An Automated Bioinformatics Pipeline Informing Near-Real-Time Public Health Responses to New HIV Diagnoses in a Statewide HIV Epidemic.

Molecular HIV cluster data can guide public health responses towards ending the HIV epidemic. Currently, real-time data integration, analysis, and interpretation are challenging, leading to a delayed public health response. We present a comprehensive methodology for addressing these challenges through data integration, analysis, and reporting. We integrated heterogeneous data sources across systems and developed an open-source, automatic bioinformatics pipeline that provides molecular HIV cluster data to inform public health responses to new statewide HIV-1 diagnoses, overcoming data management, computational, and analytical challenges. We demonstrate implementation of this pipeline in a statewide HIV epidemic and use it to compare the impact of specific phylogenetic and distance-only methods and datasets on molecular HIV cluster analyses. The pipeline was applied to 18 monthly datasets generated between January 2020 and June 2022 in Rhode Island, USA, that provide statewide molecular HIV data to support routine public health case management by a multi-disciplinary team. The resulting cluster analyses and near-real-time reporting guided public health actions in 37 phylogenetically clustered cases out of 57 new HIV-1 diagnoses. Of the 37, only 21 (57%) clustered by distance-only methods. Through a unique academic-public health partnership, an automated open-source pipeline was developed and applied to prospective, routine analysis of statewide molecular HIV data in near-real-time. This collaboration informed public health actions to optimize disruption of HIV transmission.

Not all clusters are equal: dynamics of molecular HIV-1 clusters in a statewide Rhode Island epidemic.

OBJECTIVES: Molecular epidemiology is a powerful tool to characterize HIV epidemics and prioritize public health interventions. Typically, HIV clusters are assumed to have uniform patterns over time. We hypothesized that assessment of cluster evolution would reveal distinct cluster behavior, possibly improving molecular epidemic characterization, towards disrupting HIV transmission. DESIGN: Retrospective cohort. METHODS: Annual phylogenies were inferred by cumulative aggregation of all available HIV-1 pol sequences of individuals with HIV-1 in Rhode Island (RI) between 1990 and 2020, representing a statewide epidemic. Molecular clusters were detected in annual phylogenies by strict and relaxed cluster definition criteria, and the impact of annual newly-diagnosed HIV-1 cases to the structure of individual clusters was examined over time. RESULTS: Of 2153 individuals, 31% (strict criteria) - 47% (relaxed criteria) clustered. Longitudinal tracking of individual clusters identified three cluster types: normal, semi-normal and abnormal. Normal clusters (83-87% of all identified clusters) showed predicted growing/plateauing dynamics, with approximately three-fold higher growth rates in large (15-18%) vs. small (∼5%) clusters. Semi-normal clusters (1-2% of all clusters) temporarily fluctuated in size and composition. Abnormal clusters (11-16% of all clusters) demonstrated collapses and re-arrangements over time. Borderline values of cluster-defining parameters explained dynamics of non-normal clusters. CONCLUSIONS: Comprehensive tracing of molecular HIV clusters over time in a statewide epidemic identified distinct cluster types, likely missed in cross-sectional analyses, demonstrating that not all clusters are equal. This knowledge challenges current perceptions of consistent cluster behavior over time and could improve molecular surveillance of local HIV epidemics to better inform public health strategies.

Beyond HIV outbreaks: protocol, rationale and implementation of a prospective study quantifying the benefit of incorporating viral sequence clustering analysis into routine public health interventions.

INTRODUCTION: HIV continues to have great impact on millions of lives. Novel methods are needed to disrupt HIV transmission networks. In the USA, public health departments routinely conduct contact tracing and partner services and interview newly HIV-diagnosed index cases to obtain information on social networks and guide prevention interventions. Sequence clustering methods able to infer HIV networks have been used to investigate and halt outbreaks. Incorporation of such methods into routine, not only outbreak-driven, contact tracing and partner services holds promise for further disruption of HIV transmissions. METHODS AND ANALYSIS: Building on a strong academic-public health collaboration in Rhode Island, we designed and have implemented a state-wide prospective study to evaluate an intervention that incorporates real-time HIV molecular clustering information with routine contact tracing and partner services. We present the rationale and study design of our approach to integrate sequence clustering methods into routine public health interventions as well as related important ethical considerations. This prospective study addresses key questions about the benefit of incorporating a clustering analysis triggered intervention into the routine workflow of public health departments, going beyond outbreak-only circumstances. By developing an intervention triggered by, and incorporating information from, viral sequence clustering analysis, and evaluating it with a novel design that avoids randomisation while allowing for methods comparison, we are confident that this study will inform how viral sequence clustering analysis can be routinely integrated into public health to support the ending of the HIV pandemic in the USA and beyond. ETHICS AND DISSEMINATION: The study was approved by both the Lifespan and Rhode Island Department of Health Human Subjects Research Institutional Review Boards and study results will be published in peer-reviewed journals.