Literature DB >> 29860098

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

David S Campo1, Yury Khudyakov2.   

Abstract

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 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. Published by Elsevier B.V.

Entities:  

Keywords:  Computational models; Hepatitis C virus; Simulation experiments; Targeted public health intervention; Transmission network

Mesh:

Year:  2018        PMID: 29860098      PMCID: PMC6103852          DOI: 10.1016/j.meegid.2018.05.028

Source DB:  PubMed          Journal:  Infect Genet Evol        ISSN: 1567-1348            Impact factor:   3.342


  6 in total

1.  Hepatitis C virus transmission cluster among injection drug users in Pakistan.

Authors:  Kashif Iqbal Sahibzada; Lilia Ganova-Raeva; Zoya Dimitrova; Sumathi Ramachandran; Yulin Lin; Garrett Longmire; Leonard Arthur; Guo-Liang Xia; Yury Khudyakov; Idrees Khan; Saima Sadaf
Journal:  PLoS One       Date:  2022-07-15       Impact factor: 3.752

Review 2.  A review of network simulation models of hepatitis C virus and HIV among people who inject drugs.

Authors:  Meghan Bellerose; Lin Zhu; Liesl M Hagan; William W Thompson; Liisa M Randall; Yelena Malyuta; Joshua A Salomon; Benjamin P Linas
Journal:  Int J Drug Policy       Date:  2019-11-15

3.  A latent class approach to identify multi-risk profiles associated with phylogenetic clustering of recent hepatitis C virus infection in Australia and New Zealand from 2004 to 2015.

Authors:  Sofia R Bartlett; Tanya L Applegate; Brendan P Jacka; Marianne Martinello; Francois Mj Lamoury; Mark Danta; Daniel Bradshaw; David Shaw; Andrew R Lloyd; Margaret Hellard; Gregory J Dore; Gail V Matthews; Jason Grebely
Journal:  J Int AIDS Soc       Date:  2019-02       Impact factor: 5.396

4.  Using machine learning methods to determine a typology of patients with HIV-HCV infection to be treated with antivirals.

Authors:  Antonio Rivero-Juárez; David Guijo-Rubio; Francisco Tellez; Rosario Palacios; Dolores Merino; Juan Macías; Juan Carlos Fernández; Pedro Antonio Gutiérrez; Antonio Rivero; César Hervás-Martínez
Journal:  PLoS One       Date:  2020-01-10       Impact factor: 3.240

5.  Hepatitis C Virus Transmission Clusters in Public Health and Correctional Settings, Wisconsin, USA, 2016-20171.

Authors:  Karli R Hochstatter; Damien C Tully; Karen A Power; Ruth Koepke; Wajiha Z Akhtar; Audrey F Prieve; Thomas Whyte; David J Bean; David W Seal; Todd M Allen; Ryan P Westergaard
Journal:  Emerg Infect Dis       Date:  2021-02       Impact factor: 6.883

6.  Primary case inference in viral outbreaks through analysis of intra-host variant population.

Authors:  J Walker Gussler; David S Campo; Zoya Dimitrova; Pavel Skums; Yury Khudyakov
Journal:  BMC Bioinformatics       Date:  2022-02-08       Impact factor: 3.169

  6 in total

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