Simple epidemiological dynamics explain phylogenetic clustering of HIV from patients with recent infection

Modeling homophily in dynamic networks with application to HIV molecular surveillance

BACKGROUND

Efforts to control the HIV epidemic can benefit from knowledge of the relationships between the characteristics of people who have transmitted HIV and those who became infected by them. Investigation of this relationship is facilitated by the use of HIV genetic linkage analyses, which allows inference about possible transmission events among people with HIV infection. Two persons with HIV (PWH) are considered linked if the genetic distance between their HIV sequences is less than a given threshold, which implies proximity in a transmission network. The tendency of pairs of nodes (in our case PWH) that share (or differ in) certain attributes to be linked is denoted homophily. Below, we describe a novel approach to modeling homophily with application to analyses of HIV viral genetic sequences from clinical series of participants followed in San Diego. Over the 22-year period of follow-up, increases in cluster size results from HIV transmissions to new people from those already in the cluster-either directly or through intermediaries.

METHODS

Our analytical approach makes use of a logistic model to describe homophily with regard to demographic, clinical, and behavioral characteristics-that is we investigate whether similarities (or differences) between PWH in these characteristics are associated with their sequences being linked. To investigate the performance of our methods, we conducted on a simulation study for which data sets were generated in a way that reproduced the structure of the observed database.

RESULTS

Our results demonstrated strong positive homophily associated with hispanic ethnicity, and strong negative homophily, with birth year difference. The second result implies that the larger the difference between the age of a newly-infected PWH and the average age for an available cluster, the lower the odds of a newly infected person joining that cluster. We did not observe homophily associated with prior diagnosis of sexually transmitted diseases. Our simulation studies demonstrated the validity of our approach for modeling homophily, by showing that the estimates it produced matched the specified values of the statistical network generating model.

CONCLUSIONS

Our novel methods provide a simple and flexible statistical network-based approach for modeling the growth of viral (or other microbial) genetic clusters from linkage to new infections based on genetic distance.

Predictors of HIV Molecular Cluster Membership and Implications for Partner Services

Public health surveillance data used in HIV molecular cluster analyses lack contextual information that is available from partner services (PS) data. Integrating these data sources in retrospective analyses can enrich understanding of the risk profile of people in clusters. In this study, HIV molecular clusters were identified and matched to information on partners and other information gleaned at the time of diagnosis, including coinfection with syphilis. We aimed to produce a more complete understanding of molecular cluster membership in Houston, Texas, a city ranking ninth nationally in rate of new HIV diagnoses that may benefit from retrospective matched analyses between molecular and PS data to inform future intervention. Data from PS were matched to molecular HIV records of people newly diagnosed from 2012 to 2018. By conducting analyses in HIV-TRACE (TRAnsmission Cluster Engine) using viral genetic sequences, molecular clusters were detected. Multivariable logistic regression models were used to estimate the association between molecular cluster membership and completion of a PS interview, number of named partners, and syphilis coinfection. Using data from 4,035 people who had a viral genetic sequence and matched PS records, molecular cluster membership was not significantly associated with completion of a PS interview. Among those with sequences who completed a PS interview (n = 3,869), 45.3% (n = 1,753) clustered. Molecular cluster membership was significantly associated with naming 1 or 3+ partners compared with not naming any partners [adjusted odds ratio, aOR: 1.27 (95% confidence interval, CI: 1.08-1.50), p = .003 and aOR: 1.38 (95% CI: 1.06-1.81), p = .02]. Alone, coinfection with syphilis was not significantly associated with molecular cluster membership. Syphilis coinfection was associated with molecular cluster membership when coupled with incarceration [aOR: 1.91 (95% CI: 1.08-3.38), p = .03], a risk for treatment interruption. Enhanced intervention among those with similar profiles, such as people coinfected with other risks, may be warranted.

Application of Phylodynamic Tools to Inform the Public Health Response to COVID-19: Qualitative Analysis of Expert Opinions

BACKGROUND

In the wake of the SARS-CoV-2 pandemic, scientists have scrambled to collect and analyze SARS-CoV-2 genomic data to inform public health responses to COVID-19 in real time. Open source phylogenetic and data visualization platforms for monitoring SARS-CoV-2 genomic epidemiology have rapidly gained popularity for their ability to illuminate spatial-temporal transmission patterns worldwide. However, the utility of such tools to inform public health decision-making for COVID-19 in real time remains to be explored.

OBJECTIVE

The aim of this study is to convene experts in public health, infectious diseases, virology, and bioinformatics-many of whom were actively engaged in the COVID-19 response-to discuss and report on the application of phylodynamic tools to inform pandemic responses.

METHODS

In total, 4 focus groups (FGs) occurred between June 2020 and June 2021, covering both the pre- and postvariant strain emergence and vaccination eras of the ongoing COVID-19 crisis. Participants included national and international academic and government researchers, clinicians, public health practitioners, and other stakeholders recruited through purposive and convenience sampling by the study team. Open-ended questions were developed to prompt discussion. FGs I and II concentrated on phylodynamics for the public health practitioner, while FGs III and IV discussed the methodological nuances of phylodynamic inference. Two FGs per topic area to increase data saturation. An iterative, thematic qualitative framework was used for data analysis.

RESULTS

We invited 41 experts to the FGs, and 23 (56%) agreed to participate. Across all the FG sessions, 15 (65%) of the participants were female, 17 (74%) were White, and 5 (22%) were Black. Participants were described as molecular epidemiologists (MEs; n=9, 39%), clinician-researchers (n=3, 13%), infectious disease experts (IDs; n=4, 17%), and public health professionals at the local (PHs; n=4, 17%), state (n=2, 9%), and federal (n=1, 4%) levels. They represented multiple countries in Europe, the United States, and the Caribbean. Nine major themes arose from the discussions: (1) translational/implementation science, (2) precision public health, (3) fundamental unknowns, (4) proper scientific communication, (5) methods of epidemiological investigation, (6) sampling bias, (7) interoperability standards, (8) academic/public health partnerships, and (9) resources. Collectively, participants felt that successful uptake of phylodynamic tools to inform the public health response relies on the strength of academic and public health partnerships. They called for interoperability standards in sequence data sharing, urged careful reporting to prevent misinterpretations, imagined that public health responses could be tailored to specific variants, and cited resource issues that would need to be addressed by policy makers in future outbreaks.

CONCLUSIONS

This study is the first to detail the viewpoints of public health practitioners and molecular epidemiology experts on the use of viral genomic data to inform the response to the COVID-19 pandemic. The data gathered during this study provide important information from experts to help streamline the functionality and use of phylodynamic tools for pandemic responses.

Optimized phylogenetic clustering of HIV-1 sequence data for public health applications

Clusters of genetically similar infections suggest rapid transmission and may indicate priorities for public health action or reveal underlying epidemiological processes. However, clusters often require user-defined thresholds and are sensitive to non-epidemiological factors, such as non-random sampling. Consequently the ideal threshold for public health applications varies substantially across settings. Here, we show a method which selects optimal thresholds for phylogenetic (subset tree) clustering based on population. We evaluated this method on HIV-1 pol datasets (n = 14, 221 sequences) from four sites in USA (Tennessee, Washington), Canada (Northern Alberta) and China (Beijing). Clusters were defined by tips descending from an ancestral node (with a minimum bootstrap support of 95%) through a series of branches, each with a length below a given threshold. Next, we used pplacer to graft new cases to the fixed tree by maximum likelihood. We evaluated the effect of varying branch-length thresholds on cluster growth as a count outcome by fitting two Poisson regression models: a null model that predicts growth from cluster size, and an alternative model that includes mean collection date as an additional covariate. The alternative model was favoured by AIC across most thresholds, with optimal (greatest difference in AIC) thresholds ranging 0.007-0.013 across sites. The range of optimal thresholds was more variable when re-sampling 80% of the data by location (IQR 0.008 - 0.016, n = 100 replicates). Our results use prospective phylogenetic cluster growth and suggest that there is more variation in effective thresholds for public health than those typically used in clustering studies.

Molecular Epidemiology of Individuals Experiencing Unstable Housing or Living Homeless at HIV Diagnosis: Analysis of HIV Surveillance Data in King County, Washington

We examined patterns of genetic clustering among individuals diagnosed with HIV between 2010 and 2018 using data from King County, Washington's National HIV Surveillance System. Among 2,371 individuals newly diagnosed with HIV, 231 (10%) experienced unstable housing or were living homeless at the time of diagnosis. Among the 1,658 (70%) people with an available HIV-1 pol gene sequence, 1,071 (65%) were identified to be part of 296 genetic clusters. In our analysis, housing status was not associated with genetic clustering (OR 1.02; 95%CI:0.75,1.39). After adjusting for demographic and behavioral factors, people who were living homeless at HIV diagnosis had 35% lower odds of being identified as part of a genetic cluster (AOR 0.65; 95%CI:0.44,0.95) compared to people with stable housing. Our findings highlight that people experiencing unstable housing are disproportionately burdened by HIV, and that within this population in King County, being in a genetic cluster is predominantly associated with substance use.

Sorting by Race/Ethnicity Across HIV Genetic Transmission Networks in Three Major Metropolitan Areas in the United States

An important component underlying the disparity in HIV risk between race/ethnic groups is the preferential transmission between individuals in the same group. We sought to quantify transmission between different race/ethnicity groups and measure racial assortativity in HIV transmission networks in major metropolitan areas in the United States. We reconstructed HIV molecular transmission networks from viral sequences collected as part of HIV surveillance in New York City, Los Angeles County, and Cook County, Illinois. We calculated assortativity (the tendency for individuals to link to others with similar characteristics) across the network for three candidate characteristics: transmission risk, age at diagnosis, and race/ethnicity. We then compared assortativity between race/ethnicity groups. Finally, for each race/ethnicity pair, we performed network permutations to test whether the number of links observed differed from that expected if individuals were sorting at random. Transmission networks in all three jurisdictions were more assortative by race/ethnicity than by transmission risk or age at diagnosis. Despite the different race/ethnicity proportions in each metropolitan area and lower proportions of clustering among African Americans than other race/ethnicities, African Americans were the group most likely to have transmission partners of the same race/ethnicity. This high level of assortativity should be considered in the design of HIV intervention and prevention strategies.

Scale-Free Spanning Trees and Their Application in Genomic Epidemiology

We study the algorithmic problem of finding the most “scale-free-like” spanning tree of a connected graph. This problem is motivated by the fundamental problem of genomic epidemiology: given viral genomes sampled from infected individuals, reconstruct the transmission network (“who infected whom”). We use two possible objective functions for this problem and introduce the corresponding algorithmic problems termedm-SF (-scale free) ands-SF Spanning Tree problems. We prove that those problems are APX- and NP-hard, respectively, even in the classes of cubic and bipartite graphs. We propose two integer linear programming (ILP) formulations for thes-SF Spanning Tree problem, and experimentally assess its performance using simulated and experimental data. In particular, we demonstrate that the ILP-based approach allows for accurate reconstruction of transmission histories of several hepatitis C outbreaks.

Longitudinal typing of molecular HIV clusters in a statewide epidemic

BACKGROUND

HIV molecular epidemiology is increasingly integrated into public health prevention. We conducted cluster typing to enhance characterization of a densely sampled statewide epidemic towards informing public health.

METHODS

We identified HIV clusters, categorized them into types, and evaluated their dynamics between 2004 and 2019 in Rhode Island. We grouped sequences by diagnosis year, assessed cluster changes between paired phylogenies, t0 and t1, representing adjacent years and categorized clusters as stable (cluster in t0 phylogeny = cluster in t1 phylogeny) or unstable (cluster in t0 ≠ cluster in t1). Unstable clusters were further categorized as emerging (t1 phylogeny only) or growing (larger in t1 phylogeny). We determined proportions of each cluster type, of individuals in each cluster type, and of newly diagnosed individuals in each cluster type, and assessed trends over time.

RESULTS

A total of 1727 individuals with available HIV-1 subtype B pol sequences were diagnosed in Rhode Island by 2019. Over time, stable clusters and individuals in them dominated the epidemic, increasing over time, with reciprocally decreasing unstable clusters and individuals in them. Conversely, proportions of newly diagnosed individuals in unstable clusters significantly increased. Within unstable clusters, proportions of emerging clusters and of individuals in them declined; whereas proportions of newly diagnosed individuals in growing clusters significantly increased over time.

CONCLUSION

Distinct molecular cluster types were identified in the Rhode Island epidemic. Cluster dynamics demonstrated increasing stable and decreasing unstable clusters driven by growing, rather than emerging clusters, suggesting consistent in-state transmission networks. Cluster typing could inform public health beyond conventional approaches and direct interventions.

HIV genetic diversity informs stage of HIV-1 infection among patients receiving antiretroviral therapy in Botswana

Background

Human immunodeficiency virus (HIV)-1 genetic diversity increases during infection and can help infer the time elapsed since infection. However, the effect of antiretroviral treatment (ART) on the inference remains unknown.

Methods

Participants with estimated duration of HIV-1 infection based on repeated testing were sourced from cohorts in Botswana (n = 1944). Full-length HIV genome sequencing was performed from proviral deoxyribonucleic acid. We optimized a machine learning model to classify infections as < or >1 year based on viral genetic diversity, demographic, and clinical data.

Results

The best predictive model included variables for genetic diversity of HIV-1 gag, pol, and env, viral load, age, sex, and ART status. Most participants were on ART. Balanced accuracy was 90.6% (95% confidence interval, 86.7%–94.1%). We tested the algorithm among newly diagnosed participants with or without documented negative HIV tests. Among those without records, those who self-reported a negative HIV test within <1 year were more frequently classified as recent than those who reported a test >1 year previously. There was no difference in classification between those self-reporting a negative HIV test <1 year, whether or not they had a record.

Conclusions

These results indicate that recency of HIV-1 infection can be inferred from viral sequence diversity even among patients on suppressive ART.

Combining traditional and molecular epidemiology methods to quantify local HIV transmission among foreign-born residents

OBJECTIVES

We evaluated the ability for molecular epidemiology to augment traditional HIV surveillance beyond the detection of clusters for outbreak investigation. To do this, we address a question of interest to Public Health - Seattle and King County: what proportion of HIV diagnoses among people born outside of the United States are acquired locally?

DESIGN

King County residents diagnosed with HIV, 2010-2018.

METHODS

We linked HIV-1 pol gene sequences to demographic information obtained from the National HIV Surveillance System, Public Health - Seattle and King County case investigation and partner services interviews. We determined the likely location of HIV acquisition based on HIV testing, travel histories and cluster-based molecular analyses.

RESULTS

Among 2409 people diagnosed with HIV, 798 (33%) were born outside of the United States. We inferred the location of acquisition for 77% of people born outside of the United States: 26% likely acquired HIV locally in King County (of whom 69% were MSM, 16% heterosexual), and 51% likely acquired HIV outside of King County (primarily outside of the United States). Of this 77% of people for whom we inferred the location of HIV acquisition, 45% were determined using traditional epidemiology methods and an additional 32% were inferred using molecular epidemiology methods.

CONCLUSION

We found that the National HIV Surveillance System misclassified the majority of HIV-infected foreign-born residents as 'new' local infections, and that these cases contribute to an overestimate of local incidence. Our findings highlight how molecular epidemiology can augment traditional HIV surveillance activities and provide useful information to local health jurisdictions beyond molecular cluster detection.

Factors Associated With Human Immunodeficiency Virus Infections Linked in Genetic Clusters But Disconnected in Partner Tracing

BACKGROUND

Successful partner notification can improve community-level outcomes by increasing the proportion of persons living with human immunodeficiency virus (HIV) who are linked to HIV care and virally suppressed, but it is resource intensive. Understanding where HIV transmission pathways may be undetected by routine partner notification may help improve case finding strategies.

METHODS

We combined partner notification interview and HIV sequence data for persons diagnosed with HIV in Wake County, NC in 2012 to 2013 to evaluate partner contact networks among persons with HIV pol gene sequences 2% or less pairwise genetic distance. We applied a set of multivariable generalized estimating equations to identify correlates of disparate membership in genetic versus partner contact networks.

RESULTS

In the multivariable model, being in a male-male pair (adjusted odds ratio [AOR], 16.7; P = 0.01), chronic HIV infection status (AOR, 4.5; P < 0.01), and increasing percent genetic distance between each dyad member's HIV pol gene sequence (AOR, 8.3 per each 1% increase, P < 0.01) were all associated with persons with HIV clustering but not being identified in the partner notification network component. Having anonymous partners or other factors typically associated with risk behavior were not associated.

CONCLUSIONS

Based on genetic networks, partnerships which may be stigmatized, may have occurred farther back in time or may have an intervening partner were more likely to be unobserved in the partner contact network. The HIV genetic cluster information contributes to public health understanding of HIV transmission networks in these settings where partner identifying information is not available.

The Relationship Between the Human Immunodeficiency Virus-1 Transmission Network and the HIV Care Continuum in Los Angeles County

BACKGROUND

Public health action combating human immunodeficiency virus (HIV) includes facilitating navigation through the HIV continuum of care: timely diagnosis followed by linkage to care and initiation of antiretroviral therapy to suppress viral replication. Molecular epidemiology can identify rapidly growing HIV genetic transmission clusters. How progression through the care continuum relates to transmission clusters has not been previously characterized.

METHODS

We performed a retrospective study on HIV surveillance data from 5226 adult cases in Los Angeles County diagnosed from 2010 through 2014. Genetic transmission clusters were constructed using HIV-TRACE. Cox proportional hazard models were used to estimate the impact of transmission cluster growth on the time intervals between care continuum events. Gamma frailty models incorporated the effect of heterogeneity associated with genetic transmission clusters.

RESULTS

In contrast to our expectations, there were no differences in time to the care continuum events among individuals in clusters with different growth dynamics. However, upon achieving viral suppression, individuals in high growth clusters were slower to experience viral rebound (hazard ratio 0.83, P = .011) compared with individuals in low growth clusters. Heterogeneity associated with cluster membership in the timing to each event in the care continuum was highly significant (P < .001), with and without adjustment for transmission risk and demographics.

CONCLUSIONS

Individuals within the same transmission cluster have more similar trajectories through the HIV care continuum than those across transmission clusters. These findings suggest molecular epidemiology can assist public health officials in identifying clusters of individuals who may benefit from assistance in navigating the HIV care continuum.

Near Real-Time Identification of Recent Human Immunodeficiency Virus Transmissions, Transmitted Drug Resistance Mutations, and Transmission Networks by Multiplexed Primer ID-Next-Generation Sequencing in North Carolina

BACKGROUND

The identification of recent human immunodeficiency virus (HIV) 1 infections among people with new HIV diagnoses is important to both tailoring and assessing the impact of HIV-1 prevention strategies.

METHODS

We developed a multiplexed Primer ID-next-generation sequencing approach to identify recent infections by measuring the intrahost viral diversity over multiple regions of the HIV-1 genome, in addition to detecting drug resistance mutations (DRMs) and phylogenetically linked clusters. We summarize the field implementation of this all-in-one platform among persons with newly diagnosed HIV-1 by the North Carolina State Laboratory of Public Health in 2018.

RESULTS

Overall, recent infection was identified in 94 (35%) of 268 patients with new HIV diagnoses. People <30 years old, and people who inject drugs were more likely to have diagnoses of recent infection. The reverse-transcriptase region K103N was the most commonly detected DRM (prevalence, approximately 15%). We found a total of 28 clusters, and persons with recent infection were more likely to be cluster members than were those with chronic infections (P = .03).

CONCLUSIONS

We demonstrate the rapid identification of recent infection and pretreatment DRMs coupled with cluster analysis that will allow prioritization of linkage to care, treatment, and prevention interventions to those at highest risk of onward transmission.

Phylodynamics Helps to Evaluate the Impact of an HIV Prevention Intervention

Assessment of the long-term population-level effects of HIV interventions is an ongoing public health challenge. Following the implementation of a Transmission Reduction Intervention Project (TRIP) in Odessa, Ukraine, in 2013-2016, we obtained HIV pol gene sequences and used phylogenetics to identify HIV transmission clusters. We further applied the birth-death skyline model to the sequences from Odessa (n = 275) and Kyiv (n = 92) in order to estimate changes in the epidemic's effective reproductive number (Re) and rate of becoming uninfectious (δ). We identified 12 transmission clusters in Odessa; phylogenetic clustering was correlated with younger age and higher average viral load at the time of sampling. Estimated Re were similar in Odessa and Kyiv before the initiation of TRIP; Re started to decline in 2013 and is now below Re = 1 in Odessa (Re = 0.4, 95%HPD 0.06-0.75), but not in Kyiv (Re = 2.3, 95%HPD 0.2-5.4). Similarly, estimates of δ increased in Odessa after the initiation of TRIP. Given that both cities shared the same HIV prevention programs in 2013-2019, apart from TRIP, the observed changes in transmission parameters are likely attributable to the TRIP intervention. We propose that molecular epidemiology analysis can be used as a post-intervention effectiveness assessment tool.

Factors influencing HIV-1 phylogenetic clustering

PURPOSE OF REVIEW

A major goal of public health in relation to HIV/AIDS is to prevent new transmissions in communities. Phylogenetic techniques have improved our understanding of the structure and dynamics of HIV transmissions. However, there is still no consensus about phylogenetic methodology, sampling coverage, gene target and/or minimum fragment size.

RECENT FINDINGS

Several studies use a combined methodology, which includes both a genetic or patristic distance cut-off and a branching support threshold to identify phylogenetic clusters. However, the choice about these thresholds remains an inherently subjective process, which affects the results of these studies. There is still a lack of consensus about the genomic region and the size of fragments that should be used, although there seems to be emerging a consensus that using longer segments, allied with the use of a realistic model of evolution and a codon alignment, increases the likelihood of inferring true transmission clusters. The pol gene is still the most used genomic region, but recent studies have suggested that whole genomes and/or sequences from nef and gp41 are also good targets for cluster reconstruction.

SUMMARY

The development and application of standard methodologies for phylogenetic clustering analysis will advance our understanding of factors associated with HIV transmission. This will lead to the design of more precise public health interventions.

Phylogenetics applied to the human immunodeficiency virus type 1 (HIV-1): from the cross-species transmissions to the contact network inferences

Phylogenetic analyses were crucial to elucidate the origin and spread of the pandemic human immunodeficiency virus type 1 (HIV-1) group M virus, both during the pre-epidemic period of cryptic dissemination in human populations as well as during the epidemic phase of spread. The use of phylogenetics and phylodynamics approaches has provided important insights to track the founder events that resulted in the spread of HIV-1 strains across vast geographic areas, specific countries and within geographically restricted communities. In the recent years, the use of phylogenetic analysis combined with the huge availability of HIV sequences has become an increasingly important approach to reconstruct HIV transmission networks and understand transmission dynamics in concentrated and generalised epidemics. Significant efforts to obtain viral sequences from newly HIV-infected individuals could certainly contribute to detect rapidly expanding HIV-1 lineages, identify key populations at high-risk and understand what public health interventions should be prioritised in different scenarios.

Pretreatment HIV drug resistance spread within transmission clusters in Mexico City

BACKGROUND

Pretreatment HIV drug resistance (HIVDR) to NNRTIs has consistently increased in Mexico City during the last decade.

OBJECTIVES

To infer the HIV genetic transmission network in Mexico City to describe the dynamics of the local HIV epidemic and spread of HIVDR.

PATIENTS AND METHODS

HIV pol sequences were obtained by next-generation sequencing from 2447 individuals before initiation of ART at the largest HIV clinic in Mexico City (April 2016 to June 2018). Pretreatment HIVDR was estimated using the Stanford algorithm at a Sanger-like threshold (≥20%). Genetic networks were inferred with HIV-TRACE, establishing putative transmission links with genetic distances <1.5%. We examined demographic associations among linked individuals with shared drug resistance mutations (DRMs) using a ≥ 2% threshold to include low-frequency variants.

RESULTS

Pretreatment HIVDR reached 14.8% (95% CI 13.4%-16.2%) in the cohort overall and 9.6% (8.5%-10.8%) to NNRTIs. Putative links with at least one other sequence were found for 963/2447 (39%) sequences, forming 326 clusters (2-20 individuals). The inferred network was assortative by age and municipality (P < 0.001). Clustering individuals were younger [adjusted OR (aOR) per year = 0.96, 95% CI 0.95-0.97, P < 0.001] and less likely to include women (aOR = 0.46, 95% CI 0.28-0.75, P = 0.002). Among clustering individuals, 175/963 (18%) shared DRMs (involving 66 clusters), of which 66/175 (38%) shared K103N/S (24 clusters). Eight municipalities (out of 75) harboured 65% of persons sharing DRMs. Among all persons sharing DRMs, those sharing K103N were younger (aOR = 0.93, 95% CI 0.88-0.98, P = 0.003).

CONCLUSIONS

Our analyses suggest age- and geographically associated transmission of DRMs within the HIV genetic network in Mexico City, warranting continuous monitoring and focused interventions.

Genetic clustering analysis for HIV infection among MSM in Nigeria: implications for intervention

BACKGROUND

The HIV epidemic continues to grow among MSM in countries across sub-Saharan Africa including Nigeria. To inform prevention efforts, we used a phylogenetic cluster method to characterize HIV genetic clusters and factors associated with cluster formation among MSM living with HIV in Nigeria.

METHODS

We analyzed HIV-1 pol sequences from 417 MSM living with HIV enrolled in the TRUST/RV368 cohort between 2013 and 2017 in Abuja and Lagos, Nigeria. A genetically linked cluster was defined among participants whose sequences had pairwise genetic distance of 1.5% or less. Binary and multinomial logistic regressions were used to estimate adjusted odds ratios (AORs) and 95% confidence intervals (CIs) for factors associated with HIV genetic cluster membership and size.

RESULTS

Among 417 MSM living with HIV, 153 (36.7%) were genetically linked. Participants with higher viral load (AOR = 1.72 95% CI: 1.04-2.86), no female partners (AOR = 3.66; 95% CI: 1.97-6.08), and self-identified as male sex (compared with self-identified as bigender) (AOR = 3.42; 95% CI: 1.08-10.78) had higher odds of being in a genetic cluster. Compared with unlinked participants, MSM who had high school education (AOR = 23.84; 95% CI: 2.66-213.49), were employed (AOR = 3.41; 95% CI: 1.89-10.70), had bacterial sexually transmitted infections (AOR = 3.98; 95% CI: 0.89-17.22) and were not taking antiretroviral therapy (AOR = 6.61; 95% CI: 2.25-19.37) had higher odds of being in a large cluster (size > 4).

CONCLUSION

Comprehensive HIV prevention packages should include behavioral and biological components, including early diagnosis and treatment of both HIV and bacterial sexually transmitted infections to optimally reduce the risk of HIV transmission and acquisition.

Phylogenetic Clustering by Linear Integer Programming (PhyCLIP)

Subspecies nomenclature systems of pathogens are increasingly based on sequence data. The use of phylogenetics to identify and differentiate between clusters of genetically similar pathogens is particularly prevalent in virology from the nomenclature of human papillomaviruses to highly pathogenic avian influenza (HPAI) H5Nx viruses. These nomenclature systems rely on absolute genetic distance thresholds to define the maximum genetic divergence tolerated between viruses designated as closely related. However, the phylogenetic clustering methods used in these nomenclature systems are limited by the arbitrariness of setting intra and intercluster diversity thresholds. The lack of a consensus ground truth to define well-delineated, meaningful phylogenetic subpopulations amplifies the difficulties in identifying an informative distance threshold. Consequently, phylogenetic clustering often becomes an exploratory, ad hoc exercise. Phylogenetic Clustering by Linear Integer Programming (PhyCLIP) was developed to provide a statistically principled phylogenetic clustering framework that negates the need for an arbitrarily defined distance threshold. Using the pairwise patristic distance distributions of an input phylogeny, PhyCLIP parameterizes the intra and intercluster divergence limits as statistical bounds in an integer linear programming model which is subsequently optimized to cluster as many sequences as possible. When applied to the hemagglutinin phylogeny of HPAI H5Nx viruses, PhyCLIP was not only able to recapitulate the current WHO/OIE/FAO H5 nomenclature system but also further delineated informative higher resolution clusters that capture geographically distinct subpopulations of viruses. PhyCLIP is pathogen-agnostic and can be generalized to a wide variety of research questions concerning the identification of biologically informative clusters in pathogen phylogenies. PhyCLIP is freely available at http://github.com/alvinxhan/PhyCLIP, last accessed March 15, 2019.

Public health in genetic spaces: a statistical framework to optimize cluster-based outbreak detection

Genetic clustering is a popular method for characterizing variation in transmission rates for rapidly evolving viruses, and could potentially be used to detect outbreaks in 'near real time'. However, the statistical properties of clustering are poorly understood in this context, and there are no objective guidelines for setting clustering criteria. Here, we develop a new statistical framework to optimize a genetic clustering method based on the ability to forecast new cases. We analysed the pairwise Tamura-Nei (TN93) genetic distances for anonymized HIV-1 subtype B pol sequences from Seattle (n = 1,653) and Middle Tennessee, USA (n = 2,779), and northern Alberta, Canada (n = 809). Under varying TN93 thresholds, we fit two models to the distributions of new cases relative to clusters of known cases: 1, a null model that assumes cluster growth is strictly proportional to cluster size, i.e. no variation in transmission rates among individuals; and 2, a weighted model that incorporates individual-level covariates, such as recency of diagnosis. The optimal threshold maximizes the difference in information loss between models, where covariates are used most effectively. Optimal TN93 thresholds varied substantially between data sets, e.g. 0.0104 in Alberta and 0.016 in Seattle and Tennessee, such that the optimum for one population would potentially misdirect prevention efforts in another. For a given population, the range of thresholds where the weighted model conferred greater predictive accuracy tended to be narrow (±0.005 units), and the optimal threshold tended to be stable over time. Our framework also indicated that variation in the recency of HIV diagnosis among clusters was significantly more predictive of new cases than sample collection dates (ΔAIC > 50). These results suggest that one cannot rely on historical precedence or convention to configure genetic clustering methods for public health applications, especially when translating methods between settings of low-level and generalized epidemics. Our framework not only enables investigators to calibrate a clustering method to a specific public health setting, but also provides a variable selection procedure to evaluate different predictive models of cluster growth.

Natural selection favoring more transmissible HIV detected in United States molecular transmission network

HIV molecular epidemiology can identify clusters of individuals with elevated rates of HIV transmission. These variable transmission rates are primarily driven by host risk behavior; however, the effect of viral traits on variable transmission rates is poorly understood. Viral load, the concentration of HIV in blood, is a heritable viral trait that influences HIV infectiousness and disease progression. Here, we reconstruct HIV genetic transmission clusters using data from the United States National HIV Surveillance System and report that viruses in clusters, inferred to be frequently transmitted, have higher viral loads at diagnosis. Further, viral load is higher in people in larger clusters and with increased network connectivity, suggesting that HIV in the United States is experiencing natural selection to be more infectious and virulent. We also observe a concurrent increase in viral load at diagnosis over the last decade. This evolutionary trajectory may be slowed by prevention strategies prioritized toward rapidly growing transmission clusters.

Exploring HIV-1 Transmission Dynamics by Combining Phylogenetic Analysis and Infection Timing

HIV-1 pol sequences obtained through baseline drug resistance testing of patients newly diagnosed between 2013 and 2017 were analyzed for genetic similarity. For 927 patients the information on genetic similarity was combined with demographic data and with information on the recency of infection. Overall, 48.3% of the patients were genetically linked with 11.4% belonging to a pair and 36.9% involved in a cluster of ≥3 members. The percentage of early diagnosed (≤4 months after infection) was 28.6%. Patients of Belgian origin were more frequently involved in transmission clusters (49.7% compared to 15.3%) and diagnosed earlier (37.4% compared to 12.2%) than patients of Sub-Saharan African origin. Of the infections reported to be locally acquired, 69.5% were linked (14.1% paired and 55.4% in a cluster). Equal parts of early and late diagnosed individuals (59.9% and 52.4%, respectively) were involved in clusters. The identification of a genetically linked individual for the majority of locally infected patients suggests a high rate of diagnosis in this population. Diagnosis however is often delayed for >4 months after infection increasing the opportunities for onward transmission. Prevention of local infection should focus on earlier diagnosis and protection of the still uninfected members of sexual networks with human immunodeficiency virus (HIV)-infected members.

HIV co-infection is associated with increased transmission risk in patients with chronic hepatitis C virus

Molecular epidemiological analysis of viral pathogens can identify factors associated with increased transmission risk. We investigated the frequency of genetic clustering in a large data set of NS34A, NS5A, and NS5B viral sequences from patients with chronic hepatitis C virus (HCV). Within a subset of patients with longitudinal samples, Receiver Operator Characteristic (ROC) analysis was applied which identified a threshold of 0.02 substitutions/site as most appropriate for clustering. From the 7457 patients with chronic HCV infection included in this analysis, we inferred 256 clusters comprising 541 patients (7.3%). We found that HCV/HIV co-infection, young age, and high HCV viral load were all associated with increased clustering frequency, an indicator of increased transmission risk. In light of previous work on HCV/HIV co-infection in acute HCV cohorts, our results suggest that patients with HCV/HIV co-infection may disproportionately be the source of new HCV infections and treatment efforts should be geared towards viral elimination in this vulnerable population.

Molecular Epidemiology of HIV-1 Subtype B Reveals Heterogeneous Transmission Risk: Implications for Intervention and Control

Background

The impact of HIV pre-exposure prophylaxis (PrEP) depends on infections averted by protecting vulnerable individuals as well as infections averted by preventing transmission by those who would have been infected if not receiving PrEP. Analysis of HIV phylogenies reveals risk factors for transmission, which we examine as potential criteria for allocating PrEP.

Methods

We analyzed 6912 HIV-1 partial pol sequences from men who have sex with men (MSM) in the United Kingdom combined with global reference sequences and patient-level metadata. Population genetic models were developed that adjust for stage of infection, global migration of HIV lineages, and changing incidence of infection through time. Models were extended to simulate the effects of providing susceptible MSM with PrEP.

Results

We found that young age <25 years confers higher risk of HIV transmission (relative risk = 2.52 [95% confidence interval, 2.32–2.73]) and that young MSM are more likely to transmit to one another than expected by chance. Simulated interventions indicate that 4-fold more infections can be averted over 5 years by focusing PrEP on young MSM.

Conclusions

Concentrating PrEP doses on young individuals can avert more infections than random allocation.

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.

Genetic Cluster Analysis for HIV Prevention

PURPOSE OF REVIEW

This review summarizes the use of genetic similarity clusters to understand HIV transmission and inform prevention efforts.

RECENT FINDINGS

Recent emphases include the development of real-time cluster identification in order to interrupt transmission chains, the use of clusters to estimate rates of transmission along the HIV care cascade, and the extension of cluster analyses to understand transmission in the generalized epidemics of sub-Saharan Africa. Importantly, this recent empirical work has been accompanied by theoretical work that elucidates the processes that underlie HIV genetic similarity clusters; multiple studies suggest that clusters are not necessarily enriched with individuals with high transmission rates, but rather can reflect variation in sampling times within a population, with individuals sampled early in infection more likely to cluster. Analyses of genetic similarity clusters have great promise to inform HIV epidemiology and prevention. Future emphases should include the collection of additional sequence data from underrepresented populations, such as those in sub-Saharan Africa, and further development and evaluation of clustering methods.

Detection of human immunodeficiency virus Type 1 phylogenetic clusters with multidrug resistance mutations among 2011 to 2017 blood donors from the highly endemic Northern Brazilian Amazon

BACKGROUND

This study describes transmitted drug resistance (TDR) in blood donors diagnosed with human immunodeficiency virus Type 1 (HIV-1) infection from 2011 to 2017 in three reference public blood centers from the Northern Brazilian Amazon.

STUDY DESIGN AND METHODS

This was a cross-sectional study on HIV-positive blood donors from HEMOAM, Manaus, Amazonas, AM (n = 198); HEMERON, Porto Velho, Rondônia, RO (n = 20); and HEMORAIMA, Boa Vista, Roraima, RR (n = 9). HIV-1 pol sequences (protease, reverse transcriptase) were analyzed for drug resistance mutations (DRMs) using the Calibrated Population Resistance tool (Stanford). TDR/DRM clusters were investigated by phylogenetic analysis after removing positions associated with drug resistance of Subtype B sequences from untreated and treated subjects from Northern Brazil.

RESULTS

Transmitted drug resistance/DRM in blood donors was 11% (25 of 227), all of them from HEMOAM. Most blood donors with TDR/DRM had multiple and similar DRMs. Nonnucleoside reverse transcriptase inhibitor (NNRTI) mutations predominated (10.1%), followed by nucleoside reverse transcriptase inhibitor (NRTI) mutations (5.3%) and protease inhibitor mutations (0.4%). Dual-class NNRTI/NRTI mutations represented 4.8%. Three highly supported Subtype B monophyletic clades mostly composed by individuals from Amazonas with TDR/DRM mutations were identified. The largest transmission cluster contained 10 sequences, eight from HEMOAM and two sequences described previously (one from a treated subject from Amazonas and the other one from Roraima). This cluster was characterized by NRTI (D67N, T69D, T215S/F/L, K219Q) and NNRTI (K101H, K103 N, G190A) mutations. The other two transmission clades comprised only three and two sequences from HEMOAM sharing the E138A NNRTI mutation.

CONCLUSIONS

The identification of transmission clusters of multidrug-resistant viruses in blood donors from Amazonas highlight the need of continued monitoring of TDR/DRM and the importance of pretreatment genotyping in the highly endemic Amazonas state.

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 transmission networks among transgender women in Los Angeles County, CA, USA: a phylogenetic analysis of surveillance data

BACKGROUND

Transgender women are among the groups at highest risk for HIV infection, with a prevalence of 27·7% in the USA; and despite this known high risk, undiagnosed infection is common in this population. We set out to identify transgender women and their partners in a molecular transmission network to prioritise public health activities.

METHODS

Since 2006, HIV protease and reverse transcriptase gene (pol) sequences from drug resistance testing have been reported to the Los Angeles County Department of Public Health and linked to demographic data, gender, and HIV transmission risk factor data for each case in the enhanced HIV/AIDS Reporting System. We reconstructed a molecular transmission network by use of HIV-TRAnsmission Cluster Engine (with a pairwise genetic distance threshold of 0·015 substitutions per site) from the earliest pol sequences from 22 398 unique individuals, including 412 (2%) self-identified transgender women. We examined the possible predictors of clustering with multivariate logistic regression. We characterised the genetically linked partners of transgender women and calculated assortativity (the tendency for people to link to other people with the same attributes) for each transmission risk group.

FINDINGS

8133 (36·3%) of 22 398 individuals clustered in the network across 1722 molecular transmission clusters. Transgender women who indicated a sexual risk factor clustered at the highest frequency in the network, with 147 (43%) of 345 being linked to at least one other person (adjusted odds ratio [aOR] 2·0, p=0·0002). Transgender women were assortative in the network (assortativity 0·06, p<0·001), indicating that they tended to link to other transgender women. Transgender women were more likely than expected to link to other transgender women (OR 4·65, p<0·001) and cisgender men who did not identify as men who have sex with men (MSM; OR 1·53, p<0·001). Transgender women were less likely than expected to link to MSM (OR 0·75, p<0·001), despite the high prevalence of HIV among MSM. Transgender women were distributed across 126 clusters, and cisgender individuals linked to one transgender woman were 9·2 times more likely to link to a second transgender woman than other individuals in the surveillance database. Reconstruction of the transmission network is limited by sample availability, but sequences were available for more than 40% of diagnoses.

INTERPRETATION

Clustering of transgender women and the observed tendency for linkage with cisgender men who did not identify as MSM, shows the potential to use molecular epidemiology both to identify clusters that are likely to include undiagnosed transgender women with HIV and to improve the targeting of public health prevention and treatment services to transgender women.

FUNDING

California HIV and AIDS Research Program and National Institutes of Health-National Institute of Allergy and Infectious Diseases.

Growth of HIV-1 Molecular Transmission Clusters in New York City

Background

HIV-1 genetic sequences can be used to infer viral transmission history and dynamics. Throughout the United States, HIV-1 sequences from drug resistance testing are reported to local public health departments.

Methods

We investigated whether inferred HIV transmission network dynamics can identify individuals and clusters of individuals most likely to give rise to future HIV cases in a surveillance setting. We used HIV-TRACE, a genetic distance-based clustering tool, to infer molecular transmission clusters from HIV-1 pro/RT sequences from 65736 people in the New York City surveillance registry. Logistic and LASSO regression analyses were used to identify correlates of clustering and cluster growth, respectively. We performed retrospective transmission network analyses to evaluate individual- and cluster-level prioritization schemes for identifying parts of the network most likely to give rise to new cases in the subsequent year.

Results

Individual-level prioritization schemes predicted network growth better than random targeting. Across the 3600 inferred molecular transmission clusters, previous growth dynamics were superior predictors of future transmission cluster growth compared to individual-level prediction schemes. Cluster-level prioritization schemes considering previous cluster growth relative to cluster size further improved network growth predictions.

Conclusions

Prevention efforts based on HIV molecular epidemiology may improve public health outcomes in a US surveillance setting.

HIV-1 Transmission Clustering and Phylodynamics Highlight the Important Role of Young Men Who Have Sex with Men

More persons living with HIV reside in the Southern United States than in any other region, yet little is known about HIV molecular epidemiology in the South. We used cluster and phylodynamic analyses to evaluate HIV transmission patterns in middle Tennessee. We performed cross-sectional analyses of HIV-1 pol sequences and clinical data collected from 2001 to 2015 among persons attending the Vanderbilt Comprehensive Care Clinic. Transmission clusters were identified using maximum likelihood phylogenetics and patristic distance differences. Demographic, risk behavior, and clinical factors were assessed evaluating “active” clusters (clusters including sequences sampled 2011–2015) and associations estimated with logistic regression. Transmission risk ratios for men who have sex with men (MSM) were estimated with phylodynamic models. Among 2915 persons (96% subtype-B sequences), 963 (33%) were members of 292 clusters (distance ≤1.5%, size range 2–39). Most clusters (62%, n = 690 persons) were active, either being newly identified (n = 80) or showing expansion on existing clusters (n = 101). Correlates of active clustering among persons with sequences collected during 2011–2015 included MSM risk and ≤30 years of age. Active clusters were significantly more concentrated in MSM and younger persons than historical clusters. Young MSM (YMSM) (≤26.4 years) had high estimated transmission risk [risk ratio = 4.04 (2.85–5.65) relative to older MSM] and were much more likely to transmit to YMSM. In this Tennessee cohort, transmission clusters over time were more concentrated by MSM and younger age, with high transmission risk among and between YMSM, highlighting the importance of interventions among this group. Detecting active clusters could help direct interventions to disrupt ongoing transmission chains.

A model-based clustering method to detect infectious disease transmission outbreaks from sequence variation

Clustering infections by genetic similarity is a popular technique for identifying potential outbreaks of infectious disease, in part because sequences are now routinely collected for clinical management of many infections. A diverse number of nonparametric clustering methods have been developed for this purpose. These methods are generally intuitive, rapid to compute, and readily scale with large data sets. However, we have found that nonparametric clustering methods can be biased towards identifying clusters of diagnosis—where individuals are sampled sooner post-infection—rather than the clusters of rapid transmission that are meant to be potential foci for public health efforts. We develop a fundamentally new approach to genetic clustering based on fitting a Markov-modulated Poisson process (MMPP), which represents the evolution of transmission rates along the tree relating different infections. We evaluated this model-based method alongside five nonparametric clustering methods using both simulated and actual HIV sequence data sets. For simulated clusters of rapid transmission, the MMPP clustering method obtained higher mean sensitivity (85%) and specificity (91%) than the nonparametric methods. When we applied these clustering methods to published sequences from a study of HIV-1 genetic clusters in Seattle, USA, we found that the MMPP method categorized about half (46%) as many individuals to clusters compared to the other methods. Furthermore, the mean internal branch lengths that approximate transmission rates were significantly shorter in clusters extracted using MMPP, but not by other methods. We determined that the computing time for the MMPP method scaled linearly with the size of trees, requiring about 30 seconds for a tree of 1,000 tips and about 20 minutes for 50,000 tips on a single computer. This new approach to genetic clustering has significant implications for the application of pathogen sequence analysis to public health, where it is critical to robustly and accurately identify clusters for the most cost-effective deployment of outbreak management and prevention resources.

Many pathogens evolve so rapidly that they accumulate genetic differences within a host before becoming transmitted to the next host. Consequently, clusters of sampled infections with nearly identical genomes may reveal outbreaks of recent or ongoing transmissions. There is rapidly growing interest in using model-free genetic clustering methods to guide public health responses to epidemics in near real-time, including HIV, Ebola virus and tuberculosis. However, we show that current methods are relatively ineffective at detecting transmission outbreaks; instead, they are predominantly influenced by how infections are sampled from the population. We describe a fundamentally new approach to genetic clustering that is based on modelling changes in transmission rates during the spread of the epidemic. We use simulated and real pathogen sequence data sets to demonstrate that this model-based approach is substantially more effective for detecting transmission outbreaks, and remains fast enough for real-time applications to large sequence databases.

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.

Biased phylodynamic inferences from analysing clusters of viral sequences

Phylogenetic methods are being increasingly used to help understand the transmission dynamics of measurably evolving viruses, including HIV. Clusters of highly similar sequences are often observed, which appear to follow a ‘power law’ behaviour, with a small number of very large clusters. These clusters may help to identify subpopulations in an epidemic, and inform where intervention strategies should be implemented. However, clustering of samples does not necessarily imply the presence of a subpopulation with high transmission rates, as groups of closely related viruses can also occur due to non-epidemiological effects such as over-sampling. It is important to ensure that observed phylogenetic clustering reflects true heterogeneity in the transmitting population, and is not being driven by non-epidemiological effects. We qualify the effect of using a falsely identified ‘transmission cluster’ of sequences to estimate phylodynamic parameters including the effective population size and exponential growth rate under several demographic scenarios. Our simulation studies show that taking the maximum size cluster to re-estimate parameters from trees simulated under a randomly mixing, constant population size coalescent process systematically underestimates the overall effective population size. In addition, the transmission cluster wrongly resembles an exponential or logistic growth model 99% of the time. We also illustrate the consequences of false clusters in exponentially growing coalescent and birth-death trees, where again, the growth rate is skewed upwards. This has clear implications for identifying clusters in large viral databases, where a false cluster could result in wasted intervention resources.

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.

Inferring HIV-1 Transmission Dynamics in Germany From Recently Transmitted Viruses

BACKGROUND

Although HIV continues to spread globally, novel intervention strategies such as treatment as prevention (TasP) may bring the epidemic to a halt. However, their effective implementation requires a profound understanding of the underlying transmission dynamics.

METHODS

We analyzed parameters of the German HIV epidemic based on phylogenetic clustering of viral sequences from recently infected seroconverters with known infection dates. Viral baseline and follow-up pol sequences (n = 1943) from 1159 drug-naïve individuals were selected from a nationwide long-term observational study initiated in 1997. Putative transmission clusters were computed based on a maximum likelihood phylogeny. Using individual follow-up sequences, we optimized our clustering threshold to maximize the likelihood of co-clustering individuals connected by direct transmission.

RESULTS

The sizes of putative transmission clusters scaled inversely with their abundance and their distribution exhibited a heavy tail. Clusters based on the optimal clustering threshold were significantly more likely to contain members of the same or bordering German federal states. Interinfection times between co-clustered individuals were significantly shorter (26 weeks; interquartile range: 13-83) than in a null model.

CONCLUSIONS

Viral intraindividual evolution may be used to select criteria that maximize co-clustering of transmission pairs in the absence of strong adaptive selection pressure. Interinfection times of co-clustered individuals may then be an indicator of the typical time to onward transmission. Our analysis suggests that onward transmission may have occurred early after infection, when individuals are typically unaware of their serological status. The latter argues that TasP should be combined with HIV testing campaigns to reduce the possibility of transmission before TasP initiation.

Phylodynamics on local sexual contact networks

Phylodynamic models are widely used in infectious disease epidemiology to infer the dynamics and structure of pathogen populations. However, these models generally assume that individual hosts contact one another at random, ignoring the fact that many pathogens spread through highly structured contact networks. We present a new framework for phylodynamics on local contact networks based on pairwise epidemiological models that track the status of pairs of nodes in the network rather than just individuals. Shifting our focus from individuals to pairs leads naturally to coalescent models that describe how lineages move through networks and the rate at which lineages coalesce. These pairwise coalescent models not only consider how network structure directly shapes pathogen phylogenies, but also how the relationship between phylogenies and contact networks changes depending on epidemic dynamics and the fraction of infected hosts sampled. By considering pathogen phylogenies in a probabilistic framework, these coalescent models can also be used to estimate the statistical properties of contact networks directly from phylogenies using likelihood-based inference. We use this framework to explore how much information phylogenies retain about the underlying structure of contact networks and to infer the structure of a sexual contact network underlying a large HIV-1 sub-epidemic in Switzerland.

Phylodynamic models relate the branching pattern of a pathogen’s phylogenetic tree to the tree-like growth of an epidemic as it spreads through a host population. Such models are increasingly used to learn about the epidemiology of different pathogens. We extend current models to consider the structure of host contact networks—the web of physical interactions through which pathogens spread. By considering how local interactions among hosts shape the phylogeny of a pathogen, our models offer a “pathogen’s eye view” of these networks. Our models also provide a statistical framework that can be used to infer network structure directly from phylogenies, which we use to estimate the properties of a sexual contact network in Switzerland from a HIV phylogeny.

Identifying Transmission Clusters with Cluster Picker and HIV-TRACE

We compared the behavior of two approaches (Cluster Picker and HIV-TRACE) at varying genetic distances to identify transmission clusters. We used three HIV gp41 sequence datasets originating from the Rakai Community Cohort Study: (1) next-generation sequence (NGS) data from nine linked couples; (2) NGS data from longitudinal sampling of 14 individuals; and (3) Sanger consensus sequences from a cross-sectional dataset (n = 1,022) containing 91 epidemiologically linked heterosexual couples. We calculated the optimal genetic distance threshold to separate linked versus unlinked NGS datasets using a receiver operating curve analysis. We evaluated the number, size, and composition of clusters detected by Cluster Picker and HIV-TRACE at six genetic distance thresholds (1%-5.3%) on all three datasets. We further tested the effect of using all NGS, versus only a single variant for each patient/time point, for datasets (1) and (2). The optimal gp41 genetic distance threshold to distinguish linked and unlinked couples and individuals was 5.3% and 4%, respectively. HIV-TRACE tended to detect larger and fewer clusters, whereas Cluster Picker detected more clusters containing only two sequences. For NGS datasets (1) and (2), HIV-TRACE and Cluster Picker detected all linked pairs at 3% and 4% genetic distances, respectively. However, at 5.3% genetic distance, 20% of couples in dataset (3) did not cluster using either program, and for >1/3 of couples cluster assignment were discordant. We suggest caution in choosing thresholds for clustering analyses in a generalized epidemic.

Expanding HIV-1 subtype B transmission networks among men who have sex with men in Poland

INTRODUCTION

Reconstruction of HIV transmission links allows to trace the spread and dynamics of infection and guide epidemiological interventions. The aim of this study was to characterize transmission networks among subtype B infected patients from Poland.

MATERIAL AND METHODS

Maximum likelihood phylogenenetic trees were inferred from 966 HIV-1 subtype B protease/reverse transcriptase sequences from patients followed up in nine Polish HIV centers. Monophyletic clusters were identified using 3% within-cluster distance and 0.9 bootstrap values. Interregional links for the clusters were investigated and time from infection to onward transmission estimated using Bayesian dated MCMC phylogeny.

RESULTS

Three hundred twenty one (33.2%) sequences formed 109 clusters, including ten clusters of ≥5 sequences (n = 81, 8.4%). Transmission networks were more common among MSM (234 sequences, 68.6%) compared to other infection routes (injection drug use: 28 (8.2%) and heterosexual transmissions: 59 (17.3%) cases, respectively [OR:3.5 (95%CI:2.6-4.6),p<0.001]. Frequency of clustering increased from 26.92% in 2009 to 50.6% in 2014 [OR:1.18 (95%CI:1.06-1.31),p = 0.0026; slope +2.8%/year] with median time to onward transmission within clusters of 1.38 (IQR:0.59-2.52) years. In multivariate models clustering was associated with both MSM transmission route [OR:2.24 (95%CI:1.38-3.65),p<0.001] and asymptomatic stage of HIV infection [OR:1.93 (95%CI:1.4-2.64),p<0.0001]. Additionally, interregional networks were linked to MSM transmissions [OR:4.7 (95%CI:2.55-8.96),p<0.001].

CONCLUSIONS

Reconstruction of the HIV-1 subtype B transmission patterns reveals increasing degree of clustering and existence of interregional networks among Polish MSM. Dated phylogeny confirms the association between onward transmission and recent infections. High transmission dynamics among Polish MSM emphasizes the necessity for active testing and early treatment in this group.

Social and Genetic Networks of HIV-1 Transmission in New York City

Background

Sexually transmitted infections spread across contact networks. Partner elicitation and notification are commonly used public health tools to identify, notify, and offer testing to persons linked in these contact networks. For HIV-1, a rapidly evolving pathogen with low per-contact transmission rates, viral genetic sequences are an additional source of data that can be used to infer or refine transmission networks.

Methods and Findings

The New York City Department of Health and Mental Hygiene interviews individuals newly diagnosed with HIV and elicits names of sexual and injection drug using partners. By law, the Department of Health also receives HIV sequences when these individuals enter healthcare and their physicians order resistance testing. Our study used both HIV sequence and partner naming data from 1342 HIV-infected persons in New York City between 2006 and 2012 to infer and compare sexual/drug-use named partner and genetic transmission networks. Using these networks, we determined a range of genetic distance thresholds suitable for identifying potential transmission partners. In 48% of cases, named partners were infected with genetically closely related viruses, compatible with but not necessarily representing or implying, direct transmission. Partner pairs linked through the genetic similarity of their HIV sequences were also linked by naming in 53% of cases. Persons who reported high-risk heterosexual contact were more likely to name at least one partner with a genetically similar virus than those reporting their risk as injection drug use or men who have sex with men.

Conclusions

We analyzed an unprecedentedly large and detailed partner tracing and HIV sequence dataset and determined an empirically justified range of genetic distance thresholds for identifying potential transmission partners. We conclude that genetic linkage provides more reliable evidence for identifying potential transmission partners than partner naming, highlighting the importance and complementarity of both epidemiological and molecular genetic surveillance for characterizing regional HIV-1 epidemics.

Understanding the path over which viruses such as HIV have been transmitted may be crucial for directing public health resources and guiding policy decisions. Contact tracing of named sexual and injection drug-use partners of people recently diagnosed with HIV is an indispensible tool for reconstructing this transmission network. Viral genetic sequence data—routinely collected by public health agencies—can also be used to infer the dynamics of HIV transmission. We analyzed partner naming and viral genetic sequence data in 1342 people living with HIV in New York City reported to the New York City Department of Health and Mental Hygiene between 2006 and 2012. Genetically linked partners were more likely to be named partners than named partners were to be genetically linked. This finding indicates that genetic sequence data are better than partner naming data for reconstructing this viral transmission network. Importantly, the success rate in naming a genetically linked partner varied by transmission risk category (e.g., men who have sex with men, heterosexuals, and injection drug users). This study validates the use viral genetic sequences in reconstructing these viral transmission networks in a public health surveillance setting.

Reconstructing contact network parameters from viral phylogenies

Models of the spread of disease in a population often make the simplifying assumption that the population is homogeneously mixed, or is divided into homogeneously mixed compartments. However, human populations have complex structures formed by social contacts, which can have a significant influence on the rate of epidemic spread. Contact network models capture this structure by explicitly representing each contact which could possibly lead to a transmission. We developed a method based on approximate Bayesian computation (ABC), a likelihood-free inference strategy, for estimating structural parameters of the contact network underlying an observed viral phylogeny. The method combines adaptive sequential Monte Carlo for ABC, Gillespie simulation for propagating epidemics though networks, and a kernel-based tree similarity score. We used the method to fit the Barabási-Albert network model to simulated transmission trees, and also applied it to viral phylogenies estimated from ten published HIV sequence datasets. This model incorporates a feature called preferential attachment (PA), whereby individuals with more existing contacts accumulate new contacts at a higher rate. On simulated data, we found that the strength of PA and the number of infected nodes in the network can often be accurately estimated. On the other hand, the mean degree of the network, as well as the total number of nodes, was not estimable with ABC. We observed sub-linear PA power in all datasets, as well as higher PA power in networks of injection drug users. These results underscore the importance of considering contact structures when performing phylodynamic inference. Our method offers the potential to quantitatively investigate the contact network structure underlying viral epidemics.

Impacts and shortcomings of genetic clustering methods for infectious disease outbreaks

For infectious diseases, a genetic cluster is a group of closely related infections that is usually interpreted as representing a recent outbreak of transmission. Genetic clustering methods are becoming increasingly popular for molecular epidemiology, especially in the context of HIV where there is now considerable interest in applying these methods to prioritize groups for public health resources such as pre-exposure prophylaxis. To date, genetic clustering has generally been performed with ad hoc algorithms, only some of which have since been encoded and distributed as free software. These algorithms have seldom been validated on simulated data where clusters are known, and their interpretation and similarities are not transparent to users outside of the field. Here, I provide a brief overview on the development and inter-relationships of genetic clustering methods, and an evaluation of six methods on data simulated under an epidemic model in a risk-structured population. The simulation analysis demonstrates that the majority of clustering methods are systematically biased to detect variation in sampling rates among subpopulations, not variation in transmission rates. I discuss these results in the context of previous work and the implications for public health applications of genetic clustering.

The International Dimension of the U.S. HIV Transmission Network and Onward Transmission of HIV Recently Imported into the United States

The majority of HIV infections in the United States can be traced back to a single introduction in late 1960s or early 1970s. However, it remains unclear whether subsequent introductions of HIV into the United States have given rise to onward transmission. Genetic transmission networks can aid in understanding HIV transmission. We constructed a genetic distance-based transmission network using HIV-1 pol sequences reported to the U.S. National HIV Surveillance System (n = 41,539) and all publicly available non-U.S. HIV-1 pol sequences (n = 86,215). Of the 13,145 U.S. persons clustered in the network, 457 (3.5%) were genetically linked to a potential transmission partner outside the United States. For internationally connected persons residing in but born outside the United States, 61% had a connection to their country of birth or to another country that shared a language with their country of birth. Bayesian molecular clock phylogenetic analyses indicate that introduced nonsubtype B infections have resulted in onward transmission within the United States.

How well can the exponential-growth coalescent approximate constant-rate birth-death population dynamics?

One of the central objectives in the field of phylodynamics is the quantification of population dynamic processes using genetic sequence data or in some cases phenotypic data. Phylodynamics has been successfully applied to many different processes, such as the spread of infectious diseases, within-host evolution of a pathogen, macroevolution and even language evolution. Phylodynamic analysis requires a probability distribution on phylogenetic trees spanned by the genetic data. Because such a probability distribution is not available for many common stochastic population dynamic processes, coalescent-based approximations assuming deterministic population size changes are widely employed. Key to many population dynamic models, in particular epidemiological models, is a period of exponential population growth during the initial phase. Here, we show that the coalescent does not well approximate stochastic exponential population growth, which is typically modelled by a birth–death process. We demonstrate that introducing demographic stochasticity into the population size function of the coalescent improves the approximation for values of R₀ close to 1, but substantial differences remain for large R₀. In addition, the computational advantage of using an approximation over exact models vanishes when introducing such demographic stochasticity. These results highlight that we need to increase efforts to develop phylodynamic tools that correctly account for the stochasticity of population dynamic models for inference.

Effects of memory on the shapes of simple outbreak trees

Genomic tools, including phylogenetic trees derived from sequence data, are increasingly used to understand outbreaks of infectious diseases. One challenge is to link phylogenetic trees to patterns of transmission. Particularly in bacteria that cause chronic infections, this inference is affected by variable infectious periods and infectivity over time. It is known that non-exponential infectious periods can have substantial effects on pathogens’ transmission dynamics. Here we ask how this non-Markovian nature of an outbreak process affects the branching trees describing that process, with particular focus on tree shapes. We simulate Crump-Mode-Jagers branching processes and compare different patterns of infectivity over time. We find that memory (non-Markovian-ness) in the process can have a pronounced effect on the shapes of the outbreak’s branching pattern. However, memory also has a pronounced effect on the sizes of the trees, even when the duration of the simulation is fixed. When the sizes of the trees are constrained to a constant value, memory in our processes has little direct effect on tree shapes, but can bias inference of the birth rate from trees. We compare simulated branching trees to phylogenetic trees from an outbreak of tuberculosis in Canada, and discuss the relevance of memory to this dataset.

Transmission of Non-B HIV Subtypes in the United Kingdom Is Increasingly Driven by Large Non-Heterosexual Transmission Clusters

Background. The United Kingdom human immunodeficiency virus (HIV) epidemic was historically dominated by HIV subtype B transmission among men who have sex with men (MSM). Now 50% of diagnoses and prevalent infections are among heterosexual individuals and mainly involve non-B subtypes. Between 2002 and 2010, the prevalence of non-B diagnoses among MSM increased from 5.4% to 17%, and this study focused on the drivers of this change.

Methods. Growth between 2007 and 2009 in transmission clusters among 14 000 subtype A1, C, D, and G sequences from the United Kingdom HIV Drug Resistance Database was analysed by risk group.

Results. Of 1148 clusters containing at least 2 sequences in 2007, >75% were pairs and >90% were heterosexual. Most clusters (71.4%) did not grow during the study period. Growth was significantly lower for small clusters and higher for clusters of ≥7 sequences, with the highest growth observed for clusters comprising sequences from MSM and people who inject drugs (PWID). Risk group (P < .0001), cluster size (P < .0001), and subtype (P < .01) were predictive of growth in a generalized linear model.

Discussion. Despite the increase in non-B subtypes associated with heterosexual transmission, MSM and PWID are at risk for non-B infections. Crossover of subtype C from heterosexuals to MSM has led to the expansion of this subtype within the United Kingdom.