Development of Phylodynamic Methods for Bacterial Pathogens

Harnessing genomic technologies for one health solutions in the tropics

BACKGROUND

The targeted application of cutting-edge high-throughput molecular data technologies provides an enormous opportunity to address key health, economic and environmental issues in the tropics within the One Health framework. The Earth's tropical regions are projected to contain > 50% of the world's population by 2050 coupled with 80% of its biodiversity however these regions are relatively less developed economically, with agricultural productivity substantially lower than temperate zones, a large percentage of its population having limited health care options and much of its biodiversity understudied and undescribed. The generation of high-throughput molecular data and bespoke bioinformatics capability to address these unique challenges offers an enormous opportunity for people living in the tropics. MAIN: In this review we discuss in depth solutions to challenges to populations living in tropical zones across three critical One Health areas: human health, biodiversity and food production. This review will examine how some of the challenges in the tropics can be addressed through the targeted application of advanced omics and bioinformatics and will discuss how local populations can embrace these technologies through strategic outreach and education ensuring the benefits of the One Health approach is fully realised through local engagement.

CONCLUSION

Within the context of the One Health framework, we will demonstrate how genomic technologies can be utilised to improve the overall quality of life for half the world's population.

Integrative genomics would strengthen AMR understanding through ONE health approach

Emergence of drug-induced antimicrobial resistance (AMR) forms a crippling health and economic crisis worldwide, causing high mortality from otherwise treatable diseases and infections. Next Generation Sequencing (NGS) has significantly augmented detection of culture independent microbes, potential AMR in pathogens and elucidation of mechanisms underlying it. Here, we review recent findings of AMR evolution in pathogens aided by integrated genomic investigation strategies inclusive of bacteria, virus, fungi and AMR alleles. While AMR monitoring is dominated by data from hospital-related infections, we review genomic surveillance of both biotic and abiotic components involved in global AMR emergence and persistence. Identification of pathogen-intrinsic as well as environmental and/or host factors through robust genomics/bioinformatics, along with monitoring of type and frequency of antibiotic usage will greatly facilitate prediction of regional and global patterns of AMR evolution. Genomics-enabled AMR prediction and surveillance will be crucial - in shaping health and economic policies within the One Health framework to combat this global concern.

Impacts of coprophagic foraging behaviour on the avian gut microbiome

Avian gut microbial communities are complex and play a fundamental role in regulating biological functions within an individual. Although it is well established that diet can influence the structure and composition of the gut microbiota, foraging behaviour may also play a critical, yet unexplored role in shaping the composition, dynamics, and adaptive potential of avian gut microbiota. In this review, we examine the potential influence of coprophagic foraging behaviour on the establishment and adaptability of wild avian gut microbiomes. Coprophagy involves the ingestion of faeces, sourced from either self (autocoprophagy), conspecific animals (allocoprophagy), or heterospecific animals. Much like faecal transplant therapy, coprophagy may (i) support the establishment of the gut microbiota of young precocial species, (ii) directly and indirectly provide nutritional and energetic requirements, and (iii) represent a mechanism by which birds can rapidly adapt the microbiota to changing environments and diets. However, in certain contexts, coprophagy may also pose risks to wild birds, and their microbiomes, through increased exposure to chemical pollutants, pathogenic microbes, and antibiotic-resistant microbes, with deleterious effects on host health and performance. Given the potentially far-reaching consequences of coprophagy for avian microbiomes, and the dearth of literature directly investigating these links, we have developed a predictive framework for directing future research to understand better when and why wild birds engage in distinct types of coprophagy, and the consequences of this foraging behaviour. There is a need for comprehensive investigation into the influence of coprophagy on avian gut microbiotas and its effects on host health and performance throughout ontogeny and across a range of environmental perturbations. Future behavioural studies combined with metagenomic approaches are needed to provide insights into the function of this poorly understood behaviour.

High performance Legionella pneumophila source attribution using genomics-based machine learning classification

Fundamental to effective Legionnaires' disease outbreak control is the ability to rapidly identify the environmental source(s) of the causative agent, Legionella pneumophila. Genomics has revolutionized pathogen surveillance, but L. pneumophila has a complex ecology and population structure that can limit source inference based on standard core genome phylogenetics. Here, we present a powerful machine learning approach that assigns the geographical source of Legionnaires' disease outbreaks more accurately than current core genome comparisons. Models were developed upon 534 L. pneumophila genome sequences, including 149 genomes linked to 20 previously reported Legionnaires' disease outbreaks through detailed case investigations. Our classification models were developed in a cross-validation framework using only environmental L. pneumophila genomes. Assignments of clinical isolate geographic origins demonstrated high predictive sensitivity and specificity of the models, with no false positives or false negatives for 13 out of 20 outbreak groups, despite the presence of within-outbreak polyclonal population structure. Analysis of the same 534-genome panel with a conventional phylogenomic tree and a core genome multi-locus sequence type allelic distance-based classification approach revealed that our machine learning method had the highest overall classification performance-agreement with epidemiological information. Our multivariate statistical learning approach maximizes the use of genomic variation data and is thus well-suited for supporting Legionnaires' disease outbreak investigations.IMPORTANCEIdentifying the sources of Legionnaires' disease outbreaks is crucial for effective control. Current genomic methods, while useful, often fall short due to the complex ecology and population structure of Legionella pneumophila, the causative agent. Our study introduces a high-performing machine learning approach for more accurate geographical source attribution of Legionnaires' disease outbreaks. Developed using cross-validation on environmental L. pneumophila genomes, our models demonstrate excellent predictive sensitivity and specificity. Importantly, this new approach outperforms traditional methods like phylogenomic trees and core genome multi-locus sequence typing, proving more efficient at leveraging genomic variation data to infer outbreak sources. Our machine learning algorithms, harnessing both core and accessory genomic variation, offer significant promise in public health settings. By enabling rapid and precise source identification in Legionnaires' disease outbreaks, such approaches have the potential to expedite intervention efforts and curtail disease transmission.

Application of phylodynamics to identify spread of antimicrobial-resistant Escherichia coli between humans and canines in an urban environment

The transmission of antimicrobial resistant bacteria in the urban environment is poorly understood. We utilized genomic sequencing and phylogenetics to characterize the transmission dynamics of antimicrobial resistant Escherichia coli (AMR-Ec) cultured from putative canine (caninep) and human feces present on urban sidewalks in San Francisco, California. We isolated a total of fifty-six AMR-Ec isolates from human (n = 20) and caninep (n = 36) fecal samples from the Tenderloin and South of Market (SoMa) neighborhoods of San Francisco. We then analyzed phenotypic and genotypic antimicrobial resistance (AMR) of the isolates, as well as clonal relationships based on cgMLST and single nucleotide polymorphisms (SNPs) of the core genomes. Using Bayesian inference, we reconstructed the transmission dynamics between humans and caninesp from multiple local outbreak clusters using the marginal structured coalescent approximation (MASCOT). Our results provide evidence for multiple sharing events of AMR-Ec between humans and caninesp. In particular, we found one instance of likely transmission from caninesp to humans as well as an additional local outbreak cluster consisting of one caninep and one human sample. Based on this analysis, it appears that non-human feces act as an important reservoir of clinically relevant AMR-Ec within the urban environment for this study population. This work showcases the utility of genomic epidemiology to reconstruct potential pathways by which antimicrobial resistance spreads.

Genomic surveillance for antimicrobial resistance - a One Health perspective

Antimicrobial resistance (AMR) - the ability of microorganisms to adapt and survive under diverse chemical selection pressures - is influenced by complex interactions between humans, companion and food-producing animals, wildlife, insects and the environment. To understand and manage the threat posed to health (human, animal, plant and environmental) and security (food and water security and biosecurity), a multifaceted 'One Health' approach to AMR surveillance is required. Genomic technologies have enabled monitoring of the mobilization, persistence and abundance of AMR genes and mutations within and between microbial populations. Their adoption has also allowed source-tracing of AMR pathogens and modelling of AMR evolution and transmission. Here, we highlight recent advances in genomic AMR surveillance and the relative strengths of different technologies for AMR surveillance and research. We showcase recent insights derived from One Health genomic surveillance and consider the challenges to broader adoption both in developed and in lower- and middle-income countries.

Evaluation of recombination detection methods for viral sequencing

Recombination is a key evolutionary driver in shaping novel viral populations and lineages. When unaccounted for, recombination can impact evolutionary estimations or complicate their interpretation. Therefore, identifying signals for recombination in sequencing data is a key prerequisite to further analyses. A repertoire of recombination detection methods (RDMs) have been developed over the past two decades; however, the prevalence of pandemic-scale viral sequencing data poses a computational challenge for existing methods. Here, we assessed eight RDMs: PhiPack (Profile), 3SEQ, GENECONV, recombination detection program (RDP) (OpenRDP), MaxChi (OpenRDP), Chimaera (OpenRDP), UCHIME (VSEARCH), and gmos; to determine if any are suitable for the analysis of bulk sequencing data. To test the performance and scalability of these methods, we analysed simulated viral sequencing data across a range of sequence diversities, recombination frequencies, and sample sizes. Furthermore, we provide a practical example for the analysis and validation of empirical data. We find that RDMs need to be scalable, use an analytical approach and resolution that is suitable for the intended research application, and are accurate for the properties of a given dataset (e.g. sequence diversity and estimated recombination frequency). Analysis of simulated and empirical data revealed that the assessed methods exhibited considerable trade-offs between these criteria. Overall, we provide general guidelines for the validation of recombination detection results, the benefits and shortcomings of each assessed method, and future considerations for recombination detection methods for the assessment of large-scale viral sequencing data.

Population genomics of diarrheagenic Escherichia coli uncovers high connectivity between urban and rural communities in Ecuador

Human movement may be an important driver of transmission dynamics for enteric pathogens but has largely been underappreciated except for international 'travelers' diarrhea or cholera. Phylodynamic methods, which combine genomic and epidemiological data, are used to examine rates and dynamics of disease matching underlying evolutionary history and biogeographic distributions, but these methods often are not applied to enteric bacterial pathogens. We used phylodynamics to explore the phylogeographic and evolutionary patterns of diarrheagenic E. coli in northern Ecuador to investigate the role of human travel in the geographic distribution of strains across the country. Using whole genome sequences of diarrheagenic E. coli isolates, we built a core genome phylogeny, reconstructed discrete ancestral states across urban and rural sites, and estimated migration rates between E. coli populations. We found minimal structuring based on site locations, urban vs. rural locality, pathotype, or clinical status. Ancestral states of phylogenomic nodes and tips were inferred to have 51% urban ancestry and 49% rural ancestry. Lack of structuring by location or pathotype E. coli isolates imply highly connected communities and extensive sharing of genomic characteristics across isolates. Using an approximate structured coalescent model, we estimated rates of migration among circulating isolates were 6.7 times larger for urban towards rural populations compared to rural towards urban populations. This suggests increased inferred migration rates of diarrheagenic E. coli from urban populations towards rural populations. Our results indicate that investments in water and sanitation prevention in urban areas could limit the spread of enteric bacterial pathogens among rural populations.

Leveraging insect-specific viruses to elucidate mosquito population structure and dynamics

Several aspects of mosquito ecology that are important for vectored disease transmission and control have been difficult to measure at epidemiologically important scales in the field. In particular, the ability to describe mosquito population structure and movement rates has been hindered by difficulty in quantifying fine-scale genetic variation among populations. The mosquito virome represents a possible avenue for quantifying population structure and movement rates across multiple spatial scales. Mosquito viromes contain a diversity of viruses, including several insect-specific viruses (ISVs) and "core" viruses that have high prevalence across populations. To date, virome studies have focused on viral discovery and have only recently begun examining viral ecology. While nonpathogenic ISVs may be of little public health relevance themselves, they provide a possible route for quantifying mosquito population structure and dynamics. For example, vertically transmitted viruses could behave as a rapidly evolving extension of the host's genome. It should be possible to apply established analytical methods to appropriate viral phylogenies and incidence data to generate novel approaches for estimating mosquito population structure and dispersal over epidemiologically relevant timescales. By studying the virome through the lens of spatial and genomic epidemiology, it may be possible to investigate otherwise cryptic aspects of mosquito ecology. A better understanding of mosquito population structure and dynamics are key for understanding mosquito-borne disease ecology and methods based on ISVs could provide a powerful tool for informing mosquito control programs.

Genome-wide phylodynamic approach reveals the epidemic dynamics of the main Mycoplasma bovis subtype circulating in France

Mycoplasma bovis is a major aetiological agent of bovine respiratory disease worldwide. Genome-based analyses are increasingly being used to monitor the genetic diversity and global distribution of M. bovis, complementing existing subtyping schemes based on locus sequencing. However, these analyses have so far provided limited information on the spatiotemporal and population dynamics of circulating subtypes. Here we applied a genome-wide phylodynamic approach to explore the epidemic dynamics of 88 French M. bovis strains collected between 2000 and 2019 in France and belonging to the currently dominant polC subtype 2 (st2). A strong molecular clock signal detected in the genomic data enabled robust phylodynamic inferences, which estimated that the M. bovis st2 population in France is composed of two lineages that successively emerged from independent introductions of international strains. The first lineage appeared around 2000 and supplanted the previously established antimicrobial-susceptible polC subtype 1. The second lineage, which is likely more transmissible, progressively replaced the first M. bovis st2 lineage population from 2005 onward and became predominant after 2010. Analyses also showed a brief decline in this second M. bovis st2 lineage population in around 2011, possibly due to the challenge from the concurrent emergence of M. bovis polC subtype 3 in France. Finally, we identified non-synonymous mutations in genes associated with lineages, which raises prospects for identifying new surveillance molecular markers. A genome-wide phylodynamic approach provides valuable resources for monitoring the evolution and epidemic dynamics of circulating M. bovis subtypes, and may prove critical for developing more effective surveillance systems and disease control strategies.

Phylodynamics Uncovers the Transmission of Antibiotic-Resistant Escherichia coli between Canines and Humans in an Urban Environment

The role of canines in transmitting antibiotic resistant bacteria to humans in the urban environment is poorly understood. To elucidate this role, we utilized genomic sequencing and phylogenetics to characterize the burden and transmission dynamics of antibiotic resistant Escherichia coli (ABR-Ec) cultured from canine and human feces present on urban sidewalks in San Francisco, California. We collected a total of fifty-nine ABR-Ec from human (n=12) and canine (n=47) fecal samples from the Tenderloin and South of Market (SoMa) neighborhoods of San Francisco. We then analyzed phenotypic and genotypic antibiotic resistance (ABR) of the isolates, as well as clonal relationships based on cgMLST and single nucleotide polymorphisms (SNPs) of the core genomes. Using Bayesian inference, we reconstructed the transmission dynamics between humans and canines from multiple local outbreak clusters using the marginal structured coalescent approximation (MASCOT). Overall, we found human and canine samples to carry similar amounts and profiles of ABR genes. Our results provide evidence for multiple transmission events of ABR-Ec between humans and canines. In particular, we found one instance of likely transmission from canines to humans as well as an additional local outbreak cluster consisting of one canine and one human sample. Based on this analysis, it appears that canine feces act as an important reservoir of clinically relevant ABR-Ec within the urban environment. Our findings support that public health measures should continue to emphasize proper canine feces disposal practices, access to public toilets and sidewalk and street cleaning. Importance: Antibiotic resistance in E. coli is a growing public health concern with global attributable deaths projected to reach millions annually. Current research has focused heavily on clinical routes of antibiotic resistance transmission to design interventions while the role of alternative reservoirs such as domesticated animals remain less well understood. Our results suggest canines are part of the transmission network that disseminates high-risk multidrug resistance in E. coli within the urban San Francisco community. As such, this study highlights the need to consider canines, and potentially domesticated animals more broadly, when designing interventions to reduce the prevalence of antibiotic resistance in the community. Additionally, it showcases the utility of genomic epidemiology to reconstruct the pathways by which antimicrobial resistance spreads.

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'.

Phylogenetic and phylodynamic approaches to understanding and combating the early SARS-CoV-2 pandemic

Determining the transmissibility, prevalence and patterns of movement of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections is central to our understanding of the impact of the pandemic and to the design of effective control strategies. Phylogenies (evolutionary trees) have provided key insights into the international spread of SARS-CoV-2 and enabled investigation of individual outbreaks and transmission chains in specific settings. Phylodynamic approaches combine evolutionary, demographic and epidemiological concepts and have helped track virus genetic changes, identify emerging variants and inform public health strategy. Here, we review and synthesize studies that illustrate how phylogenetic and phylodynamic techniques were applied during the first year of the pandemic, and summarize their contributions to our understanding of SARS-CoV-2 transmission and control.

Using Genomics to Understand the Epidemiology of Infectious Diseases in the Northern Territory of Australia

The Northern Territory (NT) is a geographically remote region of northern and central Australia. Approximately a third of the population are First Nations Australians, many of whom live in remote regions. Due to the physical environment and climate, and scale of social inequity, the rates of many infectious diseases are the highest nationally. Molecular typing and genomic sequencing in research and public health have provided considerable new knowledge on the epidemiology of infectious diseases in the NT. We review the applications of genomic sequencing technology for molecular typing, identification of transmission clusters, phylogenomics, antimicrobial resistance prediction, and pathogen detection. We provide examples where these methodologies have been applied to infectious diseases in the NT and discuss the next steps in public health implementation of this technology.

Research Progress and Future Trends of Microfluidic Paper-Based Analytical Devices in In-Vitro Diagnosis

In vitro diagnosis (IVD) has become a hot topic in laboratory research and achievement transformation. However, due to the high cost, and time-consuming and complex operation of traditional technologies, some new technologies are being introduced into IVD, to solve the existing problems. As a result, IVD has begun to develop toward point-of-care testing (POCT), a subdivision field of IVD. The pandemic has made governments and health institutions realize the urgency of accelerating the development of POCT. Microfluidic paper-based analytical devices (μPADs), a low-cost, high-efficiency, and easy-to-operate detection platform, have played a significant role in advancing the development of IVD. μPADs are composed of paper as the core material, certain unique substances as reagents for processing the paper, and sensing devices, as auxiliary equipment. The published reviews on the same topic lack a comprehensive and systematic introduction to μPAD classification and research progress in IVD segmentation. In this paper, we first briefly introduce the origin of μPADs and their role in promoting IVD, in the introduction section. Then, processing and detection methods for μPADs are summarized, and the innovative achievements of μPADs in IVD are reviewed. Finally, we discuss and prospect the upgrade and improvement directions of μPADs, in terms of portability, sensitivity, and automation, to help researchers clarify the progress and overcome the difficulties in subsequent μPAD research.

Epidemiological inference from pathogen genomes: A review of phylodynamic models and applications

Phylodynamics requires an interdisciplinary understanding of phylogenetics, epidemiology, and statistical inference. It has also experienced more intense application than ever before amid the SARS-CoV-2 pandemic. In light of this, we present a review of phylodynamic models beginning with foundational models and assumptions. Our target audience is public health researchers, epidemiologists, and biologists seeking a working knowledge of the links between epidemiology, evolutionary models, and resulting epidemiological inference. We discuss the assumptions linking evolutionary models of pathogen population size to epidemiological models of the infected population size. We then describe statistical inference for phylodynamic models and list how output parameters can be rearranged for epidemiological interpretation. We go on to cover more sophisticated models and finish by highlighting future directions.

Phylodynamic Inference of Bacterial Outbreak Parameters Using Nanopore Sequencing

Nanopore sequencing and phylodynamic modeling have been used to reconstruct the transmission dynamics of viral epidemics, but their application to bacterial pathogens has remained challenging. Cost-effective bacterial genome sequencing and variant calling on nanopore platforms would greatly enhance surveillance and outbreak response in communities without access to sequencing infrastructure. Here, we adapt random forest models for single nucleotide polymorphism (SNP) polishing developed by Sanderson and colleagues (2020. High precision Neisseria gonorrhoeae variant and antimicrobial resistance calling from metagenomic nanopore sequencing. Genome Res. 30(9):1354-1363) to estimate divergence and effective reproduction numbers (Re) of two methicillin-resistant Staphylococcus aureus (MRSA) outbreaks from remote communities in Far North Queensland and Papua New Guinea (PNG; n = 159). Successive barcoded panels of S. aureus isolates (2 × 12 per MinION) sequenced at low coverage (>5× to 10×) provided sufficient data to accurately infer genotypes with high recall when compared with Illumina references. Random forest models achieved high resolution on ST93 outbreak sequence types (>90% accuracy and precision) and enabled phylodynamic inference of epidemiological parameters using birth-death skyline models. Our method reproduced phylogenetic topology, origin of the outbreaks, and indications of epidemic growth (Re > 1). Nextflow pipelines implement SNP polisher training, evaluation, and outbreak alignments, enabling reconstruction of within-lineage transmission dynamics for infection control of bacterial disease outbreaks on portable nanopore platforms. Our study shows that nanopore technology can be used for bacterial outbreak reconstruction at competitive costs, providing opportunities for infection control in hospitals and communities without access to sequencing infrastructure, such as in remote northern Australia and PNG.

Genomic comparisons of Escherichia coli ST131 from Australia

Escherichia coli ST131 is a globally dispersed extraintestinal pathogenic E. coli lineage contributing significantly to hospital and community acquired urinary tract and bloodstream infections. Here we describe a detailed phylogenetic analysis of the whole genome sequences of 284 Australian ST131 E. coli isolates from diverse sources, including clinical, food and companion animals, wildlife and the environment. Our phylogeny and the results of single nucleotide polymorphism (SNP) analysis show the typical ST131 clade distribution with clades A, B and C clearly displayed, but no niche associations were observed. Indeed, interspecies relatedness was a feature of this study. Thirty-five isolates (29 of human and six of wild bird origin) from clade A (32 fimH41, 2 fimH89, 1 fimH141) were observed to differ by an average of 76 SNPs. Forty-five isolates from clade C1 from four sources formed a cluster with an average of 46 SNPs. Within this cluster, human sourced isolates differed by approximately 37 SNPs from isolates sourced from canines, approximately 50 SNPs from isolates from wild birds, and approximately 52 SNPs from isolates from wastewater. Many ST131 carried resistance genes to multiple antibiotic classes and while 41 (14 %) contained the complete class one integron-integrase intI1, 128 (45 %) isolates harboured a truncated intI1 (462-1014 bp), highlighting the ongoing evolution of this element. The module intI1-dfrA17-aadA5-qacEΔ1-sul1-ORF-chrA-padR-IS1600-mphR-mrx-mphA, conferring resistance to trimethoprim, aminoglycosides, quaternary ammonium compounds, sulphonamides, chromate and macrolides, was the most common structure. Most (73 %) Australian ST131 isolates carry at least one extended spectrum β-lactamase gene, typically bla_CTX-M-15 and bla_CTX-M-27. Notably, dual parC-1aAB and gyrA-1AB fluoroquinolone resistant mutations, a unique feature of clade C ST131 isolates, were identified in some clade A isolates. The results of this study indicate that the the ST131 population in Australia carries diverse antimicrobial resistance genes and plasmid replicons and indicate cross-species movement of ST131 strains across diverse reservoirs.

Transmission dynamics of an antimicrobial resistant Campylobacter jejuni lineage in New Zealand's commercial poultry network

Understanding the relative contribution of different between-farm transmission pathways is essential in guiding recommendations for mitigating disease spread. This study investigated the association between contact pathways linking poultry farms in New Zealand and the genetic relatedness of antimicrobial resistant Campylobacter jejuni Sequence Type 6964 (ST-6964), with the aim of identifying the most likely contact pathways that contributed to its rapid spread across the industry. Whole-genome sequencing was performed on 167C. jejuni ST-6964 isolates sampled from across 30 New Zealand commercial poultry enterprises. The genetic relatedness between isolates was determined using whole genome multilocus sequence typing (wgMLST). Permutational multivariate analysis of variance and distance-based linear models were used to explore the strength of the relationship between pairwise genetic associations among the C. jejuni isolates and each of several pairwise distance matrices, indicating either the geographical distance between farms or the network distance of transportation vehicles. Overall, a significant association was found between the pairwise genetic relatedness of the C. jejuni isolates and the parent company, the road distance and the network distance of transporting feed vehicles. This result suggests that the transportation of feed within the commercial poultry industry as well as other local contacts between flocks, such as the movements of personnel, may have played a significant role in the spread of C. jejuni. However, further information on the historical contact patterns between farms is needed to fully characterise the risk of these pathways and to understand how they could be targeted to reduce the spread of C. jejuni.

Progress and challenges in virus genomic epidemiology

Genomic epidemiology, which links pathogen genomes with associated metadata to understand disease transmission, has become a key component of outbreak response. Decreasing costs of genome sequencing and increasing computational power provide opportunities to generate and analyse large viral genomic datasets that aim to uncover the spatial scales of transmission, the demographics contributing to transmission patterns, and to forecast epidemic trends. Emerging sources of genomic data and associated metadata provide new opportunities to further unravel transmission patterns. Key challenges include how to integrate genomic data with metadata from multiple sources, how to generate efficient computational algorithms to cope with large datasets, and how to establish sampling frameworks to enable robust conclusions.