Plot transmission networks

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Description

Plot inferred, naive and true transmission trees in a circular figure.

Usage

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plot_transnetwork(mcmcoutput, epidata=NULL, type=c(1,2,3), 
plotthresh=0.05, labels="ID", text.cex=1, adj=0.25, ID=NULL, n=NULL)

Arguments

mcmcoutput

MCMC output returned by transmission_analysis function.

epidata

Epidemiological data in the format provided by simulate_data. Required if true or naive networks are to be plotted.

type

Type of tranmission network to plot. 1=Inferred transmission network from MCMC output; 2=naive network, in which equal weighting is provided to all potential sources of infection; 3=True network, as provided by the infection source details in the epidata object.

plotthresh

Probability above which edges in the inferred network are to be marked.

labels

Text labels to be marked beside each node. Options are "ID", which prints patient ID, "import", which provides the probability of importation (probability that patient was infected outside of the ward prior to admission), and "secondary" which provides the estimated number of secondary infections.

text.cex

Text size.

adj

Factor governing the space between nodes and labels.

ID

Vector of patient IDs for plotting order. By default this is a random reordering of patient IDs to position nodes at random around the circle. Must contain at least one positive host ID.

n

Number of patients. This is automatically calculated from the dimensions of mcmcoutput if not specified, however, if this is nonstandard, n should be specified here.

Details

Nodes are randomly ordered around the circle. Red corresponds to acquisitions, while black nodes are importations. For the inferred and naive networks, the color ranges from red to black depending on the probability of importation. Edges are colored and weighted according to the probability of each potential transmission route (blue & bold high probability, green & light, low probability). To generate an identical ordering in successive plots, specify an order with ID.

Examples

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  data(mcmcoutput)
  data(hospitaldata)
  par(mar=c(0,0,2,0))
  w <- sample(1:100,100)
  plot_transnetwork(mcmcoutput, epidata=hospitaldata$epi, 
    type=c(3,1), plotthresh=0.05, labels="ID", text.cex=1.2, 
    adj=0.5, ID=w, n=100)