58 results found
Dennis AM, Volz E, Frost SDW, et al., 2018, HIV-1 Transmission Clustering and Phylodynamics Highlight the Important Role of Young Men Who Have Sex with Men, AIDS RESEARCH AND HUMAN RETROVIRUSES, Vol: 34, Pages: 879-888, ISSN: 0889-2229
Geidelberg E, Volz E, 2018, A6 Using phylodynamic modelling to estimate the population attributable fraction of HIV spread due to key populations in Dakar, Senegal., Virus evolution, Vol: 4, ISSN: 2057-1577
Le Vu S, Ratmann O, Delpech V, et al., 2018, Mixing patterns of HIV transmission among men who have sex with men in the United Kingdom
Background: Near 60% of new HIV infections in the United Kingdom are estimated to occur in men who have sex with men (MSM). Patterns of mixing between different risk groups of MSM have been suggested to spread the HIV epidemics through age-disassortative partnerships and to contribute to ethnic disparities in infection rates. Understanding these mixing patterns in transmission can help to determine which groups are at a greater risk and guide prevention. Methods: We analyzed combined epidemiologic data and viral sequences from MSM diagnosed with HIV as of mid-2015 at the national level. We applied a phylodynamic source attribution model to infer patterns of transmission between groups of patients by age, ethnicity and region. Results: From pair probabilities of transmission between 19 847 MSM patients, we found that potential transmitters of HIV subtype B were on average 5 months older than recipients. We also found a moderate overall assortativity of transmission by ethnic group and a stronger assortativity by region. Conclusions: Our findings suggest that there is only a modest net flow of transmissions from older to young MSM in subtype B epidemics and that young MSM, both for Black or White groups, are more likely to be infected by one another than expected in a sexual network with random mixing.
Le Vu S, Ratmann O, Delpech V, et al., 2018, Comparison of cluster-based and source-attribution methods for estimating transmission risk using large HIV sequence databases, EPIDEMICS, Vol: 23, Pages: 1-10, ISSN: 1755-4365
Mukandavire C, Walker J, Schwartz S, et al., 2018, Estimating the contribution of key populations towards the spread of HIV in Dakar, Senegal, JOURNAL OF THE INTERNATIONAL AIDS SOCIETY, Vol: 21, ISSN: 1758-2652
Volz E, Siveroni I, 2018, Bayesian phylodynamic inference with complex models
Population genetic modeling can enhance Bayesian phylogenetic inference by providing a realistic prior on the distribution of branch lengths and times of common ancestry. The parameters of a population genetic model may also have intrinsic importance, and simultaneous estimation of a phylogeny and model parameters has enabled phylodynamic inference of population growth rates, reproduction numbers, and effective population size through time. Phylodynamic inference based on pathogen genetic sequence data has emerged as useful supplement to epidemic surveillance, however commonly-used mechanistic models that are typically fitted to non-genetic surveillance data are rarely fitted to pathogen genetic data due to a dearth of software tools, and the theory required to conduct such inference has been developed only recently. We present a framework for coalescent-based phylogenetic and phylodynamic inference which enables highly-flexible modeling of demographic and epidemiological processes. This approach builds upon previous structured coalescent approaches and includes enhancements for computational speed, accuracy, and stability. A flexible markup language is described for translating parametric demographic or epidemiological models into a structured coalescent model enabling simultaneous estimation of demographic or epidemiological parameters and time-scaled phylogenies. We demonstrate the utility of these approaches by fitting compartmental epidemiological models to Ebola virus and Influenza A virus sequence data, demonstrating how important features of these epidemics, such as the reproduction number and epidemic curves, can be gleaned from genetic data. These approaches are provided as an open-source package PhyDyn for the BEAST phylogenetics platform.
Volz EM, Didelot X, 2018, Modeling the Growth and Decline of Pathogen Effective Population Size Provides Insight into Epidemic Dynamics and Drivers of Antimicrobial Resistance, SYSTEMATIC BIOLOGY, Vol: 67, Pages: 719-728, ISSN: 1063-5157
Volz EM, Le Vu S, Ratmann O, et al., 2018, Molecular Epidemiology of HIV-1 Subtype B Reveals Heterogeneous Transmission Risk: Implications for Intervention and Control., J Infect Dis, Vol: 217, Pages: 1522-1529
Background: The impact of HIV pre-exposure prophylaxis (PrEP) depends on infections averted by protecting vulnerable individuals as well as infections averted by preventing transmission by those who would have been infected if not receiving PrEP. Analysis of HIV phylogenies reveals risk factors for transmission, which we examine as potential criteria for allocating PrEP. Methods: We analyzed 6912 HIV-1 partial pol sequences from men who have sex with men (MSM) in the United Kingdom combined with global reference sequences and patient-level metadata. Population genetic models were developed that adjust for stage of infection, global migration of HIV lineages, and changing incidence of infection through time. Models were extended to simulate the effects of providing susceptible MSM with PrEP. Results: We found that young age <25 years confers higher risk of HIV transmission (relative risk = 2.52 [95% confidence interval, 2.32-2.73]) and that young MSM are more likely to transmit to one another than expected by chance. Simulated interventions indicate that 4-fold more infections can be averted over 5 years by focusing PrEP on young MSM. Conclusions: Concentrating PrEP doses on young individuals can avert more infections than random allocation.
Volz EM, Siveroni I, 2018, Bayesian phylodynamic inference with complex models., PLoS Comput Biol, Vol: 14
Population genetic modeling can enhance Bayesian phylogenetic inference by providing a realistic prior on the distribution of branch lengths and times of common ancestry. The parameters of a population genetic model may also have intrinsic importance, and simultaneous estimation of a phylogeny and model parameters has enabled phylodynamic inference of population growth rates, reproduction numbers, and effective population size through time. Phylodynamic inference based on pathogen genetic sequence data has emerged as useful supplement to epidemic surveillance, however commonly-used mechanistic models that are typically fitted to non-genetic surveillance data are rarely fitted to pathogen genetic data due to a dearth of software tools, and the theory required to conduct such inference has been developed only recently. We present a framework for coalescent-based phylogenetic and phylodynamic inference which enables highly-flexible modeling of demographic and epidemiological processes. This approach builds upon previous structured coalescent approaches and includes enhancements for computational speed, accuracy, and stability. A flexible markup language is described for translating parametric demographic or epidemiological models into a structured coalescent model enabling simultaneous estimation of demographic or epidemiological parameters and time-scaled phylogenies. We demonstrate the utility of these approaches by fitting compartmental epidemiological models to Ebola virus and Influenza A virus sequence data, demonstrating how important features of these epidemics, such as the reproduction number and epidemic curves, can be gleaned from genetic data. These approaches are provided as an open-source package PhyDyn for the BEAST2 phylogenetics platform.
Ratmann O, Hodcroft EB, Pickles M, et al., 2017, Phylogenetic Tools for Generalized HIV-1 Epidemics: Findings from the PANGEA-HIV Methods Comparison, MOLECULAR BIOLOGY AND EVOLUTION, Vol: 34, Pages: 185-203, ISSN: 0737-4038
Sadasivam RS, Cutrona SL, Luger TM, et al., 2017, Share2Quit: Online Social Network Peer Marketing of Tobacco Cessation Systems, NICOTINE & TOBACCO RESEARCH, Vol: 19, Pages: 314-323, ISSN: 1462-2203
Siveroni IA, Volz EM, 2017, PhyDyn: Epidemiological Modelling in BEAST
PhyDyn is a BEAST2 package for performing Bayesian phylogenetic inference under models that deal with structured populations with complex population dynamics. This package enables simultaneous estimation of epidemiological parameters and pathogen phylogenies.PhyDyn implements a structured coalescent model for a large class of epidemic processes specified by a deterministic nonlinear dynamical system, and computes the log-likelihood of a gene genealogy conditional on a complex demographic history. Genealogies are specified as timed phylogenetic trees in which lineages are associated with the distinct subpopulation in which they are sampled. Epidemic models are defined by a series of ordinary differential equations (ODEs) specifying the rates that new lineages introduced in the population (birth matrix) and the rates at which migrations, or transition between states occur (migration matrix).
Non-parametric population genetic modeling provides a simple and flexible approach for studying demographic history and epidemic dynamics using pathogen sequence data. Existing Bayesian approaches are premised on stationary stochastic processes which may provide an unrealistic prior for epidemic histories which feature extended period of exponential growth or decline. We show that non-parametric models defined in terms of the growth rate of the effective population size can provide a more realistic prior for epidemic history. We propose a non-parametric autoregressive model on the growth rate as a prior for effective population size, which corresponds to the dynamics expected under many epidemic situations. We demonstrate the use of this model within a Bayesian phylodynamic inference framework. Our method correctly reconstructs trends of epidemic growth and decline from pathogen genealogies even when genealogical data is sparse and conventional skyline estimators erroneously predict stable population size. We also propose a regression approach for relating growth rates of pathogen effective population size and time-varying variables that may impact the replicative fitness of a pathogen. The model is applied to real data from rabies virus and Staphylococcus aureus epidemics. We find a close correspondence between the estimated growth rates of a lineage of methicillin-resistant S. aureus and population-level prescription rates of beta-lactam antibiotics. The new models are implemented in an open source R package called skygrowth which is available at https://mrc-ide.github.io/skygrowth/.
Volz EM, Frost SDW, 2017, Scalable relaxed clock phylogenetic dating, VIRUS EVOLUTION, Vol: 3, ISSN: 2057-1577
Volz EM, Ndembi N, Nowak R, et al., 2017, Phylodynamic analysis to inform prevention efforts in mixed HIV epidemics., Virus Evol, Vol: 3, ISSN: 2057-1577
In HIV epidemics of Sub Saharan Africa, the utility of HIV prevention efforts focused on key populations at higher risk of HIV infection and transmission is unclear. We conducted a phylodynamic analysis of HIV-1 pol sequences from four different risk groups in Abuja, Nigeria to estimate transmission patterns between men who have sex with men (MSM) and a representative sample of newly enrolled treatment naive HIV clients without clearly recorded HIV acquisition risks. We develop a realistic dynamical infectious disease model which was fitted to time-scaled phylogenies for subtypes G and CRF02_AG using a structured-coalescent approach. We compare the infectious disease model and structured coalescent to commonly used genetic clustering methods. We estimate HIV incidence among MSM of 7.9% (95%CI, 7.0-10.4) per susceptible person-year, and the population attributable fraction of HIV transmissions from MSM to reproductive age females to be 9.1% (95%CI, 3.8-18.6), and from the reproductive age women to MSM as 0.2% (95%CI, 0.06-0.3). Applying these parameter estimates to evaluate a test-and-treat HIV strategy that target MSM reduces the total HIV infections averted by half with a 2.5-fold saving. These results suggest the importance of addressing the HIV treatment needs of MSM in addition to cost-effectiveness of specific scale-up of treatment for MSM in the context of the mixed HIV epidemic observed in Nigeria.
Volz EM, Romero-Severson E, Leitner T, 2017, Phylodynamic Inference across Epidemic Scales, MOLECULAR BIOLOGY AND EVOLUTION, Vol: 34, Pages: 1276-1288, ISSN: 0737-4038
Aiello AE, Simanek AM, Eisenberg MC, et al., 2016, Design and methods of a social network isolation study for reducing respiratory infection transmission: The eX-FLU cluster randomized trial, EPIDEMICS, Vol: 15, Pages: 38-55, ISSN: 1755-4365
Volz E, Nowak R, Ndembi N, et al., 2016, Genetic Diversity of HIV Reveals the Epidemiological Role of High Risk Groups in Nigeria, 17th Annual International Meeting of the Institute-of-Human-Virology at the University-of-Maryland-School-of-Medicine, Publisher: LIPPINCOTT WILLIAMS & WILKINS, Pages: 47-47, ISSN: 1525-4135
Romero-Severson EO, Volz E, Koopman JS, et al., 2015, Dynamic Variation in Sexual Contact Rates in a Cohort of HIV-Negative Gay Men, AMERICAN JOURNAL OF EPIDEMIOLOGY, Vol: 182, Pages: 255-262, ISSN: 0002-9262
Rasmussen DA, Volz EM, Koelle K, 2014, Phylodynamic Inference for Structured Epidemiological Models, PLOS COMPUTATIONAL BIOLOGY, Vol: 10, ISSN: 1553-734X
Romero-Severson EO, Meadors GD, Volz EM, 2014, A Generating Function Approach to HIV Transmission with Dynamic Contact Rates, MATHEMATICAL MODELLING OF NATURAL PHENOMENA, Vol: 9, Pages: 121-135, ISSN: 0973-5348
Romero-Severson EO, Meadors GD, Volz EM, 2014, Erratum: A generating function approach to HIV transmission with dynamic contact rates (Mathematical Modelling of Natural Phenomena), Mathematical Modelling of Natural Phenomena, Vol: 9, Pages: 178-181, ISSN: 0973-5348
Volz E, Pond S, 2014, Phylodynamic analysis of ebola virus in the 2014 sierra leone epidemic., PLoS Curr, Vol: 6
BACKGROUND: The Ebola virus (EBOV) epidemic in Western Africa is the largest in recorded history and control efforts have so far failed to stem the rapid growth in the number of infections. Mathematical models serve a key role in estimating epidemic growth rates and the reproduction number (R0) from surveillance data and, recently, molecular sequence data. Phylodynamic analysis of existing EBOV time-stamped sequence data may provide independent estimates of the unobserved number of infections, reveal recent epidemiological history, and provide insight into selective pressures acting upon viral genes. METHODS: We fit a series mathematical models of infectious disease dynamics to phylogenies estimated from 78 whole EBOV genomes collected from distinct patients in May and June of 2014 in Sierra Leone, and perform evolutionary analysis on these genomes combined with closely related EBOV genomes from previous outbreaks. Two analyses are conducted with values of the latent period that have been used in recent modelling efforts. We also examined the EBOV sequences for evidence of possible episodic adaptive molecular evolution during the 2014 outbreak. RESULTS: We find evidence for adaptive evolution affecting L and GP protein coding regions of the EBOV genome, which is unlikely to bias molecular clock and phylodynamic analyses. We estimate R0=2.40 (95% HPD:1.54-3.87 ) if the mean latent period is 5.3 days, and R0=3.81, (95% HPD:2.47-6.3) if the mean latent period is 12.7 days. The estimated coefficient of variation (CV) of the number of transmissions per infected host is very high, and a large proportion of infections yield no transmissions. CONCLUSIONS: Estimates of R0 are sensitive to the unknown latent infectious period which can not be reliably estimated from genetic data alone. EBOV phylogenies show significant evidence for superspreading and extreme variance in the number of transmissions per infected individual during the early epidemic in Sierra Leone.
Volz EM, Frost SDW, 2014, Sampling through time and phylodynamic inference with coalescent and birth-death models, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 11, ISSN: 1742-5689
Alam SJ, Zhang X, Romero-Severson EO, et al., 2013, Detectable signals of episodic risk effects on acute HIV transmission: Strategies for analyzing transmission systems using genetic data, EPIDEMICS, Vol: 5, Pages: 44-55, ISSN: 1755-4365
Frost SDW, Volz EM, 2013, Modelling tree shape and structure in viral phylodynamics, PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 368, ISSN: 0962-8436
Miller JC, Volz EM, 2013, Model hierarchies in edge-based compartmental modeling for infectious disease spread, JOURNAL OF MATHEMATICAL BIOLOGY, Vol: 67, Pages: 869-899, ISSN: 0303-6812
Miller JC, Volz EM, 2013, Incorporating Disease and Population Structure into Models of SIR Disease in Contact Networks, PLOS ONE, Vol: 8, ISSN: 1932-6203
Romero-Severson EO, Alam SJ, Volz E, et al., 2013, Acute-Stage Transmission of HIV: Effect of Volatile Contact Rates, EPIDEMIOLOGY, Vol: 24, Pages: 516-521, ISSN: 1044-3983
Sadasivam RS, Cutrona SL, Volz E, et al., 2013, Web-based Peer-Driven Chain Referrals for Smoking Cessation, MEDINFO 2013: PROCEEDINGS OF THE 14TH WORLD CONGRESS ON MEDICAL AND HEALTH INFORMATICS, PTS 1 AND 2, Vol: 192, Pages: 357-361, ISSN: 0926-9630
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