The Impact of Clinical, Demographic and Risk Factors on Rates of HIV Transmission: A Population-based Phylogenetic Analysis in British Columbia, Canada

Poon, Art F. Y.; Joy, Jeffrey B.; Woods, Conan K.; Shurgold, Susan; Colley, Guillaume; Brumme, Chanson J.; Hogg, Robert S.; Montaner, Julio S. G.; Harrigan, P. Richard

doi:10.1093/infdis/jiu560

Cited by 93 publications

(107 citation statements)

References 30 publications

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“…20 In a public health setting, demographics and transmission category may assist in identifying groups of highest priority for targeted intervention. 18,30,31 Given the ability of HIV to spread across large geographic distances and establish onward transmission clusters, these surveillance efforts should be broad in scope.…”

mentioning

confidence: 99%

The International Dimension of the U.S. HIV Transmission Network and Onward Transmission of HIV Recently Imported into the United States

WertheimJoel

OsterAlexandra

HernandezAngela

et al. 2016

AIDS Research and Human Retroviruses

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mentioning

confidence: 99%

The International Dimension of the U.S. HIV Transmission Network and Onward Transmission of HIV Recently Imported into the United States

WertheimJoel

OsterAlexandra

HernandezAngela

et al. 2016

AIDS Research and Human Retroviruses

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“…Typically, q is chosen to be the difference between one or more summary statistics calculated for each data point. Our group (Poon 2015) and others (Mijung et al 2015) have demonstrated that a more accurate ABC approximation can be produced by using a kernel function. These functions, popular for machine learning applications, are able to calculate the similarity between data points when considering a very large or even infinite number of data features.…”

Section: Introductionmentioning

confidence: 82%

“…The particles are weighted according to the similarity between their associated simulated trees and the observed tree. To quantify this similarity, we used the tree kernel developed by Poon et al (2013). Particles are iteratively perturbed by applying a Metropolis-Hastings kernel and, if the move is accepted, simulating new datasets under the new parameters.…”

Section: Netabc: Phylogenetic Inference Of Contact Network Parametersmentioning

confidence: 99%

“…Since the tree kernel incorporates both of these sources of information, we expected it to outperform the other two statistics. Finally, the tree kernel can be tuned by adjusting the values of the meta-parameters k and r (the 'decay factor' and 'radial basis function variance', see Poon et al Poon et al 2013). k is used to penalize large subset trees which tend to dominate the kernel score.…”

Section: Classifiers For Ba Model Parameters From Tree Shapesmentioning

confidence: 99%

“…The distance function we use is the tree kernel developed by Poon et al (2013), which considers all possible subset trees with branch lengths when calculating the similarity between two trees. We apply the method to investigate the parameters of the BA network model on a variety of simulated and real datasets.…”

Section: Introductionmentioning

confidence: 99%

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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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Characteristics and growth of the genetic HIV transmission network of Mexico City during 2020

Dávila-Conn¹,

García-Morales²,

Matías-Florentino³

et al. 2021

J Int AIDS Soc.

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IntroductionMolecular surveillance systems could provide public health benefits to focus strategies to improve the HIV care continuum. Here, we infer the HIV genetic network of Mexico City in 2020, and identify actively growing clusters that could represent relevant targets for intervention.MethodsAll new diagnoses, referrals from other institutions, as well as persons returning to care, enrolling at the largest HIV clinic in Mexico City were invited to participate in the study. The network was inferred from HIV pol sequences, using pairwise genetic distance methods, with a locally hosted, secure version of the HIV‐TRACE tool: Seguro HIV‐TRACE. Socio‐demographic, clinical and behavioural metadata were overlaid across the network to design focused prevention interventions.ResultsA total of 3168 HIV sequences from unique individuals were included. One thousand and one‐hundred and fifty (36%) sequences formed 1361 links within 386 transmission clusters in the network. Cluster size varied from 2 to 14 (63% were dyads). After adjustment for covariates, lower age (adjusted odds ratio [aOR]: 0.37, p<0.001; >34 vs. <24 years), being a man who has sex with men (MSM) (aOR: 2.47, p = 0.004; MSM vs. cisgender women), having higher viral load (aOR: 1.28, p<0.001) and higher CD4+ T cell count (aOR: 1.80, p<0.001; ≥500 vs. <200 cells/mm3) remained associated with higher odds of clustering. Compared to MSM, cisgender women and heterosexual men had significantly lower education (none or any elementary: 59.1% and 54.2% vs. 16.6%, p<0.001) and socio‐economic status (low income: 36.4% and 29.0% vs. 18.6%, p = 0.03) than MSM. We identified 10 (2.6%) clusters with constant growth, for prioritized intervention, that included intersecting sexual risk groups, highly connected nodes and bridge nodes between possible sub‐clusters with high growth potential.ConclusionsHIV transmission in Mexico City is strongly driven by young MSM with higher education level and recent infection. Nevertheless, leveraging network inference, we identified actively growing clusters that could be prioritized for focused intervention with demographic and risk characteristics that do not necessarily reflect the ones observed in the overall clustering population. Further studies evaluating different models to predict growing clusters are warranted. Focused interventions will have to consider structural and risk disparities between the MSM and the heterosexual populations.

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The Impact of Clinical, Demographic and Risk Factors on Rates of HIV Transmission: A Population-based Phylogenetic Analysis in British Columbia, Canada

Abstract: Hotspots of ongoing HIV transmission can be characterized in near real time by the secondary analysis of HIV resistance genotypes, providing an important potential resource for targeting public health initiatives for HIV prevention.

Cited by 93 publications

References 30 publications

The International Dimension of the U.S. HIV Transmission Network and Onward Transmission of HIV Recently Imported into the United States

The International Dimension of the U.S. HIV Transmission Network and Onward Transmission of HIV Recently Imported into the United States

Reconstructing contact network parameters from viral phylogenies

Characteristics and growth of the genetic HIV transmission network of Mexico City during 2020

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