Detection of a historic reservoir of bedaquiline/clofazimine resistance-associated variants in Mycobacterium tuberculosis.

BACKGROUND: Drug resistance in tuberculosis (TB) poses a major ongoing challenge to public health. The recent inclusion of bedaquiline into TB drug regimens has improved treatment outcomes, but this advance is threatened by the emergence of strains of Mycobacterium tuberculosis (Mtb) resistant to bedaquiline. Clinical bedaquiline resistance is most frequently conferred by off-target resistance-associated variants (RAVs) in the mmpR5 gene (Rv0678), the regulator of an efflux pump, which can also confer cross-resistance to clofazimine, another TB drug. METHODS: We compiled a dataset of 3682 Mtb genomes, including 180 carrying variants in mmpR5, and its immediate background (i.e. mmpR5 promoter and adjacent mmpL5 gene), that have been associated to borderline (henceforth intermediate) or confirmed resistance to bedaquiline. We characterised the occurrence of all nonsynonymous mutations in mmpR5 in this dataset and estimated, using time-resolved phylogenetic methods, the age of their emergence. RESULTS: We identified eight cases where RAVs were present in the genomes of strains collected prior to the use of bedaquiline in TB treatment regimes. Phylogenetic reconstruction points to multiple emergence events and circulation of RAVs in mmpR5, some estimated to predate the introduction of bedaquiline. However, epistatic interactions can complicate bedaquiline drug-susceptibility prediction from genetic sequence data. Indeed, in one clade, Ile67fs (a RAV when considered in isolation) was estimated to have emerged prior to the antibiotic era, together with a resistance reverting mmpL5 mutation. CONCLUSIONS: The presence of a pre-existing reservoir of Mtb strains carrying bedaquiline RAVs prior to its clinical use augments the need for rapid drug susceptibility testing and individualised regimen selection to safeguard the use of bedaquiline in TB care and control.

Genomic epidemiology of the clinically dominant clonal complex 1 in the Listeria monocytogenes population in the UK.

Listeria monocytogenes is a food-borne pathogen, typically affecting the elderly, immunocompromised patients and pregnant women. The aim of this study was to determine the population structure of L. monocytogenes clonal complex 1 (CC1) in the UK and describe the genomic epidemiology of this clinically significant CC. We interrogated a working dataset of 4073 sequences of L. monocytogenes isolated between January 2015 and December 2020 from human clinical specimens, food and/or food-production environments. A minimum spanning tree was reconstructed to determine the population structure of L. monocytogenes in the UK. Subsequent analysis focused on L. monocytogenes CC1, as the cause of the highest proportion of invasive listeriosis in humans. Sequencing data was integrated with metadata on food and environmental isolates, and information from patient questionnaires, including age, sex and clinical outcomes. All isolates either belonged to lineage I (n=1299/4073, 32%) or lineage II (n=2774/4073, 68%), with clinical isolates from human cases more likely to belong to lineage I (n=546/928, 59%) and food isolates more likely to belong to lineage II (n=2352/3067, 77%). Of the four largest CCs, CC1 (n=237) had the highest proportion of isolates from human cases of disease (CC1 n=160/237, 67.5 %; CC121 n=13/843, 2 %; CC9 n=53/360, 15 %; CC2 n=69/339, 20%). Within CC1, most cases were female (n=95/160, 59%, P=0.01771) and the highest proportion of cases were in people >60 years old (39/95, 41%, P=1.314×10-6) with a high number of them aged 20-39 years old (n=35/95, 37%) most linked to pregnancy-related listeriosis (n=29/35, 83%). Most of the male cases were in men aged over 60 years old (40/65, 62%), and most of the fatal cases in both males and females were identified in this age group (42/55, 76%). Phylogenetic analysis revealed 23 5 SNP single linkage clusters comprising 80/237 (34 %) isolates with cluster sizes ranging from 2 to 19. Five 5 SNP clusters comprised isolates from human cases and an implicated food item. Expanding the analysis to 25 SNP single linkage clusters resolved an additional two clusters linking human cases to a potential food vehicle. Analysis of demographic and clinical outcome data identified CC1 as a clinically significant cause of invasive listeriosis in the elderly population and in women of child-bearing age. Phylogenetic analysis revealed the population structure of CC1 in the UK comprised small, sparsely populated genomic clusters. Only clusters containing isolates from an implicated food vehicle, or food processing or farming environments, were resolved, emphasizing the need for clinical, food and animal-health agencies to share sequencing data in real time, and the importance of a One Health approach to public-health surveillance of listeriosis.

Integrated analysis of patient networks and plasmid genomes reveals a regional, multi-species outbreak of carbapenemase-producing Enterobacterales carrying both blaIMP and mcr-9 genes.

BACKGROUND: Carbapenemase-producing Enterobacterales (CPE) are challenging in healthcare, with resistance to multiple classes of antibiotics. This study describes the emergence of IMP-encoding CPE amongst diverse Enterobacterales species between 2016 and 2019 across a London regional network. METHODS: We performed a network analysis of patient pathways, using electronic health records, to identify contacts between IMP-encoding CPE positive patients. Genomes of IMP-encoding CPE isolates were overlayed with patient contacts to imply potential transmission events. RESULTS: Genomic analysis of 84 Enterobacterales isolates revealed diverse species (predominantly Klebsiella spp, Enterobacter spp, E. coli); 86% (72/84) harboured an IncHI2 plasmid carrying blaIMP and colistin resistance gene mcr-9 (68/72). Phylogenetic analysis of IncHI2 plasmids identified three lineages showing significant association with patient contacts and movements between four hospital sites and across medical specialities, which was missed on initial investigations. CONCLUSIONS: Combined, our patient network and plasmid analyses demonstrate an interspecies, plasmid-mediated outbreak of blaIMPCPE, which remained unidentified during standard investigations. With DNA sequencing and multi-modal data incorporation, the outbreak investigation approach proposed here provides a framework for real-time identification of key factors causing pathogen spread. Plasmid-level outbreak analysis reveals that resistance spread may be wider than suspected, allowing more interventions to stop transmission within hospital networks.

Inference of Infectious Disease Transmission through a Relaxed Bottleneck Using Multiple Genomes Per Host.

In recent times, pathogen genome sequencing has become increasingly used to investigate infectious disease outbreaks. When genomic data is sampled densely enough amongst infected individuals, it can help resolve who infected whom. However, transmission analysis cannot rely solely on a phylogeny of the genomes but must account for the within-host evolution of the pathogen, which blurs the relationship between phylogenetic and transmission trees. When only a single genome is sampled for each host, the uncertainty about who infected whom can be quite high. Consequently, transmission analysis based on multiple genomes of the same pathogen per host has a clear potential for delivering more precise results, even though it is more laborious to achieve. Here, we present a new methodology that can use any number of genomes sampled from a set of individuals to reconstruct their transmission network. Furthermore, we remove the need for the assumption of a complete transmission bottleneck. We use simulated data to show that our method becomes more accurate as more genomes per host are provided, and that it can infer key infectious disease parameters such as the size of the transmission bottleneck, within-host growth rate, basic reproduction number, and sampling fraction. We demonstrate the usefulness of our method in applications to real datasets from an outbreak of Pseudomonas aeruginosa amongst cystic fibrosis patients and a nosocomial outbreak of Klebsiella pneumoniae.

A smooth tubercle bacillus from Ethiopia phylogenetically close to the Mycobacterium tuberculosis complex.

The Mycobacterium tuberculosis complex (MTBC) includes several human- and animal-adapted pathogens. It is thought to have originated in East Africa from a recombinogenic Mycobacterium canettii-like ancestral pool. Here, we describe the discovery of a clinical tuberculosis strain isolated in Ethiopia that shares archetypal phenotypic and genomic features of M. canettii strains, but represents a phylogenetic branch much closer to the MTBC clade than to the M. canettii strains. Analysis of genomic traces of horizontal gene transfer in this isolate and previously identified M. canettii strains indicates a persistent albeit decreased recombinogenic lifestyle near the emergence of the MTBC. Our findings support that the MTBC emergence from its putative free-living M. canettii-like progenitor is evolutionarily very recent, and suggest the existence of a continuum of further extant derivatives from ancestral stages, close to the root of the MTBC, along the Great Rift Valley.

Analysis of Escherichia coli O157 strains in cattle and humans between Scotland and England & Wales: implications for human health.

For the last two decades, the human infection frequency of Escherichia coli O157 (O157) in Scotland has been 2.5-fold higher than in England and Wales. Results from national cattle surveys conducted in Scotland and England and Wales in 2014/2015 were combined with data on reported human clinical cases from the same time frame to determine if strain differences in national populations of O157 in cattle could be associated with higher human infection rates in Scotland. Shiga toxin subtype (Stx) and phage type (PT) were examined within and between host (cattle vs human) and nation (Scotland vs England and Wales). For a subset of the strains, whole genome sequencing (WGS) provided further insights into geographical and host association. All three major O157 lineages (I, II, I/II) and most sub-lineages (Ia, Ib, Ic, IIa, IIb, IIc) were represented in cattle and humans in both nations. While the relative contribution of different reservoir hosts to human infection is unknown, WGS analysis indicated that the majority of O157 diversity in human cases was captured by isolates from cattle. Despite comparable cattle O157 prevalence between nations, strain types were localized. PT21/28 (sub-lineage Ic, Stx2a+) was significantly more prevalent in Scottish cattle [odds ratio (OR) 8.7 (2.3-33.7; P<0.001] and humans [OR 2.2 (1.5-3.2); P<0.001]. In England and Wales, cattle had a significantly higher association with sub-lineage IIa strains [PT54, Stx2c; OR 5.6 (1.27-33.3); P=0.011] while humans were significantly more closely associated with sub-lineage IIb [PT8, Stx1 and Stx2c; OR 29 (4.9-1161); P<0.001]. Therefore, cattle farms in Scotland were more likely to harbour Stx2a+O157 strains compared to farms in E and W (P<0.001). There was evidence of limited cattle strain migration between nations and clinical isolates from one nation were more similar to cattle isolates from the same nation, with sub-lineage Ic (mainly PT21/28) exhibiting clear national association and evidence of local transmission in Scotland. While we propose the higher rate of O157 clinical cases in Scotland, compared to England and Wales, is a consequence of the nationally higher level of Stx2a+O157 strains in Scottish cattle, we discuss the multiple additional factors that may also contribute to the different infection rates between these nations.

Within-host diversity improves phylogenetic and transmission reconstruction of SARS-CoV-2 outbreaks.

Accurate inference of who infected whom in an infectious disease outbreak is critical for the delivery of effective infection prevention and control. The increased resolution of pathogen whole-genome sequencing has significantly improved our ability to infer transmission events. Despite this, transmission inference often remains limited by the lack of genomic variation between the source case and infected contacts. Although within-host genetic diversity is common among a wide variety of pathogens, conventional whole-genome sequencing phylogenetic approaches exclusively use consensus sequences, which consider only the most prevalent nucleotide at each position and therefore fail to capture low-frequency variation within samples. We hypothesized that including within-sample variation in a phylogenetic model would help to identify who infected whom in instances in which this was previously impossible. Using whole-genome sequences from SARS-CoV-2 multi-institutional outbreaks as an example, we show how within-sample diversity is partially maintained among repeated serial samples from the same host, it can transmitted between those cases with known epidemiological links, and how this improves phylogenetic inference and our understanding of who infected whom. Our technique is applicable to other infectious diseases and has immediate clinical utility in infection prevention and control.

During an infectious disease outbreak, tracing who infected whom allows public health scientists to see how a pathogen is spreading and to establish effective control measures. Traditionally, this involves identifying the individuals an infected person comes into contact with and monitoring whether they also become unwell. However, this information is not always available and can be inaccurate. One alternative is to track the genetic data of pathogens as they spread. Over time, pathogens accumulate mutations in their genes that can be used to distinguish them from one another. Genetically similar pathogens are more likely to have spread during the same outbreak, while genetically dissimilar pathogens may have come from different outbreaks. However, there are limitations to this approach. For example, some pathogens accumulate genetic mutations very slowly and may not change enough during an outbreak to be distinguishable from one another. Additionally, some pathogens can spread rapidly, leaving less time for mutations to occur between transmission events. To overcome these challenges, Torres Ortiz et al. developed a more sensitive approach to pathogen genetic testing that took advantage of the multiple pathogen populations that often coexist in an infected patient. Rather than tracking only the most dominant genetic version of the pathogen, this method also looked at the less dominant ones. Torres Ortiz et al. performed genome sequencing of SARS-CoV-2 (the virus that causes COVID-19) samples from 451 healthcare workers, patients, and patient contacts at participating London hospitals. Analysis showed that it was possible to detect multiple genetic populations of the virus within individual patients. These subpopulations were often more similar in patients that had been in contact with one another than in those that had not. Tracking the genetic data of all viral populations enabled Torres Ortiz et al. to trace transmission more accurately than if only the dominant population was used. More accurate genetic tracing could help public health scientists better track pathogen transmission and control outbreaks. This may be especially beneficial in hospital settings where outbreaks can be smaller, and it is important to understand if transmission is occurring within the hospital or if the pathogen is imported from the community. Further research will help scientists understand how pathogen population genetics evolve during outbreaks and may improve the detection of subpopulations present at very low frequencies.

Estimating the fitness cost and benefit of antimicrobial resistance from pathogen genomic data.

Increasing levels of antibiotic resistance in many bacterial pathogen populations are a major threat to public health. Resistance to an antibiotic provides a fitness benefit when the bacteria are exposed to this antibiotic, but resistance also often comes at a cost to the resistant pathogen relative to susceptible counterparts. We lack a good understanding of these benefits and costs of resistance for many bacterial pathogens and antibiotics, but estimating them could lead to better use of antibiotics in a way that reduces or prevents the spread of resistance. Here, we propose a new model for the joint epidemiology of susceptible and resistant variants, which includes explicit parameters for the cost and benefit of resistance. We show how Bayesian inference can be performed under this model using phylogenetic data from susceptible and resistant lineages and that by combining data from both we are able to disentangle and estimate the resistance cost and benefit parameters separately. We applied our inferential methodology to several simulated datasets to demonstrate good scalability and accuracy. We analysed a dataset of Neisseria gonorrhoeae genomes collected between 2000 and 2013 in the USA. We found that two unrelated lineages resistant to fluoroquinolones shared similar epidemic dynamics and resistance parameters. Fluoroquinolones were abandoned for the treatment of gonorrhoea due to increasing levels of resistance, but our results suggest that they could be used to treat a minority of around 10% of cases without causing resistance to grow again.

Phylogenetic analysis of the origin and spread of plague in Madagascar.

BACKGROUND: Plague is a zoonotic disease caused by the bacterium Yersinia pestis, highly prevalent in the Central Highlands, a mountainous region in the center of Madagascar. After a plague-free period of over 60 years in the northwestern coast city of Mahajanga, the disease reappeared in 1991 and caused several outbreaks until 1999. Previous research indicates that the disease was reintroduced to the city of Mahajanga from the Central Highlands instead of reemerging from a local reservoir. However, it is not clear how many reintroductions occurred and when they took place. METHODOLOGY/PRINCIPAL FINDINGS: In this study we applied a Bayesian phylogeographic model to detect and date migrations of Y. pestis between the two locations that could be linked to the re-emergence of plague in Mahajanga. Genome sequences of 300 Y. pestis strains sampled between 1964 and 2012 were analyzed. Four migrations from the Central Highlands to Mahajanga were detected. Two resulted in persistent transmission in humans, one was responsible for most of the human cases recorded between 1995 and 1999, while the other produced plague cases in 1991 and 1992. We dated the emergence of the Y. pestis sub-branch 1.ORI3, which is only present in Madagascar and Turkey, to the beginning of the 20th century, using a Bayesian molecular dating analysis. The split between 1.ORI3 and its ancestor lineage 1.ORI2 was dated to the second half of the 19th century. CONCLUSIONS/SIGNIFICANCE: Our results indicate that two independent migrations from the Central Highlands caused the plague outbreaks in Mahajanga during the 1990s, with both introductions occurring during the early 1980s. They happened over a decade before the detection of human cases, thus the pathogen likely survived in wild reservoirs until the spillover to humans was possible. This study demonstrates the value of Bayesian phylogenetics in elucidating the re-emergence of infectious diseases.

Model design for nonparametric phylodynamic inference and applications to pathogen surveillance.

Inference of effective population size from genomic data can provide unique information about demographic history and, when applied to pathogen genetic data, can also provide insights into epidemiological dynamics. The combination of nonparametric models for population dynamics with molecular clock models which relate genetic data to time has enabled phylodynamic inference based on large sets of time-stamped genetic sequence data. The methodology for nonparametric inference of effective population size is well-developed in the Bayesian setting, but here we develop a frequentist approach based on nonparametric latent process models of population size dynamics. We appeal to statistical principles based on out-of-sample prediction accuracy in order to optimize parameters that control shape and smoothness of the population size over time. Our methodology is implemented in a new R package entitled mlesky. We demonstrate the flexibility and speed of this approach in a series of simulation experiments and apply the methodology to a dataset of HIV-1 in the USA. We also estimate the impact of non-pharmaceutical interventions for COVID-19 in England using thousands of SARS-CoV-2 sequences. By incorporating a measure of the strength of these interventions over time within the phylodynamic model, we estimate the impact of the first national lockdown in the UK on the epidemic reproduction number.

Phylogenetic Analysis of Bacterial Pathogen Genomes.

The development of high-throughput sequencing technology has led to a significant reduction in the time and cost of sequencing whole genomes of bacterial pathogens. Studies can sequence and compare hundreds or even thousands of genomes within a given bacterial population. A phylogenetic tree is the most frequently used method of depicting the relationships between these bacterial pathogen genomes. However, the presence of homologous recombination in most bacterial pathogen species can invalidate the application of standard phylogenetic tools. Here we describe a method to produce phylogenetic analyses that accounts for the disruptive effect of recombination. This allows users to investigate the recombination events that have occurred, as well as to produce more meaningful phylogenetic analyses which recover the clonal genealogy representing the clonal relationships between genomes.

Penicillin Binding Protein Substitutions Cooccur with Fluoroquinolone Resistance in Epidemic Lineages of Multidrug-Resistant Clostridioides difficile.

Clostridioides difficile remains a key cause of healthcare-associated infection, with multidrug-resistant (MDR) lineages causing high-mortality (≥20%) outbreaks. Cephalosporin treatment is a long-established risk factor, and antimicrobial stewardship is a key control. A mechanism underlying raised cephalosporin MICs has not been identified in C. difficile, but among other species, this is often acquired via amino acid substitutions in cell wall transpeptidases (penicillin binding proteins [PBPs]). Here, we investigated five C. difficile transpeptidases (PBP1 to PBP5) for recent substitutions, associated cephalosporin MICs, and co-occurrence with fluoroquinolone resistance. Previously published genome assemblies (n = 7,096) were obtained, representing 16 geographically widespread lineages, including healthcare-associated ST1(027). Recent amino acid substitutions were found within PBP1 (n = 50) and PBP3 (n = 48), ranging from 1 to 10 substitutions per genome. ß-Lactam MICs were measured for closely related pairs of wild-type and PBP-substituted isolates separated by 20 to 273 single nucleotide polymorphisms (SNPs). Recombination-corrected phylogenies were constructed to date substitution acquisition. Key substitutions such as PBP3 V497L and PBP1 T674I/N/V emerged independently across multiple lineages. They were associated with extremely high cephalosporin MICs; 1 to 4 doubling dilutions >wild-type, up to 1,506 µg/mL. Substitution patterns varied by lineage and clade, showed geographic structure, and occurred post-1990, coincident with the gyrA and/or gyrB substitutions conferring fluoroquinolone resistance. In conclusion, recent PBP1 and PBP3 substitutions are associated with raised cephalosporin MICs in C. difficile. Their co-occurrence with fluoroquinolone resistance hinders attempts to understand the relative importance of these drugs in the dissemination of epidemic lineages. Further controlled studies of cephalosporin and fluoroquinolone stewardship are needed to determine their relative effectiveness in outbreak control. IMPORTANCE Fluoroquinolone and cephalosporin use in healthcare settings has triggered outbreaks of high-mortality, multidrug-resistant C. difficile infection. Here, we identify a mechanism associated with raised cephalosporin MICs in C. difficile comprising amino acid substitutions in two cell wall transpeptidase enzymes (penicillin binding proteins). The higher the number of substitutions, the greater the impact on phenotype. Dated phylogenies revealed that substitutions associated with raised cephalosporin and fluoroquinolone MICs were co-acquired immediately before clinically important outbreak strains emerged. PBP substitutions were geographically structured within genetic lineages, suggesting adaptation to local antimicrobial prescribing. Antimicrobial stewardship of cephalosporins and fluoroquinolones is an effective means of C. difficile outbreak control. Genetic changes associated with raised MIC may impart a "fitness cost" after antibiotic withdrawal. Our study therefore identifies a mechanism that may explain the contribution of cephalosporin stewardship to resolving outbreak conditions. However, due to the co-occurrence of raised cephalosporin MICs and fluoroquinolone resistance, further work is needed to determine the relative importance of each.

Genomic Diversity among Actinomyces naeslundii Strains and Closely Related Species.

The aim of this study was to investigate and clarify the ambiguous taxonomy of Actinomyces naeslundii and its closely related species using state-of-the-art high-throughput sequencing techniques, and, furthermore, to determine whether sub-clusters identified within Actinomyces oris and Actinomyces naeslundii in a previous study by multi locus sequence typing (MLST) using concatenation of seven housekeeping genes should either be classified as subspecies or distinct species. The strains in this study were broadly classified under Actinomyces naeslundii group as A. naeslundii genospecies I and genospecies II. Based on MLST data analysis, these were further classified as A. oris and A. naeslundii. The whole genome sequencing of selected strains of A. oris (n = 17) and A. naeslundii (n = 19) was carried out using Illumina Genome Analyzer IIxe and Roche 454 allowing paired-end and single-reads sequencing, respectively. The sequences obtained were aligned using CLC Genomic workbench version 5.1 and annotated using RAST (Rapid Annotation using Subsystem Technology) release version 59 accessible online. Additionally, genomes of seven publicly available strains of Actinomyces (k20, MG1, c505, OT175, OT171, OT170, and A. johnsonii) were also included. Comparative genomic analysis (CGA) using Mauve, Progressive Mauve, gene-by-gene, Core, and Pan Genome, and finally Digital DNA-DNA homology (DDH) analysis was carried out. DDH values were obtained using in silico genome-genome comparison. Evolutionary analysis using ClonalFrame was also undertaken. The mutation and recombination events were compared using chi-square test among A. oris and A. naeslundii isolates (analysis methods are not included in the study). CGA results were consistent with previous traditional classification using MLST. It was found that strains of Actinomyces k20, MG1, c505, and OT175 clustered in A. oris group of isolates, while OT171, OT170, and A. johnsonii appeared as separate branches. Similar clustering to MLST was observed for other isolates. The mutation and recombination events were significantly higher in A. oris than A. naeslundii, highlighting the diversity of A. oris strains in the oral cavity. These findings suggest that A. oris forms six distinct groups, whereas A. naeslundii forms three. The correct designation of isolates will help in the identification of clinical Actinomyces isolates found in dental plaque. Easily accessible online genomic sequence data will also accelerate the investigation of the biochemical characterisation and pathogenesis of this important group of micro-organisms.

Epidemiological impacts of the NHS COVID-19 app in England and Wales throughout its first year.

The NHS COVID-19 app was launched in England and Wales in September 2020, with a Bluetooth-based contact tracing functionality designed to reduce transmission of SARS-CoV-2. We show that user engagement and the app's epidemiological impacts varied according to changing social and epidemic characteristics throughout the app's first year. We describe the interaction and complementarity of manual and digital contact tracing approaches. Results of our statistical analyses of anonymised, aggregated app data include that app users who were recently notified were more likely to test positive than app users who were not recently notified, by a factor that varied considerably over time. We estimate that the app's contact tracing function alone averted about 1 million cases (sensitivity analysis 450,000-1,400,000) during its first year, corresponding to 44,000 hospital cases (SA 20,000-60,000) and 9,600 deaths (SA 4600-13,000).

Distinguishing imported cases from locally acquired cases within a geographically limited genomic sample of an infectious disease.

MOTIVATION: The ability to distinguish imported cases from locally acquired cases has important consequences for the selection of public health control strategies. Genomic data can be useful for this, for example, using a phylogeographic analysis in which genomic data from multiple locations are compared to determine likely migration events between locations. However, these methods typically require good samples of genomes from all locations, which is rarely available. RESULTS: Here, we propose an alternative approach that only uses genomic data from a location of interest. By comparing each new case with previous cases from the same location, we are able to detect imported cases, as they have a different genealogical distribution than that of locally acquired cases. We show that, when variations in the size of the local population are accounted for, our method has good sensitivity and excellent specificity for the detection of imports. We applied our method to data simulated under the structured coalescent model and demonstrate relatively good performance even when the local population has the same size as the external population. Finally, we applied our method to several recent genomic datasets from both bacterial and viral pathogens, and show that it can, in a matter of seconds or minutes, deliver important insights on the number of imports to a geographically limited sample of a pathogen population. AVAILABILITY AND IMPLEMENTATION: The R package DetectImports is freely available from https://github.com/xavierdidelot/DetectImports. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

London parochial burial records from 1563 to 1665 indicate higher plague death rates for males than females: Some possible demographic and social explanations.

The burial rates of males and females in early modern central London were compared to investigate a possible bias towards male mortality in the plague years of 1563, 1593, 1603, 1625 and 1665. The burial records of sixteen parishes were examined and compared with the five-year periods immediately preceding each plague year when recorded burials were substantially less. A markedly higher burial rate for males was detected in each plague year but this can be partly attributed to a general preponderance of males in the central London population since there was a similar but lesser bias in non-plague years. In the plague years the difference between the frequency of male and female adult burials appears to have been enhanced by the preferential migration of women of childbearing age out of the city since fewer births were recorded in months when plague was rife. Furthermore, when a sample of households was investigated, husbands were significantly more likely to have been buried than their wives. These findings were largely applicable to the plague years of 1603, 1625 and 1665 but were far less apparent in 1563 and 1593. In general, there were more burials of boys than girls in non-plague years which is the expected consequence of their greater vulnerability to childhood diseases. This difference diminished in plague years so that the burials of girls and boys approached parity at a time when burials of children of both sexes were significantly increased. Possibly, plague did not discriminate between the sexes and this characteristic tended to mask the usual vulnerability of boys.

A scalable analytical approach from bacterial genomes to epidemiology.

Recent years have seen a remarkable increase in the practicality of sequencing whole genomes from large numbers of bacterial isolates. The availability of this data has huge potential to deliver new insights into the evolution and epidemiology of bacterial pathogens, but the scalability of the analytical methodology has been lagging behind that of the sequencing technology. Here we present a step-by-step approach for such large-scale genomic epidemiology analyses, from bacterial genomes to epidemiological interpretations. A central component of this approach is the dated phylogeny, which is a phylogenetic tree with branch lengths measured in units of time. The construction of dated phylogenies from bacterial genomic data needs to account for the disruptive effect of recombination on phylogenetic relationships, and we describe how this can be achieved. Dated phylogenies can then be used to perform fine-scale or large-scale epidemiological analyses, depending on the proportion of cases for which genomes are available. A key feature of this approach is computational scalability and in particular the ability to process hundreds or thousands of genomes within a matter of hours. This is a clear advantage of the step-by-step approach described here. We discuss other advantages and disadvantages of the approach, as well as potential improvements and avenues for future research. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.

Transmission history of SARS-CoV-2 in humans and white-tailed deer.

The emergence of a novel pathogen in a susceptible population can cause rapid spread of infection. High prevalence of SARS-CoV-2 infection in white-tailed deer (Odocoileus virginianus) has been reported in multiple locations, likely resulting from several human-to-deer spillover events followed by deer-to-deer transmission. Knowledge of the risk and direction of SARS-CoV-2 transmission between humans and potential reservoir hosts is essential for effective disease control and prioritisation of interventions. Using genomic data, we reconstruct the transmission history of SARS-CoV-2 in humans and deer, estimate the case finding rate and attempt to infer relative rates of transmission between species. We found no evidence of direct or indirect transmission from deer to human. However, with an estimated case finding rate of only 4.2%, spillback to humans cannot be ruled out. The extensive transmission of SARS-CoV-2 within deer populations and the large number of unsampled cases highlights the need for active surveillance at the human-animal interface.

Within-host diversity improves phylogenetic and transmission reconstruction of SARS-CoV-2 outbreaks.

Accurate inference of who infected whom in an infectious disease outbreak is critical for the delivery of effective infection prevention and control. The increased resolution of pathogen whole-genome sequencing has significantly improved our ability to infer transmission events. Despite this, transmission inference often remains limited by the lack of genomic variation between the source case and infected contacts. Although within-host genetic diversity is common among a wide variety of pathogens, conventional whole-genome sequencing phylogenetic approaches to reconstruct outbreaks exclusively use consensus sequences, which consider only the most prevalent nucleotide at each position and therefore fail to capture low frequency variation within samples. We hypothesized that including within-sample variation in a phylogenetic model would help to identify who infected whom in instances in which this was previously impossible. Using whole-genome sequences from SARS-CoV-2 multi-institutional outbreaks as an example, we show how within-sample diversity is stable among repeated serial samples from the same host, is transmitted between those cases with known epidemiological links, and how this improves phylogenetic inference and our understanding of who infected whom. Our technique is applicable to other infectious diseases and has immediate clinical utility in infection prevention and control.

The bounded coalescent model: Conditioning a genealogy on a minimum root date.

The coalescent model represents how individuals sampled from a population may have originated from a last common ancestor. The bounded coalescent model is obtained by conditioning the coalescent model such that the last common ancestor must have existed after a certain date. This conditioned model arises in a variety of applications, such as speciation, horizontal gene transfer or transmission analysis, and yet the bounded coalescent model has not been previously analysed in detail. Here we describe a new algorithm to simulate from this model directly, without resorting to rejection sampling. We show that this direct simulation algorithm is more computationally efficient than the rejection sampling approach. We also show how to calculate the probability of the last common ancestor occurring after a given date, which is required to compute the probability density of realisations under the bounded coalescent model. Our results are applicable in both the isochronous (when all samples have the same date) and heterochronous (where samples can have different dates) settings. We explore the effect of setting a bound on the date of the last common ancestor, and show that it affects a number of properties of the resulting phylogenies. All our methods are implemented in a new R package called BoundedCoalescent which is freely available online.