The Role of Viral Introductions in Sustaining Community-Based HIV Epidemics in Rural Uganda: Evidence from Spatial Clustering, Phylogenetics, and Egocentric Transmission Models

Grabowski, M Kate; Lessler, Justin; Redd, Andrew D.; Kagaayi, Joseph; Laeyendecker, Oliver; Ndyanabo, Anthony; Nelson, Martha I.; Cummings, Derek A. T.; Bwanika, John Baptiste; Mueller, Amy C.; Reynolds, Steven J.; Munshaw, Supriya; Ray, Stuart C.; Lutalo, Tom; Manucci, Jordyn; Tobian, Aaron A.R.; Chang, Larry W.; Beyrer, Chris; Jennings, Jacky M.; Nalugoda, Fred; Serwadda, David; Wawer, Maria J.; Quinn, Thomas C.; Gray, Ronald H.

doi:10.1371/journal.pmed.1001610

Cited by 120 publications

(158 citation statements)

References 35 publications

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“…[17][18][19] Datasets (1) and (2) contained viral sequences spanning a 325-330 base pair segments of the HIV gp41 gene, generated using the Roche 454 method. 17,18 Dataset (1) comprised of nine epidemiologically linked couples with prior evidence of a virally linked transmission event (donor and recipient).…”

Section: Hiv-1 Sequence Datasetsmentioning

confidence: 99%

“…19 Consensus sequences were obtained using Sanger sequencing (gp41 fragment, HXB2 nt 7858 to 8260) as previously described. 20 Of the 1,022 individuals, 182 individuals were identified to be part of an epidemiologically linked heterosexual couple (91 couples), where either one or both partners named the other as a sexual partner.…”

Section: Hiv-1 Sequence Datasetsmentioning

confidence: 99%

“…20 Of the 1,022 individuals, 182 individuals were identified to be part of an epidemiologically linked heterosexual couple (91 couples), where either one or both partners named the other as a sexual partner. 19 Of these 91 couples, 28 were defined as ''incident,'' in which one (n = 21) or both (n = 7) partners were diagnosed with HIV during the intersurvey interval (*1.5 years). Couples were defined as ''prevalent'' otherwise.…”

Section: Hiv-1 Sequence Datasetsmentioning

confidence: 99%

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Identifying Transmission Clusters with Cluster Picker and HIV-TRACE

Rose¹,

Lamers²,

Dollar³

et al. 2017

AIDS Research and Human Retroviruses

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We compared the behavior of two approaches (Cluster Picker and HIV-TRACE) at varying genetic distances to identify transmission clusters. We used three HIV gp41 sequence datasets originating from the Rakai Community Cohort Study: (1) next-generation sequence (NGS) data from nine linked couples; (2) NGS data from longitudinal sampling of 14 individuals; and (3) Sanger consensus sequences from a cross-sectional dataset (n = 1,022) containing 91 epidemiologically linked heterosexual couples. We calculated the optimal genetic distance threshold to separate linked versus unlinked NGS datasets using a receiver operating curve analysis. We evaluated the number, size, and composition of clusters detected by Cluster Picker and HIV-TRACE at six genetic distance thresholds (1%-5.3%) on all three datasets. We further tested the effect of using all NGS, versus only a single variant for each patient/time point, for datasets (1) and (2). The optimal gp41 genetic distance threshold to distinguish linked and unlinked couples and individuals was 5.3% and 4%, respectively. HIV-TRACE tended to detect larger and fewer clusters, whereas Cluster Picker detected more clusters containing only two sequences. For NGS datasets (1) and (2), HIV-TRACE and Cluster Picker detected all linked pairs at 3% and 4% genetic distances, respectively. However, at 5.3% genetic distance, 20% of couples in dataset (3) did not cluster using either program, and for >1/3 of couples cluster assignment were discordant. We suggest caution in choosing thresholds for clustering analyses in a generalized epidemic.

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Section: Hiv-1 Sequence Datasetsmentioning

confidence: 99%

Section: Hiv-1 Sequence Datasetsmentioning

confidence: 99%

Section: Hiv-1 Sequence Datasetsmentioning

confidence: 99%

See 1 more Smart Citation

Identifying Transmission Clusters with Cluster Picker and HIV-TRACE

Rose¹,

Lamers²,

Dollar³

et al. 2017

AIDS Research and Human Retroviruses

Self Cite

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show abstract

“…3,5,7,[11][12][13][14][15]20 While HIV clustering patterns have been well characterized among men who have sex with men (MSM), [7][8][9][10][11][13][14][15][16][17][18][19][20][21][22] the structure and dynamics of heterosexual HIV transmission networks in sub-Saharan Africa are understudied. 12,[23][24][25][26][27] High sampling density in local communities has been associated with a higher extent of HIV clustering. 23 Studies with low sampling density showed minimal HIV clustering.…”

Section: Introductionmentioning

confidence: 99%

Impact of Sampling Density on the Extent of HIV Clustering

NovitskyVlad

Moyo²,

LeiQuanhong³

et al. 2014

AIDS Research and Human Retroviruses

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Identifying and monitoring HIV clusters could be useful in tracking the leading edge of HIV transmission in epidemics. Currently, greater specificity in the definition of HIV clusters is needed to reduce confusion in the interpretation of HIV clustering results. We address sampling density as one of the key aspects of HIV cluster analysis. The proportion of viral sequences in clusters was estimated at sampling densities from 1.0% to 70%. A set of 1,248 HIV-1C env gp120 V1C5 sequences from a single community in Botswana was utilized in simulation studies. Matching numbers of HIV-1C V1C5 sequences from the LANL HIV Database were used as comparators. HIV clusters were identified by phylogenetic inference under bootstrapped maximum likelihood and pairwise distance cut-offs. Sampling density below 10% was associated with stochastic HIV clustering with broad confidence intervals. HIV clustering increased linearly at sampling density > 10%, and was accompanied by narrowing confidence intervals. Patterns of HIV clustering were similar at bootstrap thresholds 0.7 to 1.0, but the extent of HIV clustering decreased with higher bootstrap thresholds. The origin of sampling (local concentrated vs. scattered global) had a substantial impact on HIV clustering at sampling densities ‡ 10%. Pairwise distances at 10% were estimated as a threshold for cluster analysis of HIV-1 V1C5 sequences. The node bootstrap support distribution provided additional evidence for 10% sampling density as the threshold for HIV cluster analysis. The detectability of HIV clusters is substantially affected by sampling density. A minimal genotyping density of 10% and sampling density of 50-70% are suggested for HIV-1 V1C5 cluster analysis.

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“…This kind of growth curve has three qualitative phases: exponential growth, linear growth, and a slow final phase when the susceptible population is almost depleted. The waiting times until the next transmission, which determine the coalescence times in the tree, are MSM/USA (Little et al 2014) MSM/Taiwan (Kao et al 2011) MSM/Beijing (Wang et al 2015) HET/Uganda (Grabowski et al 2014) HET/Malawi (McCormack et al 2002) HET/Botswana (Novitsky et al 2013(Novitsky et al & 2014 IDU/Romania (Niculescu et al 2015) IDU/Estonia (Zetterberg et al 2004) dependent on the growth phase of the epidemic. Therefore, we hypothesize that it is the growth phase at the time of sampling which most affects tree shape, rather than the specific values of I or N ( Supplementary Fig.…”

Section: Analysis Of Ba Model With Synthetic Datamentioning

confidence: 99%

Reconstructing contact network parameters from viral phylogenies

McCloskey

Liang

Poon

2016

Preprint

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Models of the spread of disease in a population often make the simplifying assumption that the population is homogeneously mixed, or is divided into homogeneously mixed compartments. However, human populations have complex structures formed by social contacts, which can have a significant influence on the rate of epidemic spread. Contact network models capture this structure by explicitly representing each contact which could possibly lead to a transmission. We developed a method based on approximate Bayesian computation (ABC), a likelihood-free inference strategy, for estimating structural parameters of the contact network underlying an observed viral phylogeny. The method combines adaptive sequential Monte Carlo for ABC, Gillespie simulation for propagating epidemics though networks, and a kernel-based tree similarity score. We used the method to fit the Barab asi-Albert network model to simulated transmission trees, and also applied it to viral phylogenies estimated from ten published HIV sequence datasets. This model incorporates a feature called preferential attachment (PA), whereby individuals with more existing contacts accumulate new contacts at a higher rate. On simulated data, we found that the strength of PA and the number of infected nodes in the network can often be accurately estimated. On the other hand, the mean degree of the network, as well as the total number of nodes, was not estimable with ABC. We observed sub-linear PA power in all datasets, as well as higher PA power in networks of injection drug users. These results underscore the importance of considering contact structures when performing phylodynamic inference. Our method offers the potential to quantitatively investigate the contact network structure underlying viral epidemics.

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The Role of Viral Introductions in Sustaining Community-Based HIV Epidemics in Rural Uganda: Evidence from Spatial Clustering, Phylogenetics, and Egocentric Transmission Models

Abstract: Using different approaches to investigate HIV transmission patterns, Justin Lessler and colleagues find that extra-community HIV introductions are frequent and likely play a role in sustaining the epidemic in the Rakai community. Please see later in the article for the Editors' Summary

Cited by 120 publications

References 35 publications

Identifying Transmission Clusters with Cluster Picker and HIV-TRACE

Identifying Transmission Clusters with Cluster Picker and HIV-TRACE

Impact of Sampling Density on the Extent of HIV Clustering

Reconstructing contact network parameters from viral phylogenies

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