Quantifying the community transmission of <i>Mycobacterium tuberculosis</i> in a rapidly growing Chinese city: a nine-year population-based genomic and spatial analysis

Tuberculosis (TB) remains a significant public health threat in urbanizing regions of China, where shifting population dynamics and migration may amplify TB transmission. We conducted a nine-year prospective epidemiological study of culture-positive TB patients diagnosed in Shenzhen, between 1 January 2014 and 31 December 2022, and employed whole-genome sequencing analysis to describe local transmission of Mycobacterium tuberculosis (Mtb). Mtb transmission hotspots were identified using a non-parametric distance-based mapping approach. We applied a spatially structured logistic regression and hierarchical Bayesian pairwise regression analysis to identify demographic, pathogen, and spatial factors associated with local transmission. A Bayesian phylogenetic analysis was used to infer probable transmission events. Among 4,560 individuals with culture-positive TB, 93.4% (4,261) were internal migrants in China. 21.8% (996/4,560) of individuals had Mtb isolates that belonged to genomic clusters, with multiple transmission foci detected in the central business district and suburban industrial areas. Transmission was more likely to occur among patients of similar age, close geographic proximity, and migrants with a shared provincial origin. Mtb isolates from communities with higher proportions of migrants had an increased risk of clustering. In this rapidly growing urban setting, a significant proportion of cases were documented to arise as a result of local transmission, which appears to be driven by specific social and geographical factors, particularly within migrant populations. Control strategies should therefore move beyond static models toward dynamic interventions that specifically target social networks, high-risk communities, and urban hotspots in dynamic urban environments.