Publications
144 results found
Volz E, 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
Nonparametric 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 nonparametric 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 nonparametric 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 are 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 ofβ-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.
Mukandavire C, Walker J, Schwartz S, et al., 2018, Estimating the contribution of key populations towards spread of HIV in Dakar, Senegal, Journal of the International AIDS Society, Vol: 21, ISSN: 1758-2652
IntroductionKey populations including female sex workers (FSW) and men who have sex with men (MSM) bear a disproportionate burden of HIV. However, the role of focusing prevention efforts on these groups for reducing a country’s HIV epidemic is debated. We estimate the extent to which HIV transmission amongst FSW and MSM contributes to overall HIV transmission in Dakar, Senegal, using a dynamic assessment of the population attributable fraction (PAF).MethodsA dynamic transmission model of HIV among FSW, their clients, MSM and the lower-risk adult population was parameterized and calibrated within a Bayesian framework using setting-specific demographic, behavioural, HIV epidemiological, and antiretroviral treatment (ART) coverage data for 1985-2015. We used the model to estimate the 10-year PAF of commercial sex between FSW and their clients, and sex between men, to overall HIV transmission (defined as the percentage of new infections prevented when these modes of transmission are removed). Additionally, we estimated the prevention benefits associated with historical increases in condom use and ART uptake, and impact of further increases in prevention and treatment.ResultsThe model projections suggest that unprotected sex between men contributed to 42% (2.5 to 97.5th percentile range 24-59%) of transmissions between 1995-2005, increasing to 64% (37-79%) from 2015-2025. The 10-year PAF of commercial sex is smaller, diminishing from 21% (7-39%) in 1995 to 14% (5-35%) in 2015. Without ART, 49% (32-71%) more HIV infections would have occurred since 2000, when ART was initiated, whereas without condom use since 1985, 67% (27-179%) more HIV infections would have occurred, and the overall HIV prevalence would have been 60% (29-211%) greater than what it is now. Further large decreases in HIV incidence (68%) can be achieved by scaling up ART in MSM to 74% coverage and reducing their susceptibility to HIV by a two-thirds through any prevention modality.ConclusionsUnprote
Le Vu SOK, 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
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.
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
<jats:title>Abstract</jats:title><jats:p>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<jats:italic>PhyDyn</jats:italic>for the BEAST phylogenetics platform.</jats:p>
Volz E, Didelot X, 2017, Modeling the growth and decline of pathogen effective population size provides insight into epidemic dynamics and drivers of antimicrobial resistance, Publisher: bioRxiv
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
Molecular clock models relate observed genetic diversity to calendar time, enabling estimation of times of common ancestry. Many large datasets of fast-evolving viruses are not well fitted by molecular clock models that assume a constant substitution rate through time, and more flexible relaxed clock models are required for robust inference of rates and dates. Estimation of relaxed molecular clocks using Bayesian Markov chain Monte Carlo is computationally expensive and may not scale well to large datasets. We build on recent advances in maximum likelihood and least-squares phylogenetic and molecular clock dating methods to develop a fast relaxed-clock method based on a Gamma-Poisson mixture model of substitution rates. This method estimates a distinct substitution rate for every lineage in the phylogeny while being scalable to large phylogenies. Unknown lineage sample dates can be estimated as well as unknown root position. We estimate confidence intervals for rates, dates, and tip dates using parametric and non-parametric bootstrap approaches. This method is implemented as an open-source R package, treedater.
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, 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 E, Romero-Severson E, Leitner TK, 2017, Phylodynamic inference across epidemic scales, Molecular Biology and Evolution, Vol: 34, Pages: 1276-1288, ISSN: 1537-1719
Within-host genetic diversity and large transmission bottlenecks confound phylodynamic inference ofepidemiological dynamics. Conventional phylodynamic approaches assume that nodes in a time-scaledpathogen phylogeny correspond closely to the time of transmission between hosts that are ancestral tothe sample. However, when hosts harbour diverse pathogen populations, node times can substantiallypre-date infection times. Imperfect bottlenecks can cause lineages sampled in different individuals tocoalesce in unexpected patterns. To address realistic violations of standard phylodynamic assumptionswe developed a new inference approach based on a multi-scale coalescent model, accounting for nonlinearepidemiological dynamics, heterogeneous sampling through time, non-negligible genetic diversity ofpathogens within hosts, and imperfect transmission bottlenecks. We apply this method to HIV-1 andEbola virus outbreak sequence data, illustrating how and when conventional phylodynamic inference maygive misleading results. Within-host diversity of HIV-1 causes substantial upwards bias in the numberof infected hosts using conventional coalescent models, but estimates using the multi-scale model havegreater consistency with reported number of diagnoses through time. In contrast, we find that within-host diversity of Ebola virus has little influence on estimated numbers of infected hosts or reproductionnumbers, and estimates are highly consistent with the reported number of diagnoses through time.The multi-scale coalescent also enables estimation of within-host effective population size using singlesequences from a random sample of patients. We find within-host population genetic diversity of HIV-1p17 to be 2Nμ= 0.012(95% CI:0.0066−0.023), which is lower than estimates based on HIV envelopeserial sequencing of individual patients.
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: 1537-1719
Viral phylogenetic methods contribute to understanding how HIV spreads in populations, and thereby help guide the design of prevention interventions. So far, most analyses have been applied to well-sampled concentrated HIV-1 epidemics in wealthy countries. To direct the use of phylogenetic tools to where the impact of HIV-1 is greatest, the Phylogenetics And Networks for Generalized HIV Epidemics in Africa (PANGEA-HIV) consortium generates full-genome viral sequences from across sub-Saharan Africa. Analyzing these data presents new challenges, since epidemics are principally driven by heterosexual transmission and a smaller fraction of cases is sampled. Here, we show that viral phylogenetic tools can be adapted and used to estimate epidemiological quantities of central importance to HIV-1 prevention in sub-Saharan Africa. We used a community-wide methods comparison exercise on simulated data, where participants were blinded to the true dynamics they were inferring. Two distinct simulations captured generalized HIV-1 epidemics, before and after a large community-level intervention that reduced infection levels. Five research groups participated. Structured coalescent modeling approaches were most successful: phylogenetic estimates of HIV-1 incidence, incidence reductions, and the proportion of transmissions from individuals in their first 3 months of infection correlated with the true values (Pearson correlation > 90%), with small bias. However, on some simulations, true values were markedly outside reported confidence or credibility intervals. The blinded comparison revealed current limits and strengths in using HIV phylogenetics in challenging settings, provided benchmarks for future methods’ development, and supports using the latest generation of phylogenetic tools to advance HIV surveillance and prevention.
Sadasivam RS, Cutrona SL, Luger TM, et al., 2016, Share2Quit: Online Social Network Peer Marketing of Tobacco Cessation Systems, Nicotine & Tobacco Research, Vol: 19, Pages: 314-323, ISSN: 1469-994X
INTRODUCTION: Although technology-assisted tobacco interventions (TATIs) are effective, they are underused due to recruitment challenges. We tested whether we could successfully recruit smokers to a TATI using peer marketing through a social network (Facebook). METHODS: We recruited smokers on Facebook using online advertisements. These recruited smokers (seeds) and subsequent waves of smokers (peer recruits) were provided the Share2Quit peer recruitment Facebook app and other tools. Smokers were incentivized for up to seven successful peer recruitments and had 30 days to recruit from date of registration. Successful peer recruitment was defined as a peer recruited smoker completing the registration on the TATI following a referral. Our primary questions were (1) whether smokers would recruit other smokers and (2) whether peer recruitment would extend the reach of the intervention to harder-to-reach groups, including those not ready to quit and minority smokers. RESULTS: Overall, 759 smokers were recruited (seeds: 190; peer recruits: 569). Fifteen percent (n = 117) of smokers successfully recruited their peers (seeds: 24.7%; peer recruits: 7.7%) leading to four recruitment waves. Compared to seeds, peer recruits were less likely to be ready to quit (peer recruits 74.2% vs. seeds 95.1%), more likely to be male (67.1% vs. 32.9%), and more likely to be African American (23.8% vs. 10.8%) (p < .01 for all comparisons). CONCLUSIONS: Peer marketing quadrupled our engaged smokers and enriched the sample with not-ready-to-quit and African American smokers. Peer recruitment is promising, and our study uncovered several important challenges for future research. IMPLICATIONS: This study demonstrates the successful recruitment of smokers to a TATI using a Facebook-based peer marketing strategy. Smokers on Facebook were willing and able to recruit other smokers to a TATI, yielding a large and diverse population of smokers.
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
Background: Social networks are increasingly recognized as important points of intervention, yet relatively few intervention studies of respiratory infection transmission have utilized a network design. Here we describe the design, methods, and social network structure of a randomized intervention for isolating respiratory infection cases in a university setting over a 10-week period. Methodology/principal findings: 590 students in six residence halls enrolled in the eX-FLU study during a chain-referral recruitment process from September 2012-January 2013. Of these, 262 joined as "seed" participants, who nominated their social contacts to join the study, of which 328 "nominees" enrolled. Participants were cluster-randomized by 117 residence halls. Participants were asked to respond to weekly surveys on health behaviors, social interactions, and influenza-like illness (ILI) symptoms. Participants were randomized to either a 3-Day dorm room isolation intervention or a control group (no isolation) upon illness onset. ILI cases reported on their isolation behavior during illness and provided throat and nasal swab specimens at onset, day-three, and day-six of illness. A subsample of individuals (= 103) participated in a sub-study using a novel smartphone application, iEpi, which collected sensor and contextually-dependent survey data on social interactions. Within the social network, participants were significantly positively assortative by intervention group, enrollment type, residence hall, iEpi participation, age, gender, race, and alcohol use (all P < 0.002). Conclusions/significance: We identified a feasible study design for testing the impact of isolation from social networks in a university setting. These data provide an unparalleled opportunity to address questions about isolation and infection transmission, as well as insights into social networks and behaviors among college-aged students. Several important lessons were learned over the co
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
Human immunodeficiency virus (HIV) transmission models that include variability in sexual behavior over timehave shown increased incidence, prevalence, and acute-state transmission rates for a given population risk profile.This raises the question of whether dynamic variation in individual sexual behavior is a real phenomenon that can beobserved and measured. To study this dynamic variation, we developed a model incorporating heterogeneity inboth between-person and within-person sexual contact patterns. Using novel methodology that we call iterated filteringfor longitudinal data, we fitted this model by maximum likelihood to longitudinal survey data from the Centersfor Disease Control and Prevention’s Collaborative HIV Seroincidence Study (1992–1995). We found evidence forindividual heterogeneity in sexual behavior over time. We simulated an epidemic process and found that inclusion ofempirically measured levels of dynamic variation in individual-level sexual behavior brought the theoretical predictionsof HIV incidence into closer alignment with reality given the measured per-act probabilities of transmission.The methods developed here provide a framework for quantifying variation in sexual behaviors that helps in understandingthe HIV epidemic among gay men.
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
Many population genetic models have been developed for the purpose of inferring population size and growth rates from random samples of genetic data. We examine two popular approaches to this problem, the coalescent and the birth–death-sampling model (BDM), in the context of estimating population size and birth rates in a population growing exponentially according to the birth–death branching process. For sequences sampled at a single time, we found the coalescent and the BDM gave virtually indistinguishable results in terms of the growth rates and fraction of the population sampled, even when sampling from a small population. For sequences sampled at multiple time points, we find that the birth–death model estimators are subject to large bias if the sampling process is misspecified. Since BDMs incorporate a model of the sampling process, we show how much of the statistical power of BDMs arises from the sequence of sample times and not from the genealogical tree. This motivates the development of a new coalescent estimator, which is augmented with a model of the known sampling process and is potentially more precise than the coalescent that does not use sample time information.
Volz E, Pond S, 2014, Phylodynamic analysis of ebola virus in the 2014 sierra leone epidemic., PLoS Currents, Vol: 6, ISSN: 2157-3999
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.
Rasmussen DA, Volz EM, Koelle K, 2014, Phylodynamic Inference for Structured Epidemiological Models, PLOS COMPUTATIONAL BIOLOGY, Vol: 10, ISSN: 1553-734X
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- Citations: 56
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 EM, Frost SDW, 2013, Inferring the Source of Transmission with Phylogenetic Data, PLOS COMPUTATIONAL BIOLOGY, Vol: 9
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- Citations: 50
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: 1549-1277
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- Citations: 89
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
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- Citations: 20
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.
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
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- Citations: 12
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
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- Citations: 45
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
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- Citations: 13
Volz EM, Koelle K, Bedford T, 2013, Viral Phylodynamics, PLOS COMPUTATIONAL BIOLOGY, Vol: 9, ISSN: 1553-734X
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- Citations: 227
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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- Citations: 12
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
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