Determining the appropriate weighting between number of steps (<i>α</i> = 0) and the edge weight (<i>α</i> = 1) to measure the distance through the hospital network.

<p>A) The correlation between network distance and time to infection in the stochastic simulations shows a peak around <i>α</i> = 0.25. The exact value does not strongly depend on the modelled <i>R</i>₀, or other model parameters (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005622#pcbi.1005622.s002" target="_blank">S2</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005622#pcbi.1005622.s004" target="_blank">S4</a> Figs for further sensitivity analyses), with longer delays and higher introduction probabilities only slightly shifting the optimum to lower <i>α</i>. The mean time to infection, reflecting the risk of a hospital getting infected, relates to the hospital’s centrality. B) Although closeness showed the best correlation (at <i>α</i> = 0.30), C) degree centrality performs only marginally less well (at <i>α</i> = 0.50 − 0.60), while D) betweenness showed a poor correlation, with no clear optimal value for <i>α</i>. E) The time to infection as a function of shortest path length between all hospitals using <i>α</i> = 0.25, is strongly correlated with network distance. F) The correlation between the mean time to infection and closeness for each hospital.