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The COVID-19 pandemic led to a large global effort to sequence SARS-CoV-2 genomes from patient samples to track viral evolution and inform the public health response. Millions of SARS-CoV-2 genome sequences have been deposited in global public repositories. The Canadian COVID-19 Genomics Network (CanCOGeN - VirusSeq), a consortium tasked with coordinating expanded sequencing of SARS-CoV-2 genomes across Canada early in the pandemic, created the Canadian VirusSeq Data Portal, with associated data pipelines and procedures, to support these efforts. The goal of VirusSeq was to allow open access to Canadian SARS-CoV-2 genomic sequences and enhanced, standardized contextual data that were unavailable in other repositories and that meet FAIR standards (Findable, Accessible, Interoperable and Reusable). In addition, the portal data submission pipeline contains data quality checking procedures and appropriate acknowledgement of data generators that encourages collaboration. From inception to execution, the portal was developed with a conscientious focus on strong data governance principles and practices. Extensive efforts ensured a commitment to Canadian privacy laws, data security standards, and organizational processes. This portal has been coupled with other resources, such as Viral AI, and was further leveraged by the Coronavirus Variants Rapid Response Network (CoVaRR-Net) to produce a suite of continually updated analytical tools and notebooks. Here we highlight this portal (https://virusseq-dataportal.ca/), including its contextual data not available elsewhere, and the Duotang (https://covarr-net.github.io/duotang/duotang.html), a web platform that presents key genomic epidemiology and modelling analyses on circulating and emerging SARS-CoV-2 variants in Canada. Duotang presents dynamic changes in variant composition of SARS-CoV-2 in Canada and by province, estimates variant growth, and displays complementary interactive visualizations, with a text overview of the current situation. The VirusSeq Data Portal and Duotang resources, alongside additional analyses and resources computed from the portal (COVID-MVP, CoVizu), are all open source and freely available. Together, they provide an updated picture of SARS-CoV-2 evolution to spur scientific discussions, inform public discourse, and support communication with and within public health authorities. They also serve as a framework for other jurisdictions interested in open, collaborative sequence data sharing and analyses.

In the management of infectious disease outbreaks, grouping cases into clusters and understanding their underlying epidemiology are fundamental tasks. In genomic epidemiology, clusters are typically identified either using pathogen sequences alone or with sequences in combination with epidemiological data such as location and time of collection. However, it may not be feasible to culture and sequence all pathogen isolates, so sequence data may not be available for all cases. This presents challenges for identifying clusters and understanding epidemiology, because these cases may be important for transmission. Demographic, clinical and location data are likely to be available for unsequenced cases, and comprise partial information about their clustering. Here, we use statistical modelling to assign unsequenced cases to clusters already identified by genomic methods, assuming that a more direct method of linking individuals, such as contact tracing, is not available. We build our model on pairwise similarity between cases to predict whether cases cluster together, in contrast to using individual case data to predict the cases' clusters. We then develop methods that allow us to determine whether a pair of unsequenced cases are likely to cluster together, to group them into their most probable clusters, to identify which are most likely to be members of a specific (known) cluster, and to estimate the true size of a known cluster given a set of unsequenced cases. We apply our method to tuberculosis data from Valencia, Spain. Among other applications, we find that clustering can be predicted successfully using spatial distance between cases and whether nationality is the same. We can identify the correct cluster for an unsequenced case, among 38 possible clusters, with an accuracy of approximately 35 %, higher than both direct multinomial regression (17 %) and random selection (< 5 %).

The evolution and emergence of drug-resistant tuberculosis (TB) has been studied extensively in some contexts, but the ecological drivers of these two processes remain poorly understood. This study sought to describe the joint evolutionary and epidemiological histories of a novel multidrug-resistant Mycobacterium tuberculosis strain recently identified in the capital city of the Republic of Moldova (MDR Ural/4.2), where genomic surveillance of drug-resistant M. tuberculosis has been limited thus far. Using whole genome sequence data and Bayesian phylogenomic methods, we reconstruct the stepwise acquisition of drug resistance mutations in the MDR Ural/4.2 strain, estimate its historical bacterial population size over time, and infer the migration history of this strain between Eastern European countries. We infer that MDR Ural/4.2 likely evolved (via acquisition of rpoB S450L, which confers resistance to rifampin) in the early 1990s, during a period of social turmoil following Moldovan independence from the Soviet Union. This strain subsequently underwent substantial population size expansion in the early 2000s, at a time when national guidelines encouraged inpatient treatment of TB patients. We infer exportation of this strain and its isoniazid-resistant ancestral precursor from Moldova to neighbouring countries starting as early as 1985. Our findings suggest temporal and ecological associations between specific public health practices, including inpatient hospitalization of drug-resistant TB cases from the early 2000s until 2013, and the evolution of drug-resistant M. tuberculosis in Moldova. These findings underscore the need for regional coordination in TB control and expanded genomic surveillance efforts across Eastern Europe.

Outbreaks of tuberculosis (TB) - such as the large isoniazid-resistant outbreak centred on London, UK, which originated in 1995 - provide excellent opportunities to model transmission of this devastating disease. Transmission chains for TB are notoriously difficult to ascertain, but mathematical modelling approaches, combined with whole-genome sequencing data, have strong potential to contribute to transmission analyses. Using such data, we aimed to reconstruct transmission histories for the outbreak using a Bayesian approach, and to use machine-learning techniques with patient-level data to identify the key covariates associated with transmission. By using our transmission reconstruction method that accounts for phylogenetic uncertainty, we are able to identify 21 transmission events with reasonable confidence, 9 of which have zero SNP distance, and a maximum distance of 3. Patient age, alcohol abuse and history of homelessness were found to be the most important predictors of being credible TB transmitters.

Pathogen genomic data are increasingly used to characterize global and local transmission patterns of important human pathogens and to inform public health interventions. Yet, there is no current consensus on how to measure genomic variation. To test the effect of the variant-identification approach on transmission inferences for Mycobacterium tuberculosis, we conducted an experiment in which five genomic epidemiology groups applied variant-identification pipelines to the same outbreak sequence data. We compared the variants identified by each group in addition to transmission and phylogenetic inferences made with each variant set. To measure the performance of commonly used variant-identification tools, we simulated an outbreak. We compared the performance of three mapping algorithms, five variant callers and two variant filters in recovering true outbreak variants. Finally, we investigated the effect of applying increasingly stringent filters on transmission inferences and phylogenies. We found that variant-calling approaches used by different groups do not recover consistent sets of variants, which can lead to conflicting transmission inferences. Further, performance in recovering true variation varied widely across approaches. While no single variant-identification approach outperforms others in both recovering true genome-wide and outbreak-level variation, variant-identification algorithms calibrated upon real sequence data or that incorporate local reassembly outperform others in recovering true pairwise differences between isolates. The choice of variant filters contributed to extensive differences across pipelines, and applying increasingly stringent filters rapidly eroded the accuracy of transmission inferences and quality of phylogenies reconstructed from outbreak variation. Commonly used approaches to identify M. tuberculosis genomic variation have variable performance, particularly when predicting potential transmission links from pairwise genetic distances. Phylogenetic reconstruction may be improved by less stringent variant filtering. Approaches that improve variant identification in repetitive, hypervariable regions, such as long-read assemblies, may improve transmission inference.

In many countries the incidence of tuberculosis (TB) is low and is largely shaped by immigrant populations from high-burden countries. This is the case in Norway, where more than 80 % of TB cases are found among immigrants from high-incidence countries. A variable latent period, low rates of evolution and structured social networks make separating import from within-border transmission a major conundrum to TB control efforts in many low-incidence countries. Clinical Mycobacterium tuberculosis isolates belonging to an unusually large genotype cluster associated with people born in the Horn of Africa have been identified in Norway over the last two decades. We modelled transmission based on whole-genome sequence data to estimate infection times for individual patients. By contrasting these estimates with time of arrival in Norway, we estimate on a case-by-case basis whether patients were likely to have been infected before or after arrival. Independent import was responsible for the majority of cases, but we estimate that about one-quarter of the patients had contracted TB in Norway. This study illuminates the transmission dynamics within an immigrant community. Our approach is broadly applicable to many settings where TB control programmes can benefit from understanding when and where patients acquired TB.

We report an updated method for inferring the time at which an infectious disease was transmitted between persons from a time-labelled pathogen genome phylogeny. We applied the method to 48 Mycobacterium tuberculosis genomes as part of a real-time public health outbreak investigation, demonstrating that although active tuberculosis (TB) cases were diagnosed through 2013, no transmission events took place beyond mid-2012. Subsequent cases were the result of progression from latent TB infection to active disease, and not recent transmission. This evolutionary genomic approach was used to declare the outbreak over in January 2015.