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Genomic epidemiology enables tracking of pathogen transmission over all spatial scales, from local outbreaks to global pandemics, thereby highlighting the scale of interventions required.Combining genomic data with individual-level metadata can identify demographic factors driving transmission patterns.The power of genomic epidemiology is increasing due to better models, methods, and novel technologies, including new sample-collection protocols and metadata.Future integration of virus genomic data may enable forecasting of the spatial spread and ignition/decline of epidemics. Genomic epidemiology, which links pathogen genomes with associated metadata to understand disease transmission, has become a key component of outbreak response. Decreasing costs of genome sequencing and increasing computational power provide opportunities to generate and analyse large viral genomic datasets that aim to uncover the spatial scales of transmission, the demographics contributing to transmission patterns, and to forecast epidemic trends. Emerging sources of genomic data and associated metadata provide new opportunities to further unravel transmission patterns. Key challenges include how to integrate genomic data with metadata from multiple sources, how to generate efficient computational algorithms to cope with large datasets, and how to establish sampling frameworks to enable robust conclusions. Genomic epidemiology, which links pathogen genomes with associated metadata to understand disease transmission, has become a key component of outbreak response. Decreasing costs of genome sequencing and increasing computational power provide opportunities to generate and analyse large viral genomic datasets that aim to uncover the spatial scales of transmission, the demographics contributing to transmission patterns, and to forecast epidemic trends. Emerging sources of genomic data and associated metadata provide new opportunities to further unravel transmission patterns. Key challenges include how to integrate genomic data with metadata from multiple sources, how to generate efficient computational algorithms to cope with large datasets, and how to establish sampling frameworks to enable robust conclusions. The decreasing cost of genomic sequencing combined with increasing computational power has led to an explosion of interest in the application of whole-genome sequencing to public health 1.Ladner J.T. et al.Precision epidemiology for infectious disease control.Nat. 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These crises demanded a variety of approaches, including intensive genomic sequencing to understand transmission dynamics during the acute phase of epidemics (Ebola virus in the DRC), and broader genomic surveillance to identify cryptic increases in cases (polio). During the SARS-CoV-2 pandemic, many countries that did not previously utilise genomic data have begun to produce and rely on its outputs. With the SARS-CoV-2 genomic dataset now containing >2.5 million sequences from >185 countries (www.gisaid.org), and subsequent pressure from policy makers and funders to see returns on investment from genomic epidemiology, there has been a rapid development of new methodologies to fully utilise this dataset to help control the pandemic. This article explores the avenues of research that genomic epidemiology can help to elucidate and the challenges that remain in fully realising its potential. Transmission of all infections occurs at different spatial scales 18.Levin S.A. 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Dis. 2020; 20: 1263-1271Abstract Full Text Full Text PDF PubMed Scopus (147) Google Scholar. However, the link between the scales of transmission (where and how pathogens are related) and the optimal intervention scenario (spatial targeting of non-pharmaceutical interventions, isolation of superspreaders, etc.) has not been explored in detail. A relatively simple and well-tried way to examine transmission among regions is through a phylogeographic analysis. 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