{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T16:04:19Z","timestamp":1791821059646,"version":"4.3.4"},"reference-count":62,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:00:00Z","timestamp":1758844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Makerere University Data Science Research Training Program","award":["U2RTW012116"],"award-info":[{"award-number":["U2RTW012116"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Pathogens"],"abstract":"<jats:p>Drug-resistant Mycobacterium tuberculosis remains a significant global public health threat. While whole-genome sequencing (WGS) holds immense promise for understanding transmission dynamics and drug resistance mechanisms, its integration into routine surveillance remains limited. Additionally, insights from WGS are increasingly contributing to vaccine discovery by identifying novel antigenic targets and understanding pathogen evolution. The COVID-19 pandemic catalyzed an unprecedented expansion of genomic capacity in many low- and middle-income countries (LMICs), with public health institutions acquiring next-generation sequencing (NGS) platforms and developing local expertise in real-time pathogen surveillance. This hard-won capacity now represents a transformative opportunity to accelerate TB control enabling rapid detection of drug-resistant strains and high-resolution mapping of transmission networks that are critical for timely, targeted interventions. Furthermore, the integration of machine learning with genomic and clinical data offers a powerful avenue to improve the prediction of drug resistance and to tailor patient-specific TB management strategies. This article examines the practical challenges, emerging opportunities, and policy considerations necessary to embed genomic epidemiology within national TB control programs, particularly in high-burden, resource-constrained settings.<\/jats:p>","DOI":"10.3390\/pathogens14100975","type":"journal-article","created":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T10:39:37Z","timestamp":1758883177000},"page":"975","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Reimagining Tuberculosis Control in the Era of Genomics: The Case for Global Investment in Mycobacterium tuberculosis Genomic Surveillance"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8445-9414","authenticated-orcid":false,"given":"Gerald","family":"Mboowa","sequence":"first","affiliation":[{"name":"Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA"},{"name":"African Centre of Excellence in Bioinformatics and Data-Intensive Sciences, Infectious Diseases Institute, College of Health Sciences, Makerere University, Kampala P.O. Box 22418, Uganda"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,26]]},"reference":[{"key":"ref_1","unstructured":"(2025, July 29). 3 Drug-Resistant TB. Available online: https:\/\/www.who.int\/teams\/global-programme-on-tuberculosis-and-lung-health\/tb-reports\/global-tuberculosis-report-2023\/tb-disease-burden\/1-3-drug-resistant-tb."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1007\/s40264-015-0267-y","article-title":"Safety and Tolerability Profile of Second-Line Anti-Tuberculosis Medications","volume":"38","author":"Ramachandran","year":"2015","journal-title":"Drug Saf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"15","DOI":"10.4049\/jimmunol.0903856","article-title":"Understanding Latent Tuberculosis: A Moving Target","volume":"185","author":"Lin","year":"2010","journal-title":"J. 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