A graph-based evidence synthesis approach to detecting outbreak clusters: An application to dog rabies
Fig 5
Summary of the simulation study.
A, B: For the baseline simulation (mimicking rabies transmission in Bangui), performance of the method using different reconstruction scenarios, varying in terms of cutoff used at the pruning step and assumed reporting rate. C, D: Performance of the method, using the control reconstruction scenario (i.e. assuming transmission and evolution parameters as well as reporting rate are known), applied to different simulation scenarios varying in terms of reporting rate and diversity of the pathogen in the imported cases. See materials and methods and S1 Text for definition of all the simulation and reconstruction scenarios. Panels A and C show, across these scenarios, the ability of the model to correctly identify outbreak clusters, as measured by the true positive rate (TPR, proportion of pairs of cases belonging to the same transmission tree who are inferred to be in the same outbreak cluster), the true negative rate (TNR, proportion of pairs of cases not belonging to the same transmission tree who are assigned to different outbreak clusters), and the mean between TPR and TNR. Panels A and C show, across all scenarios, the distribution of the relative error in the estimated reproduction number (R) and importation rate.