Long-term refined genomic analysis of tuberculosis clusters to distinguish between ongoing transmission, reactivations or diagnostic delays
Tuberculosis (TB) clusters are interpreted as ongoing transmission events. Our aim is to evaluate whether other reasons could be behind the inclusion of new cases in clusters, to better manage each new clustered case. First, genomic data were quantitatively analysed to identify clustered cases (<12 SNPs). An evolutionary analysis was then performed, positioning the cases in genomic networks based on the SNPs distribution. The position of the new clustered cases in relation to those preceding them was considered to interpret the reasons for the growth of each cluster, supported by epidemiological and clinical data. We identified 106 clusters (years 2003-2024), in Almería, Spain, containing 537 cases. 106 (34.6%) of the cases diagnosed in the last four years were included in 53 clusters; 22 were new clusters, while the rest were growing clusters already identified before 2021. New cluster entries were due to ongoing transmission (linked in the genomic network to a recently diagnosed case at 0-2 SNPs) in only 29% of the growing clusters and in 63.6% of the new clusters. For new clustered cases not due to ongoing transmission, genomic network analysis allowed us to identify clusters with the likely involvement of i) reactivations of past exposures (new case close to another case diagnosed >4 years before), ii) prolonged diagnostic delays or subclinical periods (new case positioned in branches with a high number of preceding SNPs, suggesting persistent bacterial viability), or iii) multifactorial clusters growing by reactivations, diagnostic delays and/or ongoing transmission. Genomic evolutionary analysis is required for accurate interpretation of growing clusters. Only one third of the growing clusters in Almería correspond to ongoing transmission. Reactivation of past exposures, prolonged diagnostic delay or subclinical TB also played a role in growing clusters. Precise identification of the causes of growing clusters allows specific management of each new clustered case.