simulate_data: Nosocomial transmission data simulation

In bitrugs: Bayesian Inference of Transmission Routes Using Genome Sequences

Description

Simulation of hospital admission, discharge and disease transmission. Swab test results and genetic information are generated.

Usage

simulate_data(n, D, LOS = 7, p = 0.05, z = 0.8, b = 0.005, gamma = 0.3, gamma_gl = 0.03, 
genpar = 0.8, testdays = 3, model = 2)

Arguments

n	Number of patient admissions to be generated.
D	Number of days over which patient admissions may be randomly distributed.
LOS	Mean length of hospital stay; lengths are drawn from a Poisson distribution.
p	Probability of carriage on admission.
z	Swab test sensitivity.
b	Transmission rate.
gamma	Within host/group genetic diversity (geometric distribution parameter).
gamma_gl	Between host/group genetic diversity (geometric distribution parameter).
genpar	Group clustering parameter (model 1), or transmission chain parameter (model 2). See details below.
testdays	Number of days between each carriage test/isolate collection.
model	Genetic diversity model (1: Importation clustering model, 2: Transmission chain model). See details below.

Details

We implement two different models of genetic diversification. Model 1 is the importation clustering model, in which each infected host belongs to a group. The probability that a new importation belongs to a new group is genpar. All hosts within the same transmission chain belong to the same group. Pairwise genetic distances are sampled from geometric distributions with parameters gamma and gamma_gl for within-group and between-group pairs respectively. Model 2 is the transmission chain model, in which genetic distances increase as hosts are further separated in the transmission tree. Genetic distances between hosts separated by k transmission links are drawn independently from the geometric distribution with parameter gamma x genpar^k. Genetic distances between hosts in independent transmission chains are drawn from a geometric distribution with parameter gamma_gl.

Value

Returns a list of simulated data;

epi	Matrix of epidemiological data, consisting of columns patient ID, day of admission, day of discharge, time of colonization, source of infection, and infection group.
resmat	Matrix of test results. Each row represents the corresponding patient in epi. Each column represents a day, spanning the interval over which data were collected. 0=Negative, 1=Positive, -1=Missing
distmat	Pairwise genetic distance data. Each row and column corresponds to patient ID in patientseqIDs.
patientseqIDs	Vector of patient IDs corresponding to the rows and columns of distmat.

See Also

simulate_data_dates to pre-specify admission and discharge dates.

Examples

  Tsim <- simulate_data(n=100,D=50,p=0.1,b=0.01)

bitrugs documentation built on May 2, 2019, 8:55 a.m.