Identifying linked cases of an infectious disease can improve our understanding of its epidemiology by distinguishing sustained local transmission from frequent introductions with little onward transmission. This evidence can, in turn, inform decisions on the most appropriate interventions. Knowledge of key epidemiological distributions and reporting probabilities is key in identifying linked cases. However, with multi-host pathogens quantitative differences between hosts may need consideration, which are not incorporated in existing methods.

In this study, an existing graph-based approach to detecting outbreak clusters was extended to allow for group-specific reporting probabilities and epidemiological distributions and to assess the level and importance of assortative mixing. This method was applied to data on probable animal rabies cases in south-east Tanzania where wildlife comprised over 40% of detected animal rabies cases.

Group-specific differences (in reporting probabilities and epidemiological distributions) and the level of assortative mixing had a marked impact on the size and composition of identified clusters. The scenario most compatible with the data involved higher reporting probabilities for cases in domestic animals compared to wildlife, no difference between the mean transmission distance between domestic animals versus wildlife and substantial assortative mixing with frequent inter-species transmission.

The method described here could be applied to other multi-host systems or to single-host systems with multiple groups (such as age-classes) in which heterogeneities in reporting probabilities, distributional parameters and/or levels of mixing exist between groups. This would allow more accurate characterisation of transmission dynamics which would facilitate implementation of more effective interventions.