Spatial and Genomic Data to Characterize Endemic Typhoid Transmission

Gauld, Jillian S and Olgemoeller, Franziska and Heinz, Eva and Nkhata, Rose and Bilima, Sithembile and Wailan, Alexander M and Kennedy, Neil and Mallewa, Jane and Gordon, Melita A and Read, Jonathan M and Heyderman, Robert S and Thomson, Nicholas R and Diggle, Peter J and Feasey, Nicholas A (2022) Spatial and Genomic Data to Characterize Endemic Typhoid Transmission. Clinical Infectious Diseases, 74 (11). pp. 1993-2000. ISSN 1058-4838

Abstract

BACKGROUND: Diverse environmental exposures and risk factors have been implicated in the transmission of Salmonella Typhi, but the dominant transmission pathways through the environment to susceptible humans remain unknown. Here, we use spatial, bacterial genomic, and hydrological data to refine our view of typhoid transmission in an endemic setting. METHODS: A total of 546 patients presenting to Queen Elizabeth Central Hospital in Blantyre, Malawi, with blood culture-confirmed typhoid fever between April 2015 and January 2017 were recruited to a cohort study. The households of a subset of these patients were geolocated, and 256 S. Typhi isolates were whole-genome sequenced. Pairwise single-nucleotide variant distances were incorporated into a geostatistical modeling framework using multidimensional scaling. RESULTS: Typhoid fever was not evenly distributed across Blantyre, with estimated minimum incidence ranging across the city from <15 to >100 cases per 100 000 population per year. Pairwise single-nucleotide variant distance and physical household distances were significantly correlated (P = .001). We evaluated the ability of river catchment to explain the spatial patterns of genomics observed, finding that it significantly improved the fit of the model (P = .003). We also found spatial correlation at a smaller spatial scale, of households living <192 m apart. CONCLUSIONS: These findings reinforce the emerging view that hydrological systems play a key role in the transmission of typhoid fever. By combining genomic and spatial data, we show how multifaceted data can be used to identify high incidence areas, explain the connections between them, and inform targeted environmental surveillance, all of which will be critical to shape local and regional typhoid control strategies.