Spatial and Genomic Data to Characterize Endemic Typhoid Transmission

Background Diverse environmental exposures and risk factors have been implicated in the transmission of Salmonella Typhi, however, the dominant transmission pathways through the environment to susceptible humans remain unknown. Here, we utilize spatial, bacterial genomic, and hydrological data to refine our view of Typhoid transmission in an endemic setting. Methods 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 (SNV) 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 less than 15 to over 100 cases/100,000/year. Pairwise SNV distance and physical household distances were significantly correlated (p=0.001). 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=0.003). We also found spatial correlation at a smaller spatial scale, of households living <192 meters 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 multi-faceted data can be used to identify high incidence areas, understand the connections between them, and inform targeted environmental surveillance, all of which will be critical to shape local and regional typhoid control strategies.