Estimation of the generation interval using pairwise relative transmission probabilities.

The generation interval (the time between infection of primary and secondary cases) and its often used proxy, the serial interval (the time between symptom onset of primary and secondary cases) are critical parameters in understanding infectious disease dynamics. Because it is difficult to determine who infected whom, these important outbreak characteristics are not well understood for many diseases. We present a novel method for estimating transmission intervals using surveillance or outbreak investigation data that, unlike existing methods, does not require a contact tracing data or pathogen whole genome sequence data on all cases. We start with an expectation maximization algorithm and incorporate relative transmission probabilities with noise reduction. We use simulations to show that our method can accurately estimate the generation interval distribution for diseases with different reproductive numbers, generation intervals, and mutation rates. We then apply our method to routinely collected surveillance data from Massachusetts (2010-2016) to estimate the serial interval of tuberculosis in this setting.

What Can Genetic Relatedness Tell Us About Risk Factors for Tuberculosis Transmission?

BACKGROUND: To stop tuberculosis (TB), the leading infectious cause of death globally, we need to better understand transmission risk factors. Although many studies have identified associations between individual-level covariates and pathogen genetic relatedness, few have identified characteristics of transmission pairs or explored how closely covariates associated with genetic relatedness mirror those associated with transmission. METHODS: We simulated a TB-like outbreak with pathogen genetic data and estimated odds ratios (ORs) to correlate each covariate and genetic relatedness. We used a naive Bayes approach to modify the genetic links and nonlinks to resemble the true links and nonlinks more closely and estimated modified ORs with this approach. We compared these two sets of ORs with the true ORs for transmission. Finally, we applied this method to TB data in Hamburg, Germany, and Massachusetts, USA, to find pair-level covariates associated with transmission. RESULTS: Using simulations, we found that associations between covariates and genetic relatedness had the same relative magnitudes and directions as the true associations with transmission, but biased absolute magnitudes. Modifying the genetic links and nonlinks reduced the bias and increased the confidence interval widths, more accurately capturing error. In Hamburg and Massachusetts, pairs were more likely to be probable transmission links if they lived in closer proximity, had a shorter time between observations, or had shared ethnicity, social risk factors, drug resistance, or genotypes. CONCLUSIONS: We developed a method to improve the use of genetic relatedness as a proxy for transmission, and aid in understanding TB transmission dynamics in low-burden settings.

Molecular Epidemiology of Mycobacterium tuberculosis To Describe the Transmission Dynamics Among Inuit Residing in Iqaluit Nunavut Using Whole-Genome Sequencing.

BACKGROUND: In the last decade, tuberculosis (TB) incidence among Inuit in the Canadian Arctic has been rising. Our aim was to better understand the transmission dynamics of TB in this remote region of Canada using whole-genome sequencing. METHODS: Isolates from patients who had culture-positive pulmonary TB in Iqaluit, Nunavut, between 2009 and 2015 underwent whole-genome sequencing (WGS). The number of transmission events between cases within clusters was calculated using a threshold of a ≤3 single nucleotide polymorphism (SNP) difference between isolates and then combined with detailed epidemiological data using a reproducible novel algorithm. Social network analysis of epidemiological data was used to support the WGS data analysis. RESULTS: During the study period, 140 Mycobacterium tuberculosis isolates from 135 cases were sequenced. Four clusters were identified, all from Euro-American lineage. One cluster represented 62% of all cases that were sequenced over the entire study period. In this cluster, 2 large chains of transmission were associated with 3 superspreading events in a homeless shelter. One of the superspreading events was linked to a nonsanctioned gambling house that resulted in further transmission. Shelter to nonshelter transmission was also confirmed. An algorithm developed for the determination of transmission events demonstrated very good reproducibility (κ score .98, 95% confidence interval, .97-1.0). CONCLUSIONS: Our study suggests that socioeconomic factors, namely residing in a homeless shelter and spending time in a gambling house, combined with the superspreading event effect may have been significant factors explaining the rise in cases in this predominantly Inuit Arctic community.

Using Genetic Distance from Archived Samples for the Prediction of Antibiotic Resistance in Escherichia coli.

The rising rates of antibiotic resistance increasingly compromise empirical treatment. Knowing the antibiotic susceptibility of a pathogen's close genetic relative(s) may improve empirical antibiotic selection. Using genomic and phenotypic data for Escherichia coli isolates from three separate clinically derived databases, we evaluated multiple genomic methods and statistical models for predicting antibiotic susceptibility, focusing on potentially rapidly available information, such as lineage or genetic distance from archived isolates. We applied these methods to derive and validate the prediction of antibiotic susceptibility to common antibiotics. We evaluated 968 separate episodes of suspected and confirmed infection with Escherichia coli from three geographically and temporally separated databases in Ontario, Canada, from 2010 to 2018. Across all approaches, model performance (area under the curve [AUC]) ranges for predicting antibiotic susceptibility were the greatest for ciprofloxacin (AUC, 0.76 to 0.97) and the lowest for trimethoprim-sulfamethoxazole (AUC, 0.51 to 0.80). When a model predicted that an isolate was susceptible, the resulting (posttest) probabilities of susceptibility were sufficient to warrant empirical therapy for most antibiotics (mean, 92%). An approach combining multiple models could permit the use of narrower-spectrum oral agents in 2 out of every 3 patients while maintaining high treatment adequacy (∼90%). Methods based on genetic relatedness to archived samples of E. coli could be used to predict antibiotic resistance and improve antibiotic selection.

The changing landscape of vancomycin-resistant Enterococcus faecium in Australia: a population-level genomic study.

Background: Vancomycin-resistant Enterococcus faecium (VREfm) represent a major source of nosocomial infection worldwide. In Australia, there has been a recent concerning increase in bacteraemia associated with the vanA genotype, prompting investigation into the genomic epidemiology of VREfm. Methods: A population-level study of VREfm (10 November-9 December 2015) was conducted. A total of 321 VREfm isolates (from 286 patients) across Victoria State were collected and sequenced with Illumina NextSeq. SNPs were used to assess relatedness. STs and genes associated with resistance and virulence were identified. The vanA-harbouring plasmid from an isolate from each ST was assembled using long-read data. Illumina reads from remaining isolates were then mapped to these assemblies to identify their probable vanA-harbouring plasmid. Results: vanA-VREfm comprised 17.8% of isolates. ST203, ST80 and a pstS(-) clade, ST1421, predominated (30.5%, 30.5% and 37.2%, respectively). Most vanB-VREfm were ST796 (77.7%). vanA-VREfm were more closely related within hospitals versus between them [core SNPs 10 (IQR 1-357) versus 356 (179-416), respectively], suggesting discrete introductions of vanA-VREfm, with subsequent intra-hospital transmission. In contrast, vanB-VREfm had similar core SNP distributions within versus between hospitals, due to widespread dissemination of ST796. Different vanA-harbouring plasmids were found across STs. With the exception of ST78 and ST796, Tn1546 transposons also varied. Phylogenetic analysis revealed Australian strains were often interspersed with those from other countries, suggesting ongoing cross-continental transmission. Conclusions: Emerging vanA-VREfm in Australia is polyclonal, indicating repeat introductions of vanA-VREfm into hospitals and subsequent dissemination. The close relationship to global strains reinforces the need for ongoing screening and control of VREfm in Australia and abroad.

Genomic epidemiology and antimicrobial resistance of Neisseria gonorrhoeae in New Zealand.

Background: Antimicrobial-resistant Neisseria gonorrhoeae is a major threat to public health. No studies to date have examined the genomic epidemiology of gonorrhoea in the Western Pacific Region, where the incidence of gonorrhoea is particularly high. Methods: A population-level study of N. gonorrhoeae in New Zealand (October 2014 to May 2015). Comprehensive susceptibility testing and WGS data were obtained for 398 isolates. Relatedness was inferred using phylogenetic trees, and pairwise core SNPs. Mutations and genes known to be associated with resistance were identified, and correlated with phenotype. Results: Eleven clusters were identified. In six of these clusters, >25% of isolates were from females, while in eight of them, >15% of isolates were from females. Drug resistance was common; 98%, 32% and 68% of isolates were non-susceptible to penicillin, ciprofloxacin and tetracycline, respectively. Elevated MICs to extended-spectrum cephalosporins (ESCs) were observed in 3.5% of isolates (cefixime MICs ≥ 0.12 mg/L, ceftriaxone MICs ≥ 0.06 mg/L). Only nine isolates had penA XXXIV genotypes, three of which had decreased susceptibility to ESCs (MIC = 0.12 mg/L). Azithromycin non-susceptibility was identified in 43 isolates (10.8%); two of these isolates had 23S mutations (C2611T, 4/4 alleles), while all had mutations in mtrR or its promoter. Conclusions: The high proportion of females in clusters suggests transmission is not exclusively among MSM in New Zealand; re-assessment of risk factors for transmission may be warranted in this context. As elevated MICs of ESCs and/or azithromycin were found in closely related strains, targeted public health interventions to halt transmission are urgently needed.

Population genomics of Mycobacterium tuberculosis in the Inuit.

Nunavik, Québec suffers from epidemic tuberculosis (TB), with an incidence 50-fold higher than the Canadian average. Molecular studies in this region have documented limited bacterial genetic diversity among Mycobacterium tuberculosis isolates, consistent with a founder strain and/or ongoing spread. We have used whole-genome sequencing on 163 M. tuberculosis isolates from 11 geographically isolated villages to provide a high-resolution portrait of bacterial genetic diversity in this setting. All isolates were lineage 4 (Euro-American), with two sublineages present (major, n = 153; minor, n = 10). Among major sublineage isolates, there was a median of 46 pairwise single-nucleotide polymorphisms (SNPs), and the most recent common ancestor (MRCA) was in the early 20th century. Pairs of isolates within a village had significantly fewer SNPs than pairs from different villages (median: 6 vs. 47, P < 0.00005), indicating that most transmission occurs within villages. There was an excess of nonsynonymous SNPs after the diversification of M. tuberculosis within Nunavik: The ratio of nonsynonymous to synonymous substitution rates (dN/dS) was 0.534 before the MRCA but 0.777 subsequently (P = 0.010). Nonsynonymous SNPs were detected across all gene categories, arguing against positive selection and toward genetic drift with relaxation of purifying selection. Supporting the latter possibility, 28 genes were partially or completely deleted since the MRCA, including genes previously reported to be essential for M. tuberculosis growth. Our findings indicate that the epidemiologic success of M. tuberculosis in this region is more likely due to an environment conducive to TB transmission than a particularly well-adapted strain.

Real-Time Sequencing of Mycobacterium tuberculosis: Are We There Yet?

Whole-genome sequencing has taken a leading role in epidemiologic studies of tuberculosis, but thus far, its real-time clinical utility has been low, in part because of the requirement for culture. In their report in this issue, Votintseva et al. (A. A. Votintseva, P. Bradley, L. Pankhurst, C. del Ojo Elias, M. Loose, K. Nilgiriwala, A. Chatterjee, E. G. Smith, N. Sanderson, T. M. Walker, M. R. Morgan, D. H. Wyllie, A. S. Walker, T. E. A. Peto, D. W. Crook, and Z. Iqbal, J Clin Microbiol 55:1285-1298, 2017, https://doi.org/10.1128/JCM.02483-16) present a new method for extracting Mycobacterium tuberculosis DNA directly from smear-positive respiratory samples, making it feasible to generate drug resistance predictions and phylogenetic trees in 44 h with the Illumina MiSeq. They also illustrate the potential for a <24-h turnaround time from DNA extraction to clinically relevant results with Illumina MiniSeq and Oxford Nanopore Technologies MinION. We comment on the promise and limitations of these approaches.

Progression to tuberculosis disease increases with multiple exposures.

During a single year, a Canadian village had 34 individuals with microbiologically confirmed tuberculosis (TB) among 169 people with a new infection (20%). A contact investigation revealed multiple exposures for each person. We investigated whether the intensity of exposure might contribute to this extraordinary risk of disease.We carried out a case-control study using a public health database. Among those with a new infection, 34 had culture-confirmed TB (cases) and 118 did not progress to disease (controls). 17 patients with probable disease were excluded. Contact investigation data were utilised to tabulate the number of potential sources (total exposures). Generalised estimating equations with a logit link were used to identify associations between exposures and progression, and to investigate other potential risk factors.The median (interquartile range) number of total exposures was 15 (3-23) for cases and 3 (2-12) for controls (p=0.001). The adjusted OR for disease was 1.11 (95% CI 1.06-1.16) per additional exposure, corresponding to an OR of 3.4 for disease when comparing the medians of 15 versus 3 total exposures. This association increased when restricting to tuberculin skin test conversions.Increased exposure could be a marker of greater risk of progression to TB disease. Therefore, this risk may not be transportable across epidemiologic settings with variable exposure intensities.

Does Choice Matter? Reference-Based Alignment for Molecular Epidemiology of Tuberculosis.

When using genome sequencing for molecular epidemiology, short sequence reads are aligned to an arbitrary reference strain to detect single nucleotide polymorphisms. We investigated whether reference genome selection influences epidemiological inferences of Mycobacterium tuberculosis transmission by aligning sequence reads from 162 closely related lineage 4 (Euro-American) isolates to 7 different genomes. Phylogenetic trees were consistent with use of all but the most divergent genomes, suggesting that reference choice can be based on considerations other than M. tuberculosis lineage.

Reemergence and amplification of tuberculosis in the Canadian arctic.

BACKGROUND: Between November 2011 and November 2012, a Canadian village of 933 persons had 50 culture-positive cases of tuberculosis, with 49 sharing the same genotype. METHODS: We performed Illumina-based whole-genome sequencing on Mycobacterium tuberculosis isolates from this village, during and before the outbreak. Phylogenetic trees were generated using the maximum likelihood method. RESULTS: Three distinct genotypes were identified. Strain I (n = 7) was isolated in 1991-1996. Strain II (n = 8) was isolated in 1996-2004. Strain III (n = 62) first appeared in 2007 and did not arise from strain I or II. Within strain III, there were 3 related but distinct clusters: IIIA, IIIB, and IIIC. Between 2007 and 2010, cluster IIIA predominated (11 of 22 vs 2 of 40; P < .001), whereas in 2011-2012 clusters IIIB (n = 18) and IIIC (n = 20) predominated over cluster IIIA (n = 11). Combined evolutionary and epidemiologic analysis of strain III cases revealed that the outbreak in 2011-2012 was the result of ≥6 temporally staggered events, spanning from 1 reactivation case to a point-source outbreak of 20 cases. CONCLUSIONS: After the disappearance of 2 strains of M. tuberculosis in this village, its reemergence in 2007 was followed by an epidemiologic amplification, affecting >5% of the population.

SARS-CoV-2 Outbreak Investigation Using Contact Tracing and Whole-Genome Sequencing in an Ontario Tertiary Care Hospital.

Genomic epidemiology can facilitate an understanding of evolutionary history and transmission dynamics of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak. We used next-generation sequencing techniques to study SARS-CoV-2 genomes isolated from patients and health care workers (HCWs) across five wards of a Canadian hospital with an ongoing SARS-CoV-2 outbreak. Using traditional contact tracing methods, we show transmission events between patients and HCWs, which were also supported by the SARS-CoV-2 lineage assignments. The outbreak predominantly involved SARS-CoV-2 B.1.564.1 across all five wards, but we also show evidence of community introductions of lineages B.1, B.1.1.32, and B.1.231, falsely assumed to be outbreak related. Altogether, our study exemplifies the value of using contact tracing in combination with genomic epidemiology to understand the transmission dynamics and genetic underpinnings of a SARS-CoV-2 outbreak. IMPORTANCE Our manuscript describes a SARS-CoV-2 outbreak investigation in an Ontario tertiary care hospital. We use traditional contract tracing paired with whole-genome sequencing to facilitate an understanding of the evolutionary history and transmission dynamics of this SARS-CoV-2 outbreak in a clinical setting. These advancements have enabled the incorporation of phylogenetics and genomic epidemiology into the understanding of clinical outbreaks. We show that genomic epidemiology can help to explore the genetic evolution of a pathogen in real time, enabling the identification of the index case and helping understand its transmission dynamics to develop better strategies to prevent future spread of SARS-CoV-2 in congregate, clinical settings such as hospitals.

Genomic epidemiology of Mycobacterium abscessus in a Canadian cystic fibrosis centre.

The Mycobacterium abscessus complex causes significant morbidity and mortality among patients with Cystic Fibrosis (CF). It has been hypothesized that these organisms are transmitted from patient to patient based on genomics. However, few studies incorporate epidemiologic data to confirm this hypothesis. We longitudinally sampled 27 CF and 7 non-CF patients attending a metropolitan hospital in Ontario, Canada from 2013 to 2018. Whole genome sequencing along with epidemiological data was used to evaluate the likelihood of transmission. Overall, the genetic diversity of M. abscessus was large, with a median pairwise distance (IQR) of 1,279 (143-134) SNVs between all Ontario M. abscessus isolates and 2,908 (21-3,204) single nucleotide variants (SNVs) between M. massiliense isolates. This reflects the global diversity of this pathogen, with Ontario isolates widely dispersed throughout global phylogenetic trees of each subspecies. Using a maximum distance of 25 SNVs as a threshold to identify possible transmission, we identified 23 (of 276 total) pairs of closely-related isolates. However, transmission was probable for only one pair based on both genomic and epidemiological data. This suggests that person-to-person transmission of M. abscessus among CF patients is indeed rare and reinforces the critical importance of epidemiological data for inferences of transmission.

Multi-site implementation of whole genome sequencing for hospital infection control: A prospective genomic epidemiological analysis.

Background: Current microbiological methods lack the resolution to accurately identify multidrug-resistant organism (MDRO) transmission, however, whole genome sequencing can identify highly-related patient isolates providing opportunities for precision infection control interventions. We investigated the feasibility and potential impact of a prospective multi-centre genomics workflow for hospital infection control. Methods: We conducted a prospective genomics implementation study across eight Australian hospitals over 15 months (2017,2018), collecting all clinical and screening isolates from inpatients with vanA VRE, MRSA, ESBL Escherichia coli (ESBL-Ec), or ESBL Klebsiella pneumoniae (ESBL-Kp). Genomic and epidemiologic data were integrated to assess MDRO transmission. Findings: In total, 2275 isolates were included from 1970 patients, predominantly ESBL-Ec (40·8%) followed by MRSA (35·6%), vanA VRE (15·2%), and ESBL-Kp (8·3%).Overall, hospital and genomic epidemiology showed 607 patients (30·8%) acquired their MDRO in hospital, including the majority of vanA VRE (266 patients, 86·4%), with lower proportions of ESBL-Ec (186 patients, 23·0%), ESBL-Kp (42 patients, 26·3%), and MRSA (113 patients, 16·3%). Complex patient movements meant the majority of MDRO transmissions would remain undetected without genomic data.The genomics implementation had major impacts, identifying unexpected MDRO transmissions prompting new infection control interventions, and contributing to vanA VRE becoming a notifiable condition. We identified barriers to implementation and recommend strategies for mitigation. Interpretation: Implementation of a multi-centre genomics-informed infection control workflow is feasible and identifies many unrecognised MDRO transmissions. This provides critical opportunities for interventions to improve patient safety in hospitals. Funding: Melbourne Genomics Health Alliance (supported by State Government of Victoria, Australia), and National Health and Medical Research Council (Australia).

Reporting practices for genomic epidemiology of tuberculosis: a systematic review of the literature using STROME-ID guidelines as a benchmark.

BACKGROUND: Pathogen genomics have become increasingly important in infectious disease epidemiology and public health. The Strengthening the Reporting of Molecular Epidemiology for Infectious Diseases (STROME-ID) guidelines were developed to outline a minimum set of criteria that should be reported in genomic epidemiology studies to facilitate assessment of study quality. We evaluate such reporting practices, using tuberculosis as an example. METHODS: For this systematic review, we initially searched MEDLINE, Embase Classic, and Embase on May 3, 2017, using the search terms "tuberculosis" and "genom* sequencing". We updated this initial search on April 23, 2019, and also included a search of bioRxiv at this time. We included studies in English, French, or Spanish that recruited patients with microbiologically confirmed tuberculosis and used whole genome sequencing for typing of strains. Non-human studies, conference abstracts, and literature reviews were excluded. For each included study, the number and proportion of fulfilled STROME-ID criteria were recorded by two reviewers. A comparison of the mean proportion of fulfilled STROME-ID criteria before and after publication of the STROME-ID guidelines (in 2014) was done using a two-tailed t test. Quasi-Poisson regression and tobit regression were used to examine associations between study characteristics and the number and proportion of fulfilled STROME-ID criteria. This study was registered with PROSPERO, CRD42017064395. FINDINGS: 976 titles and abstracts were identified by our primary search, with an additional 16 studies identified in bioRxiv. 114 full texts (published between 2009 and 2019) were eligible for inclusion. The mean proportion of STROME-ID criteria fulfilled was 50% (SD 12; range 16-75). The proportion of criteria fulfilled was similar before and after STROME-ID publication (51% [SD 11] vs 46% [14], p=0·26). The number of criteria reported (among those applicable to all studies) was not associated with impact factor, h-index, country of affiliation of senior author, or sample size of isolates. Similarly, the proportion of criteria fulfilled was not associated with these characteristics, with the exception of a sample size of isolates of 277 or more (the highest quartile). In terms of reproducibility, 100 (88%) studies reported which bioinformatic tools were used, but only 33 (33%) reported corresponding version numbers. Sequencing data were available for 86 (75%) studies. INTERPRETATION: The reporting of STROME-ID criteria in genomic epidemiology studies of tuberculosis between 2009 and 2019 was low, with implications for assessment of study quality. The considerable proportion of studies without bioinformatics version numbers or sequencing data available highlights a key concern for reproducibility.

Pilot study of a combined genomic and epidemiologic surveillance program for hospital-acquired multidrug-resistant pathogens across multiple hospital networks in Australia.

OBJECTIVES: To conduct a pilot study implementing combined genomic and epidemiologic surveillance for hospital-acquired multidrug-resistant organisms (MDROs) to predict transmission between patients and to estimate the local burden of MDRO transmission. DESIGN: Pilot prospective multicenter surveillance study. SETTING: The study was conducted in 8 university hospitals (2,800 beds total) in Melbourne, Australia (population 4.8 million), including 4 acute-care, 1 specialist cancer care, and 3 subacute-care hospitals. METHODS: All clinical and screening isolates from hospital inpatients (April 24 to June 18, 2017) were collected for 6 MDROs: vanA VRE, MRSA, ESBL Escherichia coli (ESBL-Ec) and Klebsiella pneumoniae (ESBL-Kp), and carbapenem-resistant Pseudomonas aeruginosa (CRPa) and Acinetobacter baumannii (CRAb). Isolates were analyzed and reported as routine by hospital laboratories, underwent whole-genome sequencing at the central laboratory, and were analyzed using open-source bioinformatic tools. MDRO burden and transmission were assessed using combined genomic and epidemiologic data. RESULTS: In total, 408 isolates were collected from 358 patients; 47.5% were screening isolates. ESBL-Ec was most common (52.5%), then MRSA (21.6%), vanA VRE (15.7%), and ESBL-Kp (7.6%). Most MDROs (88.3%) were isolated from patients with recent healthcare exposure.Combining genomics and epidemiology identified that at least 27.1% of MDROs were likely acquired in a hospital; most of these transmission events would not have been detected without genomics. The highest proportion of transmission occurred with vanA VRE (88.4% of patients). CONCLUSIONS: Genomic and epidemiologic data from multiple institutions can feasibly be combined prospectively, providing substantial insights into the burden and distribution of MDROs, including in-hospital transmission. This analysis enables infection control teams to target interventions more effectively.

Previously undetected super-spreading of Mycobacterium tuberculosis revealed by deep sequencing.

Tuberculosis disproportionately affects the Canadian Inuit. To address this, it is imperative we understand transmission dynamics in this population. We investigate whether 'deep' sequencing can provide additional resolution compared to standard sequencing, using a well-characterized outbreak from the Arctic (2011-2012, 50 cases). Samples were sequenced to ~500-1000x and reads were aligned to a novel local reference genome generated with PacBio SMRT sequencing. Consensus and heterogeneous variants were identified and compared across genomes. In contrast with previous genomic analyses using ~50x depth, deep sequencing allowed us to identify a novel super-spreader who likely transmitted to up to 17 other cases during the outbreak (35% of the remaining cases that year). It is increasingly evident that within-host diversity should be incorporated into transmission analyses; deep sequencing may facilitate more accurate detection of super-spreaders and transmission clusters. This has implications not only for TB, but all genomic studies of transmission - regardless of pathogen.

In Canada, tuberculosis disproportionately affects the Inuit, a group of indigenous people inhabiting the Arctic regions. Canada is aiming to eliminate tuberculosis among the Inuit by 2030. One way to help stop transmission and prevent future outbreaks is to trace how and where the disease spreads using DNA sequencing. This information can then be used by public health organizations to identify possible interventions. Typically, the DNA of the bacterium that causes tuberculosis ­ Mycobacterium tuberculosis, or Mtb for short ­ is sequenced 50­100 times and a consensus DNA sequence is then generated for each patient from this data. These consensus DNA sequences are then compared to help piece together who infected whom. Recently, scientists have realized that the bacteria a person is infected with may have different DNA sequences due to people being infected with more than one bacterium or the bacterium developing variations in its genome after the infection. However, current DNA sequencing practices may miss these differences, making it harder to trace how the disease spreads. Now, Lee et al. show that sequencing the DNA of Mtb from an infected person 500­1000 times (i.e. ∼10-20 times more than usual) makes it easier to detect genetic differences and determine how tuberculosis spreads. This approach, also known as 'deep sequencing', was used to analyze DNA samples of Mtb collected from about 50 people during an outbreak of tuberculosis in 2011-2012, which had previously undergone standard DNA sequencing. This deep sequencing approach identified a 'super-spreading event' where one person had likely transmitted tuberculosis to up to 17 others during the outbreak. Lee et al. found that most of these people had visited the same 'gathering houses' which are social venues in the community. Implementing targeted public health interventions at these sites may help stop future outbreaks. To fully understand how useful this method will be for tracking the spread of tuberculosis, deep and routine sequencing will need to be compared against each other in different settings and outbreaks. Furthermore, the approach used in this study may be useful for tracking the transmission of other infectious diseases.

Estimating the relative probability of direct transmission between infectious disease patients.

BACKGROUND: Estimating infectious disease parameters such as the serial interval (time between symptom onset in primary and secondary cases) and reproductive number (average number of secondary cases produced by a primary case) are important in understanding infectious disease dynamics. Many estimation methods require linking cases by direct transmission, a difficult task for most diseases. METHODS: Using a subset of cases with detailed genetic and/or contact investigation data to develop a training set of probable transmission events, we build a model to estimate the relative transmission probability for all case-pairs from demographic, spatial and clinical data. Our method is based on naive Bayes, a machine learning classification algorithm which uses the observed frequencies in the training dataset to estimate the probability that a pair is linked given a set of covariates. RESULTS: In simulations, we find that the probabilities estimated using genetic distance between cases to define training transmission events are able to distinguish between truly linked and unlinked pairs with high accuracy (area under the receiver operating curve value of 95%). Additionally, only a subset of the cases, 10-50% depending on sample size, need to have detailed genetic data for our method to perform well. We show how these probabilities can be used to estimate the average effective reproductive number and apply our method to a tuberculosis outbreak in Hamburg, Germany. CONCLUSIONS: Our method is a novel way to infer transmission dynamics in any dataset when only a subset of cases has rich contact investigation and/or genetic data.

Rapid inference of antibiotic resistance and susceptibility by genomic neighbour typing.

Surveillance of drug-resistant bacteria is essential for healthcare providers to deliver effective empirical antibiotic therapy. However, traditional molecular epidemiology does not typically occur on a timescale that could affect patient treatment and outcomes. Here, we present a method called 'genomic neighbour typing' for inferring the phenotype of a bacterial sample by identifying its closest relatives in a database of genomes with metadata. We show that this technique can infer antibiotic susceptibility and resistance for both Streptococcus pneumoniae and Neisseria gonorrhoeae. We implemented this with rapid k-mer matching, which, when used on Oxford Nanopore MinION data, can run in real time. This resulted in the determination of resistance within 10 min (91% sensitivity and 100% specificity for S. pneumoniae and 81% sensitivity and 100% specificity for N. gonorrhoeae from isolates with a representative database) of starting sequencing, and within 4 h of sample collection (75% sensitivity and 100% specificity for S. pneumoniae) for clinical metagenomic sputum samples. This flexible approach has wide application for pathogen surveillance and may be used to greatly accelerate appropriate empirical antibiotic treatment.