Fig 1.
Data filtering process.
Table 1.
Description of health insurance datasets.
Table 2.
Basic description of AOK hospital networks.
Table 3.
Basic description of AOK patient characteristics.
Fig 2.
Impact of sampling and scale-up on direct hospital network measures (visualized in boxplots).
The weighted absolute value of relative biases (ARBs) across different network measures on sampled (inset) and scale-up (main plot) datasets for various incompleteness levels. Incompleteness was defined as the percentage of removed patients from the original datasets.
Fig 3.
Weighted cosine similarities (CSs).
Weighted CSs in directions “in” and “out” between the sampled networks and the whole hospital networks as a function of the incompleteness levels for three insurance datasets. The solid and dashed lines represented the weighted CSs on direct and indirect hospital networks based on these AOK datasets, respectively.
Fig 4.
Prevalence in community nodes obtained from transmission model simulations for different AOK datasets.
The dashed lines in each graph point the x-axis of prevalence peaks.
Fig 5.
Final PRBs of simulated networks for each AOK plotted against percentage of incompleteness.
Fig 6.
Final prevalence relative biases (PRBs) in hospital nodes for varying transmission parameters.
The final PRBs in hospital nodes were calculated based on the simulations on AOK PLUS patient-movement networks, built by scale-up at incompleteness levels of 96% and 98%. In (a) we presented the final PRBs in hospital nodes for different values of β. In (b) we presented the final PRBs in hospital nodes for different values of γ.