Targeting HIV Prevention Based on Molecular Epidemiology Among Deeply Sampled Subnetworks of Men Who Have Sex With Men

Wang, Xicheng; Wu, Yuan; Lin, Mao; Xia, Wei; Zhang, Weiwei; Dai, Lili; Mehta, Sanjay R.; Wertheim, Joel O.; Dong, Xingqi; Zhang, Tong; Wu, Hao; Smith, Davey M.

doi:10.1093/cid/civ526

Cited by 51 publications

(47 citation statements)

References 38 publications

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“…Contact networks can have a strong influence on epidemic progression, and are potentially useful as a public health tool (Little et al 2014; Wang et al 2015). Despite this, few methods exist for investigating contact network parameters in a phylodynamic framework [although see Leventhal et al 2012, Groendyke et al 2011, Volz 2009, and Leigh Brown et al 2011 for related work].…”

Section: Discussionmentioning

confidence: 99%

“…In turn, these estimates affect the estimates of quantities such as effective viral population size (Goodreau 2006). From a public health perspective, contact networks have been explored as tools for curtailing epidemic spread, by way of interventions targeted to well-connected nodes (Wang et al 2015). True contact networks are a challenging type of data to collect, requiring extensive epidemiological investigation (Welch, Bansal, and Hunter, 2011; Eames et al 2015 ).…”

Section: Introductionmentioning

confidence: 99%

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Reconstructing contact network parameters from viral phylogenies

2016

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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 Barabási-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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reconstructing contact network parameters from viral phylogenies

2016

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show abstract

“…Contact networks can have a strong influence on epidemic progression, and are potentially useful as a public health tool (Little et al 2014;Wang et al 2015). Despite this, few methods exist for investigating contact network parameters in a phylodynamic framework [although see Leventhal et al 2012, Groendyke et al 2011, Volz 2009, and Leigh Brown et al 2011.…”

Section: Discussionmentioning

confidence: 99%

“…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 distribution of cluster sizes in an HIV-1 population sequence is usually found to be heterogeneous in network analysis, consistent with different rates of transmission for different subpopulations or individuals [5657]. This heterogeneity suggests that interventions to prevent transmission should be targeted towards those with the highest risk of transmitting the virus to others [5859]. Not surprisingly, studies in which sequence data are combined with behavioral risk factors have shown that sequences from individuals with the same risk factors are likely to cluster with one another [56].…”

Section: Transmission Network Analysis Of Hivmentioning

confidence: 96%

Molecular Epidemiology of Human Immunodeficiency Virus

Chin

2017

Infect Chemother

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During the evolution of human immunodeficiency virus (HIV), transmissions between humans and primates resulted in multiple HIV lineages in humans. This evolution has been rapid, giving rise to a complex classification and allowing for worldwide spread and intermixing of subtypes, which has consequently led to dozens of circulating recombinant forms. In the Republic of Korea, 12,522 cases of HIV infection have been reported between 1985, when AIDS was first identified, and 2015. This review focuses on the evolution of HIV infection worldwide and the molecular epidemiologic characteristics of HIV in Korea.

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Targeting HIV Prevention Based on Molecular Epidemiology Among Deeply Sampled Subnetworks of Men Who Have Sex With Men

Cited by 51 publications

References 38 publications

Reconstructing contact network parameters from viral phylogenies

Reconstructing contact network parameters from viral phylogenies

Reconstructing contact network parameters from viral phylogenies

Molecular Epidemiology of Human Immunodeficiency Virus

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