GHOST: global hepatitis outbreak and surveillance technology

Longmire, Atkinson G.; Sims, Seth; Rytsareva, Inna; Campo, David S.; Skums, Pavel; Dimitrova, Zoya; Ramachandran, Sumathi; Medrzycki, Magdalena; Thai, Hong; Ganova-Raeva, Lilia; Lin, Yulin; Punkova, Lili; Sue, Amanda; Mirabito, Massimo; Wang, Silver; Tracy, Robin; Bolet, Victor; Sukalac, Thom; Lynberg, Chris; Khudyakov, Yury

doi:10.1186/s12864-017-4268-3

Cited by 39 publications

(50 citation statements)

References 30 publications

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“…Availability of transmission networks enables the development of targeted strategies for elimination of HCV infections in high-risk communities. ITN used in this study (Ramachandran et al, 2016) was generated by GHOST, a new computational and molecular technology for automatic detection of HCV transmission networks from next-generation sequence data (Campo et al, 2015; Longmire et al, 2017). Here, we used this network for the development and evaluation of targeted approaches for preventing prevention of new HCV infections in two epidemiological settings: (1) long-established infection with many HCV strains as was observed in a PWID community during investigation in Indiana and (2) a hypothetical rapid spread of a single HCV strain as observed during an outbreak.…”

Section: Discussionmentioning

confidence: 99%

“…Genetic data are more objective than personal recollections of contacts for inferring transmission networks. A low cost and broad availability of next-generation sequencing coupled with bioinformatics tools freely available to users (Longmire et al, 2017) make genetic analyses for the detection of transmission networks accessible and provide real opportunities for field applications of INDRA. Nevertheless, epidemiological variables such as an estimated size of community, duration of drug use, drug itself, rate of injection as well as gender, age, and ethnicity of network members are very useful for accurate assessment of network topology and application of specific PHI and PEI.…”

Section: Discussionmentioning

confidence: 99%

“…GHOST (Campo et al, 2015; Longmire et al, 2017; Rytsareva et al, 2017) was used to genetically characterize HCV strains and detect a transmission network. GHOST generates networks where 2 nodes representing infected individuals are linked by transmission if the minimal Hamming distance between any pair of HCV HVR1 sequences obtained from these individuals is below a relatedness threshold of 3.77% (Campo et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

“…Here, we take advantage of a complex HCV transmission network, which was recently identified among PWID in Indiana (Conrad et al, 2015; Peters et al, 2016; Ramachandran et al, 2016) using the Global Hepatitis Outbreak and Surveillance Technology (GHOST) (Campo et al, 2015; Longmire et al, 2017; Rytsareva et al, 2017), to devise and evaluate a new targeted, network-based strategy for reducing circulation and dissemination of HCV infection in 2 epidemiological settings: (1) long-established HCV infection as was observed in a PWID community during investigation in Indiana (Peters et al, 2016) and (2) a hypothetical rapid spread of a single HCV strain as observed during an outbreak.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Network DisRuption Analysis (INDRA): A targeted strategy for efficient interruption of hepatitis C transmissions

Campo

Khudyakov

2018

Infection, Genetics and Evolution

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Hepatitis C virus (HCV) infection is a global public health problem. The implementation of public health interventions (PHI) to control HCV infection could effectively interrupt HCV transmission. PHI targeting high-risk populations, e.g., people who inject drugs (PWID), are the most efficient but there is a lack of tools for prioritizing individuals within a high-risk community. Here, we present Intelligent Network DisRuption Analysis (INDRA), a targeted strategy for efficient interruption of hepatitis C transmissions. Using a large HCV transmission network among PWID in Indiana as an example, we compare effectiveness of random and targeted strategies in reducing the rate of HCV transmission in two settings: (1) long-established and (2) rapidly spreading infections (outbreak). Identification of high centrality for the network nodes co-infected with HIV or >1 HCV subtype indicates that the network structure properly represents the underlying contacts among PWID relevant to the transmission of these infections. Changes in the network’s global efficiency (GE) were used as a measure of the PHI effects. In setting 1, simulation experiments showed that a 50% GE reduction can be achieved by removing 11.2 times less nodes using targeted vs random strategies. A greater effect of targeted strategies on GE was consistently observed when networks were simulated: (1) with a varying degree of errors in node sampling and link assignment, and (2) at different levels of transmission reduction at affected nodes. In simulations considering a 10% removal of infected nodes, targeted strategies were ~2.8 times more effective than random in reducing incidence. Peer-education intervention (PEI) was modeled as a probabilistic distribution of actionable knowledge of safe injection practices from the affected node to adjacent nodes in the network. Addition of PEI to the models resulted in a 2–3 times greater reduction in incidence than from direct PHI alone. In setting 2, however, random direct PHI were ~3.2 times more effective in reducing incidence at the simulated conditions. Nevertheless, addition of PEI resulted in a ~1.7-fold greater efficiency of targeted PHI. In conclusion, targeted PHI facilitated by INDRA outperforms random strategies in decreasing circulation of long-established infections. Network-based PEI may amplify effects of PHI on incidence reduction in both settings.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intelligent Network DisRuption Analysis (INDRA): A targeted strategy for efficient interruption of hepatitis C transmissions

Campo

Khudyakov

2018

Infection, Genetics and Evolution

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“…The proposed prediction models may serve as cyber-molecular assays for staging infection, that could potentially complement and substitute standard laboratory assays. In particular, the proposed models are currently being incorporated into Global Hepatitis Outbreak and Surveillance Technology (GHOST) 33 -a web-based molecular surveillance system developed and maintained by CDC. They could also be applicable to other highly mutable viruses, such as HIV.…”

Section: Discussionmentioning

confidence: 99%

Quantitative differences between intra-host HCV populations from persons with recently established and persistent infections

Baykal

Lara

Khudyakov

et al. 2020

Preprint

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Background Detection of incident hepatitis C virus (HCV) infections is crucial for identification of outbreaks and development of public health interventions. However, there is no single diagnostic assay for distinguishing recent and persistent HCV infections. HCV exists in each infected host as a heterogeneous population of genomic variants, whose evolutionary dynamics remain incompletely understood. Genetic analysis of such viral populations can be applied to the detection of incident HCV infections and used to understand intra-host viral evolution.Methods We studied intra-host HCV populations sampled using next-generation sequencing from 98 recently and 256 persistently infected individuals. Genetic structure of the populations was evaluated using 245,878 viral sequences from these individuals and a set of selected parameters measuring their diversity, topological structure, complexity, strength of selection, epistasis, evolutionary dynamics, and physico-chemical properties.Findings Distributions of the viral population parameters differ significantly between recent and persistent infections. A general increase in viral genetic diversity from recent to persistent infections is frequently accompanied by decline in genomic complexity and increase in structuredness of the HCV population, likely reflecting a high level of intra-host adaptation at later stages of infection. Using these findings, we developed a Machine Learning classifier for the infection staging, which yielded a detection accuracy of 95.22%, thus providing a higher accuracy than other genomic-based models.Interpretation The detection of a strong association between several HCV genetic factors and stages of infection suggests that intra-host HCV population develops in a complex but regular and predictable manner in the course of infection. The proposed models may serve as a foundation of cyber-molecular assays for staging infection, that could potentially complement and/or substitute standard laboratory assays.

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