Towards a genomics-informed, real-time, global pathogen surveillance system.

The recent Ebola and Zika epidemics demonstrate the need for the continuous surveillance, rapid diagnosis and real-time tracking of emerging infectious diseases. Fast, affordable sequencing of pathogen genomes - now a staple of the public health microbiology laboratory in well-resourced settings - can affect each of these areas. Coupling genomic diagnostics and epidemiology to innovative digital disease detection platforms raises the possibility of an open, global, digital pathogen surveillance system. When informed by a One Health approach, in which human, animal and environmental health are considered together, such a genomics-based system has profound potential to improve public health in settings lacking robust laboratory capacity.

Comparing Mycobacterium tuberculosis transmission reconstruction models from whole genome sequence data.

Genomic epidemiology is routinely used worldwide to interrogate infectious disease dynamics. Multiple computational tools exist that reconstruct transmission networks by coupling genomic data with epidemiological models. Resulting inferences can improve our understanding of pathogen transmission dynamics, and yet the performance of these tools has not been evaluated for tuberculosis (TB), a disease process with complex epidemiology including variable latency and within-host heterogeneity. Here, we performed a systematic comparison of six publicly available transmission reconstruction models, evaluating their accuracy when predicting transmission events in simulated and real-world Mycobacterium tuberculosis outbreaks. We observed variability in the number of transmission links that were predicted with high probability (P ≥ 0.5) and low accuracy of these predictions against known transmission in simulated outbreaks. We also found a low proportion of epidemiologically supported case-contact pairs were identified in our real-world TB clusters. The specificity of all models was high, and a relatively high proportion of the total transmission events predicted by some models were true links, notably with TransPhylo, Outbreaker2, and Phybreak. Our findings may inform the choice of tools in TB transmission analyses and underscore the need for caution when interpreting transmission networks produced using probabilistic approaches.

Using Whole-genome Sequencing to Determine the Timing of Secondary Tuberculosis in British Columbia, Canada.

Combined with epidemiological data, whole-genome sequencing (WGS) can help better resolve individual tuberculosis (TB) transmission events to a degree not possible with traditional genotyping. We combine WGS data with patient-level data to calculate the timing of secondary TB among contacts of people diagnosed with active TB in British Columbia, Canada.

Prediction of Susceptibility to First-Line Tuberculosis Drugs by DNA Sequencing.

BACKGROUND: The World Health Organization recommends drug-susceptibility testing of Mycobacterium tuberculosis complex for all patients with tuberculosis to guide treatment decisions and improve outcomes. Whether DNA sequencing can be used to accurately predict profiles of susceptibility to first-line antituberculosis drugs has not been clear. METHODS: We obtained whole-genome sequences and associated phenotypes of resistance or susceptibility to the first-line antituberculosis drugs isoniazid, rifampin, ethambutol, and pyrazinamide for isolates from 16 countries across six continents. For each isolate, mutations associated with drug resistance and drug susceptibility were identified across nine genes, and individual phenotypes were predicted unless mutations of unknown association were also present. To identify how whole-genome sequencing might direct first-line drug therapy, complete susceptibility profiles were predicted. These profiles were predicted to be susceptible to all four drugs (i.e., pansusceptible) if they were predicted to be susceptible to isoniazid and to the other drugs or if they contained mutations of unknown association in genes that affect susceptibility to the other drugs. We simulated the way in which the negative predictive value changed with the prevalence of drug resistance. RESULTS: A total of 10,209 isolates were analyzed. The largest proportion of phenotypes was predicted for rifampin (9660 [95.4%] of 10,130) and the smallest was predicted for ethambutol (8794 [89.8%] of 9794). Resistance to isoniazid, rifampin, ethambutol, and pyrazinamide was correctly predicted with 97.1%, 97.5%, 94.6%, and 91.3% sensitivity, respectively, and susceptibility to these drugs was correctly predicted with 99.0%, 98.8%, 93.6%, and 96.8% specificity. Of the 7516 isolates with complete phenotypic drug-susceptibility profiles, 5865 (78.0%) had complete genotypic predictions, among which 5250 profiles (89.5%) were correctly predicted. Among the 4037 phenotypic profiles that were predicted to be pansusceptible, 3952 (97.9%) were correctly predicted. CONCLUSIONS: Genotypic predictions of the susceptibility of M. tuberculosis to first-line drugs were found to be correlated with phenotypic susceptibility to these drugs. (Funded by the Bill and Melinda Gates Foundation and others.).

Ethical challenges in the treatment of non-refugee migrants with tuberculosis in Canada.

While attention to the ethical issues that migrants face in accessing tuberculosis care has increased in the last few years, most of the attention has focused on challenges that refugees face when emigrating. Less attention has been given to ethical challenges that arise in the context of providing tuberculosis treatment and care to non-refugee migrants in high-income countries (HIC), particularly those that do not face immediate danger or violence. In this paper, we analyze some of the ethical challenges associated with treating migrants with tuberculosis in the Canadian context. In particular, we will discuss (i) inter- and intra-jurisdictional issues that challenge quotidian public health governance structures, and (ii) the ethical imperative for the Canadian government and its provinces to clearly differentiate access to healthcare from a person's immigration status to help overcome power imbalances that may exist between public health workers and their clients. The arguments presented herein could potentially apply to other HIC with some form of universal health coverage.

A Web-Based and In-Person Risk Reframing Intervention to Influence Mothers' Tolerance for, and Parenting Practices Associated With, Children's Outdoor Risky Play: Randomized Controlled Trial.

BACKGROUND: Outdoor risky play, such as climbing, racing, and independent exploration, is an important part of childhood and is associated with various positive physical, mental, and developmental outcomes for children. Parental attitudes and fears, particularly mothers', are a major deterrent to children's opportunities for outdoor risky play. OBJECTIVE: The aim of this study was to evaluate the efficacy of 2 versions of an intervention to reframe mothers' perceptions of risk and change parenting behaviors: a web-based intervention or an in-person workshop, compared with the control condition. METHODS: The Go Play Outside! randomized controlled trial was conducted in Canada from 2017 to 2018. Participants were recruited through social media, snowball sampling, and community notices. Mothers of children aged 6-12 years were self-assessed through eligibility questions, and those eligible and consented to participate in the study were randomized into a fully automated web-based intervention, the in-person workshop, or the control condition. The intervention was underpinned by social cognitive theory, incorporating behavior change techniques. Participants progressed through a series of self-reflection exercises and developed a goal for change. Control participants received the Position Statement on Active Outdoor Play. The primary outcome was increase in tolerance of risky play and the secondary outcome was goal attainment. Data were collected online via REDCap at baseline, 1 week, and 3 months after the intervention. Randomization was conducted using sealed envelope. Allocations were concealed to researchers at assignment and data analysis. We conducted mediation analyses to examine whether the intervention influenced elements of social cognitive theory, as hypothesized. RESULTS: A total of 451 mothers were randomized and completed baseline sociodemographic assessments: 150 in the web-based intervention, 153 in the in-person workshop, and 148 in the control condition. Among these, a total of 351 mothers completed the intervention. At 1 week after the intervention, 113, 85, and 135 mothers completed assessments for each condition, respectively, and at 3 months after the intervention, 105, 84, and 123 completed the assessments, respectively. Compared with mothers in the control condition, mothers in the web-based intervention had significantly higher tolerance of risky play at 1 week (P=.004) and 3 months after the intervention (P=.007); and mothers in the in-person workshop had significantly higher tolerance of risky play at 1 week after the intervention (P=.02). No other significant outcomes were found. None of the potential mediators were found to significantly mediate the outcomes. CONCLUSIONS: The trial demonstrates that the web-based intervention was effective in increasing mothers' tolerance for risk in play. TRIAL REGISTRATION: ClinicalTrials.gov NCT03374683; https://clinicaltrials.gov/ct2/show/NCT03374683. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-018-2552-4.

Beyond the SNP Threshold: Identifying Outbreak Clusters Using Inferred Transmissions.

Whole-genome sequencing (WGS) is increasingly used to aid the understanding of pathogen transmission. A first step in analyzing WGS data is usually to define "transmission clusters," sets of cases that are potentially linked by direct transmission. This is often done by including two cases in the same cluster if they are separated by fewer single-nucleotide polymorphisms (SNPs) than a specified threshold. However, there is little agreement as to what an appropriate threshold should be. We propose a probabilistic alternative, suggesting that the key inferential target for transmission clusters is the number of transmissions separating cases. We characterize this by combining the number of SNP differences and the length of time over which those differences have accumulated, using information about case timing, molecular clock, and transmission processes. Our framework has the advantage of allowing for variable mutation rates across the genome and can incorporate other epidemiological data. We use two tuberculosis studies to illustrate the impact of our approach: with British Columbia data by using spatial divisions; with Republic of Moldova data by incorporating antibiotic resistance. Simulation results indicate that our transmission-based method is better in identifying direct transmissions than a SNP threshold, with dissimilarity between clusterings of on average 0.27 bits compared with 0.37 bits for the SNP-threshold method and 0.84 bits for randomly permuted data. These results show that it is likely to outperform the SNP-threshold method where clock rates are variable and sample collection times are spread out. We implement the method in the R package transcluster.

Adjutant: an R-based tool to support topic discovery for systematic and literature reviews.

SUMMARY: Adjutant is an open-source, interactive and R-based application to support mining PubMed for literature reviews. Given a PubMed-compatible search query, Adjutant downloads the relevant articles and allows the user to perform an unsupervised clustering analysis to identify data-driven topic clusters. Following clustering, users can also sample documents using different strategies to obtain a more manageable dataset for further analysis. Adjutant makes explicit trade-offs between speed and accuracy, which are modifiable by the user, such that a complete analysis of several thousand documents can take a few minutes. All analytic datasets generated by Adjutant are saved, allowing users to easily conduct other downstream analyses that Adjutant does not explicitly support. AVAILABILITY AND IMPLEMENTATION: Adjutant is implemented in R, using Shiny, and is available at https://github.com/amcrisan/Adjutant. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A systematic method for surveying data visualizations and a resulting genomic epidemiology visualization typology: GEViT.

MOTIVATION: Data visualization is an important tool for exploring and communicating findings from genomic and healthcare datasets. Yet, without a systematic way of organizing and describing the design space of data visualizations, researchers may not be aware of the breadth of possible visualization design choices or how to distinguish between good and bad options. RESULTS: We have developed a method that systematically surveys data visualizations using the analysis of both text and images. Our method supports the construction of a visualization design space that is explorable along two axes: why the visualization was created and how it was constructed. We applied our method to a corpus of scientific research articles from infectious disease genomic epidemiology and derived a Genomic Epidemiology Visualization Typology (GEViT) that describes how visualizations were created from a series of chart types, combinations and enhancements. We have also implemented an online gallery that allows others to explore our resulting design space of visualizations. Our results have important implications for visualization design and for researchers intending to develop or use data visualization tools. Finally, the method that we introduce is extensible to constructing visualizations design spaces across other research areas. AVAILABILITY AND IMPLEMENTATION: Our browsable gallery is available at http://gevit.net and all project code can be found at https://github.com/amcrisan/gevitAnalysisRelease. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Building the Framework for Standardized Clinical Laboratory Reporting of Next-generation Sequencing Data for Resistance-associated Mutations in Mycobacterium tuberculosis Complex.

Tuberculosis is the primary infectious disease killer worldwide, with a growing threat from multidrug-resistant cases. Unfortunately, classic growth-based phenotypic drug susceptibility testing (DST) remains difficult, costly, and time consuming, while current rapid molecular testing options are limited by the diversity of antimicrobial-resistant genotypes that can be detected at once. Next-generation sequencing (NGS) offers the opportunity for rapid, comprehensive DST without the time or cost burden of phenotypic tests and can provide useful information for global surveillance. As access to NGS expands, it will be important to ensure that results are communicated clearly, consistent, comparable between laboratories, and associated with clear guidance on clinical interpretation of results. In this viewpoint article, we summarize 2 expert workshops regarding a standardized report format, focusing on relevant variables, terminology, and required minimal elements for clinical and laboratory reports with a proposed standardized template for clinical reporting NGS results for Mycobacterium tuberculosis.

Trust and the ethical challenges in the use of whole genome sequencing for tuberculosis surveillance: a qualitative study of stakeholder perspectives.

BACKGROUND: Emerging genomic technologies promise more efficient infectious disease control. Whole genome sequencing (WGS) is increasingly being used in tuberculosis (TB) diagnosis, surveillance, and epidemiology. However, while the use of WGS by public health agencies may raise ethical, legal, and socio-political concerns, these challenges are poorly understood. METHOD: Between November 2017 and April 2018, we conducted semi-structured interviews with 22 key stakeholders across the fields of governance and policy, public health, and laboratory sciences representing the major jurisdictions currently using WGS in national TB programs. Thematic analysis of the interviews was conducted using NVivo 11. RESULTS: Respondents identified several ethical and practical challenges associated with WGS in TB care and surveillance, all related to issues of trust, including: 1) the power of public health; 2) data sharing and profits derived from surveillance efforts; and 3) concerns regarding who has access to, and can benefit from, the technology. Additional challenges included: the potential utility that WGS adds to a public health program, the risks associated with linking necessary epidemiological metadata to the genomic data, and challenges associated with jurisdictional capacity to implement the technology. CONCLUSIONS: Successful implementation of WGS is dependent on fostering relationships of trust between those working with genomics technology and those directly impacted by it, including clinicians. Building trust (a) between the public and the public health agencies and (b) within public health agencies themselves is critical due to the inherent complexity of WGS and its implementation for communicable disease control purposes.

Genotyping and Whole-Genome Sequencing to Identify Tuberculosis Transmission to Pediatric Patients in British Columbia, Canada, 2005-2014.

Background: Tuberculosis (TB) in children is often an indicator of recent transmission. Genotyping and whole-genome sequencing (WGS) can enhance pediatric TB investigations by confirming or refuting transmission events. Methods: Mycobacterium tuberculosis isolates from all pediatric patients <18 years with culture-confirmed TB in British Columbia (BC) from 2005 to 2014 (n = 49) were genotyped by Mycobacterial Interspersed Repetitive Units-Variable Number Tandem Repeat (MIRU-VNTR) and compared with adult isolates. Genotypically clustered cases underwent WGS. Clinical, demographic, and contact data were reviewed for each case. Results: Twenty-three children were Canadian-born, 7 to Canadian-born parents (CBP) and 16 to foreign-born parents (FBP). Of the 26 foreign-born children, all were born in Asia (81%) or Africa (19%). Using molecular and epidemiological data, we determined that 15 children had acquired their infection within BC, and household transmission explained all 7 Canadian-born (FBP) children that acquired TB locally. In contrast, 6 of 7 Canadian-born (CBP) children were exposed via a non-household community source. Eight Canadian-born (FBP) children acquired their infections through travel to their parents' place of birth. All but 1 of the foreign-born children acquired their infection outside of BC. Conclusions: Genotyping and genomic data reveal that drivers of pediatric transmission vary according to a child's age, birthplace, and their parents' place of birth.

Molecular Epidemiology of Tuberculosis in British Columbia, Canada: A 10-Year Retrospective Study.

Background: Understanding regional molecular epidemiology allows for the development of more efficient tuberculosis prevention strategies in low-incidence settings. Methods: We analyzed 24-locus mycobacterial interspersed repetitive-unit-variable-number tandem repeat (MIRU-VNTR) genotyping for 2290 Mycobacterium tuberculosis clinical isolates collected in the province of British Columbia (BC), Canada, in 2005-2014. Laboratory data for each isolate were linked to case-level clinical and demographic data. These data were used to describe the molecular epidemiology of tuberculosis across the province. Results: We detected >1500 distinct genotypes across the 4 major M. tuberculosis lineages, reflecting BC's diverse population. Disease site and clustering rates varied across lineages, and MIRU-VNTR was used to group the 2290 isolates into 189 clusters (2-70 isolates per cluster), with an overall clustering rate of 42.4% and an estimated local transmission rate of 34.1%. Risk factors for clustering varied between Canadian-born and foreign-born individuals; the former had increased odds (odds ratio, 7.8; 95% confidence interval [CI], 6.2-9.6) of belonging to a genotypic cluster, although nearly one-quarter of clusters included both Canadian- and foreign-born persons. Large clusters (≥10 cases) occurred more frequently within the M. tuberculosis Euro-American lineage, and individual-level risk factors associated with belonging to a large cluster included being Canadian born (adjusted odds ratio, 3.3; 95% CI, 2.3-4.8), residing in a rural area (2.3; 1.2-4.5), and illicit drug use (2.0; 1.2-3.4). Conclusions: Although tuberculosis in BC largely arises through reactivation of latent tuberculosis in foreign-born persons, locally transmitted infections occur in discrete populations with distinct disease and risk factor profiles, representing groups for targeted interventions.

Genomic Infectious Disease Epidemiology in Partially Sampled and Ongoing Outbreaks.

Genomic data are increasingly being used to understand infectious disease epidemiology. Isolates from a given outbreak are sequenced, and the patterns of shared variation are used to infer which isolates within the outbreak are most closely related to each other. Unfortunately, the phylogenetic trees typically used to represent this variation are not directly informative about who infected whom-a phylogenetic tree is not a transmission tree. However, a transmission tree can be inferred from a phylogeny while accounting for within-host genetic diversity by coloring the branches of a phylogeny according to which host those branches were in. Here we extend this approach and show that it can be applied to partially sampled and ongoing outbreaks. This requires computing the correct probability of an observed transmission tree and we herein demonstrate how to do this for a large class of epidemiological models. We also demonstrate how the branch coloring approach can incorporate a variable number of unique colors to represent unsampled intermediates in transmission chains. The resulting algorithm is a reversible jump Monte-Carlo Markov Chain, which we apply to both simulated data and real data from an outbreak of tuberculosis. By accounting for unsampled cases and an outbreak which may not have reached its end, our method is uniquely suited to use in a public health environment during real-time outbreak investigations. We implemented this transmission tree inference methodology in an R package called TransPhylo, which is freely available from https://github.com/xavierdidelot/TransPhylo.

Universal Genotyping for Tuberculosis Prevention Programs: a 5-Year Comparison with On-Request Genotyping.

Prospective universal genotyping of tuberculosis (TB) isolates is used by many laboratories to detect clusters of cases and inform contact investigations. Prior to universal genotyping, most TB prevention programs genotyped isolates on request only, relying on requests from public health professionals whose knowledge of a patient's clinical, demographic, and epidemiological characteristics suggested potential transmission. To justify the switch from on-request to universal genotyping-particularly in the public health domain, with its limited resources and competing priorities-it is important to demonstrate the additional benefit provided by a universal genotyping program. We compared the clustering patterns revealed by retrospective 24-locus mycobacterial interspersed repetitive unit-variable-number tandem repeat genotyping of all culture-positive isolates over a 5-year period to the patterns previously established by our genotyping-on-request program in the low-incidence setting of British Columbia, Canada. We found that 23.8% of isolates were requested during the study period, and while requested isolates had increased odds of belonging to a genotype cluster (adjusted odds ratio, 2.3; 95% confidence interval, 1.5 to 3.3), only 54.6% clustered with the requested comparator strain. Universal genotyping revealed 94 clusters ranging in size from 2 to 53 isolates (mean = 5) and involving 432 individuals. On-request genotyping missed 54 (57.4%) of these clusters and 130 (30.1%) clustered individuals. Our results underscore that TB patient networks are complex, with unrecognized linkages between patients, and a prospective province-wide universal genotyping program provides an informative, bias-free tool to explore transmission to a degree not possible with on-request genotyping.

RNA-Seq Analysis of Gene Expression, Viral Pathogen, and B-Cell/T-Cell Receptor Signatures in Complex Chronic Disease.

Background: Chronic fatigue syndrome (CFS) remains poorly understood. Although infections are speculated to trigger the syndrome, a specific infectious agent and underlying pathophysiological mechanism remain elusive. In a previous study, we described similar clinical phenotypes in CFS patients and alternatively diagnosed chronic Lyme syndrome (ADCLS) patients­individuals diagnosed with Lyme disease by testing from private Lyme specialty laboratories but who test negative by reference 2-tiered serologic analysis. Methods: Here, we performed blinded RNA-seq analysis of whole blood collected from 25 adults diagnosed with CFS and 13 ADCLS patients, comparing these cases to 25 matched controls and 11 patients with well-controlled systemic lupus erythematosus (SLE). Samples were collected at patient enrollment and not during acute symptom flares. RNA-seq data were used to study host gene expression, B-cell/T-cell receptor profiles (BCR/TCR), and potential viral infections. Results: No differentially expressed genes (DEGs) were found to be significant when CFS or ADCLS cases were compared to controls. Forty-two DEGs were found when SLE cases were compared to controls, consistent with activation of interferon signaling pathways associated with SLE disease. BCR/TCR repertoire analysis did not show significant differences between CFS and controls or ADCLS and controls. Finally, viral sequences corresponding to anelloviruses, human pegivirus 1, herpesviruses, and papillomaviruses were detected in RNA-seq data, but proportions were similar (P = .73) across all genus-level taxonomic categories. Conclusions: Our observations do not support a theory of transcriptionally mediated immune cell dysregulation in CFS and ADCLS, at least outside of periods of acute symptom flares.

A microbiological revolution meets an ancient disease: improving the management of tuberculosis with genomics.

Tuberculosis (TB) is an ancient disease with an enormous global impact. Despite declining global incidence, the diagnosis, phenotyping, and epidemiological investigation of TB require significant clinical microbiology laboratory resources. Current methods for the detection and characterization of Mycobacterium tuberculosis consist of a series of laboratory tests varying in speed and performance, each of which yields incremental information about the disease. Since the sequencing of the first M. tuberculosis genome in 1998, genomic tools have aided in the diagnosis, treatment, and control of TB. Here we summarize genomics-based methods that are positioned to be introduced in the modern clinical TB laboratory, and we highlight how recent advances in genomics will improve the detection of antibiotic resistance-conferring mutations and the understanding of M. tuberculosis transmission dynamics and epidemiology. We imagine the future TB clinic as one that relies heavily on genomic interrogation of the M. tuberculosis isolate, allowing for more rapid diagnosis of TB and real-time monitoring of outbreak emergence.

Whole-Genome Sequencing of Measles Virus Genotypes H1 and D8 During Outbreaks of Infection Following the 2010 Olympic Winter Games Reveals Viral Transmission Routes.

We used whole-genome sequencing to investigate a dual-genotype outbreak of measles occurring after the XXI Olympic Winter Games in Vancouver, Canada. By sequencing 27 complete genomes from H1 and D8 genotype measles viruses isolated from outbreak cases, we estimated the virus mutation rate, determined that person-to-person transmission is typically associated with 0 mutations between isolates, and established that a single introduction of H1 virus led to the expansion of the outbreak beyond Vancouver. This is the largest measles genomics project to date, revealing novel aspects of measles virus genetics and providing new insights into transmission of this reemerging viral pathogen.

Lyme Disease Diagnosed by Alternative Methods: A Phenotype Similar to That of Chronic Fatigue Syndrome.

BACKGROUND: A subset of patients reporting a diagnosis of Lyme disease can be described as having alternatively diagnosed chronic Lyme syndrome (ADCLS), in which diagnosis is based on laboratory results from a nonreference Lyme specialty laboratory using in-house criteria. Patients with ADCLS report symptoms similar to those reported by patients with chronic fatigue syndrome (CFS). METHODS: We performed a case-control study comparing patients with ADCLS and CFS to each other and to both healthy controls and controls with systemic lupus erythematosus (SLE). Subjects completed a history, physical exam, screening laboratory tests, 7 functional scales, reference serology for Lyme disease using Centers for Disease Control and Prevention criteria, reference serology for other tick-associated pathogens, and cytokine expression studies. RESULTS: The study enrolled 13 patients with ADCLS (12 of whom were diagnosed by 1 alternative US laboratory), 25 patients with CFS, 25 matched healthy controls, and 11 SLE controls. Baseline clinical data and functional scales indicate significant disability among ADCLS and CFS patients and many important differences between these groups and controls, but no significant differences between each other. No ADCLS patient was confirmed as having positive Lyme serology by reference laboratory testing, and there was no difference in distribution of positive serology for other tick-transmitted pathogens or cytokine expression across the groups. CONCLUSIONS: In British Columbia, a setting with low Lyme disease incidence, ADCLS patients have a similar phenotype to that of CFS patients. Disagreement between alternative and reference laboratory Lyme testing results in this setting is most likely explained by false-positive results from the alternative laboratory.

Bayesian inference of infectious disease transmission from whole-genome sequence data.

Genomics is increasingly being used to investigate disease outbreaks, but an important question remains unanswered--how well do genomic data capture known transmission events, particularly for pathogens with long carriage periods or large within-host population sizes? Here we present a novel Bayesian approach to reconstruct densely sampled outbreaks from genomic data while considering within-host diversity. We infer a time-labeled phylogeny using Bayesian evolutionary analysis by sampling trees (BEAST), and then infer a transmission network via a Monte Carlo Markov chain. We find that under a realistic model of within-host evolution, reconstructions of simulated outbreaks contain substantial uncertainty even when genomic data reflect a high substitution rate. Reconstruction of a real-world tuberculosis outbreak displayed similar uncertainty, although the correct source case and several clusters of epidemiologically linked cases were identified. We conclude that genomics cannot wholly replace traditional epidemiology but that Bayesian reconstructions derived from sequence data may form a useful starting point for a genomic epidemiology investigation.