Inferring person-to-person networks of pathogen transmission: is routine surveillance data up to the task?

Huber, J. H.; Hsiang, M. S.; Dlamini, N.; Murphy, M.; Vilakati, S.; Nhlabathi, N.; Lerch, A.; Nielsen, R.; Ntshalintshali, N.; Greenhouse, B.; Perkins, A.

Inference of person-to-person transmission networks using routinely collected surveillance data is being used increasingly to estimate spatiotemporal patterns of pathogen transmission. Several data types can be used to inform transmission network inferences, yet the sensitivity of those inferences to different data types is not routinely evaluated. We evaluated the influence of different combinations of spatial, temporal, and travel-history data on transmission network inferences for Plasmodium falciparum, the pathogen responsible for most human malaria. After developing a new inference framework and applying it to simulated data, we found that these data types have limited utility for inferring transmission networks and, in some combinations, tend to overestimate transmission. Only when outbreaks were highly focal in time or when travel histories were highly accurate was the inference algorithm able to accurately estimate the reproduction number under control, Rc, a key metric of transmission. Applying this approach to surveillance data from Eswatini indicated that inferences of Rc and spatiotemporal patterns therein are sensitive to the choice of data types and assumptions about the accuracy of travel-history data. Taken together, these results suggest that transmission network inferences made with routinely collected surveillance data should be interpreted with caution. As we have done here, future studies inferring transmission networks should apply their algorithm to data simulated under alternative assumptions to assess the robustness of their inferences.