Genomic Epidemiology Analysis of Infectious Disease Outbreaks Using TransPhylo

Emergence and interstate spread of highly pathogenic avian influenza A(H5N1) in dairy cattle in the United States

Highly pathogenic avian influenza (HPAI) viruses cross species barriers and have the potential to cause pandemics. In North America, HPAI A(H5N1) viruses related to the goose/Guangdong 2.3.4.4b hemagglutinin phylogenetic clade have infected wild birds, poultry, and mammals. Our genomic analysis and epidemiological investigation showed that a reassortment event in wild bird populations preceded a single wild bird-to-cattle transmission episode. The movement of asymptomatic or presymptomatic cattle has likely played a role in the spread of HPAI within the United States dairy herd. Some molecular markers that may lead to changes in transmission efficiency and phenotype were detected at low frequencies. Continued transmission of H5N1 HPAI within dairy cattle increases the risk for infection and subsequent spread of the virus to human populations.

The NOSTRA model: Coherent estimation of infection sources in the case of possible nosocomial transmission

Nosocomial, or hospital-acquired, infections are a key determinant of patient health in healthcare facilities, leading to longer stays and increased mortality. In addition to the direct effects on infected patients, the burden imposed by nosocomial infections impacts both staff and other patients by increasing the load on the healthcare system. The appropriate infection control response may differ depending on whether the infection was acquired in the hospital or the community. For example, nosocomial outbreaks may require ward closures to reduce the risk of onward transmission, whilst this may not be an appropriate response to repeated importations of infections from outside the facility. Unfortunately, it is often unclear whether an infection detected in a healthcare facility is nosocomial, as the time of infection is unobserved. Given this, there is a strong case for the development of models that can integrate multiple datasets available in hospitals to assess whether an infection detected in a hospital is nosocomial. When assessing nosocomiality, it is beneficial to take into account both whether the timing of infection is consistent with hospital acquisition and whether there are any likely candidates within the hospital who could have been the source of the infection. In this work, we developed a Bayesian model which jointly estimates whether a given infection detected in hospital is nosocomial and whether it came from a set of individuals identified as candidates by hospital staff. The model coherently integrates pathogen genetic information, the timings of epidemiological events, such as symptom onset, and location data on the infected patient and candidate infectors. We illustrated this model on a real hospital dataset showing both its output and how the impact of the different data sources on the assessed probabilities are contingent on what other data has been included in the model, and validated the calibration of the predictions against simulated data.

Bayesian Inference of Pathogen Phylogeography using the Structured Coalescent Model

Over the past decade, pathogen genome sequencing has become well established as a powerful approach to study infectious disease epidemiology. In particular, when multiple genomes are available from several geographical locations, comparing them is informative about the relative size of the local pathogen populations as well as past migration rates and events between locations. The structured coalescent model has a long history of being used as the underlying process for such phylogeographic analysis. However, the computational cost of using this model does not scale well to the large number of genomes frequently analysed in pathogen genomic epidemiology studies. Several approximations of the structured coalescent model have been proposed, but their effects are difficult to predict. Here we show how the exact structured coalescent model can be used to analyse a precomputed dated phylogeny, in order to perform Bayesian inference on the past migration history, the effective population sizes in each location, and the directed migration rates from any location to another. We describe an efficient reversible jump Markov Chain Monte Carlo scheme which is implemented in a new R package StructCoalescent. We use simulations to demonstrate the scalability and correctness of our method and to compare it with existing software. We also applied our new method to several state-of-the-art datasets on the population structure of real pathogens to showcase the relevance of our method to current data scales and research questions.

JUNIPER: Reconstructing Transmission Events from Next-Generation Sequencing Data at Scale

Transmission reconstruction-the inference of who infects whom in disease outbreaks-offers critical insights into how pathogens spread and provides opportunities for targeted control measures. We developed JUNIPER (Joint Underlying Network Inference for Phylogenetic and Epidemiological Reconstructions), a highly-scalable pathogen outbreak reconstruction tool that incorporates intrahost variation, incomplete sampling, and algorithmic parallelization. Central to JUNIPER is a statistical model for within-host variant frequencies observed by next generation sequencing, which we validated on a dataset of over 160,000 deep-sequenced SARS-CoV-2 genomes. Combining this within-host variation model with population-level evolutionary and transmission models, we developed a method for inferring phylogenies and transmission trees simultaneously. We benchmarked JUNIPER on computer-generated and real outbreaks in which transmission links were known or epidemiologically confirmed. We demonstrated JUNIPER's real-world utility on two large-scale datasets: over 1,500 bovine H5N1 cases and over 13,000 human COVID-19 cases. Based on these analyses, we quantified the elevated H5N1 transmission rates in California and identified high-confidence transmission events, and demonstrated the efficacy of vaccination for reducing SARS-CoV-2 transmission. By overcoming computational and methodological limitations in existing outbreak reconstruction tools, JUNIPER provides a robust framework for studying pathogen spread at scale.

JUNIPER: Reconstructing Transmission Events from Next-Generation Sequencing Data at Scale

Transmission reconstruction--the inference of who infects whom in disease outbreaks--offers critical insights into how pathogens spread and provides opportunities for targeted control measures. We developed JUNIPER (Joint Underlying Network Inference for Phylogenetic and Epidemiological Reconstructions), a highly-scalable pathogen outbreak reconstruction tool that incorporates intrahost variation, incomplete sampling, and algorithmic parallelization. Central to JUNIPER is a statistical model for within-host variant frequencies observed by next generation sequencing, which we validated on a dataset of over 160,000 deep-sequenced SARS-CoV-2 genomes. Combining this within-host variation model with population-level evolutionary and transmission models, we developed a method for inferring phylogenies and transmission trees simultaneously. We benchmarked JUNIPER on computer-generated and real outbreaks in which transmission links were known or epidemiologically confirmed. We demonstrated JUNIPER's real-world utility on two large-scale datasets: over 1,500 bovine H5N1 cases and over 13,000 human COVID-19 cases. Based on these analyses, we quantified the elevated H5N1 transmission rates in California and identified high-confidence transmission events, and demonstrated the efficacy of vaccination for reducing SARS-CoV-2 transmission. By overcoming computational and methodological limitations in existing outbreak reconstruction tools, JUNIPER provides a robust framework for studying pathogen spread at scale.

Implications for TB control among migrants in large cities in China: a prospective population-based genomic epidemiology study in Shenzhen

Internal migrants are a challenge for TB control in large Chinese cities and understanding this epidemiology is crucial for designing effective control and prevention strategies. We conducted a prospective genomic epidemiological study of culture-positive TB patients diagnosed between June 1, 2018 and May 31, 2021 in the Longhua District of Shenzhen. Treatment status was obtained from local and national TB registries and all isolates were sequenced. Genomic clusters were defined as strains differing by ≤12 SNPs. Risk factors for clustering were identified with multivariable analysis and then Bayesian models and TransPhylo were used to infer the timing of transmission within clusters. Of the 2277 culture-positive patients, 70.1% (1596/2277) were migrants: 72.1% (1043/1446) of the migrants patients developed TB within two years of arriving in Longhua; 38.8% within 6 months of arriving; and 12.3% (104/843) had TB symptoms when they arrived. Only 15.4% of Longhua strains were in genomic clusters. More than one third (33.6%) of patients were not treated in Shenzhen but were involved in nearly one third of the recent transmission events. Clustering was associated with migrants not treated in Shenzhen, males, and teachers/trainers. TB in Longhua is prinicipally due to reactivation of infections in migrants, but a proportion may have had clinical or incipient TB upon arrival in the district. Patients diagnosed but not treated in Longhua were involved in recent local TB transmission. Controlling TB in Shenzhen will require strategies to comprehensively diagnose and treat active TB in the internal migrant population.

Apollo: A comprehensive GPU-powered within-host simulator for viral evolution and infection dynamics across population, tissue, and cell

Modern sequencing instruments bring unprecedented opportunity to study within-host viral evolution in conjunction with viral transmissions between hosts. However, no computational simulators are available to assist the characterization of within-host dynamics. This limits our ability to interpret epidemiological predictions incorporating within-host evolution and to validate computational inference tools. To fill this need we developed Apollo, a GPU-accelerated, out-of-core tool for within-host simulation of viral evolution and infection dynamics across population, tissue, and cellular levels. Apollo is scalable to hundreds of millions of viral genomes and can handle complex demographic and population genetic models. Apollo can replicate real within-host viral evolution; accurately recapturing observed viral sequences from an HIV cohort derived from initial population-genetic configurations. For practical applications, using Apollo-simulated viral genomes and transmission networks, we validated and uncovered the limitations of a widely used viral transmission inference tool.

Genomic epidemiology describes introduction and outbreaks of antifungal drug-resistant Candida auris

Candida auris is a globally emerged fungal pathogen causing nosocomial invasive infections. Here, we use cutting-edge genomic approaches to elucidate the temporal and geographic epidemiology of drug-resistant C. auris within the UK. We analysed a representative sample of over 200 isolates from multiple UK hospitals to assess the number and timings of C. auris introductions and infer subsequent patterns of inter- and intra-hospital transmission of azole drug-resistant isolates. We identify at least one introduction from Clade I and two from Clade III into the UK, and observe temporal and geographical evidence for multiple transmission events of antifungal drug resistant isolates between hospitals and identified local within-hospital patient-to-patient transmission events. Our study confirms outbreaks of drug-resistant C. auris are linked and that transmission amongst patients occurs, explaining local hospital outbreaks, and demonstrating a need for improved epidemiological surveillance of C. auris to protect patients and healthcare services.

An overview of next generation sequencing strategies and genomics tools used for tuberculosis research

Tuberculosis (TB) is a grave public health concern and is considered the foremost contributor to human mortality resulting from infectious disease. Due to the stringent clonality and extremely restricted genomic diversity, conventional methods prove inefficient for in-depth exploration of minor genomic variations and the evolutionary dynamics operating in Mycobacterium tuberculosis (M.tb) populations. Until now, the majority of reviews have primarily focused on delineating the application of whole-genome sequencing (WGS) in predicting antibiotic resistant genes, surveillance of drug resistance strains, and M.tb lineage classifications. Despite the growing use of next generation sequencing (NGS) and WGS analysis in TB research, there are limited studies that provide a comprehensive summary of there role in studying macroevolution, minor genetic variations, assessing mixed TB infections, and tracking transmission networks at an individual level. This highlights the need for systematic effort to fully explore the potential of WGS and its associated tools in advancing our understanding of TB epidemiology and disease transmission. We delve into the recent bioinformatics pipelines and NGS strategies that leverage various genetic features and simultaneous exploration of host-pathogen protein expression profile to decipher the genetic heterogeneity and host-pathogen interaction dynamics of the M.tb infections. This review highlights the potential benefits and limitations of NGS and bioinformatics tools and discusses their role in TB detection and epidemiology. Overall, this review could be a valuable resource for researchers and clinicians interested in NGS-based approaches in TB research.

Sustained transmission over two decades of a previously unrecognised MPT64 negative Mycobacterium tuberculosis strain in Queensland, Australia: a whole genome sequencing study

Background

MPT64 is a key protein used for Mycobacterium tuberculosis (MTB) complex strain identification. We describe protracted transmission of an MPT64 negative MTB strain in Queensland, Australia, and explore genomic factors related to its successful spread.

Methods

All MPT64 negative strains identified between 2002 and 2022 by the Queensland Mycobacteria Reference Laboratory, and an additional 2 isolates from New South Wales (NSW), were whole genome sequenced. Bayesian modelling and phylogeographical analyses were used to assess their evolutionary history and transmission dynamics. Protein structural modelling to understand the putative functional effects of the mutated gene coding for MPT64 protein was performed.

Findings

Forty-three MPT64 negative isolates were sequenced, belonging to a single MTB cluster of Lineage 4.1.1.1 strains. Combined with a UK dataset of the same lineage, molecular dating estimated 1990 (95% HPD 1987-1993) as the likely time of strain introduction into Australia. Although the strain has spread over a wide geographic area and new cases linked to the cluster continue to arise, phylodynamic analysis suggest the outbreak peaked around 2003. All MPT64 negative strains had a frame shift mutation (delAT, p.Val216fs) within the MPT64 gene, which confers two major structural rearrangements at the C-terminus of the protein.

Interpretation

This study uncovered the origins of an MPT64 negative MTB outbreak in Australia, providing a richer understanding of its biology and transmission dynamics, as well as guidance for clinical diagnosis and public health action. The potential spread of MPT64 negative strains undermines the diagnostic utility of the MPT64 immunochromatographic test.

Funding

This study was funded from an operational budget provided to the Queensland Mycobacterium Reference Laboratory by Pathology Queensland, Queensland Department of Health.

Changing genomic epidemiology of COVID-19 in long-term care facilities during the 2020-2022 pandemic, Washington State

BACKGROUND

Long-term care facilities (LTCFs) are vulnerable to disease outbreaks. Here, we jointly analyze SARS-CoV-2 genomic and paired epidemiologic data from LTCFs and surrounding communities in Washington state (WA) to assess transmission patterns during 2020-2022, in a setting of changing policy. We describe sequencing efforts and genomic epidemiologic findings across LTCFs and perform in-depth analysis in a single county.

METHODS

We assessed genomic data representativeness, built phylogenetic trees, and conducted discrete trait analysis to estimate introduction sizes over time, and explored selected outbreaks to further characterize transmission events.

RESULTS

We found that transmission dynamics among cases associated with LTCFs in WA changed over the course of the COVID-19 pandemic, with variable introduction rates into LTCFs, but decreasing amplification within LTCFs. SARS-CoV-2 lineages circulating in LTCFs were similar to those circulating in communities at the same time. Transmission between staff and residents was bi-directional.

CONCLUSIONS

Understanding transmission dynamics within and between LTCFs using genomic epidemiology on a broad scale can assist in targeting policies and prevention efforts. Tracking facility-level outbreaks can help differentiate intra-facility outbreaks from high community transmission with repeated introduction events. Based on our study findings, methods for routine tree building and overlay of epidemiologic data for hypothesis generation by public health practitioners are recommended. Discrete trait analysis added valuable insight and can be considered when representative sequencing is performed. Cluster detection tools, especially those that rely on distance thresholds, may be of more limited use given current data capture and timeliness. Importantly, we noted a decrease in data capture from LTCFs over time. Depending on goals for use of genomic data, sentinel surveillance should be increased or targeted surveillance implemented to ensure available data for analysis.

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

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

Genomic epidemiological investigation of an outbreak of Serratia marcescens neurosurgical site infections associated with contaminated haircutting toolkits in a hospital barber shop

BACKGROUND

Nine surgical site infections caused by Serratia marcescens were diagnosed in neurosurgical patients in a 3500-bed hospital between 2nd February and 6th April 2022.

OBJECTIVE

To trace the source of infections caused by S. marcescens to expedite termination of the outbreak and prevent future epidemics.

METHODS

A review of all surgical procedures and cultures yielding S. marcescens since February 2022 was conducted. Samples were collected from patients and environmental sources. S. marcescens isolates were characterized by antibiotic susceptibility testing. Whole-genome sequencing (WGS) was used to investigate genetic relationships. Resistance genes, virulence genes and plasmid replicons were identified.

RESULTS

S. marcescens was isolated from patients' puncture fluid, cerebrospinal fluid and other secretions, and was also cultured from the barbers' haircutting tools, including leather knives, slicker scrapers and razors. In total, 15 isolates were obtained from patients and eight isolates were obtained from haircutting tools. All isolates exhibited identical antibiotic resistance patterns. WGS revealed close clustering among the 23 isolates which differed significantly from previous strains. Three resistance genes and nine virulence-associated genes were detected in all isolates, and 19 of 23 isolates harboured an MOBP-type plasmid. The results confirmed an outbreak of S. marcescens, which was traced to contaminated haircutting tools in the hospital barber shop. The outbreak ended after extensive reinforcement of infection control procedures and re-education of the barbers.

CONCLUSIONS

These results highlight the risk of postoperative infections related to pre-operative skin preparation, and demonstrate the value of next-generation sequencing tools to expedite outbreak investigations.

A method to estimate the serial interval distribution under partially-sampled data

The serial interval of an infectious disease is an important variable in epidemiology. It is defined as the period of time between the symptom onset times of the infector and infectee in a direct transmission pair. Under partially sampled data, purported infector-infectee pairs may actually be separated by one or more unsampled cases in between. Misunderstanding such pairs as direct transmissions will result in overestimating the length of serial intervals. On the other hand, two cases that are infected by an unseen third case (known as coprimary transmission) may be classified as a direct transmission pair, leading to an underestimation of the serial interval. Here, we introduce a method to jointly estimate the distribution of serial intervals factoring in these two sources of error. We simultaneously estimate the distribution of the number of unsampled intermediate cases between purported infector-infectee pairs, as well as the fraction of such pairs that are coprimary. We also extend our method to situations where each infectee has multiple possible infectors, and show how to factor this additional source of uncertainty into our estimates. We assess our method's performance on simulated data sets and find that our method provides consistent and robust estimates. We also apply our method to data from real-life outbreaks of four infectious diseases and compare our results with published results. With similar accuracy, our method of estimating serial interval distribution provides unique advantages, allowing its application in settings of low sampling rates and large population sizes, such as widespread community transmission tracked by routine public health surveillance.

Inferring bacterial transmission dynamics using deep sequencing genomic surveillance data

Identifying and interrupting transmission chains is important for controlling infectious diseases. One way to identify transmission pairs - two hosts in which infection was transmitted from one to the other - is using the variation of the pathogen within each single host (within-host variation). However, the role of such variation in transmission is understudied due to a lack of experimental and clinical datasets that capture pathogen diversity in both donor and recipient hosts. In this work, we assess the utility of deep-sequenced genomic surveillance (where genomic regions are sequenced hundreds to thousands of times) using a mouse transmission model involving controlled spread of the pathogenic bacterium Citrobacter rodentium from infected to naïve female animals. We observe that within-host single nucleotide variants (iSNVs) are maintained over multiple transmission steps and present a model for inferring the likelihood that a given pair of sequenced samples are linked by transmission. In this work we show that, beyond the presence and absence of within-host variants, differences arising in the relative abundance of iSNVs (allelic frequency) can infer transmission pairs more precisely. Our approach further highlights the critical role bottlenecks play in reserving the within-host diversity during transmission.

Inferring Viral Transmission Pathways from Within-Host Variation

Genome sequencing can offer critical insight into pathogen spread in viral outbreaks, but existing transmission inference methods use simplistic evolutionary models and only incorporate a portion of available genetic data. Here, we develop a robust evolutionary model for transmission reconstruction that tracks the genetic composition of within-host viral populations over time and the lineages transmitted between hosts. We confirm that our model reliably describes within-host variant frequencies in a dataset of 134,682 SARS-CoV-2 deep-sequenced genomes from Massachusetts, USA. We then demonstrate that our reconstruction approach infers transmissions more accurately than two leading methods on synthetic data, as well as in a controlled outbreak of bovine respiratory syncytial virus and an epidemiologically-investigated SARS-CoV-2 outbreak in South Africa. Finally, we apply our transmission reconstruction tool to 5,692 outbreaks among the 134,682 Massachusetts genomes. Our methods and results demonstrate the utility of within-host variation for transmission inference of SARS-CoV-2 and other pathogens, and provide an adaptable mathematical framework for tracking within-host evolution.

P-DOR, an easy-to-use pipeline to reconstruct bacterial outbreaks using genomics

SUMMARY

Bacterial Healthcare-Associated Infections (HAIs) are a major threat worldwide, which can be counteracted by establishing effective infection control measures, guided by constant surveillance and timely epidemiological investigations. Genomics is crucial in modern epidemiology but lacks standard methods and user-friendly software, accessible to users without a strong bioinformatics proficiency. To overcome these issues we developed P-DOR, a novel tool for rapid bacterial outbreak characterization. P-DOR accepts genome assemblies as input, it automatically selects a background of publicly available genomes using k-mer distances and adds it to the analysis dataset before inferring a Single-Nucleotide Polymorphism (SNP)-based phylogeny. Epidemiological clusters are identified considering the phylogenetic tree topology and SNP distances. By analyzing the SNP-distance distribution, the user can gauge the correct threshold. Patient metadata can be inputted as well, to provide a spatio-temporal representation of the outbreak. The entire pipeline is fast and scalable and can be also run on low-end computers.

AVAILABILITY AND IMPLEMENTATION

P-DOR is implemented in Python3 and R and can be installed using conda environments. It is available from GitHub https://github.com/SteMIDIfactory/P-DOR under the GPL-3.0 license.

Phylogenetic Analysis of Bacterial Pathogen Genomes

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

Clonal Spread of Carbapenem-Resistant Klebsiella pneumoniae Sequence Type 11 in Chinese Pediatric Patients

Klebsiella pneumoniae often causes life-threatening infections in patients globally. Despite its notability, little is known about potential nosocomial outbreak and spread of K. pneumoniae among pediatric patients in low- and middle-income countries. Ninety-eight K. pneumoniae strains isolated from pediatric patients in a large general hospital in China between February 2018 and May 2019 were subjected to nanopore and Illumina sequencing and genomic analysis to elucidate transmission and genetic diversity. The temporal distribution patterns of K. pneumoniae revealed a cluster of sequence type 11 (ST11) strains comprising two clades. Most inferred transmissions were of clade 1, which could be traced to a common ancestor dating to mid-2017. An infant in the coronary care unit played a central role, potentially seeding transmission clusters in other wards. Major genomic changes during the outbreak included chromosomal mutations associated with virulence and gains and losses of plasmids encoding resistance. In summary, we report a nosocomial outbreak among pediatric patients caused by clonal dissemination of KPC-2-producing ST11 K. pneumoniae. Our findings highlight the value of whole-genome sequencing during outbreak investigations and illustrate that transmission chains can be identified during hospital stays. IMPORTANCE We report a nosocomial outbreak among pediatric patients caused by clonal dissemination of blaKPC-2-carrying ST11 K. pneumoniae. Strains of various sequence types coexist in the complex hospital environment; the quick emergence and spread of ST11 strains were mainly due to the plasmid-mediated acquisition of resistance genes. The spread of hospital infection was highly associated with several specific wards, suggesting the importance of genomic surveillance on wards at high risk of infection.

Modeling approaches for early warning and monitoring of pandemic situations as well as decision support

The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and assessing as well as predicting the effectiveness of (non-) pharmaceutical interventions. However, in several countries, these modeling efforts have only limited impact on governmental decision-making so far. In light of this situation, the review aims to provide a critical review of existing modeling approaches and to discuss the potential for future developments.

Early evolution and transmission of GII.P16-GII.2 norovirus in China

Norovirus is the most common cause of acute gastroenteritis worldwide. During 2016-2017, a novel recombinant GII.P16-GII.2 genotype of norovirus suddenly appeared and over the next several years became the predominant strain in both China and worldwide. To better understand the origin and diffusion of the GII.P16-GII.2 genotype in China, we conducted molecular evolutionary analyses, including phylodynamics and phylogeography. Moreover, to trace person-to-person transmission of GII.P16-GII.2 norovirus, we applied the novel method, TransPhylo, to a historical phylogeny using sequences obtained from a publicly available database. A time-scaled phylogenetic tree indicated that the time to the most recent common ancestor of the GII.P16-GII.2 major capsid protein (VP1) gene diverged from the GII.P2-GII.2 VP1 gene at 2,001.03 with an evolutionary rate of 3.32 × 10-3 substitutions/site/year. The time to the most recent common ancestor of the GII.P16-GII.2 RNA-dependent RNA polymerase region diverged from the GII.P16-GII.4 RNA-dependent RNA polymerase region at 2,013.28 with an evolutionary rate of 9.44 × 10-3 substitutions/site/year. Of these 2 genomic regions, VP1 gene sequence variations were the most influenced by selective pressure. A phylogeographic analysis showed that GII.P16-GII.2 strains in China communicated most frequently with those in the United States, Australia, Thailand, and Russia, suggesting import from Australia to Taiwan and from the United States to Guangdong. TransPhylo analyses indicated that the basic reproductive number (R0) and sampling proportion (pi) of GII.P16-GII.2 norovirus were 1.99 (95% confidence interval: 1.58-2.44) and 0.76 (95% confidence interval: 0.63-0.88), respectively. Strains from the United States and Australia were responsible for large spread during the evolution and transmission of the virus. Coastal cities and places with high population densities should be closely monitored for norovirus.

Clinical and Molecular Characterizations of Carbapenem-Resistant Klebsiella pneumoniae Causing Bloodstream Infection in a Chinese Hospital

Bloodstream infection (BSI) caused by carbapenem-resistant Klebsiella pneumoniae (CRKP) is a serious and urgent threat for hospitalized patients. This study aims to describe the clinical and molecular characteristics of CRKP causing BSI in a tertiary-care hospital in Beijing, China. A total of 146 CRKP strains and 39 carbapenem-susceptible K. pneumoniae (CSKP) strains collected in the hospital from 2017 to 2020 were sent for whole-genome sequencing. Univariate and multivariate analyses were used to evaluate risk factors for in-hospital mortality of CRKP-BSI cases. Thirty (20.5%) of 146 CRKP-BSI patients and three (7.7%) of 39 CSKP-BSI patients died at discharge (χ² = 3.471, P = 0.062). Multivariate logistic regression analysis indicated that age and use of urinary catheters were independent risk factors for the death of CRKP-BSI. The 146 CRKP isolates belonged to 9 sequence types (STs) and 11 serotypes, while the 39 CSKP isolates belonged to 23 STs and 27 serotypes. The mechanism of carbapenem resistance for all the CRKP strains was the acquisition of carbapenemase, mainly KPC-2 (n = 127). There were 2 predominant serotypes for ST11 CRKP, namely, KL47 (n = 82) and KL64 (n = 42). Some virulent genes, including rmpA2, iucABCD and iutA, and repB gene, which was involved in plasmid replication, were detected in all ST11-KL64 strains. Evolutionary transmission analysis suggested that ST11 CRKP strains might have evolved from KL47 into KL64 and were accompanied by multiple outbreak events. This study poses an urgent need for enhancing infection control measures in the hospital, especially in the intensive care unit where the patients are at high-risk for acquiring CRKP-BSI. IMPORTANCE CRKP-BSI is demonstrated to cause high mortality. In this study, we demonstrated that ST11 CRKP strains might carry many virulent genes. Meanwhile, outbreak events occurred several times in the strains collected. Carbapenemase acquisition (mainly KPC-2 carbapenemase) was responsible for carbapenem resistance of all the 146 CRKP strains. As 2 predominant strains, all ST11-KL64 strains, but not ST11-KL47 strains, carried rmpA2, iucABCD, iutA, as well as a plasmid replication initiator (repB). Our study suggested that the occurrence of region-specific recombination events manifested by the acquisition of some virulence genes might contribute to serotype switching from ST11-KL47 to ST11-KL64. The accumulation of virulent genes in epidemic resistant strains poses a great challenge for the prevention and treatment of BSI caused by K. pneumoniae in high-risk patients.

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'.

Molecular Epidemiological Investigations of Localized SARS-CoV-2 Outbreaks-Utility of Public Algorithms

The recent rapid expansion of targeted viral sequencing approaches in conjunction with available bioinformatics have provided an effective platform for studying severe acute respiratory syndrome coronavirus-2 (CoV-2) virions at the molecular level. These means can be adapted to the field of viral molecular epidemiology, wherein localized outbreak clusters can be evaluated and linked. To this end, we have integrated publicly available algorithms in conjunction with targeted RNASeq data in order to qualitatively evaluate similarity or dissimilarity between suspect outbreak strains from hospitals, or assisted living facilities. These tools include phylogenetic clustering and mutational analysis utilizing Nextclade and Ultrafast Sample placement on Existing tRee (UShER). We herein present these outbreak screening tools utilizing three case examples in the context of molecular epidemiology, along with limitations and potential future developments. We anticipate that these methods can be performed in clinical molecular laboratories equipped with CoV-2-sequencing technology.

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

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

Hunting alters viral transmission and evolution in a large carnivore

Hunting can fundamentally alter wildlife population dynamics but the consequences of hunting on pathogen transmission and evolution remain poorly understood. Here, we present a study that leverages a unique landscape-scale quasi-experiment coupled with pathogen-transmission tracing, network simulation and phylodynamics to provide insights into how hunting shapes feline immunodeficiency virus (FIV) dynamics in puma (Puma concolor). We show that removing hunting pressure enhances the role of males in transmission, increases the viral population growth rate and increases the role of evolutionary forces on the pathogen compared to when hunting was reinstated. Changes in transmission observed with the removal of hunting could be linked to short-term social changes while the male puma population increased. These findings are supported through comparison with a region with stable hunting management over the same time period. This study shows that routine wildlife management can have impacts on pathogen transmission and evolution not previously considered.

OUP accepted manuscript

A detailed understanding of how and when severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission occurs is crucial for designing effective prevention measures. Other than contact tracing, genome sequencing provides information to help infer who infected whom. However, the effectiveness of the genomic approach in this context depends on both (high enough) mutation and (low enough) transmission rates. Today, the level of resolution that we can obtain when describing SARS-CoV-2 outbreaks using just genomic information alone remains unclear. In order to answer this question, we sequenced forty-nine SARS-CoV-2 patient samples from ten local clusters in NW Spain for which partial epidemiological information was available and inferred transmission history using genomic variants. Importantly, we obtained high-quality genomic data, sequencing each sample twice and using unique barcodes to exclude cross-sample contamination. Phylogenetic and cluster analyses showed that consensus genomes were generally sufficient to discriminate among independent transmission clusters. However, levels of intrahost variation were low, which prevented in most cases the unambiguous identification of direct transmission events. After filtering out recurrent variants across clusters, the genomic data were generally compatible with the epidemiological information but did not support specific transmission events over possible alternatives. We estimated the effective transmission bottleneck size to be one to two viral particles for sample pairs whose donor–recipient relationship was likely. Our analyses suggest that intrahost genomic variation in SARS-CoV-2 might be generally limited and that homoplasy and recurrent errors complicate identifying shared intrahost variants. Reliable reconstruction of direct SARS-CoV-2 transmission based solely on genomic data seems hindered by a slow mutation rate, potential convergent events, and technical artifacts. Detailed contact tracing seems essential in most cases to study SARS-CoV-2 transmission at high resolution.

Reconstructing SARS-CoV-2 infection dynamics through the phylogenetic inference of unsampled sources of infection

The COVID-19 pandemic has illustrated the importance of infection tracking. The role of asymptomatic, undiagnosed individuals in driving infections within this pandemic has become increasingly evident. Modern phylogenetic tools that take into account asymptomatic or undiagnosed individuals can help guide public health responses. We finetuned established phylogenetic pipelines using published SARS-CoV-2 genomic data to examine reasonable estimate transmission networks with the inference of unsampled infection sources. The system utilised Bayesian phylogenetics and TransPhylo to capture the evolutionary and infection dynamics of SARS-CoV-2. Our analyses gave insight into the transmissions within a population including unsampled sources of infection and the results aligned with epidemiological observations. We were able to observe the effects of preventive measures in Canada's "Atlantic bubble" and in populations such as New York State. The tools also inferred the cross-species disease transmission of SARS-CoV-2 transmission from humans to lions and tigers in New York City's Bronx Zoo. These phylogenetic tools offer a powerful approach in response to both the COVID-19 and other emerging infectious disease outbreaks.

Genomic Epidemiology Analysis of Infectious Disease Outbreaks Using TransPhylo

Comparing the pathogen genomes from several cases of an infectious disease has the potential to help us understand and control outbreaks. Many methods exist to reconstruct a phylogeny from such genomes, which represents how the genomes are related to one another. However, such a phylogeny is not directly informative about transmission events between individuals. TransPhylo is a software tool implemented as an R package designed to bridge the gap between pathogen phylogenies and transmission trees. TransPhylo is based on a combined model of transmission between hosts and pathogen evolution within each host. It can simulate both phylogenies and transmission trees jointly under this combined model. TransPhylo can also reconstruct a transmission tree based on a dated phylogeny, by exploring the space of transmission trees compatible with the phylogeny. A transmission tree can be represented as a coloring of a phylogeny where each color represents a different host of the pathogen, and TransPhylo provides convenient ways to plot these colorings and explore the results. This article presents the basic protocols that can be used to make the most of TransPhylo. © 2021 The Authors. Basic Protocol 1: First steps with TransPhylo Basic Protocol 2: Simulation of outbreak data Basic Protocol 3: Inference of transmission Basic Protocol 4: Exploring the results of inference.