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

Dynamics of clustering rates in the Rhode Island HIV-1 epidemic

BACKGROUND

Characterizing HIV clustering rates and their trends over time can improve understanding a local epidemic and enhance its control.

METHODS

Leveraging an academic-public health partnership in Rhode Island, we explored longitudinal dynamics of statewide clustering rates among key populations from 1991 to 2023. Partial HIV-1 pol sequences were grouped by year of HIV-1 diagnosis. Molecular clusters were identified in cumulative annual phylogenies. Overall clustering rates, and clustering rates of newly diagnosed and prevalent infections, and of specific sociodemographic characteristics of key populations over time were determined. Mann-Kendall statistics were used to estimate clustering rate trends and relationships among groups.

RESULTS

By the end of 2023, 2630 individuals with sequences represented the statewide epidemic in Rhode Island. Overall clustering rates increased from 7% in 1991 to 46% in 2023, correlating with cumulative sequence increase. Clustering rates of newly diagnosed and prevalent infections significantly increased over time, higher in newly diagnosed individuals since the early 2000s. Increases were also observed among groups defined by gender, age, transmission risks, race, mental illness, HIV-1 subtypes, and country of birth, with some crossovers and divergence patterns over time.

CONCLUSION

Exploring dynamics of HIV clustering rates over three decades in a statewide HIV-1 epidemic expanded its characterization and provided insight into its evolving changes. These dynamics may indicate a gradual shift towards a more concentrated and localized HIV-1 epidemic, highlighting important opportunities for targeted interventions to effectively prevent new HIV transmissions.

Community Insights in Phylogenetic HIV Research: The CIPHR Project Protocol

Inferring HIV transmission networks from HIV sequences is gaining popularity in the field of HIV molecular epidemiology. However, HIV sequences are often analyzed at distance from those affected by HIV epidemics, namely without the involvement of communities most affected by HIV. These remote analyses often mean that knowledge is generated in absence of lived experiences and socio-economic realities that could inform the ethical application of network-derived information in 'real world' programmes. Procedures to engage communities are noticeably absent from the HIV molecular epidemiology literature. Here we present our team's protocol for engaging community activists living in Nairobi, Kenya in a knowledge exchange process - The CIPHR Project (Community Insights in Phylogenetic HIV Research). Drawing upon a community-based participatory approach, our team will (1) explore the possibilities and limitations of HIV molecular epidemiology for key population programmes, (2) pilot a community-based HIV molecular study, and (3) co-develop policy guidelines on conducting ethically safe HIV molecular epidemiology. Critical dialogue with activist communities will offer insight into the potential uses and abuses of using such information to sharpen HIV prevention programmes. The outcome of this process holds importance to the development of policy frameworks that will guide the next generation of the global response.

Impact of SARS-CoV-2 lockdown on expansion of HIV transmission clusters among key populations: A retrospective phylogenetic analysis

Background

Public health measures designed to reduce SARS-CoV-2 transmission led to reduced access to care and prevention services for people living with or at risk of acquiring HIV, particularly during the initial introduction of extensive restrictions. This reduction in access may have contributed to increases in HIV transmission not outweighed by decreases in transmission occurring as a result of reduced contact rates promoted by the same public health measures.

Methods

We synthesize available province-wide HIV data in British Columbia, Canada, together with public mobility data to phylogenetically investigate the early impacts of SARS-CoV-2 on HIV transmission. Cluster growth, coalescent branching events and lineage-level diversification rates were assessed in "pre-lockdown" (January 22-March 21, 2020), "lockdown" (March 22-May 20, 2020) and "post-lockdown" (May 21-July 19, 2020) to facilitate comparison of transmission trends across key populations.

Findings

Results reveal increased HIV transmission in a limited number of clusters in association with reduced access to health services during the initial introduction of SARS-CoV-2-related restrictions. In particular, clusters associated with people who inject drugs (PWID) show rapid growth, extensive branching events in phylogenetic trees during and following the lockdown period, and elevated median change in individuals' viral diversification rates during lockdown compared to clusters associated with men who have sex with men (MSM), consistent with increased transmission rates between PWID.

Interpretation

Increased vigilance and innovative targeted solutions are critical to offset potential negative impacts of SARS-CoV-2 or future pandemic-related restrictions on HIV epidemic dynamics.

Funding

Funding sources include Genome Canada and Genome BC, the Public Health Agency of Canada, the BC Centre for Excellence in HIV/AIDS, and the Canadian Institutes of Health Research Coronavirus Rapid Response Programme. Student funding includes a NSERC CREATE scholarship and a Canadian Institutes of Health Research graduate fellowship.

The potential of genomics for infectious disease forecasting

Genomic technologies have led to tremendous gains in understanding how pathogens function, evolve and interact. Pathogen diversity is now measurable at high precision and resolution, in part because over the past decade, sequencing technologies have increased in speed and capacity, at decreased cost. Alongside this, the use of models that can forecast emergence and size of infectious disease outbreaks has risen, highlighted by the coronavirus disease 2019 pandemic but also due to modelling advances that allow for rapid estimates in emerging outbreaks to inform monitoring, coordination and resource deployment. However, genomics studies have remained largely retrospective. While they contain high-resolution views of pathogen diversification and evolution in the context of selection, they are often not aligned with designing interventions. This is a missed opportunity because pathogen diversification is at the core of the most pressing infectious public health challenges, and interventions need to take the mechanisms of virulence and understanding of pathogen diversification into account. In this Perspective, we assess these converging fields, discuss current challenges facing both surveillance specialists and modellers who want to harness genomic data, and propose next steps for integrating longitudinally sampled genomic data with statistical learning and interpretable modelling to make reliable predictions into the future.

An Evaluation of Phylogenetic Workflows in Viral Molecular Epidemiology

The use of viral sequence data to inform public health intervention has become increasingly common in the realm of epidemiology. Such methods typically utilize multiple sequence alignments and phylogenies estimated from the sequence data. Like all estimation techniques, they are error prone, yet the impacts of such imperfections on downstream epidemiological inferences are poorly understood. To address this, we executed multiple commonly used viral phylogenetic analysis workflows on simulated viral sequence data, modeling Human Immunodeficiency Virus (HIV), Hepatitis C Virus (HCV), and Ebolavirus, and we computed multiple methods of accuracy, motivated by transmission-clustering techniques. For multiple sequence alignment, MAFFT consistently outperformed MUSCLE and Clustal Omega, in both accuracy and runtime. For phylogenetic inference, FastTree 2, IQ-TREE, RAxML-NG, and PhyML had similar topological accuracies, but branch lengths and pairwise distances were consistently most accurate in phylogenies inferred by RAxML-NG. However, FastTree 2 was the fastest, by orders of magnitude, and when the other tools were used to optimize branch lengths along a fixed FastTree 2 topology, the resulting phylogenies had accuracies that were indistinguishable from their original counterparts, but with a fraction of the runtime.

Phylogenetic prioritization of HIV-1 transmission clusters with viral lineage-level diversification rates

Background and objectives

Public health officials faced with a large number of transmission clusters require a rapid, scalable and unbiased way to prioritize distribution of limited resources to maximize benefits. We hypothesize that transmission cluster prioritization based on phylogenetically derived lineage-level diversification rates will perform as well as or better than commonly used growth-based prioritization measures, without need for historical data or subjective interpretation.

Methodology

9822 HIV pol sequences collected during routine drug resistance genotyping were used alongside simulated sequence data to infer sets of phylogenetic transmission clusters via patristic distance threshold. Prioritized clusters inferred from empirical data were compared to those prioritized by the current public health protocols. Prioritization of simulated clusters was evaluated based on correlation of a given prioritization measure with future cluster growth, as well as the number of direct downstream transmissions from cluster members.

Results

Empirical data suggest diversification rate-based measures perform comparably to growth-based measures in recreating public heath prioritization choices. However, unbiased simulated data reveals phylogenetic diversification rate-based measures perform better in predicting future cluster growth relative to growth-based measures, particularly long-term growth. Diversification rate-based measures also display advantages over growth-based measures in highlighting groups with greater future transmission events compared to random groups of the same size. Furthermore, diversification rate measures were notably more robust to effects of decreased sampling proportion.

Conclusions and implications

Our findings indicate diversification rate-based measures frequently outperform growth-based measures in predicting future cluster growth and offer several additional advantages beneficial to optimizing the public health prioritization process.