50 results found
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 stochastic processes with stationary increments 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 β-lactam antibiotics. The new models are implemented in an open source R package called skygrowth which is available at https://github.com/mrc-ide/skygrowth.
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
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.
Le Vu S, Ratmann O, Delpech V, et al., 2017, Comparison of cluster-based and source-attribution methods for estimating transmission risk using large HIV sequence databases., Epidemics
Phylogenetic clustering of HIV sequences from a random sample of patients can reveal epidemiological transmission patterns, but interpretation is hampered by limited theoretical support and statistical properties of clustering analysis remain poorly understood. Alternatively, source attribution methods allow fitting of HIV transmission models and thereby quantify aspects of disease transmission. A simulation study was conducted to assess error rates of clustering methods for detecting transmission risk factors. We modeled HIV epidemics among men having sex with men and generated phylogenies comparable to those that can be obtained from HIV surveillance data in the UK. Clustering and source attribution approaches were applied to evaluate their ability to identify patient attributes as transmission risk factors. We find that commonly used methods show a misleading association between cluster size or odds of clustering and covariates that are correlated with time since infection, regardless of their influence on transmission. Clustering methods usually have higher error rates and lower sensitivity than source attribution method for identifying transmission risk factors. But neither methods provide robust estimates of transmission risk ratios. Source attribution method can alleviate drawbacks from phylogenetic clustering but formal population genetic modeling may be required to estimate quantitative transmission risk factors.
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).
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-1polsequences 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., Math Model Nat Phenom, Vol: 9, Pages: 121-135, ISSN: 0973-5348
The basic reproduction number,R0, is often defined as the average number of infections generated by a newly infected individual in a fully susceptible population. The interpretation, meaning, and derivation ofR0are controversial. However, in the context of mean field models,R0demarcates the epidemic threshold below which the infected population approaches zero in the limit of time. In this manner,R0has been proposed as a method for understanding the relative impact of public health interventions with respect to disease eliminations from a theoretical perspective. The use ofR0is made more complex by both the strong dependency ofR0on the model form and the stochastic nature of transmission. A common assumption in models of HIV transmission that have closed form expressions forR0is that a single individual's behavior is constant over time. In this paper we derive expressions for bothR0and probability of an epidemic in a finite population under the assumption that people periodically change their sexual behavior over time. We illustrate the use of generating functions as a general framework to model the effects of potentially complex assumptions on the number of transmissions generated by a newly infected person in a susceptible population. We find that the relationship between the probability of an epidemic andR0is not straightforward, but, that as the rate of change in sexual behavior increases bothR0and the probability of an epidemic also decrease.
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
Frost SDW, Volz EM, 2013, Modelling tree shape and structure in viral phylodynamics., Philos Trans R Soc Lond B Biol Sci, Vol: 368
Epidemiological models have highlighted the importance of population structure in the transmission dynamics of infectious diseases. Using HIV-1 as an example of a model evolutionary system, we consider how population structure affects the shape and the structure of a viral phylogeny in the absence of strong selection at the population level. For structured populations, the number of lineages as a function of time is insufficient to describe the shape of the phylogeny. We develop deterministic approximations for the dynamics of tips of the phylogeny over evolutionary time, the number of 'cherries', tips that share a direct common ancestor, and Sackin's index, a commonly used measure of phylogenetic imbalance or asymmetry. We employ cherries both as a measure of asymmetry of the tree as well as a measure of the association between sequences from different groups. We consider heterogeneity in infectiousness associated with different stages of HIV infection, and in contact rates between groups of individuals. In the absence of selection, we find that population structure may have relatively little impact on the overall asymmetry of a tree, especially when only a small fraction of infected individuals is sampled, but may have marked effects on how sequences from different subpopulations cluster and co-cluster.
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, Pages: e69162-e69162
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
Sadasivam RS, Volz EM, Kinney RL, et al., 2013, Share2Quit: Web-Based Peer-Driven Referrals for Smoking Cessation., JMIR Res Protoc, Vol: 2, ISSN: 1929-0748
BACKGROUND: Smoking is the number one preventable cause of death in the United States. Effective Web-assisted tobacco interventions are often underutilized and require new and innovative engagement approaches. Web-based peer-driven chain referrals successfully used outside health care have the potential for increasing the reach of Internet interventions. OBJECTIVE: The objective of our study was to describe the protocol for the development and testing of proactive Web-based chain-referral tools for increasing the access to Decide2Quit.org, a Web-assisted tobacco intervention system. METHODS: We will build and refine proactive chain-referral tools, including email and Facebook referrals. In addition, we will implement respondent-driven sampling (RDS), a controlled chain-referral sampling technique designed to remove inherent biases in chain referrals and obtain a representative sample. We will begin our chain referrals with an initial recruitment of former and current smokers as seeds (initial participants) who will be trained to refer current smokers from their social network using the developed tools. In turn, these newly referred smokers will also be provided the tools to refer other smokers from their social networks. We will model predictors of referral success using sample weights from the RDS to estimate the success of the system in the targeted population. RESULTS: This protocol describes the evaluation of proactive Web-based chain-referral tools, which can be used in tobacco interventions to increase the access to hard-to-reach populations, for promoting smoking cessation. CONCLUSIONS: Share2Quit represents an innovative advancement by capitalizing on naturally occurring technology trends to recruit smokers to Web-assisted tobacco interventions.
Volz EM, Frost SDW, 2013, Inferring the Source of Transmission with Phylogenetic Data, PLOS COMPUTATIONAL BIOLOGY, Vol: 9, ISSN: 1553-7358
Volz EM, Ionides E, Romero-Severson EO, et al., 2013, HIV-1 Transmission during Early Infection in Men Who Have Sex with Men: A Phylodynamic Analysis, PLOS MEDICINE, Vol: 10, ISSN: 1549-1676
Volz EM, Koelle K, Bedford T, 2013, Viral Phylodynamics, PLOS COMPUTATIONAL BIOLOGY, Vol: 9, ISSN: 1553-7358
Bauermeister JA, Zimmerman MA, Johns MM, et al., 2012, Innovative Recruitment Using Online Networks: Lessons Learned From an Online Study of Alcohol and Other Drug Use Utilizing a Web-Based, Respondent-Driven Sampling (webRDS) Strategy, JOURNAL OF STUDIES ON ALCOHOL AND DRUGS, Vol: 73, Pages: 834-838, ISSN: 1937-1888
Miller JC, Slim AC, Volz EM, 2012, Edge-based compartmental modelling for infectious disease spread, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 9, Pages: 890-906, ISSN: 1742-5689
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