accessed by index in C++. Note: When accessed via setupLogNormalICPAcquisition, parameters are set by index, so this returns an unnamed list where position matters.

Usage

LogNormalAcquisitionParams(
  time = Param(0, 0),
  constant = Param(0.001, 1),
  log_tot_inpat = Param(-1, 0),
  log_col = Param(1, 0),
  col = Param(0, 0),
  abx_col = Param(0, 0),
  onabx = Param(0, 0),
  everabx = Param(0, 0)
)

Arguments

Value

An unnamed list of 8 parameters in the correct order for LogNormalAbxICP.

Examples

LogNormalAcquisitionParams()

Model Parameters for a Log Normal Model

Description

Model Parameters for a Log Normal Model

Usage

LogNormalModelParams(
  modname,
  nstates = 2L,
  nmetro = 1L,
  forward = TRUE,
  cheat = FALSE,
  Insitu = NULL,
  SurveillanceTest = SurveillanceTestParams(),
  ClinicalTest = ClinicalTestParams(),
  OutOfUnitInfection = OutOfUnitInfectionParams(),
  InUnit = NULL,
  Abx = AbxParams(),
  AbxRate = AbxRateParams()
)

LinearAbxModel(..., InUnit = ABXInUnitParams())

Arguments

Value

A list of parameters for the model.

Functions

• LinearAbxModel(): Linear Antibiotic Model Alias

Examples

LogNormalModelParams("LogNormalModel")

Out of Unit Infection Parameters

Description

Out of Unit Infection Parameters

Usage

OutOfUnitInfectionParams(
  acquisition = Param(0.05),
  clearance = Param(0.01),
  progression = Param(0)
)

Arguments

Value

A list of parameters for out of unit infection.

Examples

OutOfUnitInfectionParams()

Construct a parameter with a prior, weight and an update flag.

Description

Construct a parameter with a prior, weight and an update flag.

Usage

Param(
  init,
  weight = if_else(init == 0, 0, 1),
  update = weight > 0,
  prior = init
)

Arguments

Value

A list with the following elements:

• init the initial value of the parameter.

• weight the weight of the prior.

• update a flag indicating if the parameter shouldbe updated in the MCMC.

• prior mean value of the prior distribution, may be used with weight to fully determine prior parameters.

Examples

# Fully specified parameter.
Param(init = 0, weight = 1, update = TRUE, prior = 0.5)
# Fixed parameter
# Weight = 0 implies update=FALSE and prior is ignored.
Param(0, 0)
# Update parameter that starts at zero.
Param(0, weight =1, update=TRUE)
# Parameters specified at zero implies fixed.
Param(0)

Specify a random testing parameter with a rate.

Description

Specify a random testing parameter with a rate.

Usage

ParamWRate(param = Param(), rate = Param())

Arguments

Value

A list of with param and rate.

Examples

ParamWRate(Param(0.5, 0), rate = Param(1, 0))

Progression Parameters

Description

Progression Parameters

Usage

ProgressionParams(
  rate = Param(0.01),
  abx = Param(1, 0),
  ever_abx = Param(1, 0)
)

Arguments

Value

A list of parameters for progression.

Examples

ProgressionParams()

Random Testing Parameter Set

Description

Random Testing Parameter Set

Usage

RandomTestParams(
  uncolonized = ParamWRate(Param(0.5, 0), Param(1, 0)),
  colonized = ParamWRate(Param(0.5, 0), Param(1, 0)),
  latent = ParamWRate(Param(0), Param(0))
)

ClinicalTestParams(
  uncolonized = ParamWRate(Param(0.5, 0), Param(1, 0)),
  colonized = ParamWRate(Param(0.5, 0), Param(1, 0)),
  latent = ParamWRate(Param(0), Param(0))
)

Arguments

Value

list of parameters for random testing.

Functions

• ClinicalTestParams(): Clinical Test Parameters Alias

Examples

RandomTestParams()

Surveillance Test Parameters

Description

Specify the rates of positive tests for each state of the model.

Usage

SurveillanceTestParams(
  colonized = Param(init = 0.8, weight = 1),
  uncolonized = Param(init = 1e-10, weight = 0),
  latent = Param(init = 0, weight = 0)
)

Arguments

Value

A list of parameters for surveillance testing.

Examples

SurveillanceTestParams()

Extract Model Parameters from C++ Model Object

Description

Convenience function to extract all parameter values from a C++ model object created with newCppModel(). This is essentially a wrapper around accessing the model's parameter properties.

Usage

getCppModelParams(model)

Arguments

Value

A named list containing all model parameter values:

• Insitu - Named numeric vector of in situ parameter values

• SurveillanceTest - Named numeric vector of surveillance test parameter values

• ClinicalTest - Named numeric vector of clinical test parameter values

• OutCol - Named numeric vector of out-of-unit colonization parameter values

• InCol - Named numeric vector of in-unit colonization parameter values

• Abx - Named numeric vector of antibiotic parameter values (if applicable)

If a component's names cannot be determined or lengths mismatch, the vector is returned unnamed.

Examples

# Create a linear antibiotic model
params <- LinearAbxModel()
model <- newCppModel(params)

# Extract all parameters
all_params <- getCppModelParams(model)

# View specific parameter groups
all_params$InCol  # In-unit colonization parameters
all_params$Insitu # In situ parameters

Get compilation flags for exposed classes

Description

Returns information about which optional classes are exposed in this build. This allows tests to conditionally skip tests for unexposed classes.

Usage

getExposureFlags()

Value

List with logical flags:

• comprehensive_testing - TRUE if comprehensive testing classes are exposed

• all_classes - TRUE if all optional classes are exposed

• minimal - TRUE if only critical classes are exposed

Convert MCMC Parameters to Data Frame

Description

Converts the nested list structure of MCMC parameters from runMCMC output into a tidy data frame format suitable for analysis and visualization.

Usage

mcmc_to_dataframe(mcmc_results)

Arguments

Details

The function extracts parameters from the nested list structure and handles missing values gracefully by inserting NA when a parameter is not present. This is particularly useful for creating trace plots and posterior distributions.

Value

A data frame with one row per MCMC iteration containing:

• iteration: The iteration number

• insitu_*: In-situ probability parameters

• surv_test_*: Surveillance test parameters

• clin_test_*: Clinical test parameters and rates

• outunit_*: Out of unit infection parameters

• inunit_*: In unit LinearAbx model parameters (base, time, mass, freq, colabx, susabx, susever, clr, clrAbx, clrEver)

• abxrate_*: Antibiotic rate parameters

• loglikelihood: Log likelihood at each iteration

Examples

results <- runMCMC(data = simulated.data,
                   modelParameters = LinearAbxModel(),
                   nsims = 10,
                   nburn = 0,
                   outputparam = TRUE,
                   outputfinal = FALSE)
param_df <- mcmc_to_dataframe(results)
head(param_df)

Create a new C++ model object with parameters

Description

Creates and initializes a C++ model object based on the provided parameters. This function wraps the underlying C++ model classes (LogNormalModel, LinearAbxModel, LinearAbxModel2, MixedModel) in appropriate R reference classes that expose the model's methods and properties.

Usage

newCppModel(modelParameters, verbose = FALSE)

Arguments

Details

The function uses the existing newModel C++ function to instantiate the model and configure all parameters, then wraps it in the appropriate R reference class based on the model type specified in modelParameters$modname.

Value

A reference class object wrapping the C++ model. The specific class depends on modelParameters$modname:

• CppLogNormalModel - For "LogNormalModel"

• CppLinearAbxModel - For "LinearAbxModel"

• CppLinearAbxModel2 - For "LinearAbxModel2"

• CppMixedModel - For "MixedModel" (if exposed in C++)

All returned objects inherit from CppBasicModel and provide access to:

• Properties:

  • InColParams - In-unit colonization parameters

  • OutColParams - Out-of-unit colonization parameters

  • InsituParams - In situ parameters

  • SurveillanceTestParams - Surveillance test parameters

  • ClinicalTestParams - Clinical test parameters

  • AbxParams - Antibiotic parameters

• Methods:

  • logLikelihood(hist) - Calculate log likelihood for a SystemHistory

  • getHistoryLinkLogLikelihoods(hist) - Get individual link log likelihoods

  • forwardSimulate(...) - Perform forward simulation

  • initEpisodeHistory(...) - Initialize episode history

  • sampleEpisodes(...) - Sample episodes

  • setAbx(...) - Set antibiotic parameters

See Also

• LogNormalModelParams() for creating model parameters

• LinearAbxModel() for linear antibiotic model parameters

• InsituParams(), SurveillanceTestParams(), etc. for parameter components

• newModelExport() for extracting parameter values from a model

Examples

# Create a linear antibiotic model (recommended - stable constructors)
params <- LinearAbxModel()
model <- newCppModel(params)

# Access model properties
inColParams <- model$InColParams
insituParams <- model$InsituParams

# Get parameter values
paramValues <- inColParams$values

# Get parameter names (if available)
paramNames <- inColParams$names

# Create a log-normal model
params <- LogNormalModelParams("LogNormalModel")
model <- newCppModel(params, verbose = TRUE)

Create a new model object

Description

Creates and initializes a model object based on the provided parameters. This allows direct creation and inspection of model objects without running MCMC. Returns a list with all model parameter values for verification.

Usage

newModelExport(modelParameters, verbose = FALSE)

Arguments

Value

A list containing the initialized model parameters:

• Insitu - In situ parameters

• SurveillanceTest - Surveillance test parameters

• ClinicalTest - Clinical test parameters

• OutCol - Out of unit colonization parameters

• InCol - In unit colonization parameters

• Abx - Antibiotic parameters

Run Bayesian Transmission MCMC

Description

Run Bayesian Transmission MCMC

Usage

runMCMC(
  data,
  modelParameters,
  nsims,
  nburn = 100L,
  outputparam = TRUE,
  outputfinal = FALSE,
  verbose = FALSE
)

Arguments

Value

A list with the following elements:

• Parameters the MCMC chain of model parameters (if outputparam=TRUE)

• LogLikelihood the log likelihood of the model at each iteration (if outputparam=TRUE)

• MCMCParameters the MCMC parameters used

• ModelParameters the model parameters used

• ModelName the name of the model

• nstates the number of states in the model

• waic1 the WAIC1 estimate

• waic2 the WAIC2 estimate

• and optionally (if outputfinal=TRUE) FinalModel the final model state.

See Also

mcmc_to_dataframe

Examples

  # Minimal example: create parameters and run a very short MCMC
  params <- LinearAbxModel(nstates = 2)
  data(simulated.data_sorted, package = "bayestransmission")
  results <- runMCMC(
    data = simulated.data_sorted,
    modelParameters = params,
    nsims = 3,
    nburn = 0,
    outputparam = TRUE,
    outputfinal = FALSE,
    verbose = FALSE
  )
  str(results)

Simulated Transmission Data

Description

This data set contains simulated transmission data for a hypothetical infectious disease. The data set contains the following columns:

Usage

simulated.data

Format

An object of class data.frame with 8360 rows and 5 columns.

Details

• facility: The facility where the event occurred.

• unit: The unit within the facility where the event occurred.

• time: The time at which the event occurred.

• patient: The patient involved in the event.

• type: The type of event.

Simulated Transmission Data (Sorted)

Description

This is a sorted version of the simulated transmission data. The data is sorted by time, then by patient. See simulated.data for column descriptions.

Usage

simulated.data_sorted

Format

An object of class data.frame with 8360 rows and 5 columns.