Imperial College London

DrErikVolz

Faculty of MedicineSchool of Public Health

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 1933e.volz Website

 
 
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Location

 

UG10Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Publication Type
Year
to

69 results found

Flaxman S, Mishra S, Gandy A, Unwin H, Coupland H, Mellan T, Zhu H, Berah T, Eaton J, Perez Guzman P, Schmit N, Cilloni L, Ainslie K, Baguelin M, Blake I, Boonyasiri A, Boyd O, Cattarino L, Ciavarella C, Cooper L, Cucunuba Perez Z, Cuomo-Dannenburg G, Dighe A, Djaafara A, Dorigatti I, van Elsland S, Fitzjohn R, Fu H, Gaythorpe K, Geidelberg L, Grassly N, Green W, Hallett T, Hamlet A, Hinsley W, Jeffrey B, Jorgensen D, Knock E, Laydon D, Nedjati Gilani G, Nouvellet P, Parag K, Siveroni I, Thompson H, Verity R, Volz E, Walters C, Wang H, Wang Y, Watson O, Winskill P, Xi X, Whittaker C, Walker P, Ghani A, Donnelly C, Riley S, Okell L, Vollmer M, Ferguson N, Bhatt Set al., 2020, Report 13: Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries

Following the emergence of a novel coronavirus (SARS-CoV-2) and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national lockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number – a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of lockdown (11th March), although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented all interventions considered in our analysis. This means that the reproducti

Report

Ferguson N, Laydon D, Nedjati Gilani G, Imai N, Ainslie K, Baguelin M, Bhatia S, Boonyasiri A, Cucunuba Perez Z, Cuomo-Dannenburg G, Dighe A, Dorigatti I, Fu H, Gaythorpe K, Green W, Hamlet A, Hinsley W, Okell L, van Elsland S, Thompson H, Verity R, Volz E, Wang H, Wang Y, Walker P, Walters C, Winskill P, Whittaker C, Donnelly C, Riley S, Ghani Aet al., 2020, Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand

The global impact of COVID-19 has been profound, and the public health threat it represents is the most serious seen in a respiratory virus since the 1918 H1N1 influenza pandemic. Here we present the results of epidemiological modelling which has informed policymaking in the UK and other countries in recent weeks. In the absence of a COVID-19 vaccine, we assess the potential role of a number of public health measures – so-called non-pharmaceutical interventions (NPIs) – aimed at reducing contact rates in the population and thereby reducing transmission of the virus. In the results presented here, we apply a previously published microsimulation model to two countries: the UK (Great Britain specifically) and the US. We conclude that the effectiveness of any one intervention in isolation is likely to be limited, requiring multiple interventions to be combined to have a substantial impact on transmission. Two fundamental strategies are possible: (a) mitigation, which focuses on slowing but not necessarily stopping epidemic spread – reducing peak healthcare demand while protecting those most at risk of severe disease from infection, and (b) suppression, which aims to reverse epidemic growth, reducing case numbers to low levels and maintaining that situation indefinitely. Each policy has major challenges. We find that that optimal mitigation policies (combining home isolation of suspect cases, home quarantine of those living in the same household as suspect cases, and social distancing of the elderly and others at most risk of severe disease) might reduce peak healthcare demand by 2/3 and deaths by half. However, the resulting mitigated epidemic would still likely result in hundreds of thousands of deaths and health systems (most notably intensive care units) being overwhelmed many times over. For countries able to achieve it, this leaves suppression as the preferred policy option. We show that in the UK and US context, suppression will minimally requi

Report

Gaythorpe K, Imai N, Cuomo-Dannenburg G, Baguelin M, Bhatia S, Boonyasiri A, Cori A, Cucunuba Perez Z, Dighe A, Dorigatti I, Fitzjohn R, Fu H, Green W, Hamlet A, Hinsley W, Laydon D, Nedjati Gilani G, Okell L, Riley S, Thompson H, van Elsland S, Volz E, Wang H, Wang Y, Whittaker C, Xi X, Donnelly C, Ghani A, Ferguson Net al., 2020, Report 8: Symptom progression of COVID-19

The COVID-19 epidemic was declared a Public Health Emergency of International Concern (PHEIC) by WHO on 30th January 2020 [1]. As of 8 March 2020, over 107,000 cases had been reported. Here, we use published and preprint studies of clinical characteristics of cases in mainland China as well as case studies of individuals from Hong Kong, Japan, Singapore and South Korea to examine the proportional occurrence of symptoms and the progression of symptoms through time.We find that in mainland China, where specific symptoms or disease presentation are reported, pneumonia is the most frequently mentioned, see figure 1. We found a more varied spectrum of severity in cases outside mainland China. In Hong Kong, Japan, Singapore and South Korea, fever was the most frequently reported symptom. In this latter group, presentation with pneumonia is not reported as frequently although it is more common in individuals over 60 years old. The average time from reported onset of first symptoms to the occurrence of specific symptoms or disease presentation, such as pneumonia or the use of mechanical ventilation, varied substantially. The average time to presentation with pneumonia is 5.88 days, and may be linked to testing at hospitalisation; fever is often reported at onset (where the mean time to develop fever is 0.77 days).

Report

Thompson H, Imai N, Dighe A, Baguelin M, Bhatia S, Boonyasiri A, Cori A, Cucunuba Perez Z, Cuomo-Dannenburg G, Dorigatti I, Fitzjohn R, Fu H, Gaythorpe K, Ghani A, Green W, Hamlet A, Hinsley W, Laydon D, Nedjati Gilani G, Okell L, Riley S, van Elsland S, Volz E, Wang H, Yuanrong W, Whittaker C, Xi X, Donnelly C, Ferguson Net al., 2020, Report 7: Estimating infection prevalence in Wuhan City from repatriation flights

Since the end of January 2020, in response to the growing COVID-19 epidemic, 55 countries have repatriated over 8000 citizens from Wuhan City, China. In addition to quarantine measures for returning citizens, many countries implemented PCR screening to test for infection regardless of symptoms. These flights therefore give estimates of infection prevalence in Wuhan over time. Between 30th January and 1st February (close to the peak of the epidemic in Wuhan), infection prevalence was 0.87% (95% CI: 0.32% - 1.89%). As countries now start to repatriate citizens from Iran and northern Italy, information from repatriated citizens could help inform the level of response necessary to help control the outbreaks unfolding in newly affected areas.

Report

Nascimento FF, Baral S, Geidelberg L, Mukandavire C, Schwartz SR, Turpin G, Turpin N, Diouf D, Diouf NL, Coly K, Kane CT, Ndour C, Vickerman P, Boily M-C, Volz EMet al., 2020, Phylodynamic analysis of HIV-1 subtypes B, C and CRF 02_AG in Senegal, EPIDEMICS, Vol: 30, ISSN: 1755-4365

Journal article

Bhatia S, Imai N, Cuomo-Dannenburg G, Baguelin M, Boonyasiri A, Cori A, Cucunuba Perez Z, Dorigatti I, Fitzjohn R, Fu H, Gaythorpe K, Ghani A, Hamlet A, Hinsley W, Laydon D, Nedjati Gilani G, Thompson H, Okell L, Riley S, van Elsland S, Volz E, Wang H, Wang Y, Whittaker C, Xi X, Donnelly C, Ferguson Net al., 2020, Report 6: Relative sensitivity of international surveillance, Report 6: Relative sensitivity of international surveillance

Since the start of the COVID-19 epidemic in late 2019, there are now 29 affected countries with over 1000 confirmed cases outside of mainland China. In previous reports, we estimated the likely epidemic size in Wuhan City based on air traffic volumes and the number of detected cases internationally. Here we analysed COVID-19 cases exported from mainland China to different regions and countries, comparing the country-specific rates of detected and confirmed cases per flight volume to estimate the relative sensitivity of surveillance in different countries. Although travel restrictions from Wuhan City and other cities across China may have reduced the absolute number of travellers to and from China, we estimated that about two thirds of COVID-19 cases exported from mainland China have remained undetected worldwide, potentially resulting in multiple chains of as yet undetected human-to-human transmission outside mainland China.

Report

Volz E, Baguelin M, Bhatia S, Boonyasiri A, Cori A, Cucunuba Perez Z, Cuomo-Dannenburg G, Donnelly C, Dorigatti I, Fitzjohn R, Fu H, Gaythorpe K, Ghani A, Hamlet A, Hinsley W, Imai N, Laydon D, Nedjati Gilani G, Okell L, Riley S, van Elsland S, Wang H, Wang Y, Xi X, Ferguson Net al., 2020, Report 5: Phylogenetic analysis of SARS-CoV-2

Genetic diversity of SARS-CoV-2 (formerly 2019-nCoV), the virus which causes COVID-19, provides information about epidemic origins and the rate of epidemic growth. By analysing 53 SARS-CoV-2 whole genome sequences collected up to February 3, 2020, we find a strong association between the time of sample collection and accumulation of genetic diversity. Bayesian and maximum likelihood phylogenetic methods indicate that the virus was introduced into the human population in early December and has an epidemic doubling time of approximately seven days. Phylodynamic modelling provides an estimate of epidemic size through time. Precise estimates of epidemic size are not possible with current genetic data, but our analyses indicate evidence of substantial heterogeneity in the number of secondary infections caused by each case, as indicated by a high level of over-dispersion in the reproduction number. Larger numbers of more systematically sampled sequences – particularly from across China – will allow phylogenetic estimates of epidemic size and growth rate to be substantially refined.

Report

Volz E, Wiuf C, Grad YH, Frost SDW, Dennis AM, Didelot Xet al., 2020, Identification of hidden population structure in time-scaled phylogenies, Systematic Biology, ISSN: 1063-5157

Abstract Population structure influences genealogical patterns, however data pertaining to how populations are structured are often unavailable or not directly observable. Inference of population structure is highly important in molecular epidemiology where pathogen phylogenetics is increasingly used to infer transmission patterns and detect outbreaks. Discrepancies between observed and idealised genealogies, such as those generated by the coalescent process, can be quantified, and where significant differences occur, may reveal the action of natural selection, host population structure, or other demographic and epidemiological heterogeneities. We have developed a fast non-parametric statistical test for detection of cryptic population structure in time-scaled phylogenetic trees. The test is based on contrasting estimated phylogenies with the theoretically expected phylodynamic ordering of common ancestors in two clades within a coalescent framework. These statistical tests have also motivated the development of algorithms which can be used to quickly screen a phylogenetic tree for clades which are likely to share a distinct demographic or epidemiological history. Epidemiological applications include identification of outbreaks in vulnerable host populations or rapid expansion of genotypes with a fitness advantage. To demonstrate the utility of these methods for outbreak detection, we applied the new methods to large phylogenies reconstructed from thousands of HIV-1 partial pol sequences. This revealed the presence of clades which had grown rapidly in the recent past, and was significantly concentrated in young men, suggesting recent and rapid transmission in that group. Furthermore, to demonstrate the utility of these methods for the study of antimicrobial resistance, we applied the new methods to a large phylogeny reconstructed from whole genome Neisseria gonorrhoeae sequences. We find that population structure detected using these methods closely overlaps with th

Journal article

Dorigatti I, Okell L, Cori A, Imai N, Baguelin M, Bhatia S, Boonyasiri A, Cucunuba Perez Z, Cuomo-Dannenburg G, Fitzjohn R, Fu H, Gaythorpe K, Hamlet A, Hinsley W, Hong N, Kwun M, Laydon D, Nedjati Gilani G, Riley S, van Elsland S, Volz E, Wang H, Walters C, Xi X, Donnelly C, Ghani A, Ferguson Net al., 2020, Report 4: Severity of 2019-novel coronavirus (nCoV)

We present case fatality ratio (CFR) estimates for three strata of 2019-nCoV infections. For cases detected in Hubei, we estimate the CFR to be 18% (95% credible interval: 11%-81%). For cases detected in travellers outside mainland China, we obtain central estimates of the CFR in the range 1.2-5.6% depending on the statistical methods, with substantial uncertainty around these central values. Using estimates of underlying infection prevalence in Wuhan at the end of January derived from testing of passengers on repatriation flights to Japan and Germany, we adjusted the estimates of CFR from either the early epidemic in Hubei Province, or from cases reported outside mainland China, to obtain estimates of the overall CFR in all infections (asymptomatic or symptomatic) of approximately 1% (95% confidence interval 0.5%-4%). It is important to note that the differences in these estimates does not reflect underlying differences in disease severity between countries. CFRs seen in individual countries will vary depending on the sensitivity of different surveillance systems to detect cases of differing levels of severity and the clinical care offered to severely ill cases. All CFR estimates should be viewed cautiously at the current time as the sensitivity of surveillance of both deaths and cases in mainland China is unclear. Furthermore, all estimates rely on limited data on the typical time intervals from symptom onset to death or recovery which influences the CFR estimates.

Report

Li Y, Liu H, Ramadhani HO, Ndembi N, Crowell TA, Kijak G, Robb ML, Ake JA, Kokogho A, Nowak RG, Gaydos C, Baral SD, Volz E, Tovanabutra S, Charurat M, TRUSTRV368 Study Groupet al., 2020, Genetic clustering analysis for HIV infection among MSM in Nigeriaimplications for intervention, AIDS, Vol: 34, Pages: 227-236, ISSN: 0269-9370

BACKGROUND: The HIV epidemic continues to grow among MSM in countries across sub-Saharan Africa including Nigeria. To inform prevention efforts, we used a phylogenetic cluster method to characterize HIV genetic clusters and factors associated with cluster formation among MSM living with HIV in Nigeria. METHODS: We analyzed HIV-1 pol sequences from 417 MSM living with HIV enrolled in the TRUST/RV368 cohort between 2013 and 2017 in Abuja and Lagos, Nigeria. A genetically linked cluster was defined among participants whose sequences had pairwise genetic distance of 1.5% or less. Binary and multinomial logistic regressions were used to estimate adjusted odds ratios (AORs) and 95% confidence intervals (CIs) for factors associated with HIV genetic cluster membership and size. RESULTS: Among 417 MSM living with HIV, 153 (36.7%) were genetically linked. Participants with higher viral load (AOR = 1.72 95% CI: 1.04-2.86), no female partners (AOR = 3.66; 95% CI: 1.97-6.08), and self-identified as male sex (compared with self-identified as bigender) (AOR = 3.42; 95% CI: 1.08-10.78) had higher odds of being in a genetic cluster. Compared with unlinked participants, MSM who had high school education (AOR = 23.84; 95% CI: 2.66-213.49), were employed (AOR = 3.41; 95% CI: 1.89-10.70), had bacterial sexually transmitted infections (AOR = 3.98; 95% CI: 0.89-17.22) and were not taking antiretroviral therapy (AOR = 6.61; 95% CI: 2.25-19.37) had higher odds of being in a large cluster (size > 4). CONCLUSION: Comprehensive HIV prevention packages should include behavioral and biological components, including early diagnosis and treatment of both HIV and bacterial sexually transmitted infections to optimally reduce the risk of HIV transmission and acquisition.

Journal article

Le Vu S, Ratmann O, Delpech V, Brown AE, Gill ON, Tostevin A, Dunn D, Fraser C, Volz Eet al., 2019, HIV-1 transmission patterns in men who have sex with men: insights from genetic source attribution analysis, AIDS Research and Human Retroviruses, Vol: 39, Pages: 805-813, ISSN: 0889-2229

BACKGROUND: Near 60% of new HIV infections in the United Kingdom are estimated to occur in men who have sex with men (MSM). Age-disassortative partnerships in MSM have been suggested to spread the HIV epidemics in many Western developed countries 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 public health interventions. METHODS: We analyzed combined epidemiologic data and viral sequences from MSM diagnosed with HIV at the national level. We applied a phylodynamic source attribution model to infer patterns of transmission between groups of patients. RESULTS: From pair probabilities of transmission between 14 603 MSM patients, we found that potential transmitters of HIV subtype B were on average 8 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.

Journal article

Volz EM, Siveroni I, 2018, Bayesian phylodynamic inference with complex models, PLoS Computational Biology, Vol: 14, ISSN: 1553-734X

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.

Journal article

Dennis AM, Volz E, Frost SDW, Hossain ASMM, Poon AFY, Rebeiro PF, Vermund SH, Sterling TR, Kalish MLet 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

More persons living with HIV reside in the Southern United States than in any other region, yet little is known about HIV molecular epidemiology in the South. We used cluster and phylodynamic analyses to evaluate HIV transmission patterns in middle Tennessee. We performed cross-sectional analyses of HIV-1 pol sequences and clinical data collected from 2001 to 2015 among persons attending the Vanderbilt Comprehensive Care Clinic. Transmission clusters were identified using maximum likelihood phylogenetics and patristic distance differences. Demographic, risk behavior, and clinical factors were assessed evaluating “active” clusters (clusters including sequences sampled 2011–2015) and associations estimated with logistic regression. Transmission risk ratios for men who have sex with men (MSM) were estimated with phylodynamic models. Among 2915 persons (96% subtype-B sequences), 963 (33%) were members of 292 clusters (distance ≤1.5%, size range 2–39). Most clusters (62%, n = 690 persons) were active, either being newly identified (n = 80) or showing expansion on existing clusters (n = 101). Correlates of active clustering among persons with sequences collected during 2011–2015 included MSM risk and ≤30 years of age. Active clusters were significantly more concentrated in MSM and younger persons than historical clusters. Young MSM (YMSM) (≤26.4 years) had high estimated transmission risk [risk ratio = 4.04 (2.85–5.65) relative to older MSM] and were much more likely to transmit to YMSM. In this Tennessee cohort, transmission clusters over time were more concentrated by MSM and younger age, with high transmission risk among and between YMSM, highlighting the importance of interventions among this group. Detecting active clusters could help direct interventions to disrupt ongoing transmission chains.

Journal article

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.

Journal article

Mukandavire C, Walker J, Schwartz S, Boily MC, Marie-Claude B, Leon D, Carrie L, Daouda D, Ben L, Nafissatou Leye D, Fatou D, Karleen C, Remy Serge MM, Safiatou T, Papa Amadou Niang D, Coumba T, Cheikh N, Erik V, Sharmistha M, Stefan B, Peter Vet 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

Journal article

Le Vu S, Ratmann O, Delpech V, Brown AE, Gill ON, Tostevin A, Dunn D, Fraser C, Volz EMet al., 2018, Mixing patterns of HIV transmission among men who have sex with men in the United Kingdom, Publisher: Cold Spring Harbor Laboratory

<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>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.</jats:p></jats:sec>

Working paper

Le Vu SOK, Ratmann O, Delpech V, Brown AE, Gill ON, Tostevin A, Fraser C, Volz EMet 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.

Journal article

Volz EM, Le Vu S, Ratmann O, Tostevin A, Dunn D, Orkin C, O'Shea S, Delpech V, Brown A, Gill N, Fraser C, UK HIV Drug Resistance Databaseet 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.

Journal article

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/.

Working paper

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.

Journal article

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).

Software

Volz EM, Ndembi N, Nowak R, Kijak GH, Idoko J, Dakum P, Royal W, Baral S, Dybul M, Blattner WA, Charurat Met 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.

Journal article

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.

Journal article

Ratmann O, Hodcroft EB, Pickles M, Cori A, Hall M, Lycett S, Colijn C, Dearlove B, Didelot X, Frost S, Hossain M, Joy JB, Kendall M, Kühnert D, Leventhal GE, Liang R, Plazzotta G, Poon A, Rasmussen DA, Stadler T, Volz E, Weis C, Leigh Brown AJ, Fraser Cet 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.

Journal article

Sadasivam RS, Cutrona SL, Luger TM, Volz E, Kinney R, Rao SR, Allison JJ, Houston TKet 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.

Journal article

Aiello AE, Simanek AM, Eisenberg MC, Walsh AR, Davis B, Volz E, Cheng C, Rainey JJ, Uzicanin A, Gao H, Osgood N, Knowles D, Stanley K, Tarter K, Monto ASet 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

Journal article

Volz E, Nowak R, Ndembi N, Kijak G, Baral S, Blattner W, Charurat Met 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

Conference paper

Romero-Severson EO, Volz E, Koopman JS, Leitner T, Ionides ELet 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.

Journal article

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.

Journal article

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.

Journal article

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