Identifying Transmission Clusters with Cluster Picker and HIV-TRACE

Rose, Rebecca; Lamers, Susanna L.; Dollar, James Jarad; Grabowski, M Kate; Hodcroft, Emma B.; Ragonnet‐Cronin, Manon; Wertheim, Joel O.; Redd, Andrew D.; German, Danielle; Laeyendecker, Oliver

doi:10.1089/aid.2016.0205

Cited by 65 publications

(61 citation statements)

References 33 publications

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“…One should consider exploring the choice of more stringent cut-offs between 0.01 and 0.02 for example to identify potential transmission partnerships and networks particularly in settings with high-risk HIV transmission to avoid additional complexity of finding spurious associations. A large study (~20,000 sequences) that evaluated the number, size, and composition of clusters detected by Cluster Picker and HIV-TRACE at six genetic distance thresholds (1%–5.3%) on three gp41 datasets showed that the optimal gp41 genetic distance threshold to distinguish linked and unlinked couples and individuals was 5.3% and 4.0%, respectively [53]. Another potential limitation was that we were unable to estimate the proportion of HIV infections attributable to community contacts or outside community, in the absence of historical contacts.…”

Section: Discussionmentioning

confidence: 99%

HIV-1 transmission networks in high risk fishing communities on the shores of Lake Victoria in Uganda: A phylogenetic and epidemiological approach

et al. 2017

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BackgroundFishing communities around Lake Victoria in sub-Saharan Africa have been characterised as a population at high risk of HIV-infection.MethodsUsing data from a cohort of HIV-positive individuals aged 13–49 years, enrolled from 5 fishing communities on Lake Victoria between 2009–2011, we sought to identify factors contributing to the epidemic and to understand the underlying structure of HIV transmission networks. Clinical and socio-demographic data were combined with HIV-1 phylogenetic analyses. HIV-1 gag-p24 and env-gp-41 sub-genomic fragments were amplified and sequenced from 283 HIV-1-infected participants. Phylogenetic clusters with ≥2 highly related sequences were defined as transmission clusters. Logistic regression models were used to determine factors associated with clustering.ResultsAltogether, 24% (n = 67/283) of HIV positive individuals with sequences fell within 34 phylogenetically distinct clusters in at least one gene region (either gag or env). Of these, 83% occurred either within households or within community; 8/34 (24%) occurred within household partnerships, and 20/34 (59%) within community. 7/12 couples (58%) within households clustered together. Individuals in clusters with potential recent transmission (11/34) were more likely to be younger 71% (15/21) versus 46% (21/46) in un-clustered individuals and had recently become resident in the community 67% (14/21) vs 48% (22/46). Four of 11 (36%) potential transmission clusters included incident-incident transmissions. Independently, clustering was less likely in HIV subtype D (adjusted Odds Ratio, aOR = 0.51 [95% CI 0.26–1.00]) than A and more likely in those living with an HIV-infected individual in the household (aOR = 6.30 [95% CI 3.40–11.68]).ConclusionsA large proportion of HIV sexual transmissions occur within house-holds and within communities even in this key mobile population. The findings suggest localized HIV transmissions and hence a potential benefit for the test and treat approach even at a community level, coupled with intensified HIV counselling to identify early infections.

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Section: Discussionmentioning

confidence: 99%

HIV-1 transmission networks in high risk fishing communities on the shores of Lake Victoria in Uganda: A phylogenetic and epidemiological approach

et al. 2017

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“…We compare two HIV network inference tools: HIV-TRACE (Kosakovsky and TreeCluster (Moshiri, 2018). HIV-TRACE is a widely-used method (Rose et al, 2017;Wertheim et al, 2017;Pérez-Losada et al, 2017) that clusters individuals such that, for all pairs of individuals u and v, if the Tamura and Nei (1993) (TN93) distance is below the threshold (default 1.5%), u and v are connected by an edge; each connected component forms a cluster. When we ran HIV-TRACE, we skipped its alignment step because we did not simulate indels.…”

Section: Transmission Network Reconstruction Methodsmentioning

confidence: 99%

FAVITES: simultaneous simulation of transmission networks, phylogenetic trees, and sequences

Moshiri

Ragonnet‐Cronin

Wertheim

et al. 2018

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Motivation:The ability to simulate epidemics as a function of model parameters allows insights that are unobtainable from real datasets. Further, reconstructing transmission networks for fast-evolving viruses like HIV may have the potential to greatly enhance epidemic intervention, but transmission network reconstruction methods have been inadequately studied, largely because it is difficult to obtain "truth" sets on which to test them and properly measure their performance.Results: We introduce FAVITES, a robust framework for simulating realistic datasets for epidemics that are caused by fast-evolving pathogens like HIV. FAVITES creates a generative model to produce contact networks, transmission networks, phylogenetic trees, and sequence datasets, and to add error to the data. FAVITES is designed to be extensible by dividing the generative model into modules, each of which is expressed as a fixed API that can be implemented using various models. We use FAVITES to simulate HIV datasets and study the realism of the simulated datasets. We then use the simulated data to study the impact of the increased treatment efforts on epidemiological outcomes. We also study two transmission network reconstruction methods and their effectiveness in detecting fast-growing clusters.Availability and implementation: FAVITES is available at https://github.com/niemasd/FAVITES, and a Docker image can be found on DockerHub (https://hub.docker.com/r/niemasd/favites).Redd, 2014). More recently, these methods have become widely used at both local and global scale . Nevertheless, several questions remain to be fully answered regarding the performance of these methods. It is not always clear which method/setting combination performs best for a specific downstream use-case or for specific epidemiological conditions. More broadly, the effectiveness of these methods in helping achieve public health goals is the subject of ongoing clinical and theoretical research.Accuracy of transmission networks is difficult to assess because the true order of transmissions is not known. Moreover, predicting the impact of parameters of interest (e.g., rate of treatment) on the epidemiological outcomes is difficult. In simulations, in contrast, the ground truth is known and parameters can be easily controlled. The simulation of transmission networks needs to combine models of social network, transmission, evolution, and ideally sampling biases and errors (Villandre et al., 2016) .We introduce FAVITES (FrAmework for VIral Transmission and Evolution Simulation), which can simulate numerous models of contact networks, viral transmission, phylogenetic and sequence evolution, data (sub)sampling, and real-world data perturbations, and which was built to be flexible such that users can seamlessly plug in statistical models at every step of the simulation process. Previous attempts to create an epidemic simulation tool include epinet (Groendyke et al., 2012), TreeSim (Stadler and Bonhoeffer, 2013), outbreaker (Jombart et al., 2014), seedy (Worby and Read, 2015), and...

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“…According to the Cluster Picker manual and the suggestions of previous studies, the parameter support threshold was set to 0 and the genetic distance threshold was set to 4% to identify a close transmission relationship between Yunnan-mIDUs and Dehong IDUs [22–24,38]. …”

Section: Methodsmentioning

confidence: 99%

Burmese injecting drug users in Yunnan play a pivotal role in the cross-border transmission of HIV-1 in the China-Myanmar border region

Chen

Wang

et al. 2018

Virulence

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Injecting drug users (IDUs) are the major risk group for HIV-1 infection in the China-Myanmar border area. There are a large number of Burmese IDUs living in Yunnan (Yunnan-mIDUs) who might be associated with the cross-border transmission of HIV-1. From 2010 to 2013, 617 Yunnan-mIDUs were recruited from three counties of Yunnan, 19.0% of whom were detected to be HIV-1 positive by serological testing. Partial HIV-1 p17, pol, vif-env, and env genes were amplified from the positive samples and were sequenced. Phylogenetic and HIV-1 subtyping analyses revealed that HIV-1 recombinant forms (RFs), including RF_BC (36.4%), RF_01BC (26.1%), RF_01C (9.1%) and RF_01B (1.1%), were predominant among this cohort. Of the identified HIV-1 strains, 14.8%, 9.1% and 3.4% belonged to subtype C, CRF01_AE and subtype B, respectively. Transmission cluster analysis showed that sequences from the Yunnan-mIDUs formed transmission clusters not only with those from Burmese IDUs but also with those from Chinese IDUs, indicating that Yunnan-mIDUs might acquire HIV-1 infection from or spread HIV-1 to both Burmese and Chinese IDUs. Phylogeographic analyses revealed three cross-border transmission patterns associated with Yunnan-mIDUs, in which Yunnan-mIDUs served as the crucial nodes linking the Burmese and Chinese IDUs. These results suggest that Yunnan-mIDUs are a potential viral reservoir for the diffusion of HIV-1 in Yunnan and play a pivotal role in the bidirectional cross-border transmission of HIV-1 in the China-Myanmar border region. More intervention efforts that focus on Yunnan-mIDUs are recommended in Yunnan’s campaign against HIV/AIDS.

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Identifying Transmission Clusters with Cluster Picker and HIV-TRACE

Cited by 65 publications

References 33 publications

HIV-1 transmission networks in high risk fishing communities on the shores of Lake Victoria in Uganda: A phylogenetic and epidemiological approach

HIV-1 transmission networks in high risk fishing communities on the shores of Lake Victoria in Uganda: A phylogenetic and epidemiological approach

FAVITES: simultaneous simulation of transmission networks, phylogenetic trees, and sequences

Burmese injecting drug users in Yunnan play a pivotal role in the cross-border transmission of HIV-1 in the China-Myanmar border region

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