Supersize me: how whole-genome sequencing and big data are transforming epidemiology

The utility of whole-genome sequencing to identify likely transmission pairs for pathogens with slow and variable evolution

Pathogen whole-genome sequencing (WGS) has been used to track the transmission of infectious diseases in extraordinary detail, especially for pathogens that undergo fast and steady evolution, as is the case with many RNA viruses. However, for other pathogens evolution is less predictable, making interpretation of these data to inform our understanding of their epidemiology more challenging and the value of densely collected pathogen genome data uncertain. Here, we assess the utility of WGS for one such pathogen, in the "who-infected-whom" identification problem. We study samples from hosts (130 cattle, 111 badgers) with confirmed infection of M. bovis (causing bovine Tuberculosis), which has an estimated clock rate as slow as ∼0.1-1 variations per year. For each potential pathway between hosts, we calculate the relative likelihood that such a transmission event occurred. This is informed by an epidemiological model of transmission, and host life history data. By including WGS data, we shrink the number of plausible pathways significantly, relative to those deemed likely on the basis of life history data alone. Despite our uncertainty relating to the evolution of M. bovis, the WGS data are therefore a valuable adjunct to epidemiological investigations, especially for wildlife species whose life history data are sparse.

ViralFlow v1.0-a computational workflow for streamlining viral genomic surveillance

ViralFlow v1.0 is a computational workflow developed for viral genomic surveillance. Several key changes turned ViralFlow into a general-purpose reference-based genome assembler for all viruses with an available reference genome. New virus-agnostic modules were implemented to further study nucleotide and amino acid mutations. ViralFlow v1.0 runs on a broad range of computational infrastructures, from laptop computers to high-performance computing (HPC) environments, and generates standard and well-formatted outputs suited for both public health reporting and scientific problem-solving. ViralFlow v1.0 is available at: https://viralflow.github.io/index-en.html.

On the ever-growing functional versatility of the CRISPR-Cas13 system

CRISPR-Cas systems evolved in prokaryotes to implement a powerful antiviral immune response as a result of sequence-specific targeting by ribonucleoproteins. One of such systems consists of an RNA-guided RNA endonuclease, known as CRISPR-Cas13. In very recent years, this system is being repurposed in different ways in order to decipher and engineer gene expression programmes. Here, we discuss the functional versatility of the CRISPR-Cas13 system, which includes the ability for RNA silencing, RNA editing, RNA tracking, nucleic acid detection and translation regulation. This functional palette makes the CRISPR-Cas13 system a relevant tool in the broad field of systems and synthetic biology.

Review: Salmonella Dublin in dairy cattle

Salmonella enterica serovar Dublin (S. Dublin) is a bacterium host-adapted to cattle with increasing prevalence in dairy facilities. It can severely affect cattle health, producing high morbidity and mortality in young calves and reducing the performance of mature animals. Salmonella Dublin is difficult to control and eradicate from herds, as it can be shed from clinically normal animals. In addition, S. Dublin is a zoonotic bacterium that can be lethal for humans and pose a risk for human and animal health due to its multi-drug resistant characteristics. This review provides an overview of S. Dublin as a pathogen in dairy facilities, the risk factors associated with infection, and current strategies for preventing and controlling this disease. Furthermore, current gaps in knowledge are also discussed.

Application of Genomic Epidemiology of Pathogens to Farmed Yellowtail Fish Mycobacteriosis in Kyushu, Japan

To investigate mycobacterial cases of farmed yellowtail fish in coastal areas of western Japan (Kagoshima, Kyushu), where aquaculture fisheries are active, Mycobacterium pseudoshottsii, the causative agent, was isolated from six neighboring fishing ports in 2012 and 2013. A phylogenetic ana-lysis revealed that the strains isolated from one fishing port were closely related to those isolated from other regions of Japan, suggesting the nationwide spread of a single strain. However, strains from Japan were phylogenetically distinct from those from the Mediterranean and the United States; therefore, worldwide transmission was not observed based on the limited data obtained on the strains exami-ned in this study. The present results demonstrate that a bacterial genomic ana-lysis of infected cases, a mole-cular epidemiology strategy for public health, provides useful data for estimating the prevalence and transmission pathways of M. pseudoshottsii in farmed fish. A bacterial genome ana-lysis of strains, such as that performed herein, may play an important role in monitoring the prevalence of this pathogen in fish farms and possible epidemics in the future as a result of international traffic, logistics, and trade in fisheries.

Are rabid raccoons (Procyon lotor) ready for the rapture? Determining the geographic origin of rabies virus-infected raccoons using RADcapture and microhaplotypes

North America is recognized for the exceptional richness of rabies virus (RV) wildlife reservoir species. Management of RV is accomplished through vaccination targeting mesocarnivore reservoir populations, such as the raccoon (Procyon lotor) in Eastern North America. Raccoons are a common generalist species, and populations may reach high densities in developed areas, which can result in contact with humans and pets with potential exposures to the raccoon variant of RV throughout the eastern United States. Understanding the spatial movement of RV by raccoon populations is important for monitoring and refining strategies supporting the landscape-level control and local elimination of this lethal zoonosis. We developed a high-throughput genotyping panel for raccoons based on hundreds of microhaplotypes to identify population structure and genetic diversity relevant to rabies management programs. Throughout the eastern United States, we identified hierarchical population genetic structure with clusters that were connected through isolation-by-distance. We also illustrate that this genotyping approach can be used to support real-time management priorities by identifying the geographic origin of a rabid raccoon that was collected in an area of the United States that had been raccoon RV-free for 8 years. The results from this study and the utility of the microhaplotype panel and genotyping method will provide managers with information on raccoon ecology that can be incorporated into future management decisions.

Origin matters: Using a local reference genome improves measures in population genomics

Genome sequencing enables answering fundamental questions about the genetic basis of adaptation, population structure and epigenetic mechanisms. Yet, we usually need a suitable reference genome for mapping population-level resequencing data. In some model systems, multiple reference genomes are available, giving the challenging task of determining which reference genome best suits the data. Here, we compared the use of two different reference genomes for the three-spined stickleback (Gasterosteus aculeatus), one novel genome derived from a European gynogenetic individual and the published reference genome of a North American individual. Specifically, we investigated the impact of using a local reference versus one generated from a distinct lineage on several common population genomics analyses. Through mapping genome resequencing data of 60 sticklebacks from across Europe and North America, we demonstrate that genetic distance among samples and the reference genomes impacts downstream analyses. Using a local reference genome increased mapping efficiency and genotyping accuracy, effectively retaining more and better data. Despite comparable distributions of the metrics generated across the genome using SNP data (i.e. π, Tajima's D and FST ), window-based statistics using different references resulted in different outlier genes and enriched gene functions. A marker-based analysis of DNA methylation distributions had a comparably high overlap in outlier genes and functions, yet with distinct differences depending on the reference genome. Overall, our results highlight how using a local reference genome decreases reference bias to increase confidence in downstream analyses of the data. Such results have significant implications in all reference-genome-based population genomic analyses.

The tumour is in the detail: Local phylogenetic, population and epidemiological dynamics of a transmissible cancer in Tasmanian devils

Infectious diseases are a major threat for biodiversity conservation and can exert strong influence on wildlife population dynamics. Understanding the mechanisms driving infection rates and epidemic outcomes requires empirical data on the evolutionary trajectory of pathogens and host selective processes. Phylodynamics is a robust framework to understand the interaction of pathogen evolutionary processes with epidemiological dynamics, providing a powerful tool to evaluate disease control strategies. Tasmanian devils have been threatened by a fatal transmissible cancer, devil facial tumour disease (DFTD), for more than two decades. Here we employ a phylodynamic approach using tumour mitochondrial genomes to assess the role of tumour genetic diversity in epidemiological and population dynamics in a devil population subject to 12 years of intensive monitoring, since the beginning of the epidemic outbreak. DFTD molecular clock estimates of disease introduction mirrored observed estimates in the field, and DFTD genetic diversity was positively correlated with estimates of devil population size. However, prevalence and force of infection were the lowest when devil population size and tumour genetic diversity was the highest. This could be due to either differential virulence or transmissibility in tumour lineages or the development of host defence strategies against infection. Our results support the view that evolutionary processes and epidemiological trade-offs can drive host-pathogen coexistence, even when disease-induced mortality is extremely high. We highlight the importance of integrating pathogen and population evolutionary interactions to better understand long-term epidemic dynamics and evaluating disease control strategies.

Unraveling the epidemiology of Mycobacterium bovis using whole-genome sequencing combined with environmental and demographic data

When studying the dynamics of a pathogen in a host population, one crucial question is whether it transitioned from an epidemic (i.e., the pathogen population and the number of infected hosts are increasing) to an endemic stable state (i.e., the pathogen population reached an equilibrium). For slow-growing and slow-evolving clonal pathogens such as Mycobacterium bovis, the causative agent of bovine (or animal) and zoonotic tuberculosis, it can be challenging to discriminate between these two states. This is a result of the combination of suboptimal detection tests so that the actual extent of the pathogen prevalence is often unknown, as well as of the low genetic diversity, which can hide the temporal signal provided by the accumulation of mutations in the bacterial DNA. In recent years, the increased availability, efficiency, and reliability of genomic reading techniques, such as whole-genome sequencing (WGS), have significantly increased the amount of information we can use to study infectious diseases, and therefore, it has improved the precision of epidemiological inferences for pathogens such as M. bovis. In this study, we use WGS to gain insights into the epidemiology of M. bovis in Cameroon, a developing country where the pathogen has been reported for decades. A total of 91 high-quality sequences were obtained from tissue samples collected in four abattoirs, 64 of which were with complete metadata. We combined these with environmental, demographic, ecological, and cattle movement data to generate inferences using phylodynamic models. Our findings suggest M. bovis in Cameroon is slowly expanding its epidemiological range over time; therefore, endemic stability is unlikely. This suggests that animal movement plays an important role in transmission. The simultaneous prevalence of M. bovis in co-located cattle and humans highlights the risk of such transmission being zoonotic. Therefore, using genomic tools as part of surveillance would vastly improve our understanding of disease ecology and control strategies.

Mycobacterium avium subsp. paratuberculosis in Sheep and Goats in Austria: Seroprevalence, Risk Factors and Detection from Boot Swab Samples

This study aimed to investigate the prevalence of Mycobacterium avium subsp. paratuberculosis (MAP) in small ruminants in Austria by testing 22,019 serum samples with ELISA for the presence of specific antibodies. Furthermore, detailed investigations in five MAP-infected goat herds were carried out by ELISA, qPCR and bacterial culture. The found animal-level apparent MAP seroprevalence was 2.0% for goats and 0.7% for sheep (calculated true prevalence 3.5% and 1.2%, respectively). Herd-level apparent MAP seroprevalence was 11.1% for goat herds and 8.9% for sheep flocks. Significant risk factors for seropositivity in goat herds were: herd size, animal trading, farmed as a dairy herd, Animal Health Service membership and cohabitation with farmed game. For sheep flocks, seroprevalence was significantly higher in flocks with animal trading and where cattle or goats were kept in the flock, respectively. The overall apparent within-herd MAP seroprevalence in the five goat farms investigated was 21.8% (11.7%-28.0%, calculated true seroprevalence 38.6%) and an overall rate of MAP shedding of 12.3% was detected (5.0%-24.7%). It was possible to identify MAP by culture using boot swab samples in each herd. The results indicated a moderate MAP infection rate in small ruminants in Austria.

SARS-CoV-2 Genomic Diversity in Households Highlights the Challenges of Sequence-Based Transmission Inference

The reliability of sequence-based inference of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission is not clear. Sequence data from infections among household members can define the expected genomic diversity of a virus along a defined transmission chain. SARS-CoV-2 cases were identified prospectively among 2,369 participants in 706 households. Specimens with a reverse transcription-PCR cycle threshold of ≤30 underwent whole-genome sequencing. Intrahost single-nucleotide variants (iSNV) were identified at a ≥5% frequency. Phylogenetic trees were used to evaluate the relationship of household and community sequences. There were 178 SARS-CoV-2 cases in 706 households. Among 147 specimens sequenced, 106 yielded a whole-genome consensus with coverage suitable for identifying iSNV. Twenty-six households had sequences from multiple cases within 14 days. Consensus sequences were indistinguishable among cases in 15 households, while 11 had ≥1 consensus sequence that differed by 1 to 2 mutations. Sequences from households and the community were often interspersed on phylogenetic trees. Identification of iSNV improved inference in 2 of 15 households with indistinguishable consensus sequences and in 6 of 11 with distinct ones. In multiple-infection households, whole-genome consensus sequences differed by 0 to 1 mutations. Identification of shared iSNV occasionally resolved linkage, but the low genomic diversity of SARS-CoV-2 limits the utility of "sequence-only" transmission inference. IMPORTANCE We performed whole-genome sequencing of SARS-CoV-2 from prospectively identified cases in three longitudinal household cohorts. In a majority of multi-infection households, SARS-CoV-2 consensus sequences were indistinguishable, and they differed by 1 to 2 mutations in the rest. Importantly, even with modest genomic surveillance of the community (3 to 5% of cases sequenced), it was not uncommon to find community sequences interspersed with household sequences on phylogenetic trees. Identification of shared minority variants only occasionally resolved these ambiguities in transmission linkage. Overall, the low genomic diversity of SARS-CoV-2 limits the utility of "sequence-only" transmission inference. Our work highlights the need to carefully consider both epidemiologic linkage and sequence data to define transmission chains in households, hospitals, and other transmission settings.

Target-enrichment sequencing yields valuable genomic data for challenging-to-culture bacteria of public health importance

Genomic data contribute invaluable information to the epidemiological investigation of pathogens of public health importance. However, whole-genome sequencing (WGS) of bacteria typically relies on culture, which represents a major hurdle for generating such data for a wide range of species for which culture is challenging. In this study, we assessed the use of culture-free target-enrichment sequencing as a method for generating genomic data for two bacterial species: (1) Bacillus anthracis, which causes anthrax in both people and animals and whose culture requires high-level containment facilities; and (2) Mycoplasma amphoriforme, a fastidious emerging human respiratory pathogen. We obtained high-quality genomic data for both species directly from clinical samples, with sufficient coverage (>15×) for confident variant calling over at least 80% of the baited genomes for over two thirds of the samples tested. Higher qPCR cycle threshold (Ct) values (indicative of lower pathogen concentrations in the samples), pooling libraries prior to capture, and lower captured library concentration were all statistically associated with lower capture efficiency. The Ct value had the highest predictive value, explaining 52 % of the variation in capture efficiency. Samples with Ct values ≤30 were over six times more likely to achieve the threshold coverage than those with a Ct > 30. We conclude that target-enrichment sequencing provides a valuable alternative to standard WGS following bacterial culture and creates opportunities for an improved understanding of the epidemiology and evolution of many clinically important pathogens for which culture is challenging.

Population genomics of Bacillus anthracis from an anthrax hyperendemic area reveals transmission processes across spatial scales and unexpected within-host diversity

Genomic sequencing has revolutionized our understanding of bacterial disease epidemiology, but remains underutilized for zoonotic pathogens in remote endemic settings. Anthrax, caused by the spore-forming bacterium Bacillus anthracis, remains a threat to human and animal health and rural livelihoods in low- and middle-income countries. While the global genomic diversity of B. anthracis has been well-characterized, there is limited information on how its populations are genetically structured at the scale at which transmission occurs, critical for understanding the pathogen's evolution and transmission dynamics. Using a uniquely rich dataset, we quantified genome-wide SNPs among 73 B. anthracis isolates derived from 33 livestock carcasses sampled over 1 year throughout the Ngorongoro Conservation Area, Tanzania, a region hyperendemic for anthrax. Genome-wide SNPs distinguished 22 unique B. anthracis genotypes (i.e. SNP profiles) within the study area. However, phylogeographical structure was lacking, as identical SNP profiles were found throughout the study area, likely the result of the long and variable periods of spore dormancy and long-distance livestock movements. Significantly, divergent genotypes were obtained from spatio-temporally linked cases and even individual carcasses. The high number of SNPs distinguishing isolates from the same host is unlikely to have arisen during infection, as supported by our simulation models. This points to an unexpectedly wide transmission bottleneck for B. anthracis, with an inoculum comprising multiple variants being the norm. Our work highlights that inferring transmission patterns of B. anthracis from genomic data will require analytical approaches that account for extended and variable environmental persistence, as well as co-infection.

Integrating genomic and epidemiologic data to accelerate progress toward schistosomiasis elimination

The global community has adopted ambitious goals to eliminate schistosomiasis as a public health problem, and new tools are needed to achieve them. Mass drug administration programs, for example, have reduced the burden of schistosomiasis, but the identification of hotspots of persistent and reemergent transmission threaten progress toward elimination and underscore the need to couple treatment with interventions that reduce transmission. Recent advances in DNA sequencing technologies make whole-genome sequencing a valuable and increasingly feasible option for population-based studies of complex parasites such as schistosomes. Here, we focus on leveraging genomic data to tailor interventions to distinct social and ecological circumstances. We consider two priority questions that can be addressed by integrating epidemiological, ecological, and genomic information: (1) how often do non-human host species contribute to human schistosome infection? and (2) what is the importance of locally acquired versus imported infections in driving transmission at different stages of elimination? These questions address processes that can undermine control programs, especially those that rely heavily on treatment with praziquantel. Until recently, these questions were difficult to answer with sufficient precision to inform public health decision-making. We review the literature related to these questions and discuss how whole-genome approaches can identify the geographic and taxonomic sources of infection, and how such information can inform context-specific efforts that advance schistosomiasis control efforts and minimize the risk of reemergence.

Genomic Epidemiology of Salmonid Alphavirus in Norwegian Aquaculture Reveals Recent Subtype-2 Transmission Dynamics and Novel Subtype-3 Lineages

Viral disease poses a major barrier to sustainable aquaculture, with outbreaks causing large economic losses and growing concerns for fish welfare. Genomic epidemiology can support disease control by providing rapid inferences on viral evolution and disease transmission. In this study, genomic epidemiology was used to investigate salmonid alphavirus (SAV), the causative agent of pancreas disease (PD) in Atlantic salmon. Our aim was to reconstruct SAV subtype-2 (SAV2) diversity and transmission dynamics in recent Norwegian aquaculture, including the origin of SAV2 in regions where this subtype is not tolerated under current legislation. Using nanopore sequencing, we captured ~90% of the SAV2 genome for n = 68 field isolates from 10 aquaculture production regions sampled between 2018 and 2020. Using time-calibrated phylogenetics, we infer that, following its introduction to Norway around 2010, SAV2 split into two clades (SAV2a and 2b) around 2013. While co-present at the same sites near the boundary of Møre og Romsdal and Trøndelag, SAV2a and 2b were generally detected in non-overlapping locations at more Southern and Northern latitudes, respectively. We provide evidence for recent SAV2 transmission over large distances, revealing a strong connection between Møre og Romsdal and SAV2 detected in 2019/20 in Rogaland. We also demonstrate separate introductions of SAV2a and 2b outside the SAV2 zone in Sognefjorden (Vestland), connected to samples from Møre og Romsdal and Trøndelag, respectively, and a likely 100 km Northward transmission of SAV2b within Trøndelag. Finally, we recovered genomes of SAV2a and SAV3 co-infecting single fish in Rogaland, involving novel SAV3 lineages that diverged from previously characterized strains >25 years ago. Overall, this study demonstrates useful applications of genomic epidemiology for tracking viral disease spread in aquaculture.

Towards a more healthy conservation paradigm: integrating disease and molecular ecology to aid biological conservation†

Parasites, and the diseases they cause, are important from an ecological and evolutionary perspective because they can negatively affect host fitness and can regulate host populations. Consequently, conservation biology has long recognized the vital role that parasites can play in the process of species endangerment and recovery. However, we are only beginning to understand how deeply parasites are embedded in ecological systems, and there is a growing recognition of the important ways in which parasites affect ecosystem structure and function. Thus, there is an urgent need to revisit how parasites are viewed from a conservation perspective and broaden the role that disease ecology plays in conservation-related research and outcomes. This review broadly focusses on the role that disease ecology can play in biological conservation. Our review specifically emphasizes on how the integration of tools and analytical approaches associated with both disease and molecular ecology can be leveraged to aid conservation biology. Our review first concentrates on disease-mediated extinctions and wildlife epidemics. We then focus on elucidating how host–parasite interactions has improved our understanding of the eco-evolutionary dynamics affecting hosts at the individual, population, community and ecosystem scales. We believe that the role of parasites as drivers and indicators of ecosystem health is especially an exciting area of research that has the potential to fundamentally alter our view of parasites and their role in biological conservation. The review concludes with a broad overview of the current and potential applications of modern genomic tools in disease ecology to aid biological conservation.

Social network analysis and whole-genome sequencing to evaluate disease transmission in a large, dynamic population: A study of avian mycobacteriosis in zoo birds

This study combined a social network analysis and whole-genome sequencing (WGS) to test for general patterns of contagious spread of a mycobacterial infection for which pathways of disease acquisition are not well understood. Our population included 275 cases diagnosed with avian mycobacteriosis that were nested in a source population of 16,430 birds at San Diego Zoo Wildlife Alliance facilities from 1992 through mid-2014. Mycobacteria species were determined using conventional methods and whole genome sequencing (WGS). Mycobacterium avium avium (MAA) and Mycobacterium genavense were the most common species of mycobacteria identified and were present in different proportions across bird taxa. A social network for the birds was constructed from the source population to identify directly and indirectly connected cases during time periods relevant to disease transmission. Associations between network connectivity and genetic similarity of mycobacteria (as determined by clusters of genotypes separated by few single nucleotide polymorphisms, or SNPs) were then evaluated in observed and randomly generated network permutations. Findings showed that some genotypes clustered along pathways of bird connectivity, while others were dispersed throughout the network. The proportion of directly connected birds having a similar mycobacterial genotype was 0.36 and significant (p<0.05). This proportion was higher (0.58) and significant for MAA but not for M. genavense. Evaluations of SNP distributions also showed genotypes of MAA were more related in connected birds than expected by chance; however, no significant patterns of genetic relatedness were identified for M. genavense, although data were sparse. Integrating the WGS analysis of mycobacteria with a social network analysis of their host birds revealed significant genetic clustering along pathways of connectivity, namely for MAA. These findings are consistent with a contagious process occurring in some, but not all, case clusters.

What can phylodynamics bring to animal health research?

Infectious diseases are a major burden to global economies, and public and animal health. To date, quantifying the spread of infectious diseases to inform policy making has traditionally relied on epidemiological data collected during epidemics. However, interest has grown in recent phylodynamic techniques to infer pathogen transmission dynamics from genetic data. Here, we provide examples of where this new discipline has enhanced disease management in public health and illustrate how it could be further applied in animal health. In particular, we describe how phylodynamics can address fundamental epidemiological questions, such as inferring key transmission parameters in animal populations and quantifying spillover events at the wildlife-livestock interface, and generate important insights for the design of more effective control strategies.

Who infects whom?-Reconstructing infection chains of Mycobacterium avium ssp. paratuberculosis in an endemically infected dairy herd by use of genomic data

Recent evidence of circulation of multiple strains within herds and mixed infections of cows marks the beginning of a rethink of our knowledge on Mycobacterium avium ssp. paratuberculosis (MAP) epidemiology. Strain typing opens new ways to investigate MAP transmission. This work presents a method for reconstructing infection chains in a setting of endemic Johne’s disease on a well-managed dairy farm. By linking genomic data with demographic field data, strain-specific differences in spreading patterns could be quantified for a densely sampled dairy herd. Mixed infections of dairy cows with MAP are common, and some strains spread more successfully. Infected cows remain susceptible for co-infections with other MAP genotypes. The model suggested that cows acquired infection from 1–4 other cows and spread infection to 0–17 individuals. Reconstructed infection chains supported the hypothesis that high shedding animals that started to shed at an early age and showed a progressive infection pattern represented a greater risk for spreading MAP. Transmission of more than one genotype between animals was recorded. In this farm with a good MAP control management program, adult-to-adult contact was proposed as the most important transmission route to explain the reconstructed networks. For each isolate, at least one more likely ancestor could be inferred. Our study results help to capture underlying transmission processes and to understand the challenges of tracing MAP spread within a herd. Only the combination of precise longitudinal field data and bacterial strain type information made it possible to trace infection in such detail.

COVID-19: Short-term forecast of ICU beds in times of crisis

By early May 2020, the number of new COVID-19 infections started to increase rapidly in Chile, threatening the ability of health services to accommodate all incoming cases. Suddenly, ICU capacity planning became a first-order concern, and the health authorities were in urgent need of tools to estimate the demand for urgent care associated with the pandemic. In this article, we describe the approach we followed to provide such demand forecasts, and we show how the use of analytics can provide relevant support for decision making, even with incomplete data and without enough time to fully explore the numerical properties of all available forecasting methods. The solution combines autoregressive, machine learning and epidemiological models to provide a short-term forecast of ICU utilization at the regional level. These forecasts were made publicly available and were actively used to support capacity planning. Our predictions achieved average forecasting errors of 4% and 9% for one- and two-week horizons, respectively, outperforming several other competing forecasting models.

Identifying likely transmissions in Mycobacterium bovis infected populations of cattle and badgers using the Kolmogorov Forward Equations

Established methods for whole-genome-sequencing (WGS) technology allow for the detection of single-nucleotide polymorphisms (SNPs) in the pathogen genomes sourced from host samples. The information obtained can be used to track the pathogen’s evolution in time and potentially identify ‘who-infected-whom’ with unprecedented accuracy. Successful methods include ‘phylodynamic approaches’ that integrate evolutionary and epidemiological data. However, they are typically computationally intensive, require extensive data, and are best applied when there is a strong molecular clock signal and substantial pathogen diversity. To determine how much transmission information can be inferred when pathogen genetic diversity is low and metadata limited, we propose an analytical approach that combines pathogen WGS data and sampling times from infected hosts. It accounts for ‘between-scale’ processes, in particular within-host pathogen evolution and between-host transmission. We applied this to a well-characterised population with an endemic Mycobacterium bovis (the causative agent of bovine/zoonotic tuberculosis, bTB) infection. Our results show that, even with such limited data and low diversity, the computation of the transmission probability between host pairs can help discriminate between likely and unlikely infection pathways and therefore help to identify potential transmission networks. However, the method can be sensitive to assumptions about within-host evolution.

Mining whole genome sequence data to efficiently attribute individuals to source populations

Whole genome sequence (WGS) data could transform our ability to attribute individuals to source populations. However, methods that efficiently mine these data are yet to be developed. We present a minimal multilocus distance (MMD) method which rapidly deals with these large data sets as well as methods for optimally selecting loci. This was applied on WGS data to determine the source of human campylobacteriosis, the geographical origin of diverse biological species including humans and proteomic data to classify breast cancer tumours. The MMD method provides a highly accurate attribution which is computationally efficient for extended genotypes. These methods are generic, easy to implement for WGS and proteomic data and have wide application.

Transmission network reconstruction for foot-and-mouth disease outbreaks incorporating farm-level covariates

Transmission network modelling to infer 'who infected whom' in infectious disease outbreaks is a highly active area of research. Outbreaks of foot-and-mouth disease have been a key focus of transmission network models that integrate genomic and epidemiological data. The aim of this study was to extend Lau's systematic Bayesian inference framework to incorporate additional parameters representing predominant species and numbers of animals held on a farm. Lau's Bayesian Markov chain Monte Carlo algorithm was reformulated, verified and pseudo-validated on 100 simulated outbreaks populated with demographic data Japan and Australia. The modified model was then implemented on genomic and epidemiological data from the 2010 outbreak of foot-and-mouth disease in Japan, and outputs compared to those from the SCOTTI model implemented in BEAST2. The modified model achieved improvements in overall accuracy when tested on the simulated outbreaks. When implemented on the actual outbreak data from Japan, infected farms that held predominantly pigs were estimated to have five times the transmissibility of infected cattle farms and be 49% less susceptible. The farm-level incubation period was 1 day shorter than the latent period, the timing of the seeding of the outbreak in Japan was inferred, as were key linkages between clusters and features of farms involved in widespread dissemination of this outbreak. To improve accessibility the modified model has been implemented as the R package 'BORIS' for use in future outbreaks.

Molecular Epidemiology of Myroides odoratimimus in Nosocomial Catheter-Related Infection at a General Hospital in China

Purpose

Catheter-related infection (CRI) is one of the most frequent causes of hospitalizations for immunocompromised patients. A major challenge is the increased prevalence of Myroides odoratimimus. The purpose of the present study was to evaluate the clinical features and molecular characteristics of M. odoratimimus collected from a general hospital in Shanghai, China.

Patients and Methods

From July 2015 to August 2016, a total of 22 isolates of M. odoratimimus were collected from inpatients respectively from the biliary and pancreatic surgery (6/22) and the urology department (16/22). Clonal relatedness among the isolates was assessed using pulsed-field gel electrophoresis (PFGE) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Moreover, the antimicrobial susceptibility tests were carried out using the Clinical and Laboratory Standards Institute (CLSI) broth microdilution method. The presence of antibiotic resistance genes was screened using the polymerase chain reaction (PCR) assay. Additionally, protein structure prediction was analyzed using PSIPRED and RaptorX.

Results

PFGE differentiated these isolates into six possibly related clones from two different departments obtained during a distinct period, indicating clonal dissemination in the two departments. We compared the dendrograms of M. odoratimimus isolates obtained by MALDI-TOF MS with those obtained by PFGE and found that the coincidence rate between them was only 68.2%. All the M. odoratimimus isolates were highly resistant to most available antibiotics, including carbapenems. Furthermore, chromosome-encoded β-lactamases MUS-1 was confirmed by PCR in 6 of 22 Myroides odoratimimus isolates. Herein, we also reported a novel variant of bla_MUS-1 in the remaining 16 isolates, which encodes MUS-3 protein at position 60 (Valine to Alanine), differing from the structure of MUS-1.

Conclusion

The opportunistic and extensively antibiotic-resistant Myroides odoratimimus has a small range of epidemics in these two different departments. Clinicians should be aware that M. odoratimimus may induce a severe nosocomial outbreak of catheter-related infections, particularly in immunocompromised patients.

Within-Host Viral Diversity: A Window into Viral Evolution

The evolutionary dynamics of a virus can differ within hosts and across populations. Studies of within-host evolution provide an important link between experimental studies of virus evolution and large-scale phylodynamic analyses. They can determine the extent to which global processes are recapitulated on local scales and how accurately experimental infections model natural ones. They may also inform epidemiologic models of disease spread and reveal how host-level dynamics contribute to a virus's evolution at a larger scale. Over the last decade, advances in viral sequencing have enabled detailed studies of viral genetic diversity within hosts. I review how within-host diversity is sampled, measured, and expressed, and how comparative studies of viral diversity can be leveraged to elucidate a virus's evolutionary dynamics. These concepts are illustrated with detailed reviews of recent research on the within-host evolution of influenza virus, dengue virus, and cytomegalovirus.

Epidemiology and Phylogenetic Analysis of Viral Respiratory Infections in Vietnam

Acute respiratory infections (ARIs) impose a major public health burden on fragile healthcare systems of developing Southeast Asian countries such as Vietnam. The epidemiology, genetic diversity and transmission patterns of respiratory viral pathogens that circulate in this region are not well characterized. We used RT-PCR to screen for 14 common respiratory viruses in nasal/throat samples from 4326 ARI patients from 5 sites in Vietnam during 2012-2016. 64% of patients tested positive for viruses; 14% tested positive multiple co-infecting viruses. The most frequently detected viruses were Respiratory syncytial virus (RSV, 23%), Human Rhinovirus (HRV, 13%), Influenza A virus (IAV, 11%) and Human Bocavirus (HBoV, 7%). RSV infections peaked in July to October, were relatively more common in children <1 year and in the northernmost hospital. IAV infections peaked in December to February and were relatively more common in patients >5 years in the central region. Coinfection with IAV or RSV was associated with increased disease severity compared with patients only infected with HBoV or HRV. Over a hundred genomes belonging to 13 families and 24 genera were obtained via metagenomic sequencing, including novel viruses and viruses less commonly associated with ARIs. Phylogenetic and phylogeographic analyses further indicated that neighboring countries were the most likely source of many virus lineages causing ARIs in Vietnam and estimated the period that specific lineages have been circulating. Our study illustrates the value of applying the state-of-the-art virus diagnostic methods (multiplex RT-PCR and metagenomic sequencing) and phylodynamic analyses at a national level to generate an integrated picture of viral ARI epidemiology.

Genomic Analyses of Human Sapoviruses Detected over a 40-Year Period Reveal Disparate Patterns of Evolution among Genotypes and Genome Regions

Human sapovirus is a causative agent of acute gastroenteritis in all age groups. The use of full-length viral genomes has proven beneficial to investigate evolutionary dynamics and transmission chains. In this study, we developed a full-length genome sequencing platform for human sapovirus and sequenced the oldest available strains (collected in the 1970s) to analyse diversification of sapoviruses. Sequence analyses from five major genotypes (GI.1, GI.2, GII.1, GII.3, and GIV.1) showed limited intra-genotypic diversification for over 20–40 years. The accumulation of amino acid mutations in VP1 was detected for GI.2 and GIV.1 viruses, while having a similar rate of nucleotide evolution to the other genotypes. Differences in the phylogenetic clustering were detected between RdRp and VP1 sequences of our archival strains as well as other reported putative recombinants. However, the lack of the parental strains and differences in diversification among genomic regions suggest that discrepancies in the phylogenetic clustering of sapoviruses could be explained, not only by recombination, but also by disparate nucleotide substitution patterns between RdRp and VP1 sequences. Together, this study shows that, contrary to noroviruses, sapoviruses present limited diversification by means of intra-genotype variation and recombination.

One model to rule them all? Modelling approaches across OneHealth for human, animal and plant epidemics

One hundred years after the 1918 influenza outbreak, are we ready for the next pandemic? This paper addresses the need to identify and develop collaborative, interdisciplinary and cross-sectoral approaches to modelling of infectious diseases including the fields of not only human and veterinary medicine, but also plant epidemiology. Firstly, the paper explains the concepts on which the most common epidemiological modelling approaches are based, namely the division of a host population into susceptible, infected and removed (SIR) classes and the proportionality of the infection rate to the size of the susceptible and infected populations. It then demonstrates how these simple concepts have been developed into a vast and successful modelling framework that has been used in predicting and controlling disease outbreaks for over 100 years. Secondly, it considers the compartmental models based on the SIR paradigm within the broader concept of a 'disease tetrahedron' (comprising host, pathogen, environment and man) and uses it to review the similarities and differences among the fields comprising the 'OneHealth' approach. Finally, the paper advocates interactions between all fields and explores the future challenges facing modellers. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.

Consolidation of Clinical Microbiology Laboratories and Introduction of Transformative Technologies

Clinical microbiology is experiencing revolutionary advances in the deployment of molecular, genome sequencing-based, and mass spectrometry-driven detection, identification, and characterization assays. Laboratory automation and the linkage of information systems for big(ger) data management, including artificial intelligence (AI) approaches, also are being introduced. The initial optimism associated with these developments has now entered a more reality-driven phase of reflection on the significant challenges, complexities, and health care benefits posed by these innovations. With this in mind, the ongoing process of clinical laboratory consolidation, covering large geographical regions, represents an opportunity for the efficient and cost-effective introduction of new laboratory technologies and improvements in translational research and development. This will further define and generate the mandatory infrastructure used in validation and implementation of newer high-throughput diagnostic approaches. Effective, structured access to large numbers of well-documented biobanked biological materials from networked laboratories will release countless opportunities for clinical and scientific infectious disease research and will generate positive health care impacts. We describe why consolidation of clinical microbiology laboratories will generate quality benefits for many, if not most, aspects of the services separate institutions already provided individually. We also define the important role of innovative and large-scale diagnostic platforms. Such platforms lend themselves particularly well to computational (AI)-driven genomics and bioinformatics applications. These and other diagnostic innovations will allow for better infectious disease detection, surveillance, and prevention with novel translational research and optimized (diagnostic) product and service development opportunities as key results.

In Search of Covariates of HIV-1 Subtype B Spread in the United States-A Cautionary Tale of Large-Scale Bayesian Phylogeography

Infections with HIV-1 group M subtype B viruses account for the majority of the HIV epidemic in the Western world. Phylogeographic studies have placed the introduction of subtype B in the United States in New York around 1970, where it grew into a major source of spread. Currently, it is estimated that over one million people are living with HIV in the US and that most are infected with subtype B variants. Here, we aim to identify the drivers of HIV-1 subtype B dispersal in the United States by analyzing a collection of 23,588 pol sequences, collected for drug resistance testing from 45 states during 2004-2011. To this end, we introduce a workflow to reduce this large collection of data to more computationally-manageable sample sizes and apply the BEAST framework to test which covariates associate with the spread of HIV-1 across state borders. Our results show that we are able to consistently identify certain predictors of spread under reasonable run times across datasets of up to 10,000 sequences. However, the general lack of phylogenetic structure and the high uncertainty associated with HIV trees make it difficult to interpret the epidemiological relevance of the drivers of spread we are able to identify. While the workflow we present here could be applied to other virus datasets of a similar scale, the characteristic star-like shape of HIV-1 phylogenies poses a serious obstacle to reconstructing a detailed evolutionary and spatial history for HIV-1 subtype B in the US.

Towards a more healthy conservation paradigm: integrating disease and molecular ecology to aid biological conservation†

Parasites, and the diseases they cause, are important from an ecological and evolutionary perspective because they can negatively affect host fitness and can regulate host populations. Consequently, conservation biology has long recognized the vital role that parasites can play in the process of species endangerment and recovery. However, we are only beginning to understand how deeply parasites are embedded in ecological systems, and there is a growing recognition of the important ways in which parasites affect ecosystem structure and function. Thus, there is an urgent need to revisit how parasites are viewed from a conservation perspective and broaden the role that disease ecology plays in conservation-related research and outcomes. This review broadly focusses on the role that disease ecology can play in biological conservation. Our review specifically emphasizes on how the integration of tools and analytical approaches associated with both disease and molecular ecology can be leveraged to aid conservation biology. Our review first concentrates on disease mediated extinctions and wildlife epidemics. We then focus on elucidating how host-parasite interactions has improved our understanding of the eco-evolutionary dynamics affecting hosts at the individual, population, community and ecosystem scales. We believe that the role of parasites as drivers and indicators of ecosystem health is especially an exciting area of research that has the potential to fundamentally alter our view of parasites and their role in biological conservation. The review concludes with a broad overview of the current and potential applications of modern genomic tools in disease ecology to aid biological conservation.

Combining genomics and epidemiology to analyse bi-directional transmission of Mycobacterium bovis in a multi-host system

Quantifying pathogen transmission in multi-host systems is difficult, as exemplified in bovine tuberculosis (bTB) systems, but is crucial for control. The agent of bTB, Mycobacterium bovis, persists in cattle populations worldwide, often where potential wildlife reservoirs exist. However, the relative contribution of different host species to bTB persistence is generally unknown. In Britain, the role of badgers in infection persistence in cattle is highly contentious, despite decades of research and control efforts. We applied Bayesian phylogenetic and machine-learning approaches to bacterial genome data to quantify the roles of badgers and cattle in M. bovis infection dynamics in the presence of data biases. Our results suggest that transmission occurs more frequently from badgers to cattle than vice versa (10.4x in the most likely model) and that within-species transmission occurs at higher rates than between-species transmission for both. If representative, our results suggest that control operations should target both cattle and badgers.

Predicting whole genome sequencing success for archived avian influenza virus (Orthomyxoviridae) samples using real-time and droplet PCRs

Long-term viral archives are valuable sources of research data. Each archive can store hundreds of thousands of diverse sample types. In the current era of whole genome sequencing, archived samples become a rich source of evolutionary and epidemiological data that can span years, and even decades. However, the ability to obtain high quality viral whole genome sequences from samples of various types, age, and quality is inconsistent. A minimum quality threshold that helps predict the best success of obtaining high quality genomic sequences for both recent and archived samples is highly valuable. Real-time reverse transcription PCR (rrt-PCR) and droplet digital PCR (ddPCR) are useful tools to evaluate nucleic acid integrity. We hypothesized that diagnostic rrt-PCR and ddPCR data for avian influenza virus (AIV) can predict viral whole genome sequencing success. To test this hypothesis we used RNA extracted from cloacal and oropharyngeal swabs stored in the USDA-APHIS National Wildlife Disease Program Wildlife Tissue Archive. We determined that a specific rrt-PCR C_q value or ddPCR copies/μL resulted in recovery of complete sequences of all eight AIV gene segments. We used logistic regression to estimate probabilities of whole genome recovery at 0.95 (C_q = 15, copies/μL = 49,350), 0.75 (C_q = 24, copies/μL = 16,800), 0.50 (C_q = 29, copies/μL = <1), and 0.25 (C_q = 235, copies/μL = <1). We also identified values at which we predictably recovered HA and NA segments for diagnosing subtypes (C_q = 27.29; copies/μL = 757.50). This approach will allow researchers to assess the potential success of AIV whole genome recovery from diagnostic samples collected in routine AIV surveillance.

Status and potential of bacterial genomics for public health practice: a scoping review

BACKGROUND

Next-generation sequencing (NGS) is increasingly being translated into routine public health practice, affecting the surveillance and control of many pathogens. The purpose of this scoping review is to identify and characterize the recent literature concerning the application of bacterial pathogen genomics for public health practice and to assess the added value, challenges, and needs related to its implementation from an epidemiologist's perspective.

METHODS

In this scoping review, a systematic PubMed search with forward and backward snowballing was performed to identify manuscripts in English published between January 2015 and September 2018. Included studies had to describe the application of NGS on bacterial isolates within a public health setting. The studied pathogen, year of publication, country, number of isolates, sampling fraction, setting, public health application, study aim, level of implementation, time orientation of the NGS analyses, and key findings were extracted from each study. Due to a large heterogeneity of settings, applications, pathogens, and study measurements, a descriptive narrative synthesis of the eligible studies was performed.

RESULTS

Out of the 275 included articles, 164 were outbreak investigations, 70 focused on strategy-oriented surveillance, and 41 on control-oriented surveillance. Main applications included the use of whole-genome sequencing (WGS) data for (1) source tracing, (2) early outbreak detection, (3) unraveling transmission dynamics, (4) monitoring drug resistance, (5) detecting cross-border transmission events, (6) identifying the emergence of strains with enhanced virulence or zoonotic potential, and (7) assessing the impact of prevention and control programs. The superior resolution over conventional typing methods to infer transmission routes was reported as an added value, as well as the ability to simultaneously characterize the resistome and virulome of the studied pathogen. However, the full potential of pathogen genomics can only be reached through its integration with high-quality contextual data.

CONCLUSIONS

For several pathogens, it is time for a shift from proof-of-concept studies to routine use of WGS during outbreak investigations and surveillance activities. However, some implementation challenges from the epidemiologist's perspective remain, such as data integration, quality of contextual data, sampling strategies, and meaningful interpretations. Interdisciplinary, inter-sectoral, and international collaborations are key for an appropriate genomics-informed surveillance.

Analysing livestock network data for infectious disease control: an argument for routine data collection in emerging economies

Livestock movements are an important mechanism of infectious disease transmission. Where these are well recorded, network analysis tools have been used to successfully identify system properties, highlight vulnerabilities to transmission, and inform targeted surveillance and control. Here we highlight the main uses of network properties in understanding livestock disease epidemiology and discuss statistical approaches to infer network characteristics from biased or fragmented datasets. We use a 'hurdle model' approach that predicts (i) the probability of movement and (ii) the number of livestock moved to generate synthetic 'complete' networks of movements between administrative wards, exploiting routinely collected government movement permit data from northern Tanzania. We demonstrate that this model captures a significant amount of the observed variation. Combining the cattle movement network with a spatial between-ward contact layer, we create a multiplex, over which we simulated the spread of 'fast' ( R0 = 3) and 'slow' ( R0 = 1.5) pathogens, and assess the effects of random versus targeted disease control interventions (vaccination and movement ban). The targeted interventions substantially outperform those randomly implemented for both fast and slow pathogens. Our findings provide motivation to encourage routine collection and centralization of movement data to construct representative networks. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

Reconstructing a transmission network and identifying risk factors of secondary transmissions in the 2010 foot-and-mouth disease outbreak in Japan

Research aimed at understanding transmission networks, representing a network of "who infected whom" for an infectious disease outbreak, have been actively conducted in recent years. Transmission network models incorporating epidemiological and genetic data are valuable for elucidating disease transmission pathways. In this study, we reconstructed the transmission network of the foot-and-mouth disease (FMD) epidemic in Japan in 2010, and explored farm-level risk factors associated with increased risk of secondary transmission. A published, systematic Bayesian transmission network model was applied to epidemiological data of 292 infected farms and whole genome sequence data of 104 of the infected farms. This model can make inferences for known infected farms even lacking genetic data. After estimating the consensus network, the accuracy of the network was examined by comparison with epidemiological data. Then, risk factors inferred to have been sources of secondary transmission were explored using zero-inflated Poisson regression model. As far as we are aware, this study represents the largest FMD outbreak transmission network to be published by such means combining epidemiological and genetic data. The consensus network reasonably generated the epidemiological links, which were estimated from the actual epidemiological investigation. Among 292 farms, 101 farms (35%) were inferred to have been the sources of secondary transmission, and amongst these farms, the median number of secondary cases was 2 (min:1-max:18) farms. The farm-type (small and large -sized pig farms), the number of days from onset to notification, and the number of susceptible farms within a 1-km radius were significantly associated with secondary transmission. Transmission network modelling enabled inference of the connections between infected farms during the FMD epidemic and identified important factors for controlling the risk of secondary transmission. This study demonstrated that the predominant susceptible species held on a farm, farm size, and animal density were associated with increased onwards transmission.

The betaboost package-a software tool for modelling bounded outcome variables in potentially high-dimensional epidemiological data

Motivation

To provide an integrated software environment for model fitting and variable selection in regression models with a bounded outcome variable.

Implementation

The proposed modelling framework is implemented in the add-on package betaboost of the statistical software environment R.

General features

The betaboost methodology is based on beta-regression, which is a state-of-the-art method for modelling bounded outcome variables. By combining traditional model fitting techniques with recent advances in statistical learning and distributional regression, betaboost allows users to carry out data-driven variable and/or confounder selection in potentially high-dimensional epidemiological data. The software package implements a flexible routine to incorporate linear and non-linear predictor effects in both the mean and the precision parameter (relating inversely to the variance) of a beta-regression model.

Availability

The software is hosted publicly at [http://github.com/boost-R/betaboost] and has been published under General Public License (GPL) version 3 or newer.

Disease management at the wildlife-livestock interface: Using whole-genome sequencing to study the role of elk in Mycobacterium bovis transmission in Michigan, USA

The role of wildlife in the persistence and spread of livestock diseases is difficult to quantify and control. These difficulties are exacerbated when several wildlife species are potentially involved. Bovine tuberculosis (bTB), caused by Mycobacterium bovis, has experienced an ecological shift in Michigan, with spillover from cattle leading to an endemically infected white-tailed deer (deer) population. It has potentially substantial implications for the health and well-being of both wildlife and livestock and incurs a significant economic cost to industry and government. Deer are known to act as a reservoir of infection, with evidence of M. bovis transmission to sympatric elk and cattle populations. However, the role of elk in the circulation of M. bovis is uncertain; they are few in number, but range further than deer, so may enable long distance spread. Combining Whole Genome Sequences (WGS) for M. bovis isolates from exceptionally well-observed populations of elk, deer and cattle with spatiotemporal locations, we use spatial and Bayesian phylogenetic analyses to show strong spatiotemporal admixture of M. bovis isolates. Clustering of bTB in elk and cattle suggests either intraspecies transmission within the two populations, or exposure to a common source. However, there is no support for significant pathogen transfer amongst elk and cattle, and our data are in accordance with existing evidence that interspecies transmission in Michigan is likely only maintained by deer. This study demonstrates the value of whole genome population studies of M. bovis transmission at the wildlife-livestock interface, providing insights into bTB management in an endemic system.

Emerging Challenges and Opportunities in Infectious Disease Epidemiology

Much of the intellectual tradition of modern epidemiology stems from efforts to understand and combat chronic diseases persisting through the 20th century epidemiologic transition of countries such as the United States and United Kingdom. After decades of relative obscurity, infectious disease epidemiology has undergone an intellectual rebirth in recent years amid increasing recognition of the threat posed by both new and familiar pathogens. Here, we review the emerging coalescence of infectious disease epidemiology around a core set of study designs and statistical methods bearing little resemblance to the chronic disease epidemiology toolkit. We offer our outlook on challenges and opportunities facing the field, including the integration of novel molecular and digital information sources into disease surveillance, the assimilation of such data into models of pathogen spread, and the increasing contribution of models to public health practice. We next consider emerging paradigms in causal inference for infectious diseases, ranging from approaches to evaluating vaccines and antimicrobial therapies to the task of ascribing clinical syndromes to etiologic microorganisms, an age-old problem transformed by our increasing ability to characterize human-associated microbiota. These areas represent an increasingly important component of epidemiology training programs for future generations of researchers and practitioners.

Can bovine TB be eradicated from the Republic of Ireland? Could this be achieved by 2030?

Background

There has been an ongoing decline in bovine tuberculosis (TB) in the Republic of Ireland, however, TB has yet to be eradicated. Further to a recent commitment by the Irish government to eradicate TB by 2030, this paper considers two questions, ‘Can bovine TB be eradicated from the Republic of Ireland?’ and ‘Could this be achieved by 2030?’, given current knowledge from research.

Main body of the abstract

Until very recently, Ireland has lacked key tools required for eradication. This gap has substantially been filled with the national roll-out of badger vaccination. Nonetheless, there is robust evidence, drawn from general national research, international experiences, and results of a recent modelling study, to suggest that all current strategies plus badger vaccination will not be sufficient to successfully eradicate TB from Ireland by 2030. We face a critical decision point in the programme, specifically the scope and intensity of control measures from this point forward. Adequate information is available, both from research and international experience, to indicate that these additional measures should broadly focus on adequately addressing TB risks from wildlife, implementing additional risk-based cattle controls, and enhancing industry engagement. These three areas are considered in some detail.

Conclusion

Based on current knowledge, it will not be possible to eradicate TB by 2030 with current control strategies plus national badger vaccination. Additional measures will be needed if Ireland is to eradicate TB within a reasonable time frame. Decisions made now will have long-term implications both in terms of time-to-eradication and cumulative programme costs.

Incorporating genomic methods into contact networks to reveal new insights into animal behavior and infectious disease dynamics

Utilization of contact networks has provided opportunities for assessing the dynamic interplay between pathogen transmission and host behavior. Genomic techniques have, in their own right, provided new insight into complex questions in disease ecology, and the increasing accessibility of genomic approaches means more researchers may seek out these tools. The integration of network and genomic approaches provides opportunities to examine the interaction between behavior and pathogen transmission in new ways and with greater resolution. While a number of studies have begun to incorporate both contact network and genomic approaches, a great deal of work has yet to be done to better integrate these techniques. In this review, we give a broad overview of how network and genomic approaches have each been used to address questions regarding the interaction of social behavior and infectious disease, and then discuss current work and future horizons for the merging of these techniques.

Reconstructing foot-and-mouth disease outbreaks: a methods comparison of transmission network models

A number of transmission network models are available that combine genomic and epidemiological data to reconstruct networks of who infected whom during infectious disease outbreaks. For such models to reliably inform decision-making they must be transparently validated, robust, and capable of producing accurate predictions within the short data collection and inference timeframes typical of outbreak responses. A lack of transparent multi-model comparisons reduces confidence in the accuracy of transmission network model outputs, negatively impacting on their more widespread use as decision-support tools. We undertook a formal comparison of the performance of nine published transmission network models based on a set of foot-and-mouth disease outbreaks simulated in a previously free country, with corresponding simulated phylogenies and genomic samples from animals on infected premises. Of the transmission network models tested, Lau’s systematic Bayesian integration framework was found to be the most accurate for inferring the transmission network and timing of exposures, correctly identifying the source of 73% of the infected premises (with 91% accuracy for sources with model support >0.80). The Structured COalescent Transmission Tree Inference provided the most accurate inference of molecular clock rates. This validation study points to which models might be reliably used to reconstruct similar future outbreaks and how to interpret the outputs to inform control. Further research could involve extending the best-performing models to explicitly represent within-host diversity so they can handle next-generation sequencing data, incorporating additional animal and farm-level covariates and combining predictions using Ensemble methods and other approaches.

Food for contagion: synthesis and future directions for studying host-parasite responses to resource shifts in anthropogenic environments

Human-provided resource subsidies for wildlife are diverse, common and have profound consequences for wildlife-pathogen interactions, as demonstrated by papers in this themed issue spanning empirical, theoretical and management perspectives from a range of study systems. Contributions cut across scales of organization, from the within-host dynamics of immune function, to population-level impacts on parasite transmission, to landscape- and regional-scale patterns of infection. In this concluding paper, we identify common threads and key findings from author contributions, including the consequences of resource subsidies for (i) host immunity; (ii) animal aggregation and contact rates; (iii) host movement and landscape-level infection patterns; and (iv) interspecific contacts and cross-species transmission. Exciting avenues for future work include studies that integrate mechanistic modelling and empirical approaches to better explore cross-scale processes, and experimental manipulations of food resources to quantify host and pathogen responses. Work is also needed to examine evolutionary responses to provisioning, and ask how diet-altered changes to the host microbiome influence infection processes. Given the massive public health and conservation implications of anthropogenic resource shifts, we end by underscoring the need for practical recommendations to manage supplemental feeding practices, limit human-wildlife conflicts over shared food resources and reduce cross-species transmission risks, including to humans.This article is part of the theme issue 'Anthropogenic resource subsidies and host-parasite dynamics in wildlife'.

Predicting reservoir hosts and arthropod vectors from evolutionary signatures in RNA virus genomes

Identifying the animal origins of RNA viruses requires years of field and laboratory studies that stall responses to emerging infectious diseases. Using large genomic and ecological datasets, we demonstrate that animal reservoirs and the existence and identity of arthropod vectors can be predicted directly from viral genome sequences via machine learning. We illustrate the ability of these models to predict the epidemiology of diverse viruses across most human-infective families of single-stranded RNA viruses, including 69 viruses with previously elusive or never-investigated reservoirs or vectors. Models such as these, which capitalize on the proliferation of low-cost genomic sequencing, can narrow the time lag between virus discovery and targeted research, surveillance, and management.

Can We Breed Cattle for Lower Bovine TB Infectivity?

Host resistance and infectivity are genetic traits affecting infectious disease transmission. This Perspective discusses the potential exploitation of genetic variation in cattle infectivity, in addition to resistance, to reduce the risk, and prevalence of bovine tuberculosis (bTB). In bTB, variability in M. bovis shedding has been previously reported in cattle and wildlife hosts (badgers and wild boars), but the observed differences were attributed to dose and route of infection, rather than host genetics. This article addresses the extent to which cattle infectivity may play a role in bTB transmission, and discusses the feasibility, and potential benefits from incorporating infectivity into breeding programmes. The underlying hypothesis is that bTB infectivity, like resistance, is partly controlled by genetics. Identifying and reducing the number of cattle with high genetic infectivity, could reduce further a major risk factor for herds exposed to bTB. We outline evidence in support of this hypothesis and describe methodologies for detecting and estimating genetic parameters for infectivity. Using genetic-epidemiological prediction models we discuss the potential benefits of selection for reduced infectivity and increased resistance in terms of practical field measures of epidemic risk and severity. Simulations predict that adding infectivity to the breeding programme could enhance and accelerate the reduction in breakdown risk compared to selection on resistance alone. Therefore, given the recent launch of genetic evaluations for bTB resistance and the UK government's goal to eradicate bTB, it is timely to consider the potential of integrating infectivity into breeding schemes.

Pathogens in space: Advancing understanding of pathogen dynamics and disease ecology through landscape genetics

Landscape genetics has provided many insights into how heterogeneous landscape features drive processes influencing spatial genetic variation in free-living organisms. This rapidly developing field has focused heavily on vertebrates, and expansion of this scope to the study of infectious diseases holds great potential for landscape geneticists and disease ecologists alike. The potential application of landscape genetics to infectious agents has garnered attention at formative stages in the development of landscape genetics, but systematic examination is lacking. We comprehensively review how landscape genetics is being used to better understand pathogen dynamics. We characterize the field and evaluate the types of questions addressed, approaches used and systems studied. We also review the now established landscape genetic methods and their realized and potential applications to disease ecology. Lastly, we identify emerging frontiers in the landscape genetic study of infectious agents, including recent phylogeographic approaches and frameworks for studying complex multihost and host-vector systems. Our review emphasizes the expanding utility of landscape genetic methods available for elucidating key pathogen dynamics (particularly transmission and spread) and also how landscape genetic studies of pathogens can provide insight into host population dynamics. Through this review, we convey how increasing awareness of the complementarity of landscape genetics and disease ecology among practitioners of each field promises to drive important cross-disciplinary advances.

Visualising and mapping the intellectual structure of medical big data

In this study, we sought to apply recent advances in informetrics to the analysis of literature related to big data in the field of medicine. Our aim was to elucidate research trends, identify knowledge clusters and decipher the links between them. We also sought to ascertain the theories most commonly applied in the processing of medical data and identify potential research gaps. The most important keywords over the last 10 years have been ‘big data’, ‘data mining’, ‘healthcare’, ‘cloud computing’, ‘machine learning’ and ‘electronic health record system’. These could be viewed as the core issues of research associated with big data in the field of medicine. We also identified a number of keywords that are expected to play a pivotal role in this field in the near future. These terms include the ‘internet of things’, ‘e-health’, ‘sensors’, ‘predictive modeling’, ‘quantified self’, ‘smart city’, ‘wearable device’ and ‘m-health’. Finally, we compiled co-word networks indicating the degree of connectivity between keywords, for use in locating knowledge gaps by revealing the overall context of issues commonly encountered when investigating big data. Our findings form a solid academic foundation on which to develop medical technologies, managerial strategies and theory related to big data.