Transmission Network Parameters Estimated From HIV Sequences for a Nationwide Epidemic

Brown, Andrew J. Leigh; Lycett, Samantha; Weinert, Lucy A.; Hughes, G.; Fearnhill, Esther; Dunn, David

doi:10.1093/infdis/jir550

Cited by 171 publications

(185 citation statements)

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“…In previous studies, we have identified clusters in trees based on mean within cluster distance [18,22]. However, we decided to use maximum genetic distance in our tool for three reasons.…”

Section: Discussionmentioning

confidence: 99%

Automated analysis of phylogenetic clusters

et al. 2013

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BackgroundAs sequence data sets used for the investigation of pathogen transmission patterns increase in size, automated tools and standardized methods for cluster analysis have become necessary. We have developed an automated Cluster Picker which identifies monophyletic clades meeting user-input criteria for bootstrap support and maximum genetic distance within large phylogenetic trees. A second tool, the Cluster Matcher, automates the process of linking genetic data to epidemiological or clinical data, and matches clusters between runs of the Cluster Picker.ResultsWe explore the effect of different bootstrap and genetic distance thresholds on clusters identified in a data set of publicly available HIV sequences, and compare these results to those of a previously published tool for cluster identification. To demonstrate their utility, we then use the Cluster Picker and Cluster Matcher together to investigate how clusters in the data set changed over time. We find that clusters containing sequences from more than one UK location at the first time point (multiple origin) were significantly more likely to grow than those representing only a single location.ConclusionsThe Cluster Picker and Cluster Matcher can rapidly process phylogenetic trees containing tens of thousands of sequences. Together these tools will facilitate comparisons of pathogen transmission dynamics between studies and countries.

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“…In previous studies, we have identified clusters in trees based on mean within cluster distance [18,22]. However, we decided to use maximum genetic distance in our tool for three reasons.…”

Section: Discussionmentioning

confidence: 99%

Automated analysis of phylogenetic clusters

et al. 2013

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“…In simulation experiments, PHI was modeled as a node removal from ITN. Targeted strategies should be especially efficient on networks having a skewed degree distribution because of a differential contribution of nodes to force of infection, with a few highly linked nodes spreading infection to many adjacent nodes and many peripheral nodes infecting one or only few nodes (Latora et al, 2006; Leigh Brown et al, 2011; Villandre et al, 2016; Wertheim et al, 2014). Thus, high-degree nodes will be removed only with low probability at random, which will have a little effect on reduction of force infection, while targeting high-degree nodes will have a significant impact on force of infection.…”

Section: Discussionmentioning

confidence: 99%

“…The difference can be explained by topological features of uninfected nodes. In setting 1, which is expected to be of some duration, uninfected nodes join the network by a preferential attachment, linking to the existing nodes with a probability proportional to their degree (Latora et al, 2006; Leigh Brown et al, 2011; Villandre et al, 2016; Wertheim et al, 2014). Thus, removal of high-centrality nodes results in a greater effect on attachment of new nodes, significantly reducing probability of the newly joining members to become infected.…”

Section: Discussionmentioning

confidence: 99%

“…Targeting interventions to the most contributing members intuitively seems to be the most efficient way to interrupt disease dissemination within the community. Indeed, among PWID, transmissions usually occur through preferential contacts, resulting in contact networks that are not randomly organized but are modular and have a skewed degree distribution (Latora et al, 2006; Leigh Brown et al, 2011; Villandre et al, 2016; Wertheim et al, 2014). However, the structure of contact or transmission networks is not always available and can be identified only through intense research, which prevents common application of the networks to public health interventions (PHI).…”

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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“…HIV sequences representing subtype B protease and partial reverse transcriptase coding regions (pro-RT region, ϳ1.1 kb long) were obtained from (i) the Los Alamos HIVDB (http://www.hiv.lanl .gov; accessed in June 2012), (ii) the UKHIVRDB (http://www.hivrdb .org; accessed on 20 March 2013, containing sequences up to the end of 2010) of strains from treatment-naive patients (17), and (iii) our in-house HIV database comprising sequences of strains from study participants residing in the Tokyo-Kanagawa metropolitan area of Japan (designated the JP-TK HIVDB) (10). Ethics approval for the use of anonymized data in the UKHIVRDB was given by the London Multicenter Research Ethics Committee.…”

Section: Methodsmentioning

confidence: 99%