Molecular Epidemiology of HIV-1 Subtype B Infection across Florida Reveals Few Large Superclusters with Metropolitan Origin

Rich, Shannan N.; Prosperi, Mattia; Dellicour, Simon; Vrancken, Bram; Cook, Robert L.; Spencer, Emma; Salemi, Marco; Mavian, Carla

doi:10.1128/spectrum.01889-22

Cited by 5 publications

(10 citation statements)

References 25 publications

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“…Of these, 27, 115 (96.5%) were classified as subtype B and used in previous clustering analysis by Rich et al . [ 19 ]. In the Rich et al .…”

Section: Resultsmentioning

confidence: 99%

“… The maximum likelihood phylogeny was generated as described in Rich et al . [ 19 ] for 27, 115 partial pol sequences sampled from individuals across the state of Florida, for whom metadata were provided. County of residence, categorized according to EHE prioritization, is shown in the corresponding heatmap.…”

Section: Resultsmentioning

confidence: 99%

“…Classification of predicted transmission growth was prevalent among sequences previously identified as belonging to transmission clusters by Rich et al . [ 19 ]. In particular, the statistical association of predicted transmission growth with larger clusters (>11 individuals) suggests a reasonable rationale for CDC prioritization of larger clusters and is indicative of groups for whom transmission is largely uncontrolled through existing public health approaches.…”

Section: Discussionmentioning

confidence: 99%

“…Using these data, we demonstrate the effectiveness of our GNN method ( DeepDynaForecast ) in predicting future dynamics from limited, early sampling and its promising capacity to improve public health efforts in preventing epidemic spread. We then sought to apply the trained model to the HIV epidemic in Florida between 2012 and 2020, for which large-scale pathogen sequencing data and metadata is available, previously analyzed using traditional distance-based methods [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

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DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction

Sun,

Fang,

Salemi

et al. 2024

PLoS Comput Biol

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In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of transmission growth or decline among specific risk groups can aid in optimizing interventions, particularly when resources are limited. Phylogenetic trees have been widely used in the detection of transmission chains and high-risk populations. Moreover, tree topology and the incorporation of population parameters (phylodynamics) can be useful in reconstructing the evolutionary dynamics of an epidemic across space and time among individuals. We now demonstrate the utility of phylodynamic trees for transmission modeling and forecasting, developing a phylogeny-based deep learning system, referred to as DeepDynaForecast. Our approach leverages a primal-dual graph learning structure with shortcut multi-layer aggregation, which is suited for the early identification and prediction of transmission dynamics in emerging high-risk groups. We demonstrate the accuracy of DeepDynaForecast using simulated outbreak data and the utility of the learned model using empirical, large-scale data from the human immunodeficiency virus epidemic in Florida between 2012 and 2020. Our framework is available as open-source software (MIT license) at github.com/lab-smile/DeepDynaForcast.

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“…Of these, 27, 115 (96.5%) were classified as subtype B and used in previous clustering analysis by Rich et al . [ 19 ]. In the Rich et al .…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction

Sun,

Fang,

Salemi

et al. 2024

PLoS Comput Biol

Self Cite

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“…MicrobeTrace is a secure web browser tool developed at the CDC [38] that remains operational without an internet connection and facilitates data integration and analysis with a suite of customizable views, including networks, phylogenetic trees, world maps, tables and charts that could be tailored as a dashboard view for real-time data analytics and reporting. MicrobeTrace has been widely used to integrate, visualize and analyze data from several pathogens, including HIV [39][40][41][42][43], hepatitis [44,45], tuberculosis [46] and SARS-CoV-2 [47][48][49]. We describe the expansion of MicrobeTrace as a pathogen-specific tool for genomic surveillance and variant classification.…”

Section: Introductionmentioning

confidence: 99%

HantaNet: A New MicrobeTrace Application for Hantavirus Classification, Genomic Surveillance, Epidemiology and Outbreak Investigations

Cintron,

Whitmer,

Moscoso

et al. 2023

Viruses

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Hantaviruses zoonotically infect humans worldwide with pathogenic consequences and are mainly spread by rodents that shed aerosolized virus particles in urine and feces. Bioinformatics methods for hantavirus diagnostics, genomic surveillance and epidemiology are currently lacking a comprehensive approach for data sharing, integration, visualization, analytics and reporting. With the possibility of hantavirus cases going undetected and spreading over international borders, a significant reporting delay can miss linked transmission events and impedes timely, targeted public health interventions. To overcome these challenges, we built HantaNet, a standalone visualization engine for hantavirus genomes that facilitates viral surveillance and classification for early outbreak detection and response. HantaNet is powered by MicrobeTrace, a browser-based multitool originally developed at the Centers for Disease Control and Prevention (CDC) to visualize HIV clusters and transmission networks. HantaNet integrates coding gene sequences and standardized metadata from hantavirus reference genomes into three separate gene modules for dashboard visualization of phylogenetic trees, viral strain clusters for classification, epidemiological networks and spatiotemporal analysis. We used 85 hantavirus reference datasets from GenBank to validate HantaNet as a classification and enhanced visualization tool, and as a public repository to download standardized sequence data and metadata for building analytic datasets. HantaNet is a model on how to deploy MicrobeTrace-specific tools to advance pathogen surveillance, epidemiology and public health globally.

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Novel insights on unraveling dynamics of transmission clusters in outbreaks using phylogeny-based methods

Magalis,

Riva,

Marini

et al. 2024

Infection, Genetics and Evolution

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Molecular Epidemiology of HIV-1 Subtype B Infection across Florida Reveals Few Large Superclusters with Metropolitan Origin

Abstract: The large number of missing phylogenetic linkages in rural Florida counties and among women and Black persons with HIV may impact timely detection of ongoing and emerging transmission clusters and ultimately hinder the success of epidemic elimination goals in Florida.

Cited by 5 publications

References 25 publications

DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction

DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction

HantaNet: A New MicrobeTrace Application for Hantavirus Classification, Genomic Surveillance, Epidemiology and Outbreak Investigations

Novel insights on unraveling dynamics of transmission clusters in outbreaks using phylogeny-based methods

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