Inferring person-to-person networks of Plasmodium falciparum transmission: are analyses of routine surveillance data up to the task?

• Published in: Malaria Journal
• Main Authors: John H. Huber, Michelle S. Hsiang, Nomcebo Dlamini, Maxwell Murphy, Sibonakaliso Vilakati, Nomcebo Nhlabathi, Anita Lerch, Rasmus Nielsen, Nyasatu Ntshalintshali, Bryan Greenhouse, T. Alex Perkins
• Format: Article in Journal/Newspaper
• Language: English
• Published: BMC 2022
• Subjects: Arctic medicine. Tropical medicine; RC955-962; Infectious and parasitic diseases; RC109-216; Arctic
• Online Access: https://doi.org/10.1186/s12936-022-04072-2; https://doaj.org/article/c87d855c274e44778d5bc4f25a1cd0f5

Abstract Background Inference of person-to-person transmission networks using surveillance data is increasingly used 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. Methods The influence of different combinations of spatial, temporal, and travel-history data on transmission network inferences for Plasmodium falciparum malaria were evaluated. Results The information content of these data types may be limited for inferring person-to-person transmission networks and may lead to an overestimate of transmission. Only when outbreaks were temporally focal or travel histories were accurate was the algorithm able to accurately estimate the reproduction number under control, R c . Applying this approach to data from Eswatini indicated that inferences of R c and spatiotemporal patterns therein depend upon the choice of data types and assumptions about travel-history data. Conclusions These results suggest that transmission network inferences made with routine malaria surveillance data should be interpreted with caution.