Inferring the source of transmission with phylogenetic data

Bayesian mixture models for phylogenetic source attribution from consensus sequences and time since infection estimates

In stopping the spread of infectious diseases, pathogen genomic data can be used to reconstruct transmission events and characterize population-level sources of infection. Most approaches for identifying transmission pairs do not account for the time passing since the divergence of pathogen variants in individuals, which is problematic in viruses with high within-host evolutionary rates. This prompted us to consider possible transmission pairs in terms of phylogenetic data and additional estimates of time since infection derived from clinical biomarkers. We develop Bayesian mixture models with an evolutionary clock as a signal component and additional mixed effects or covariate random functions describing the mixing weights to classify potential pairs into likely and unlikely transmission pairs. We demonstrate that although sources cannot be identified at the individual level with certainty, even with the additional data on time elapsed, inferences into the population-level sources of transmission are possible, and more accurate than using only phylogenetic data without time since infection estimates. We apply the proposed approach to estimate age-specific sources of HIV infection in Amsterdam tranamission networks among men who have sex with men between 2010 and 2021. This study demonstrates that infection time estimates provide informative data to characterize transmission sources, and shows how phylogenetic source attribution can then be done with multi-dimensional mixture models.

Assessing transmission attribution risk from simulated sequencing data in HIV molecular epidemiology

BACKGROUND

HIV molecular epidemiology (ME) is the analysis of sequence data together with individual-level clinical, demographic, and behavioral data to understand HIV epidemiology. The use of ME has raised concerns regarding identification of the putative source in direct transmission events. This could result in harm ranging from stigma to criminal prosecution in some jurisdictions. Here we assessed the risks of ME using simulated HIV genetic sequencing data.

METHODS

We simulated social networks of men-who-have-sex-with-men, calibrating the simulations to data from San Diego. We used these networks to simulate consensus and next-generation sequence (NGS) data to evaluate the risks of identifying direct transmissions using different HIV sequence lengths, and population sampling depths. To identify the source of transmissions, we calculated infector probability and used phyloscanner software for the analysis of consensus and NGS data, respectively.

RESULTS

Consensus sequence analyses showed that the risk of correctly inferring the source (direct transmission) within identified transmission pairs was very small and independent of sampling depth. Alternatively, NGS analyses showed that identification of the source of a transmission was very accurate, but only for 6.5% of inferred pairs. False positive transmissions were also observed, where one or more unobserved intermediaries were present when compared to the true network.

CONCLUSION

Source attribution using consensus sequences rarely infers direct transmission pairs with high confidence but is still useful for population studies. In contrast, source attribution using NGS data was much more accurate in identifying direct transmission pairs, but for only a small percentage of transmission pairs analyzed.

Lessons learned from community engagement regarding phylodynamic research with molecular HIV surveillance data

INTRODUCTION

The widespread implementation of molecular HIV surveillance (MHS) has resulted in an increased discussion about the ethical, human rights and public health implications of MHS. We narrate our process of pausing our research that uses data collected through MHS in response to these growing concerns and summarize the key lessons we learned through conversations with community members.

METHODS

The original study aimed to describe HIV transmission patterns by age and race/ethnicity among men who have sex with men in King County, Washington, by applying probabilistic phylodynamic modelling methods to HIV-1 pol gene sequences collected through MHS. In September 2020, we paused the publication of this research to conduct community engagement: we held two public-facing online presentations, met with a national community coalition that included representatives of networks of people living with HIV, and invited two members of this coalition to provide feedback on our manuscript. During each of these meetings, we shared a brief presentation of our methods and findings and explicitly solicited feedback on the perceived public health benefit and potential harm of our analyses and results.

RESULTS

Some community concerns about MHS in public health practice also apply to research using MHS data, namely those related to informed consent, inference of transmission directionality and criminalization. Other critiques were specific to our research study and included feedback about the use of phylogenetic analyses to study assortativity by race/ethnicity and the importance of considering the broader context of stigma and structural racism. We ultimately decided the potential harms of publishing our study-perpetuating racialized stigma about men who have sex with men and eroding the trust between phylogenetics researchers and communities of people living with HIV-outweighed the potential benefits.

CONCLUSIONS

HIV phylogenetics research using data collected through MHS data is a powerful scientific technology with the potential to benefit and harm communities of people living with HIV. Addressing criminalization and including people living with HIV in decision-making processes have the potential to meaningfully address community concerns and strengthen the ethical justification for using MHS data in both research and public health practice. We close with specific opportunities for action and advocacy by researchers.

Colonization and transmission of Staphylococcus aureus in schools: a citizen science project

Aggregation of children in schools has been established to be a key driver of transmission of infectious diseases. Mathematical models of transmission used to predict the impact of control measures, such as vaccination and testing, commonly depend on self-reported contact data. However, the link between self-reported social contacts and pathogen transmission has not been well described. To address this, we used Staphylococcus aureus as a model organism to track transmission within two secondary schools in England and test for associations between self-reported social contacts, test positivity and the bacterial strain collected from the same students. Students filled out a social contact survey and their S. aureus colonization status was ascertained through self-administered swabs from which isolates were sequenced. Isolates from the local community were also sequenced to assess the representativeness of school isolates. A low frequency of genome-linked transmission precluded a formal analysis of links between genomic and social networks, suggesting that S. aureus transmission within schools is too rare to make it a viable tool for this purpose. Whilst we found no evidence that schools are an important route of transmission, increased colonization rates found within schools imply that school-age children may be an important source of community transmission.

Using genetic data to identify transmission risk factors: Statistical assessment and application to tuberculosis transmission

Identifying host factors that influence infectious disease transmission is an important step toward developing interventions to reduce disease incidence. Recent advances in methods for reconstructing infectious disease transmission events using pathogen genomic and epidemiological data open the door for investigation of host factors that affect onward transmission. While most transmission reconstruction methods are designed to work with densely sampled outbreaks, these methods are making their way into surveillance studies, where the fraction of sampled cases with sequenced pathogens could be relatively low. Surveillance studies that use transmission event reconstruction then use the reconstructed events as response variables (i.e., infection source status of each sampled case) and use host characteristics as predictors (e.g., presence of HIV infection) in regression models. We use simulations to study estimation of the effect of a host factor on probability of being an infection source via this multi-step inferential procedure. Using TransPhylo-a widely-used method for Bayesian estimation of infectious disease transmission events-and logistic regression, we find that low sensitivity of identifying infection sources leads to dilution of the signal, biasing logistic regression coefficients toward zero. We show that increasing the proportion of sampled cases improves sensitivity and some, but not all properties of the logistic regression inference. Application of these approaches to real world data from a population-based TB study in Botswana fails to detect an association between HIV infection and probability of being a TB infection source. We conclude that application of a pipeline, where one first uses TransPhylo and sparsely sampled surveillance data to infer transmission events and then estimates effects of host characteristics on probabilities of these events, should be accompanied by a realistic simulation study to better understand biases stemming from imprecise transmission event inference.

Using phylogenetics to infer HIV-1 transmission direction between known transmission pairs

Identifying the transmission direction between individuals provides unparalleled power to understand infectious disease epidemiology. With epidemiological and clinical information typically unavailable to infer transmission direction, phylogenetic analysis of pathogen sequence data offers an alternative approach. While the success of this phylogenetic analysis varies, the reasons remain unknown. We analyze sequence data from over 100 transmission pairs for which both the transmission direction of HIV is known and detailed additional information is available. We find that easily quantifiable phylogenetic and sampling characteristics discriminate whether a phylogenetically inferred transmission direction is correct. Our analysis highlights that while phylogenetic approaches to infer transmission direction are unsuitable for individual-level analysis, such as forensic investigations, confidence in source attribution can be incorporated in population-level analyses.

Inferring the transmission direction between linked individuals living with HIV provides unparalleled power to understand the epidemiology that determines transmission. Phylogenetic ancestral-state reconstruction approaches infer the transmission direction by identifying the individual in whom the most recent common ancestor of the virus populations originated. While these methods vary in accuracy, it is unclear why. To evaluate the performance of phylogenetic ancestral-state reconstruction to determine the transmission direction of HIV-1 infection, we inferred the transmission direction for 112 transmission pairs where transmission direction and detailed additional information were available. We then fit a statistical model to evaluate the extent to which epidemiological, sampling, genetic, and phylogenetic factors influenced the outcome of the inference. Finally, we repeated the analysis under real-life conditions with only routinely available data. We found that whether ancestral-state reconstruction correctly infers the transmission direction depends principally on the phylogeny's topology. For example, under real-life conditions, the probability of identifying the correct transmission direction increases from 32%—when a monophyletic–monophyletic or paraphyletic–polyphyletic tree topology is observed and when the tip closest to the root does not agree with the state at the root—to 93% when a paraphyletic–monophyletic topology is observed and when the tip closest to the root agrees with the root state. Our results suggest that documenting larger differences in relative intrahost diversity increases our confidence in the transmission direction inference of linked pairs for population-level studies of HIV. These findings provide a practical starting point to determine our confidence in transmission direction inference from ancestral-state reconstruction.

Airborne Transmission and Control of Influenza and Other Respiratory Pathogens

Despite uncertainty about the specific transmission risk posed by airborne, spray-borne, and contact modes for influenza, SARS-CoV-2, and other respiratory viruses, there is evidence that airborne transmission via inhalation is important and often predominates. An early study of influenza transmission via airborne challenge quantified infectious doses as low as one influenza virion leading to illness characterized by cough and sore throat. Other studies that challenged via intranasal mucosal exposure observed high doses required for similarly symptomatic respiratory illnesses. Analysis of the Evaluating Modes of Influenza Transmission (EMIT) influenza human-challenge transmission trial—of 52 H3N2 inoculated viral donors and 75 sero-susceptible exposed individuals—quantifies airborne transmission and provides context and insight into methodology related to airborne transmission. Advances in aerosol sampling and epidemiologic studies examining the role of masking, and engineering-based air hygiene strategies provide a foundation for understanding risk and directions for new work.

Modelling: Understanding pandemics and how to control them

New disease challenges, societal demands and better or novel types of data, drive innovations in the structure, formulation and analysis of epidemic models. Innovations in modelling can lead to new insights into epidemic processes and better use of available data, yielding improved disease control and stimulating collection of better data and new data types. Here we identify key challenges for the structure, formulation, analysis and use of mathematical models of pathogen transmission relevant to current and future pandemics.

Comparison of observer based methods for source localisation in complex networks

In recent years, research on methods for locating a source of spreading phenomena in complex networks has seen numerous advances. Such methods can be applied not only to searching for the "patient zero" in epidemics, but also finding the true sources of false or malicious messages circulating in the online social networks. Many methods for solving this problem have been established and tested in various circumstances. Yet, we still lack reviews that would include a direct comparison of efficiency of these methods. In this paper, we provide a thorough comparison of several observer-based methods for source localisation on complex networks. All methods use information about the exact time of spread arrival at a pre-selected group of vertices called observers. We investigate how the precision of the studied methods depends on the network topology, density of observers, infection rate, and observers' placement strategy. The direct comparison between methods allows for an informed choice of the methods for applications or further research. We find that the Pearson correlation based method and the method based on the analysis of multiple paths are the most effective in networks with synthetic or real topologies. The former method dominates when the infection rate is low; otherwise, the latter method takes over.

Drug resistance mutations in HIV: new bioinformatics approaches and challenges

Drug resistance mutations appear in HIV under treatment pressure. Resistant variants can be transmitted to treatment-naive individuals, which can lead to rapid virological failure and can limit treatment options. Consequently, quantifying the prevalence, emergence and transmission of drug resistance is critical to effectively treating patients and to shape health policies. We review recent bioinformatics developments and in particular describe: (1) the machine learning approaches intended to predict and explain the level of resistance of HIV variants from their sequence data; (2) the phylogenetic methods used to survey the emergence and dynamics of resistant HIV transmission clusters; (3) the impact of deep sequencing in studying within-host and between-host genetic diversity of HIV variants, notably regarding minority resistant variants.

Concordance of HIV transmission risk factors elucidated using viral diversification rate and phylogenetic clustering

BACKGROUND AND OBJECTIVES

Although HIV sequence clustering is routinely used to identify subpopulations experiencing elevated transmission, it over-simplifies transmission dynamics and is sensitive to methodology. Complementarily, viral diversification rates can be used to approximate historical transmission rates. Here, we investigated the concordance and sensitivity of HIV transmission risk factors identified by phylogenetic clustering, viral diversification rate, changes in viral diversification rate and a combined approach.

METHODOLOGY

Viral sequences from 9848 people living with HIV in British Columbia, Canada, sampled between 1996 and February 2019, were used to infer phylogenetic trees, from which clusters were identified and viral diversification rates of each tip were calculated. Factors associated with heightened transmission risk were compared across models of cluster membership, viral diversification rate, changes in diversification rate, and viral diversification rate among clusters.

RESULTS

Viruses within larger clusters had higher diversification rates and lower changes in diversification rate than those within smaller clusters; however, rates within individual clusters, independent of size, varied widely. Risk factors for both cluster membership and elevated viral diversification rate included being male, young, a resident of health authority E, previous injection drug use, previous hepatitis C virus infection or a high recent viral load. In a sensitivity analysis, models based on cluster membership had wider confidence intervals and lower concordance of significant effects than viral diversification rate for lower sampling rates.

CONCLUSIONS AND IMPLICATIONS

Viral diversification rate complements phylogenetic clustering, offering a means of evaluating transmission dynamics to guide provision of treatment and prevention services.

LAY SUMMARY

Understanding HIV transmission dynamics within clusters can help prioritize public health resource allocation. We compared socio-demographic and clinical risk factors associated with phylogenetic cluster membership and viral diversification rate, a historical branching rate, in order to assess their relative concordance and sampling sensitivity.

Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections

SARS-CoV-2 is notable both for its rapid spread, and for the heterogeneity of its patterns of transmission, with multiple published incidences of superspreading behaviour. Here, we applied a novel network reconstruction algorithm to infer patterns of viral transmission occurring between patients and health care workers (HCWs) in the largest clusters of COVID-19 infection identified during the first wave of the epidemic at Cambridge University Hospitals NHS Foundation Trust, UK. Based upon dates of individuals reporting symptoms, recorded individual locations, and viral genome sequence data, we show an uneven pattern of transmission between individuals, with patients being much more likely to be infected by other patients than by HCWs. Further, the data were consistent with a pattern of superspreading, whereby 21% of individuals caused 80% of transmission events. Our study provides a detailed retrospective analysis of nosocomial SARS-CoV-2 transmission, and sheds light on the need for intensive and pervasive infection control procedures.

Sample size calculation for phylogenetic case linkage

Sample size calculations are an essential component of the design and evaluation of scientific studies. However, there is a lack of clear guidance for determining the sample size needed for phylogenetic studies, which are becoming an essential part of studying pathogen transmission. We introduce a statistical framework for determining the number of true infector-infectee transmission pairs identified by a phylogenetic study, given the size and population coverage of that study. We then show how characteristics of the criteria used to determine linkage and aspects of the study design can influence our ability to correctly identify transmission links, in sometimes counterintuitive ways. We test the overall approach using outbreak simulations and provide guidance for calculating the sensitivity and specificity of the linkage criteria, the key inputs to our approach. The framework is freely available as the R package phylosamp, and is broadly applicable to designing and evaluating a wide array of pathogen phylogenetic studies.

Sequencing the genetic material of viral and bacterial pathogens has become an important part of tracking and combating human infectious diseases. Specifically, comparing the pathogen DNA or RNA sequences collected from infected individuals can allow researchers and public health experts to determine who infected whom, or detect when a pathogen entered a specific country or geographic area. However, it is often impossible to collect samples from every single infected person, and these missing sequences can pose problems for this type of analysis, especially if there is some bias behind which samples were selected for sequencing. We have developed a mathematical framework that allows users to determine the probability their conclusions about pathogen transmission are correct given the number and proportion of samples from a pathogen outbreak they have sequenced. This framework is freely available, easy to use, and broadly generalizable to any pathogen, and we hope that it can be used to inform the design and sampling strategies behind future sequencing-based studies.

Rapid feedback on hospital onset SARS-CoV-2 infections combining epidemiological and sequencing data

Background

Rapid identification and investigation of healthcare-associated infections (HCAIs) is important for suppression of SARS-CoV-2, but the infection source for hospital onset COVID-19 infections (HOCIs) cannot always be readily identified based only on epidemiological data. Viral sequencing data provides additional information regarding potential transmission clusters, but the low mutation rate of SARS-CoV-2 can make interpretation using standard phylogenetic methods difficult.

Methods

We developed a novel statistical method and sequence reporting tool (SRT) that combines epidemiological and sequence data in order to provide a rapid assessment of the probability of HCAI among HOCI cases (defined as first positive test >48 hr following admission) and to identify infections that could plausibly constitute outbreak events. The method is designed for prospective use, but was validated using retrospective datasets from hospitals in Glasgow and Sheffield collected February-May 2020.

Results

We analysed data from 326 HOCIs. Among HOCIs with time from admission ≥8 days, the SRT algorithm identified close sequence matches from the same ward for 160/244 (65.6%) and in the remainder 68/84 (81.0%) had at least one similar sequence elsewhere in the hospital, resulting in high estimated probabilities of within-ward and within-hospital transmission. For HOCIs with time from admission 3-7 days, the SRT probability of healthcare acquisition was >0.5 in 33/82 (40.2%).

Conclusions

The methodology developed can provide rapid feedback on HOCIs that could be useful for infection prevention and control teams, and warrants further prospective evaluation. The integration of epidemiological and sequence data is important given the low mutation rate of SARS-CoV-2 and its variable incubation period.

Funding

COG-UK HOCI funded by COG-UK consortium, supported by funding from UK Research and Innovation, National Institute of Health Research and Wellcome Sanger Institute.

Using longitudinally sampled viral nucleotide sequences to characterize the drivers of HIV-1 transmission

OBJECTIVES

Understanding the drivers of HIV-1 transmission is of importance for curbing the ongoing epidemic. Phylogenetic methods based on single viral sequences allow us to assess whether two individuals are part of the same viral outbreak, but cannot on their own assess who potentially transmitted the virus. We developed and assessed a molecular epidemiology method with the main aim to screen cohort studies for and to characterize individuals who are 'potential HIV-1 transmitters', in order to understand the drivers of HIV-1 transmission.

METHODS

We developed and validated a molecular epidemiology approach using longitudinally sampled viral Sanger sequences to characterize potential HIV-1 transmitters in the Swiss HIV Cohort Study.

RESULTS

Our method was able to identify 279 potential HIV-1 transmitters and allowed us to determine the main epidemiological and virological drivers of transmission. We found that the directionality of transmission was consistent with infection times for 72.9% of 85 potential HIV-1 transmissions with accurate infection date estimates. Being a potential HIV-1 transmitter was associated with risk factors including viral load [adjusted odds ratio_{multivariable} (95% confidence interval): 1.86 (1.49-2.32)], syphilis coinfection [1.52 (1.06-2.19)], and recreational drug use [1.45 (1.06-1.98)]. By contrast for the potential HIV-1 recipients, this association was weaker or even absent [1.18 (0.82-1.72), 0.89 (0.52-1.55) and 1.53 (0.98-2.39), respectively], indicating that inferred directionality of transmission is useful at the population level.

CONCLUSIONS

Our results indicate that longitudinally sampled Sanger sequences do not provide sufficient information to identify transmitters with high certainty at the individual level, but that they allow the drivers of transmission at the population level to be characterized.

Harnessing genomics to trace the path of a viral outbreak in African lions

Predicting the emergence of novel infectious diseases requires an understanding of how pathogens infect and efficiently spread in alternative naïve hosts. A pathogen's ability to adapt to a new host (i.e. host shift) oftentimes is constrained by host phylogeny, due to limits in the molecular mechanisms available to overcome host-specific immune defences (Longdon et al., 2014). Some pathogens, such as RNA viruses, however, have a propensity to jump hosts due to rapid mutation rates. For example, canine distemper virus (CDV) infects a broad range of terrestrial carnivores, as well as noncarnivore species worldwide, with a host range that is distributed across 5 orders and 22 families (Beineke et al., 2015). In 1993-1994, a severe CDV outbreak infected multiple carnivore host species in Serengeti National Park, causing widespread mortality and the subsequent decline of the African lion (Panthera leo) population (Roelke-Parker et al., 1996). While previous studies established domestic dogs (Canis lupus familiaris) as the disease reservoir, the precise route of transmission to lions remained a mystery, and a number of wild carnivore species could have facilitated viral evolution and spread. In this issue of Molecular Ecology, Weckworth et al. (2020) used whole-genome viral sequences obtained from four carnivore species during the CDV outbreak, in combination with epidemiological data, to illuminate the pathway and evolutionary mechanisms leading to disease emergence in Serengeti lions.

Understanding disclosed and cryptic HIV transmission risk via genetic analysis: what are we missing and when does it matter?

PURPOSE OF REVIEW

To discuss the recent HIV phylogenetic analyses examining HIV transmission patterns among and within risk groups.

RECENT FINDINGS

Phylodynamic analysis has recently been applied to multiple HIV outbreaks among people who inject drugs to determine whether HIV transmission is ongoing. Large-scale analyses of datasets of HIV sequences collected for drug-resistance testing provide population-level insights into transmission patterns. One focus across world regions has been to investigate whether age-disparity is a driver of HIV transmission. In sub-Saharan Africa, researchers have examined transmission between heterosexuals and MSM and between high prevalence fishing communities and inland communities. In the US and the UK, cryptic risk groups such as nondisclosed MSM and the partners of transgender women are increasingly being uncovered based on their position in densely sampled molecular transmission networks.

SUMMARY

Analysis of HIV genetic sequence can resolve viral transmission patterns between risk groups at unprecedented scales and levels of detail. Future research should focus on understanding the effect of missing data on inferences and the biases of different methods. Uncovering groups and patterns obscured from traditional epidemiolocal analyses is exciting but should not compromise the privacy of the groups in question.

Correlates of hepatitis C viral clustering among people who inject drugs in Baltimore

This study examines correlates of hepatitis C virus (HCV) genetic clustering among community-recruited people who inject drugs enrolled in the AIDS Linked to the IntraVenous Experience cohort in Baltimore between 1988 and 1989. HCV RNA was extracted and the core/envelope-1 region was sequenced. Clusters were identified from maximum likelihood trees with 1000 bootstrap replicates using a 70% aLRT and a 4% genetic-distance threshold in Cluster Picker. Overall, 46% of participants were in a cluster, including 122 genotype-1a and 36 genotype-1b clusters with an average of 2-3 genetically linked HCV infections. The largest cluster consists of 9 participants. In univariable analysis, black race (PR = 1.66 [95% CI: 1.12-2.45]), age <35 years (PR = 1.18 [95% CI: 1.02-1.37]), and injection drug use of cocaine alone (PR = 1.30 [95% CI: 1.02-1.65]) were significantly associated with being in a cluster. Conversely, a history of medication-associated treatment (MAT) was negatively associated with being in a cluster (PR = 0.82 [95% CI: 0.71-0.95]). In multivariable analysis, black race (APR = 1.62 [95% CI: 1.11-2.38]) remained independently associated being in a cluster while MAT (APR = 0.85 [95% CI: 0.74-0.99]) remained negatively associated with clustering. Our findings suggest strong locally-propagated transmission networks during the early epidemic that was driven by younger PWID. In light of the current opioid epidemic in the US, these findings suggest an urgent need for preventive interventions to mitigate the growth of large HCV transmission networks.

HIV-1 Transmission Patterns in Men Who Have Sex with Men: Insights from Genetic Source Attribution Analysis

Near 60% of new HIV infections in the United Kingdom are estimated to occur in men who have sex with men (MSM). Age-disassortative partnerships in MSM have been suggested to spread the HIV epidemics in many Western developed countries and to contribute to ethnic disparities in infection rates. Understanding these mixing patterns in transmission can help to determine which groups are at a greater risk and guide public health interventions. We analyzed combined epidemiological data and viral sequences from MSM diagnosed with HIV at the national level. We applied a phylodynamic source attribution model to infer patterns of transmission between groups of patients. From pair probabilities of transmission between 14,603 MSM patients, we found that potential transmitters of HIV subtype B were on average 8 months older than recipients. We also found a moderate overall assortativity of transmission by ethnic group and a stronger assortativity by region. Our findings suggest that there is only a modest net flow of transmissions from older to young MSM in subtype B epidemics and that young MSM, both for Black or White groups, are more likely to be infected by one another than expected in a sexual network with random mixing.

A likelihood ratio-based approach for improved source attribution in microbiological forensic investigations

A common objective in microbial forensic investigations is to identify the origin of a recovered pathogenic bacterium by DNA sequencing. However, there is currently no consensus about how degrees of belief in such origin hypotheses should be quantified, interpreted, and communicated to wider audiences. To fill this gap, we have developed a concept based on calculating probabilistic evidential values for microbial forensic hypotheses. The likelihood-ratio method underpinning this concept is widely used in other forensic fields, such as human DNA matching, where results are readily interpretable and have been successfully communicated in juridical hearings. The concept was applied to two case scenarios of interest in microbial forensics: (1) identifying source cultures among series of very similar cultures generated by parallel serial passage of the Tier 1 pathogen Francisella tularensis, and (2) finding the production facilities of strains isolated in a real disease outbreak caused by the human pathogen Listeria monocytogenes. Evidence values for the studied hypotheses were computed based on signatures derived from whole genome sequencing data, including deep-sequenced low-frequency variants and structural variants such as duplications and deletions acquired during serial passages. In the F. tularensis case study, we were able to correctly assign fictive evidence samples to the correct culture batches of origin on the basis of structural variant data. By setting up relevant hypotheses and using data on cultivated batch sources to define the reference populations under each hypothesis, evidential values could be calculated. The results show that extremely similar strains can be separated on the basis of amplified mutational patterns identified by high-throughput sequencing. In the L. monocytogenes scenario, analyses of whole genome sequence data conclusively assigned the clinical samples to specific sources of origin, and conclusions were formulated to facilitate communication of the findings. Taken together, these findings demonstrate the potential of using bacterial whole genome sequencing data, including data on both low frequency SNP signatures and structural variants, to calculate evidence values that facilitate interpretation and communication of the results. The concept could be applied in diverse scenarios, including both epidemiological and forensic source tracking of bacterial infectious disease outbreaks.

HIV-TRACE (TRAnsmission Cluster Engine): a Tool for Large Scale Molecular Epidemiology of HIV-1 and Other Rapidly Evolving Pathogens

In modern applications of molecular epidemiology, genetic sequence data are routinely used to identify clusters of transmission in rapidly evolving pathogens, most notably HIV-1. Traditional 'shoe-leather' epidemiology infers transmission clusters by tracing chains of partners sharing epidemiological connections (e.g., sexual contact). Here, we present a computational tool for identifying a molecular transmission analog of such clusters: HIV-TRACE (TRAnsmission Cluster Engine). HIV-TRACE implements an approach inspired by traditional epidemiology, by identifying chains of partners whose viral genetic relatedness imply direct or indirect epidemiological connections. Molecular transmission clusters are constructed using codon-aware pairwise alignment to a reference sequence followed by pairwise genetic distance estimation among all sequences. This approach is computationally tractable and is capable of identifying HIV-1 transmission clusters in large surveillance databases comprising tens or hundreds of thousands of sequences in near real time, that is, on the order of minutes to hours. HIV-TRACE is available at www.hivtrace.org and from www.github.com/veg/hivtrace, along with the accompanying result visualization module from www.github.com/veg/hivtrace-viz. Importantly, the approach underlying HIV-TRACE is not limited to the study of HIV-1 and can be applied to study outbreaks and epidemics of other rapidly evolving pathogens.

PANGEA-HIV 2: Phylogenetics And Networks for Generalised Epidemics in Africa

PURPOSE OF REVIEW

The HIV epidemic in sub-Saharan Africa is far from being under control and the ambitious UNAIDS targets are unlikely to be met by 2020 as declines in per-capita incidence being largely offset by demographic trends. There is an increasing number of proven and specific HIV prevention tools, but little consensus on how best to deploy them.

RECENT FINDINGS

Traditionally, phylogenetics has been used in HIV research to reconstruct the history of the epidemic and date zoonotic infections, whereas more recent publications focus on HIV diversity and drug resistance. However, it is also the most powerful method of source attribution available for the study of HIV transmission. The PANGEA (Phylogenetics And Networks for Generalized Epidemics in Africa) consortium has generated over 18 000 NGS HIV sequences from five countries in sub-Saharan Africa. Using phylogenetic methods, we will identify characteristics of individuals or groups, which are most likely to be at risk of infection or at risk of infecting others.

SUMMARY

Combining phylogenetics, phylodynamics and epidemiology will allow PANGEA to highlight where prevention efforts should be focussed to reduce the HIV epidemic most effectively. To maximise the public health benefit of the data, PANGEA offers accreditation to external researchers, allowing them to access the data and join the consortium. We also welcome submissions of other HIV sequences from sub-Saharan Africa to the database.

Comparative analysis of HIV sequences in real time for public health

PURPOSE OF REVIEW

The purpose of this study is to summarize recent advances in public health applications of comparative methods for HIV-1 sequence analysis in real time, including genetic clustering methods.

RECENT FINDINGS

Over the past 2 years, several groups have reported the deployment of established genetic clustering methods to guide public health decisions for HIV prevention in 'near real time'. However, it remains unresolved how well the readouts of comparative methods like clusters translate to events that are actionable for public health. A small number of recent studies have begun to elucidate the linkage between clusters and HIV-1 incidence, whereas others continue to refine and develop new comparative methods for such applications.

SUMMARY

Although the use of established methods to cluster HIV-1 sequence databases has become a widespread activity, there remains a critical gap between clusters and public health value.

HIV-1 genotype diversity and distribution characteristics among heterosexually transmitted population in Jiangsu province, China

Background

Heterosexual transmission has contributed greatly to the current HIV-1 epidemic in China. However, the HIV-1 genetic characteristics in the heterosexually transmitted population in Jiangsu province remained unclear.

Methods

A molecular epidemiological investigation on heterosexual transmission of HIV-1 was conducted across Jiangsu province. 301 HIV-1 patients infected through heterosexual transmission were involved in this study. The epidemiological information was investigated by trained staff via face-to-face interviews. Blood samples were taken from each patient, HIV-1 RNA was extracted from the plasma, and used for amplifying the gag and env genes followed by further products sequencing. The genotypes of HIV-1 were determined using phylogenetic tree analyses in the neighbor-joining method.

Results

A total of 262 samples were successfully taken for genotyping. The main subtypes which accounted for 90.5% of all HIV-1 strains are CRF01_AE (45.4%), CRF07_BC (21.4%), subtype B (12.6%), CRF08_BC (11.1%). Minor subtypes were also detected, such as CRF68_01B, subtype C, CRF55_01B, CRF02_AG and subtype A. Time trend analysis suggested the prevalence of subtype B and CRF08_BC decreased gradually, but the prevalence of CRF01_AE increased over time. A relatively higher prevalence of CRF07_BC in Central Jiangsu and subtype B were detected in South Jiangsu, while a relatively lower prevalence of subtype B and CRF08_BC were detected in Central Jiangsu.

Conclusion

Complex and unbalanced HIV distribution characteristics suggest that heterosexual transmission of HIV needs to be taken seriously. It is necessary to implement more effective and comprehensive intervention strategies for further control of HIV-1 dissemination.

Phylogenies from dynamic networks

The relationship between the underlying contact network over which a pathogen spreads and the pathogen phylogenetic trees that are obtained presents an opportunity to use sequence data to learn about contact networks that are difficult to study empirically. However, this relationship is not explicitly known and is usually studied in simulations, often with the simplifying assumption that the contact network is static in time, though human contact networks are dynamic. We simulate pathogen phylogenetic trees on dynamic Erdős-Renyi random networks and on two dynamic networks with skewed degree distribution, of which one is additionally clustered. We use tree shape features to explore how adding dynamics changes the relationships between the overall network structure and phylogenies. Our tree features include the number of small substructures (cherries, pitchforks) in the trees, measures of tree imbalance (Sackin index, Colless index), features derived from network science (diameter, closeness), as well as features using the internal branch lengths from the tip to the root. Using principal component analysis we find that the network dynamics influence the shapes of phylogenies, as does the network type. We also compare dynamic and time-integrated static networks. We find, in particular, that static network models like the widely used Barabasi-Albert model can be poor approximations for dynamic networks. We explore the effects of mis-specifying the network on the performance of classifiers trained identify the transmission rate (using supervised learning methods). We find that both mis-specification of the underlying network and its parameters (mean degree, turnover rate) have a strong adverse effect on the ability to estimate the transmission parameter. We illustrate these results by classifying HIV trees with a classifier that we trained on simulated trees from different networks, infection rates and turnover rates. Our results point to the importance of correctly estimating and modelling contact networks with dynamics when using phylodynamic tools to estimate epidemiological parameters.

Evolutionary Virology at 40

RNA viruses are diverse, abundant, and rapidly evolving. Genetic data have been generated from virus populations since the late 1970s and used to understand their evolution, emergence, and spread, culminating in the generation and analysis of many thousands of viral genome sequences. Despite this wealth of data, evolutionary genetics has played a surprisingly small role in our understanding of virus evolution. Instead, studies of RNA virus evolution have been dominated by two very different perspectives, the experimental and the comparative, that have largely been conducted independently and sometimes antagonistically. Here, we review the insights that these two approaches have provided over the last 40 years. We show that experimental approaches using in vitro and in vivo laboratory models are largely focused on short-term intrahost evolutionary mechanisms, and may not always be relevant to natural systems. In contrast, the comparative approach relies on the phylogenetic analysis of natural virus populations, usually considering data collected over multiple cycles of virus-host transmission, but is divorced from the causative evolutionary processes. To truly understand RNA virus evolution it is necessary to meld experimental and comparative approaches within a single evolutionary genetic framework, and to link viral evolution at the intrahost scale with that which occurs over both epidemiological and geological timescales. We suggest that the impetus for this new synthesis may come from methodological advances in next-generation sequencing and metagenomics.

Ethical considerations in global HIV phylogenetic research

Phylogenetic analysis of pathogens is an increasingly powerful way to reduce the spread of epidemics, including HIV. As a result, phylogenetic approaches are becoming embedded in public health and research programmes, as well as outbreak responses, presenting unique ethical, legal, and social issues that are not adequately addressed by existing bioethics literature. We formed a multidisciplinary working group to explore the ethical issues arising from the design of, conduct in, and use of results from HIV phylogenetic studies, and to propose recommendations to minimise the associated risks to both individuals and groups. We identified eight key ethical domains, within which we highlighted factors that make HIV phylogenetic research unique. In this Review, we endeavoured to provide a framework to assist researchers, public health practitioners, and funding institutions to ensure that HIV phylogenetic studies are designed, done, and disseminated in an ethical manner. Our conclusions also have broader relevance for pathogen phylogenetics.

Introduction of Phylodynamics for Controlling the HIV/AIDS Epidemic in Korea

As over 1000 new cases of HIV/AIDS occur in Korea annually, preventive health programs against HIV/AIDS are urgently needed. Since phylodynamic studies have been suggested as a way to understand how infectious diseases are transmitted and evolve, phylodynamic inferences can be a useful tool for HIV/AIDS research. In particular, phylodynamic models are helpful for dating the origins of an epidemic and estimating its basic reproduction number. Thus, the introduction of phylodynamics would be a highly valuable step towards controlling the HIV/AIDS epidemic in Korea.

Molecular Analysis of Human Immunodeficiency Virus Type 1 (HIV-1)-Infected Individuals in a Network-Based Intervention (Transmission Reduction Intervention Project): Phylogenetics Identify HIV-1-Infected Individuals With Social Links

Background

The Transmission Reduction Intervention Project (TRIP) is a network-based intervention that aims at decreasing human immunodeficiency virus type 1 (HIV-1) spread. We herein explore associations between transmission links as estimated by phylogenetic analyses, and social network-based ties among persons who inject drugs (PWID) recruited in TRIP.

Methods

Phylogenetic trees were inferred from HIV-1 sequences of TRIP participants. Highly supported phylogenetic clusters (transmission clusters) were those fulfilling 3 different phylogenetic confidence criteria. Social network-based ties (injecting or sexual partners, same venue engagement) were determined based on personal interviews, recruitment links, and field observation.

Results

TRIP recruited 356 individuals (90.2% PWID) including HIV-negative controls; recently HIV-infected seeds; long-term HIV-infected seeds; and their social network members. Of the 150 HIV-infected participants, 118 (78.7%) were phylogenetically analyzed. Phylogenetic analyses suggested the existence of 13 transmission clusters with 32 sequences. Seven of these clusters included 14 individuals (14/32 [43.8%]) who also had social ties with at least 1 member of their cluster. This proportion was significantly higher than what was expected by chance.

Conclusions

Molecular methods can identify HIV-infected people socially linked with another person in about half of the phylogenetic clusters. This could help public health efforts to locate individuals in networks with high transmission rates.

Understanding dengue virus evolution to support epidemic surveillance and counter-measure development

Dengue virus (DENV) causes a profound burden of morbidity and mortality, and its global burden is rising due to the co-circulation of four divergent DENV serotypes in the ecological context of globalization, travel, climate change, urbanization, and expansion of the geographic range of the Ae.aegypti and Ae.albopictus vectors. Understanding DENV evolution offers valuable opportunities to enhance surveillance and response to DENV epidemics via advances in RNA virus sequencing, bioinformatics, phylogenetic and other computational biology methods. Here we provide a scoping overview of the evolution and molecular epidemiology of DENV and the range of ways that evolutionary analyses can be applied as a public health tool against this arboviral pathogen.

Bayesian reconstruction of transmission within outbreaks using genomic variants

Pathogen genome sequencing can reveal details of transmission histories and is a powerful tool in the fight against infectious disease. In particular, within-host pathogen genomic variants identified through heterozygous nucleotide base calls are a potential source of information to identify linked cases and infer direction and time of transmission. However, using such data effectively to model disease transmission presents a number of challenges, including differentiating genuine variants from those observed due to sequencing error, as well as the specification of a realistic model for within-host pathogen population dynamics. Here we propose a new Bayesian approach to transmission inference, BadTrIP (BAyesian epiDemiological TRansmission Inference from Polymorphisms), that explicitly models evolution of pathogen populations in an outbreak, transmission (including transmission bottlenecks), and sequencing error. BadTrIP enables the inference of host-to-host transmission from pathogen sequencing data and epidemiological data. By assuming that genomic variants are unlinked, our method does not require the computationally intensive and unreliable reconstruction of individual haplotypes. Using simulations we show that BadTrIP is robust in most scenarios and can accurately infer transmission events by efficiently combining information from genetic and epidemiological sources; thanks to its realistic model of pathogen evolution and the inclusion of epidemiological data, BadTrIP is also more accurate than existing approaches. BadTrIP is distributed as an open source package (https://bitbucket.org/nicofmay/badtrip) for the phylogenetic software BEAST2. We apply our method to reconstruct transmission history at the early stages of the 2014 Ebola outbreak, showcasing the power of within-host genomic variants to reconstruct transmission events.

We present a new tool to reconstruct transmission events within outbreaks. Our approach makes use of pathogen genetic information, notably genetic variants at low frequency within host that are usually discarded, and combines it with epidemiological information of host exposure to infection. This leads to accurate reconstruction of transmission even in cases where abundant within-host pathogen genetic variation and weak transmission bottlenecks (multiple pathogen units colonising a new host at transmission) would otherwise make inference difficult due to the transmission history differing from the pathogen evolution history inferred from pathogen isolets. Also, the use of within-host pathogen genomic variants increases the resolution of the reconstruction of the transmission tree even in scenarios with limited within-outbreak pathogen genetic diversity: within-host pathogen populations that appear identical at the level of consensus sequences can be discriminated using within-host variants. Our Bayesian approach provides a measure of the confidence in different possible transmission histories, and is published as open source software. We show with simulations and with an analysis of the beginning of the 2014 Ebola outbreak that our approach is applicable in many scenarios, improves our understanding of transmission dynamics, and will contribute to finding and limiting sources and routes of transmission, and therefore preventing the spread of infectious disease.

PHYLOSCANNER: Inferring Transmission from Within- and Between-Host Pathogen Genetic Diversity

A central feature of pathogen genomics is that different infectious particles (virions and bacterial cells) within an infected individual may be genetically distinct, with patterns of relatedness among infectious particles being the result of both within-host evolution and transmission from one host to the next. Here, we present a new software tool, phyloscanner, which analyses pathogen diversity from multiple infected hosts. phyloscanner provides unprecedented resolution into the transmission process, allowing inference of the direction of transmission from sequence data alone. Multiply infected individuals are also identified, as they harbor subpopulations of infectious particles that are not connected by within-host evolution, except where recombinant types emerge. Low-level contamination is flagged and removed. We illustrate phyloscanner on both viral and bacterial pathogens, namely HIV-1 sequenced on Illumina and Roche 454 platforms, HCV sequenced with the Oxford Nanopore MinION platform, and Streptococcus pneumoniae with sequences from multiple colonies per individual. phyloscanner is available from https://github.com/BDI-pathogens/phyloscanner.

Treatment of HIV for the Prevention of Transmission in Discordant Couples and at the Population Level

The scientific breakthrough proving that antiretroviral therapy (ART) can halt heterosexual HIV transmission came in the form of a landmark clinical trial conducted among serodiscordant couples. Study findings immediately informed global recommendations for the use of treatment as prevention in serodiscordant couples. The extent to which these findings are generalizable to other key populations or to groups exposed to HIV through nonsexual transmission routes (i.e., anal intercourse or unsafe injection of drugs) has since driven a large body of research. This review explores the history of HIV research in serodiscordant couples, the implications for management of couples, subsequent research on treatment as prevention in other key populations, and challenges in community implementation of these strategies.

Comparison of cluster-based and source-attribution methods for estimating transmission risk using large HIV sequence databases

Phylogenetic clustering of HIV sequences from a random sample of patients can reveal epidemiological transmission patterns, but interpretation is hampered by limited theoretical support and statistical properties of clustering analysis remain poorly understood. Alternatively, source attribution methods allow fitting of HIV transmission models and thereby quantify aspects of disease transmission. A simulation study was conducted to assess error rates of clustering methods for detecting transmission risk factors. We modeled HIV epidemics among men having sex with men and generated phylogenies comparable to those that can be obtained from HIV surveillance data in the UK. Clustering and source attribution approaches were applied to evaluate their ability to identify patient attributes as transmission risk factors. We find that commonly used methods show a misleading association between cluster size or odds of clustering and covariates that are correlated with time since infection, regardless of their influence on transmission. Clustering methods usually have higher error rates and lower sensitivity than source attribution method for identifying transmission risk factors. But neither methods provide robust estimates of transmission risk ratios. Source attribution method can alleviate drawbacks from phylogenetic clustering but formal population genetic modeling may be required to estimate quantitative transmission risk factors.

Phylodynamic analysis to inform prevention efforts in mixed HIV epidemics

In HIV epidemics of Sub Saharan Africa, the utility of HIV prevention efforts focused on key populations at higher risk of HIV infection and transmission is unclear. We conducted a phylodynamic analysis of HIV-1 pol sequences from four different risk groups in Abuja, Nigeria to estimate transmission patterns between men who have sex with men (MSM) and a representative sample of newly enrolled treatment naive HIV clients without clearly recorded HIV acquisition risks. We develop a realistic dynamical infectious disease model which was fitted to time-scaled phylogenies for subtypes G and CRF02_AG using a structured-coalescent approach. We compare the infectious disease model and structured coalescent to commonly used genetic clustering methods. We estimate HIV incidence among MSM of 7.9% (95%CI, 7.0-10.4) per susceptible person-year, and the population attributable fraction of HIV transmissions from MSM to reproductive age females to be 9.1% (95%CI, 3.8-18.6), and from the reproductive age women to MSM as 0.2% (95%CI, 0.06-0.3). Applying these parameter estimates to evaluate a test-and-treat HIV strategy that target MSM reduces the total HIV infections averted by half with a 2.5-fold saving. These results suggest the importance of addressing the HIV treatment needs of MSM in addition to cost-effectiveness of specific scale-up of treatment for MSM in the context of the mixed HIV epidemic observed in Nigeria.

Viral genetic variation accounts for a third of variability in HIV-1 set-point viral load in Europe

HIV-1 set-point viral load-the approximately stable value of viraemia in the first years of chronic infection-is a strong predictor of clinical outcome and is highly variable across infected individuals. To better understand HIV-1 pathogenesis and the evolution of the viral population, we must quantify the heritability of set-point viral load, which is the fraction of variation in this phenotype attributable to viral genetic variation. However, current estimates of heritability vary widely, from 6% to 59%. Here we used a dataset of 2,028 seroconverters infected between 1985 and 2013 from 5 European countries (Belgium, Switzerland, France, the Netherlands and the United Kingdom) and estimated the heritability of set-point viral load at 31% (CI 15%-43%). Specifically, heritability was measured using models of character evolution describing how viral load evolves on the phylogeny of whole-genome viral sequences. In contrast to previous studies, (i) we measured viral loads using standardized assays on a sample collected in a strict time window of 6 to 24 months after infection, from which the viral genome was also sequenced; (ii) we compared 2 models of character evolution, the classical "Brownian motion" model and another model ("Ornstein-Uhlenbeck") that includes stabilising selection on viral load; (iii) we controlled for covariates, including age and sex, which may inflate estimates of heritability; and (iv) we developed a goodness of fit test based on the correlation of viral loads in cherries of the phylogenetic tree, showing that both models of character evolution fit the data well. An overall heritability of 31% (CI 15%-43%) is consistent with other studies based on regression of viral load in donor-recipient pairs. Thus, about a third of variation in HIV-1 virulence is attributable to viral genetic variation.

A high rate of HIV-1 acquisition post immigration among migrants in Sweden determined by a CD4 T-cell decline trajectory model

OBJECTIVES

There is a lack of knowledge about the extent to which migrants become HIV-1 infected after arrival in European countries. The objective of this study was to assess the extent to which migrants to Sweden become HIV-1 infected post immigration using a CD4 T-cell decline trajectory model.

METHODS

All migrants (n = 2268) who were ≥ 15 years old, were diagnosed with HIV-1 infection in the period 1983-2013, had a known year of arrival in Sweden, did not have primary HIV infection and were not infected via mother-to-child transmission were included in the study. The CD4 T-cell decline trajectory model was applied and estimates of HIV acquisition were compared to the clinical reports. Phylogenetic analysis was performed in a subset of patients to explore whether this would favour the model or the doctor's estimate.

RESULTS

The model estimated 19% of individuals to have been infected after arrival in Sweden, whereas the physician's estimate was 12%. In 79% of cases the estimates agreed. Discordance was predominantly seen when the doctor estimated HIV acquisition to have occurred before arrival in Sweden, while the model estimated it to have occurred after arrival in Sweden, and this type of discordance was seen in 10% of all patients. The probability of a discordance was greater for older patients, those with a high first CD4 T-cell count and those infected via heterosexual transmission. The phylogenetic analysis showed a higher concordance with the CD4 model than with the clinical reports (36 vs. 13%, respectively).

CONCLUSIONS

The model indicated that a substantially higher proportion of migrants are infected after arrival in Sweden than estimated using clinical routine reports. It is therefore important to further emphasize primary preventive measures among migrants who have established themselves in their new country.

Mining for pairs: shared clinic visit dates identify steady HIV-positive partnerships

OBJECTIVES

Here we examined the hypothesis that some stable HIV-infected partnerships can be found in cohort studies, as the patients frequently attend the clinic visits together.

METHODS

Using mathematical approximations and shuffling to derive the probabilities of sharing a given number of visits by chance, we identified and validated couples that may represent either transmission pairs or serosorting couples in a stable relationship.

RESULTS

We analysed 434 432 visits for 16 139 Swiss HIV Cohort Study patients from 1990 to 2014. For 89 pairs, the number of shared visits exceeded the number expected. Of these, 33 transmission pairs were confirmed on the basis of three criteria: an extensive phylogenetic tree, a self-reported steady HIV-positive partnership, and risk group affiliation. Notably, 12 of the validated transmission pairs (36%; 12 of 33) were of a mixed ethnicity with a large median age gap [17.5 years; interquartile range (IQR) 11.8-22 years] and these patients harboured HIV-1 of predominantly non-B subtypes, suggesting imported infections.

CONCLUSIONS

In the context of the surge in research interest in HIV transmission pairs, this simple method widens the horizons of research on within-pair quasi-species exchange, transmitted drug resistance and viral recombination at the biological level and targeted prevention at the public health level.

Transmission networks and risk of HIV infection in KwaZulu-Natal, South Africa: a community-wide phylogenetic study

BACKGROUND

The incidence of HIV infection in young women in Africa is very high. We did a large-scale community-wide phylogenetic study to examine the underlying HIV transmission dynamics and the source and consequences of high rates of HIV infection in young women in South Africa.

METHODS

We did a cross-sectional household survey of randomly selected individuals aged 15-49 years in two neighbouring subdistricts (one urban and one rural) with a high burden of HIV infection in KwaZulu-Natal, South Africa. Participants completed structured questionnaires that captured general demographic, socioeconomic, psychosocial, and behavioural data. Peripheral blood samples were obtained for HIV antibody testing. Samples with HIV RNA viral load greater than 1000 copies per mL were selected for genotyping. We constructed a phylogenetic tree to identify clusters of linked infections (defined as two or more sequences with bootstrap or posterior support ≥90% and genetic distance ≤4·5%).

FINDINGS

From June 11, 2014, to June 22, 2015, we enrolled 9812 participants, 3969 of whom tested HIV positive. HIV prevalence (weighted) was 59·8% in 2835 women aged 25-40 years, 40·3% in 1548 men aged 25-40 years, 22·3% in 2224 women younger than 25 years, and 7·6% in 1472 men younger than 25 years. HIV genotyping was done in 1589 individuals with a viral load of more than 1000 copies per mL. In 90 transmission clusters, 123 women were linked to 103 men. Of 60 possible phylogenetically linked pairings with the 43 women younger than 25 years, 18 (30·0%) probable male partners were younger than 25 years, 37 (61·7%) were aged 25-40 years, and five (8·3%) were aged 41-49 years: mean age difference 8·7 years (95% CI 6·8-10·6; p<0·0001). For the 92 possible phylogenetically linked pairings with the 56 women aged 25-40 years, the age difference dropped to 1·1 years (95% CI -0·6 to 2·8; p=0·111). 16 (39·0%) of 41 probable male partners linked to women younger than 25 years were also linked to women aged 25-40 years. Of 79 men (mean age 31·5 years) linked to women younger than 40 years, 62 (78·5%) were unaware of their HIV-positive status, 76 (96·2%) were not on antiretroviral therapy, and 29 (36·7%) had viral loads of more than 50 000 copies per mL.

INTERPRETATION

Sexual partnering between young women and older men, who might have acquired HIV from women of similar age, is a key feature of the sexual networks driving transmission. Expansion of treatment and combination prevention strategies that include interventions to address age-disparate sexual partnering is crucial to reducing HIV incidence and enabling Africa to reach the goal of ending AIDS as a public health threat.

FUNDING

President's Emergency Program for AIDS Relief, US Centers for Disease Control and Prevention, South African Medical Research Council, and MAC AIDS Fund.

SCOTTI: Efficient Reconstruction of Transmission within Outbreaks with the Structured Coalescent

Exploiting pathogen genomes to reconstruct transmission represents a powerful tool in the fight against infectious disease. However, their interpretation rests on a number of simplifying assumptions that regularly ignore important complexities of real data, in particular within-host evolution and non-sampled patients. Here we propose a new approach to transmission inference called SCOTTI (Structured COalescent Transmission Tree Inference). This method is based on a statistical framework that models each host as a distinct population, and transmissions between hosts as migration events. Our computationally efficient implementation of this model enables the inference of host-to-host transmission while accommodating within-host evolution and non-sampled hosts. SCOTTI is distributed as an open source package for the phylogenetic software BEAST2. We show that SCOTTI can generally infer transmission events even in the presence of considerable within-host variation, can account for the uncertainty associated with the possible presence of non-sampled hosts, and can efficiently use data from multiple samples of the same host, although there is some reduction in accuracy when samples are collected very close to the infection time. We illustrate the features of our approach by investigating transmission from genetic and epidemiological data in a Foot and Mouth Disease Virus (FMDV) veterinary outbreak in England and a Klebsiella pneumoniae outbreak in a Nepali neonatal unit. Transmission histories inferred with SCOTTI will be important in devising effective measures to prevent and halt transmission.

We present a new tool, SCOTTI, to efficiently reconstruct transmission events within outbreaks. Our approach combines genetic information from infection samples with epidemiological information of patient exposure to infection. While epidemiological information has been traditionally used to understand who infected whom in an outbreak, detailed genetic information is increasingly becoming available with the steady progress of sequencing technologies. However, many complications, if unaccounted for, can affect the accuracy with which the transmission history is reconstructed. SCOTTI efficiently accounts for several complications, in particular within-patient genetic variation of the infectious organism, and non-sampled patients (such as asymptomatic patients). Thanks to these features, SCOTTI provides accurate reconstructions of transmission in complex scenarios, which will be important in finding and limiting the sources and routes of transmission, preventing the spread of infectious disease.

Connecting the dots: network data and models in HIV epidemiology

Effective HIV prevention requires knowledge of the structure and dynamics of the social networks across which infections are transmitted. These networks most commonly comprise chains of sexual relationships, but in some populations, sharing of contaminated needles is also an important, or even the main mechanism that connects people in the network. Whereas network data have long been collected during survey interviews, new data sources have become increasingly common in recent years, because of advances in molecular biology and the use of partner notification services in HIV prevention and treatment programmes. We review current and emerging methods for collecting HIV-related network data, as well as modelling frameworks commonly used to infer network parameters and map potential HIV transmission pathways within the network. We discuss the relative strengths and weaknesses of existing methods and models, and we propose a research agenda for advancing network analysis in HIV epidemiology. We make the case for a combination approach that integrates multiple data sources into a coherent statistical framework.

Using genomics data to reconstruct transmission trees during disease outbreaks

Genetic sequence data from pathogens present a novel means to investigate the spread of infectious disease between infected hosts or infected premises, complementing traditional contact-tracing approaches, and much recent work has gone into developing methods for this purpose. The objective is to recover the epidemic transmission tree, which identifies who infected whom. This paper reviews the various approaches that have been taken. The first step is to define a measure of difference between sequences, which must be done while taking into account such factors as recombination and convergent evolution. Three broad categories of method exist, of increasing complexity: those that assume no withinhost genetic diversity or mutation, those that assume no within-host diversity but allow mutation, and those that allow both. Until recently, the assumption was usually made that every host in the epidemic could be identified, but this is now being relaxed, and some methods are intended for sparsely sampled data, concentrating on the identification of pairs of sequences that are likely to be the result of direct transmission rather than inferring the complete transmission tree. Many of the procedures described here are available to researchers as free software.

Genomics reveals historic and contemporary transmission dynamics of a bacterial disease among wildlife and livestock

Whole-genome sequencing has provided fundamental insights into infectious disease epidemiology, but has rarely been used for examining transmission dynamics of a bacterial pathogen in wildlife. In the Greater Yellowstone Ecosystem (GYE), outbreaks of brucellosis have increased in cattle along with rising seroprevalence in elk. Here we use a genomic approach to examine Brucella abortus evolution, cross-species transmission and spatial spread in the GYE. We find that brucellosis was introduced into wildlife in this region at least five times. The diffusion rate varies among Brucella lineages (∼3 to 8 km per year) and over time. We also estimate 12 host transitions from bison to elk, and 5 from elk to bison. Our results support the notion that free-ranging elk are currently a self-sustaining brucellosis reservoir and the source of livestock infections, and that control measures in bison are unlikely to affect the dynamics of unrelated strains circulating in nearby elk populations.

ORBiT: Oak Ridge biosurveillance toolkit for public health dynamics

Background

The digitization of health-related information through electronic health records (EHR) and electronic healthcare reimbursement claims and the continued growth of self-reported health information through social media provides both tremendous opportunities and challenges in developing effective biosurveillance tools. With novel emerging infectious diseases being reported across different parts of the world, there is a need to build systems that can track, monitor and report such events in a timely manner. Further, it is also important to identify susceptible geographic regions and populations where emerging diseases may have a significant impact.

Methods

In this paper, we present an overview of Oak Ridge Biosurveillance Toolkit (ORBiT), which we have developed specifically to address data analytic challenges in the realm of public health surveillance. In particular, ORBiT provides an extensible environment to pull together diverse, large-scale datasets and analyze them to identify spatial and temporal patterns for various biosurveillance-related tasks.

Results

We demonstrate the utility of ORBiT in automatically extracting a small number of spatial and temporal patterns during the 2009-2010 pandemic H1N1 flu season using claims data. These patterns provide quantitative insights into the dynamics of how the pandemic flu spread across different parts of the country. We discovered that the claims data exhibits multi-scale patterns from which we could identify a small number of states in the United States (US) that act as "bridge regions" contributing to one or more specific influenza spread patterns. Similar to previous studies, the patterns show that the south-eastern regions of the US were widely affected by the H1N1 flu pandemic. Several of these south-eastern states act as bridge regions, which connect the north-east and central US in terms of flu occurrences.

Conclusions

These quantitative insights show how the claims data combined with novel analytical techniques can provide important information to decision makers when an epidemic spreads throughout the country. Taken together ORBiT provides a scalable and extensible platform for public health surveillance.

Bayesian inference of sampled ancestor trees for epidemiology and fossil calibration

Phylogenetic analyses which include fossils or molecular sequences that are sampled through time require models that allow one sample to be a direct ancestor of another sample. As previously available phylogenetic inference tools assume that all samples are tips, they do not allow for this possibility. We have developed and implemented a Bayesian Markov Chain Monte Carlo (MCMC) algorithm to infer what we call sampled ancestor trees, that is, trees in which sampled individuals can be direct ancestors of other sampled individuals. We use a family of birth-death models where individuals may remain in the tree process after sampling, in particular we extend the birth-death skyline model [Stadler et al., 2013] to sampled ancestor trees. This method allows the detection of sampled ancestors as well as estimation of the probability that an individual will be removed from the process when it is sampled. We show that even if sampled ancestors are not of specific interest in an analysis, failing to account for them leads to significant bias in parameter estimates. We also show that sampled ancestor birth-death models where every sample comes from a different time point are non-identifiable and thus require one parameter to be known in order to infer other parameters. We apply our phylogenetic inference accounting for sampled ancestors to epidemiological data, where the possibility of sampled ancestors enables us to identify individuals that infected other individuals after being sampled and to infer fundamental epidemiological parameters. We also apply the method to infer divergence times and diversification rates when fossils are included along with extant species samples, so that fossilisation events are modelled as a part of the tree branching process. Such modelling has many advantages as argued in the literature. The sampler is available as an open-source BEAST2 package (https://github.com/CompEvol/sampled-ancestors).

A central goal of phylogenetic analysis is to estimate evolutionary relationships and the dynamical parameters underlying the evolutionary branching process (e.g. macroevolutionary or epidemiological parameters) from molecular data. The statistical methods used in these analyses require that the underlying tree branching process is specified. Standard models for the branching process which were originally designed to describe the evolutionary past of present day species do not allow one sampled taxon to be the ancestor of another. However the probability of sampling a direct ancestor is not negligible for many types of data. For example, when fossil and living species are analysed together to infer species divergence times, fossil species may or may not be direct ancestors of living species. In epidemiology, a sampled individual (a host from which a pathogen sequence was obtained) can infect other individuals after sampling, which then go on to be sampled themselves. The models that account for direct ancestors produce phylogenetic trees with a different structure from classic phylogenetic trees and so using these models in inference requires new computational methods. Here we developed a method for phylogenetic analysis that accounts for the possibility of direct ancestors.