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PANGEA-HIV 2

Phylogenetics And Networks for Generalised Epidemics in Africa

Abeler-Dörner, Luciea; Grabowski, Mary K.b; Rambaut, Andrewc; Pillay, Deenand,e; Fraser, Christophea on behalf of the PANGEA consortium

Current Opinion in HIV and AIDS: May 2019 - Volume 14 - Issue 3 - p 173–180
doi: 10.1097/COH.0000000000000542
PHYLOGENETICS IN HIV TRANSMISSION: Edited by Morgane Rolland and Josh Herbeck
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Purpose of review The HIV epidemic in sub-Saharan Africa is far from being under control and the ambitious UNAIDS targets are unlikely to be met by 2020 as declines in per-capita incidence being largely offset by demographic trends. There is an increasing number of proven and specific HIV prevention tools, but little consensus on how best to deploy them.

Recent findings Traditionally, phylogenetics has been used in HIV research to reconstruct the history of the epidemic and date zoonotic infections, whereas more recent publications focus on HIV diversity and drug resistance. However, it is also the most powerful method of source attribution available for the study of HIV transmission. The PANGEA (Phylogenetics And Networks for Generalized Epidemics in Africa) consortium has generated over 18 000 NGS HIV sequences from five countries in sub-Saharan Africa. Using phylogenetic methods, we will identify characteristics of individuals or groups, which are most likely to be at risk of infection or at risk of infecting others.

Summary Combining phylogenetics, phylodynamics and epidemiology will allow PANGEA to highlight where prevention efforts should be focussed to reduce the HIV epidemic most effectively. To maximise the public health benefit of the data, PANGEA offers accreditation to external researchers, allowing them to access the data and join the consortium. We also welcome submissions of other HIV sequences from sub-Saharan Africa to the database.

aBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK

bDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Rakai Health Sciences Program, Baltimore, USA

cInstitute of Evolutionary Biology, University of Edinburgh, Ashworth Laboratories, Edinburgh, UK

dAfrica Health Research Institute, KwaZulu-Natal, South Africa

eDivision of Infection and Immunity, University College London, London, UK

Correspondence to Mary K. Grabowski, PhD, ScM, Johns Hopkins University, Baltimore, USA. E-mail: lucie.abeler-dorner@bdi.ox.ac.uk; mgrabows@jhu.edu

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INTRODUCTION

The current review will give a brief overview of how the PANGEA consortium (Phylogenetics And Networks for Generalized Epidemics in Africa) will contribute to understanding the HIV epidemics in sub-Saharan Africa.

In recent years, most phylogenetic studies of HIV sequences sampled in sub-Saharan Africa have focussed on drug resistance mutations and their transmission [1–4], or classification of subtypes and evolutionary questions of HIV diversity [5–8]. However, sequence data is increasingly being used to construct transmission networks to inform prevention efforts [9], and to inform epidemiological studies [10▪,11▪]. Studies which combine phylogenetics and epidemiology to characterize sources of transmission in key populations and to detect sources of transmission and outbreaks have previously been conducted in other regions [12,13,14▪,15▪,16▪▪]. However, such studies remain rare in sub-Saharan Africa as sequences are not available in as large numbers as in North American or European settings [17].

To date, PANGEA has generated over 18 000 NGS HIV sequences from five countries in sub-Saharan Africa, from diverse settings including cohorts of the population-based cohorts from surveillance sites (Rakai Community Cohort Study) [18▪▪], the Mochudi Prevention Project [19,20], the MRC/UVRI Uganda population-based cohorts, and fisherfolk cohorts [21–23] an MRC/UVRI Uganda cohort of female sex-workers [24], a cohort of HIV-1 drug-resistant individuals from northern KwaZulu-Natal in South Africa (Africa Health Research Institute Resistance Cohort) [25], participants from the Partners in Prevention HSV/HIV Transmission Study [26], and participants from the TasP trial [27]. Data from the HPTN 071-2 (PopART) trial Phylogenetics ancillary study [28] will become available in spring 2020. Below we will outline some of the key questions that PANGEA is planning to address with these data.

Box 1

Box 1

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AIMS OF THE PHYLOGENETICS AND NETWORKS FOR GENERALIZED EPIDEMICS IN AFRICA CONSORTIUM

The overarching goal of the consortium is to use phylogenetic methods to identify the characteristics of individuals and groups that make them more likely to be at higher risk of infection (sinks), at higher risk of infecting others (sources), or both (hubs), and to translate these results into information that is directly actionable in HIV prevention [29]. Potentially relevant characteristics include age, sex, geography, occupation, cultural preferences and norms, migrational behaviour, riskiness of sexual behaviour, and use of preexposure prophylaxis (PreP). Figure 1 shows a schematic overview of sources, sinks, and hubs.

FIGURE 1

FIGURE 1

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ROLE OF PREVALENCE ‘HOTSPOTS’

The HIV epidemic in sub-Saharan Africa is markedly heterogeneous with foci of high HIV incidence and prevalence communities (i.e. hotspots) among larger, relatively low-risk general populations [30]. In East Africa, hotspots include Lake Victoria fishing communities, which contain disproportionate numbers of key populations at high risk of infection, such as sex workers [21,31]. In Southern Africa, similar high prevalence foci have been observed along major trading routes and close to mines [32,33]. Using viral phylogenetics, we are measuring viral flow between high-prevalence and low-prevalence communities to quantify the attributable fraction of cases arising from hotspots. Bbosa et al.[34▪], for example, discovered that the HIV epidemic in fishing communities around Lake Victoria acts as a sink in comparison to inland communities, contrary to what was previously thought. Ratmann et al.[35] demonstrated how source-sink analyses including resolving the direction of transmission can be carried out using NGS data.

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MOBILE POPULATIONS AND THEIR ROLE IN SUSTAINING LOCAL EPIDEMICS

Migration and mobility are important components in understanding the spread of HIV in many African communities [36,37]. There is growing evidence that mobile population are at higher risk for HIV and are less likely to be tested and linked to HIV care and treatment [32,37,38]. Consequently, these populations most likely have a key role in ongoing epidemic dynamics. For example, country-wide HIV incidence rates in Botswana have not declined despite achieving 90-90-90, which may be because Botswanan policy prohibits provision of HIV treatment to migrant populations [39]. Mobility is also a key factor in the interpretation of community randomized trials of HIV prevention [40]. If HIV transmissions are common between control and intervention arms, the impact of Universal Test and Treat (UTT) inventions will likely be underestimated. With phylogenetic approaches, these events can be quantified and adjusted for when analysing treatment effects [41]. Using phylogenetics, we are characterizing the role of mobile groups and nonmobile groups to ongoing HIV spread in the geographic regions represented in the PANGEA-HIV consortium, including Botswana and the areas of the two included UTT trials – HPTN 071 (PopART) and ANRS 12249 (TasP). Furthermore, we will analyse the geographical movement of the virus to quantify movement between the cohorts and more distant geographical locations in sub-Saharan Africa.

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CHARACTERISTICS OF TRANSMITTERS AND TRANSMISSIONS

Another factor that can sustain local epidemics, as well as posing a problem for clinical trials and prevention efforts in general, is the heterogeneity of transmitters. Achieving 90-90-90 and having 72% of individuals virally suppressed might not reduce transmission by 72%, if individuals at high risk of infecting others are more likely to be missed. Targeted HIV prevention, therefore, requires an understanding of the risk factors linked to acquiring HIV and those linked to transmitting HIV. The most powerful tool of source attribution currently available to study HIV transmission is the construction of phylogenetic transmission networks [15▪,42,43,44▪▪,45].

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TRANSMISSION NETWORKS

A viral phylogeny reveals who is close to whom in the transmission network [46]. With additional epidemiological information [15▪] or modelling [42] one can identify which of these close individuals are likely sources of new infections. Following theoretical work on incorporating within-host diversity into phylogenetic inference [47▪], we have developed a tool that can be used for source attribution in HIV NGS data [48▪]. Once sources have been identified with quantified uncertainty, epidemiological questions about the characteristics of transmitters can be addressed. For example, transmission networks will enable us to detect individuals that are at high risk of infecting others and quantify their contribution to transmission. More generally, PANGEA aims to identify demographic, clinical, and virological correlates of being a transmitter compared with the background HIV-infected population. Transmission networks combined with mathematical modelling will also allow us to estimate the proportion of transmissions that arise from individuals in early or acute infection [49]. It has long been suspected that the majority of HIV transmissions derive from undiagnosed and untreated individuals in acute infection [50]. There is a worry that acutely infected individuals in hard-to-reach groups will continue to drive the epidemic even as increases in ART coverage lead to a sharp overall fall in incidence. We will construct matrices of who infects whom, stratified, for example, by age and sex. This may reveal motifs of transmission, which have been hypothesised as important drivers of the epidemic [11▪,51▪]. We will document transmissions of both drug-sensitive and drug-resistant viruses, and thus quantify important unknown parameters that influence long-term projections of the risk of drug resistance in generalized epidemics.

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EMERGENCE AND TRANSMISSION OF DRUG RESISTANCE

The rapidly expanding use of ART, both for treatment and prevention, increases the risk of HIV drug resistance. Indeed, prevalence of transmitted resistance is increasing throughout the generalized epidemics in Africa [52]. This supports modelling work suggesting that 15% of new infections could be associated with drug resistance mutations by 2020 [53]. The source of resistance transmission can either be from those with virological failure on treatment, or from those themselves untreated but having recently been infected with drug-resistant virus. The implications of this are profound. Should evidence emerge of spread of transmitted drug resistance, there is a more urgent need to change first line therapy. With dolutegravir soon to be made available, our findings will help to determine how individual countries prioritize treatment guidelines. Phylogenetic approaches can help to distinguish if a patient was likely infected with a drug-resistant virus or acquired drug-resistance mutations while on treatment. NGS data also yields information on whether different drug resistance mutations are found in different viruses within the same patient or if a multiresistant lineage is emerging. Combining both approaches with information on antiretroviral treatment will allow us to better understand how resistance spreads through a population.

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COMBINING PHYLOGENETICS WITH MATHEMATICAL MODELLING OF HIV PREVENTION

Mathematical models of HIV transmission are key tools for evidence synthesis, impact assessment, prioritization of interventions, and prediction. Yet, despite many developments in methods, statistics, and computation, many uncertainties remain in key parameters that affect long-term projections [54]. Uncertainties include the infectiousness of acute infection, estimates of mixing matrices by age and risk group, estimates of between-individual heterogeneity in transmission rates, the existence, size, magnitude and contribution of hidden high-risk subpopulations, and the degree and rate of spatial mixing. These parameters are, at least in principle, in reach of being estimated using phylogenetic analyses. We will use parameters estimated from phylogenetic analysis to inform our mathematical models of the epidemic.

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METHODS DEVELOPMENT

Some of the planned analyses will require the development of new methods. For example, PANGEA samples are not representative samples of the population. We will develop methods to adjust for sampling biases based on local knowledge, using Hidden Markov models, and repeat down-sampling. Some of our populations live in areas with a high share of A/D recombinant viruses. We are developing practical approaches to accommodating recombination in phylogenetic analyses. As part of the first phase of the PANGEA project, we held an open-community exercise to evaluate methods of inferring changes in incidence [55]. Genetic data were simulated under a range of scenarios at the regional and local community level and research groups were invited to apply their methods blinded to the true dynamics. The results of this exercise will be used to guide analysis of the actual PANGEA sequence data but common to all the approaches was the construction of phylogenetic trees from the genome sequence data. State-of-the-art phylogenetics methods are amongst the most computationally intense used in the study of infectious diseases today. We will, therefore, further develop the BEAST software packages [56,57] to deal with the challenge of large numbers of sequences and resulting phylogenetic trees. We will use web-based interactive graphics like NextStrain [58] to visualize the resulting complex phylogenies and integrate them with geographical maps and epidemiological data.

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ETHICS OF PHYLOGENETICS

In our experience, consent rates for phylogenetics work are very high among study participants. However, transmission data needs to be handled very carefully to avoid the possibility that individual pairs can be identified, especially as some sexual practices are illegal across African countries. In light of these concerns, the PANGEA consortium convened a think tank meeting in 2017, including ethicists, legal experts, and social scientists, to propose guidelines for phylogenetic analysis [59]. The guidelines highlight the importance of appropriate communication of phylogenetic results, and ensuring public health benefit is balanced against risks to individuals and communities.

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HOW TO GET INVOLVED

There are different ways in which external researchers can be involved with PANGEA (Fig. 2). Details are available on the consortium website (www.pangea-hiv.org).

  1. Sequence your samples with PANGEA: We offer subsidized state-of-the-art NGS sequencing [60] to collaborators who are willing to contribute their sequence data and associated epidemiological data to the PANGEA database 12 months after they have received the data.
  2. Contribute sequence data: If your project is at a stage where you are ready to reach out to a wider range of collaborators or if you are looking for a secure storing place for your data, consider contributing your sequence data to the PANGEA database.
  3. Join the community of PANGEA researchers: The aim of PANGEA is to make the best possible use of the data for public health and scientific discovery, and give credit to the people who generated the data. PANGEA, therefore, operates a data sharing policy that is as open as possible given the sensitivity of the data. External researchers can request access to the data via a concept sheet proposal. After 6 months and two successful updates on progress to the consortium, researchers named on the concept sheet proposal can apply to become an accredited PANGEA researcher. Accredited PANGEA researchers have access to all sequence data and less sensitive metadata. They are required to agree to a code of conduct and update the consortium regularly on their research. Collaborations with researchers from the institutes that generated the data are encouraged.
FIGURE 2

FIGURE 2

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PHYLOGENETICS AND NETWORKS FOR GENERALIZED EPIDEMICS IN AFRICA DATA

Currently the PANGEA database holds over 18 000 NGS sequence files from sub-Saharan Africa (Table 1) with basic epidemiological metadata associated with them. Details are available on the consortium website (www.pangea-hiv.org). For some cohorts, in-depth metadata is available; contact us for more information. Currently we hold 6766 sequences from Uganda, 1749 from South Africa, 2530 from Botswana, and 29 from Kenya. Sequencing from Kenya is in progress and further sequencing is planned from Uganda and South Africa. Around 7500 sequences from Zambia will become available in early 2020. Overall, 65% of the sequences span the full genome or nearly the full genome (>7000 base pairs), 18% of sequences are partial genomes with at least 3000 base pairs and 17% of sequences contain at least 1000 base pairs. Using an improved protocol [60], we currently obtain full genomes from close to 90% of high-quality samples, with many of the less-complete sequences originating from participants who are likely already virally suppressed.

Table 1

Table 1

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CONCLUSION

Studies of transmission precede the use of phylogenetic methods in epidemiology [61–67]. However, phylogenetics can be in many ways more informative and/or more feasible than classical epidemiology or longitudinal studies of serodiscordant couples. Viral phylogenetic data cannot only complement epidemiological data and fill the gaps, it can also provide clinical information, for example, drug resistance information, more cheaply. Viral data can be compared more easily across studies than questionnaires and phylogenetics is much more scalable than partner studies or contact tracing. Crucially, phylogenetics also provides information on transmissions on a different level. Classical epidemiology will tell us that circumcision modifies the transmission rate per sexual contact, but phylogenetics will tell us in addition that uncircumcised individuals are an epidemic driver in a certain population. Phylogenetics can, therefore, help determine how the HIV virus moves through a population and can be translated into directly actionable information for public health, for example, suggesting, which groups within a population need more support and should be prioritized for interventions.

Having generated over 18 000 full or partial NGS genomes from five countries in Eastern and Southern Africa, PANGEA will focus on analysing source–sink dynamics, addressing the impact of mobility and migration, identifying and interpreting patterns of drug resistance, and describing the wider phylodynamic context of HIV spread. Results will be used to guide recommendations for HIV treatment and prevention policy in sub-Saharan Africa. PANGEA is committed to maximize the public health benefit of the data and welcomes project proposals and data contributions from researchers who share our aims.

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Acknowledgements

We thank all members of the PANGEA consortium for making this project possible, Dr William Probert, Dr Chris Wymant, and Dr Tanya Golubchik, and the members of the PANGEA 2 Steering Committee for critical reading of the manuscript, and Larisa Gavrila for help with the references.

PANGEA is funded by the Bill & Melinda Gates Foundation (consecutive grants OPP1084362 and OPP1175094).

Members of the PANGEA consortium are listed below: Helen Ayles, Clinical Research Department, Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK, and Zambart, University of Zambia, Lusaka, Zambia; David Bonsall, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Rory Bowden, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; Vincent Calvez, Institut Camille Jordan, Université de Lyon, Université Claude Bernard, CNRS UMR 5208, Lyon, France; Max Essex, Research Laboratory, Botswana Harvard AIDS Institute Partnership, Gaborone, Botswana, and Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, USA; Sarah Fidler, St Mary's Hospital and Imperial College, London, UK; Tanya Golubchik, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Richard Hayes, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; Joshua T Herbeck, Department of Global Health, University of Washington, Seattle, USA; Joseph Kagaayi, Rakai Health Sciences Program, Kalisizo, Uganda; Pontiano Kaleebu, Medical Research Council/Uganda Virus Research Institute and London School of Hygiene & Tropical Medicine Uganda Research Unit, Entebbe, Uganda; Jairam R Lingappa, Departments of Global Health, Medicine and Pediatrics, University of Washington, Seattle, USA; Vladimir Novitsky, Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, USA; Thomas C Quinn, National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), Baltimore, USA, and Department of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, USA; Oliver Ratmann, Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, UK, and Department of Mathematics, Imperial College London, London, UK; Janet Seeley, Africa Health Research Institute, KwaZulu-Natal, South Africa, and Department of Global Health and Development, London School of Hygiene & Tropical Medicine, London, UK; Deogratius Ssemwanga, Medical Research Council/Uganda Virus Research Institute and London School of Hygiene & Tropical Medicine Uganda Research Unit, Entebbe, Uganda; Frank Tanser, Africa Health Research Institute, KwaZulu-Natal, South Africa; School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa; Research Department of Infection & Population Health, University College London, London, UK; and Centre for the AIDS Programme of Research in South Africa (CAPRISA), Durban, South Africa; and Maria J Wawer, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Rakai Health Sciences Program, Baltimore, USA

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Financial support and sponsorship

None.

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Conflicts of interest

There are no conflicts of interest.

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REFERENCES AND RECOMMENDED READING

Papers of particular interest, published within the annual period of review, have been highlighted as:

  • ▪ of special interest
  • ▪▪ of outstanding interest
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REFERENCES

1. Onywera H, Maman D, Inzaule S, et al. Surveillance of HIV-1 pol transmitted drug resistance in acutely and recently infected antiretroviral drug-naïve persons in rural western Kenya. PLoS One 2017; 12:e0171124.
2. Arimide DA, Abebe A, Kebede Y, et al. HIV-genetic diversity and drug resistance transmission clusters in Gondar, Northern Ethiopia, 2003–2013. PLoS One 2018; 13:e0205446.
3. Chimukangara B, Kharsany ABM, Lessells RJ, et al. Moderate to high levels of pretreatment HIV drug resistance in KwaZulu-Natal Province, South Africa. AIDS Res Hum Retroviruses 2019; 35:129–138.
4. Coetzee J, Hunt G, Jaffer M, et al. HIV-1 viraemia and drug resistance amongst female sex workers in Soweto, South Africa: a cross sectional study. PLoS One 2017; 12:e0188606.
5. Lee GQ, Bangsberg DR, Mo T, et al. Prevalence and clinical impacts of HIV-1 intersubtype recombinants in Uganda revealed by near-full-genome population and deep sequencing approaches. AIDS 2017; 31:2345–2354.
6. Rodgers MA, Wilkinson E, Vallari A, et al. Sensitive next-generation sequencing method reveals deep genetic diversity of HIV-1 in the Democratic Republic of the Congo. J Virol 2017; 91:e01841–01816.
7. Sivay MV, Hudelson SE, Wang J, et al. HIV-1 diversity among young women in rural South Africa: HPTN 068. PLoS One 2018; 13:e0198999.
8. Tongo M, Harkins GW, Dorfman JR, et al. Unravelling the complicated evolutionary and dissemination history of HIV-1M subtype A lineages. Virus Evol 2018; 4:vey003.
9. Dennis AM, Cohen MS, Rucinski KB, et al. HIV-1 transmission among persons with acute HIV-1 infection in Malawi: demographic, behavioral and phylogenetic relationships. Clinical Infectious Diseases 2018; https://doi.org/10.1093/cid/ciy1006
10▪. Kiwuwa-Muyingo S, Nazziwa J, Ssemwanga D, et al. HIV-1 transmission networks in high risk fishing communities on the shores of Lake Victoria in Uganda: a phylogenetic and epidemiological approach. PLoS One 2017; 12:e0185818.

The authors analyse the dynamics of HIV transmission in very high prevalence and marginalized fishing communities on Lake Victoria, Uganda. They infer large amounts of local transmission.

11▪. de Oliveira T, Kharsany ABM, Gräf T, et al. Transmission networks and risk of HIV infection in KwaZulu-Natal, South Africa: a community-wide phylogenetic study. Lancet HIV 2017; 4:e41–e50.

The authors use phylogenetics to identify key motifs of transmission in the epidemic, and focus in particular on the role of age-discordant male-to-female transmission as mechanism that renews and so drives the epidemic. This study has been influential in the question it addresses. As the methods have been questioned [51▪], the question requires further analysis.

12. Brenner BG, Roger M, Routy J-P, et al. Quebec Primary HIV Infection Study Group. High rates of forward transmission events after acute/early HIV-1 infection. J Infect Dis 2007; 195:951–959.
13. Oster AM, Dorell CG, Mena LA, et al. HIV risk among young African American men who have sex with men: a case-control study in Mississippi. Am J Public Health 2011; 101:137–143.
14▪. Volz EM, Ionides E, Romero-Severson EO, et al. HIV-1 transmission during early infection in men who have sex with men: a phylodynamic analysis. PLoS Med 2013; 10:e1001568.

The authors inferred that, amongst MSM in Detroit, transmission occurring during acute and early infection accounted for 40–50% of transmission. This study demonstrated the power of structured coalescent methods.

15▪. Ratmann O, van Sighem A, Bezemer D, et al. ATHENA observational cohort. cohort Ao: Sources of HIV infection among men having sex with men and implications for prevention. Sci Translat Med 2016; 8:320ra2.

The authors quantify the drivers of the Dutch HIV epidemic amongst MSM by identifying ∼600 probable transmission pairs, with directionality inferred using dates of seroconversion. The article inferred more than half of transmission (65–75%) came from undiagnosed men; that these men tested less often than average; that very little transmission occurred whilst on ART, even with viral blips; that there is a case for the use of PreP to further control the epidemic.

16▪▪. Poon AFY, Gustafson R, Daly P, et al. Near real-time monitoring of HIV transmission hotspots from routine HIV genotyping: an implementation case study. Lancet HIV 2016; 3:e231–e238.

This study demonstrated a system for real-time phylogenetic monitoring of HIV in British Columbia, enabling public health workers to respond to emerging transmission patterns. The system was based on robust randomization, and helped to control a large emerging outbreak.

17. Grabowski MK, Herbeck JT, Poon AFY. Genetic cluster analysis for HIV prevention. Curr HIV/AIDS Rep 2018; 15:182–189.
18▪▪. Grabowski MK, Serwadda DM, Gray RH, et al. Rakai Health Sciences Program. HIV prevention efforts and incidence of HIV in Uganda. New Engl J Med 2017; 377:2154–2166.

This analysis of over 20 years of follow-up data from Rakai, Uganda shows that scale up of ART and voluntary medical male circumcision has likely reduced incidence of new infections by ∼42%.

19. Novitsky V, Bussmann H, Logan A, et al. Phylogenetic relatedness of circulating HIV-1C variants in Mochudi, Botswana. PloS One 2013; 8:e80589.
20. Novitsky V, Kühnert D, Moyo S, et al. Phylodynamic analysis of HIV sub-epidemics in Mochudi, Botswana. Epidemics 2015; 13:44–55.
21. Asiki G, Mpendo J, Abaasa A, et al. HIV and syphilis prevalence and associated risk factors among fishing communities of Lake Victoria, Uganda. Sex Tranm Infect 2011; 87:511.
22. Seeley J, Nakiyingi-Miiro J, Kamali A, et al. CHIVTUM Study Team. High HIV incidence and socio-behavioral risk patterns in fishing communities on the shores of Lake Victoria. Uganda 2012; 39:433–439.
23. Asiki G, Murphy G, Nakiyingi-Miiro J, et al. The general population cohort in rural south-western Uganda: a platform for communicable and noncommunicable disease studies. Int J Epidemiol 2013; 42:129–141.
24. Vandepitte J, Bukenya J, Weiss HA, et al. HIV and other sexually transmitted infections in a cohort of women involved in high-risk sexual behavior in Kampala, Uganda. Sex Transm Dis 2011; 38:316–323.
25. Rossouw TM, Nieuwoudt M, Manasa J, et al. HIV drug resistance levels in adults failing first-line antiretroviral therapy in an urban and a rural setting in South Africa. HIV Med 2017; 18:104–114.
26. Celum C, Wald A, Lingappa JR, et al. Acyclovir and Transmission of HIV-1 from Persons Infected with HIV-1 and HSV-2. New Engl J Med 2010; 362:427–439.
27. Iwuji C, McGrath N, Calmy A, et al. Universal test and treat is not associated with sub-optimal antiretroviral therapy adherence in rural South Africa: the ANRS 12249 TasP trial. Journal of the International AIDS Society 2018; 21:e25112.
28. The HPTN 071-2 Protocol Team: HPTN 071-2: Phylogenetics Protocol, Version 2.0; HPTN 071-2 Phylogenetics in HPTN 071: An ancillary study to ‘Population Effects of Antiretroviral Therapy to Reduce HIV Transmission (PopART): a cluster-randomized trial of the impact of a combination prevention package on population-level HIV incidence in Zambia and South Africa.’ Available at: https://www.hptn.org/sites/default/files/inline-files/HPTN071-2%2CVersion2.0%2807-14-2017%29.pdf; 2017.
29. Pillay D, Herbeck J, Cohen MS, et al. PANGEA-HIV: phylogenetics for generalised epidemics in Africa. Lancet Infect Dis 2015; 15:259–261.
30. Anderson S-J, Cherutich P, Kilonzo N, et al. Maximising the effect of combination HIV prevention through prioritisation of the people and places in greatest need: a modelling study. Lancet 2014; 384:249–256.
31. Chang LW, Grabowski MK, Ssekubugu R, et al. Heterogeneity of the HIV epidemic in agrarian, trading, and fishing communities in Rakai, Uganda: an observational epidemiological study. Lancet HIV 2016; 3:e388–e396.
32. Tanser F, Bärnighausen T, Cooke GS, Newell M-L. Localized spatial clustering of HIV infections in a widely disseminated rural South African epidemic. Int J Epidemiol 2009; 38:1008–1016.
33. Zhou J, Lurie MN, Bärnighausen T, et al. Determinants and spatial patterns of adult overweight and hypertension in a high HIV prevalence rural South African population. Health Place 2012; 18:1300–1306.
34▪. Bbosa N, Ssemwanga D, Nsubuga RN, et al. Phylogeography of HIV-1 suggests that Ugandan fishing communities are a sink for, not a source of, virus from general populations. Sci Rep 2019; 9:1051.

The authors analyse the dynamics of HIV transmission in very high prevalence and marginalized fishing communities on Lake Victoria, Uganda. Contrary to expectations, the study identifies the fishing communities as sinks rather than sources of the HIV infection.

35. Ratmann O, Grabowski MK, Hall M, et al. on behalf of the PANGEA consortium and Rakai Health Sciences Program: inferring HIV-1 transmission networks and sources of ongoing viral spread in Africa with deep sequencing. Nat Commun 2019; (in press).
36. Anglewicz P, VanLandingham M, Manda-Taylor L, Kohler H-P. Migration and HIV infection in Malawi. AIDS 2016; 30:2099–2105.
37. Dobra A, Bärnighausen T, Vandormael A, Tanser F. Space-time migration patterns and risk of HIV acquisition in rural South Africa. AIDS 2017; 31:137–145.
38. Billioux VG, Chang LW, Reynolds SJ, et al. Human immunodeficiency virus care cascade among sub-populations in Rakai, Uganda: an observational study. J Int AIDS Soc 2017; 20:21590.
39. Gaolathe T, Wirth KE, Holme MP, et al. Botswana's progress toward achieving the 2020 UNAIDS 90-90-90 antiretroviral therapy and virological suppression goals: a population-based survey. Lancet HIV 2016; 3:e221–e230.
40. Hayes RJ, Moulton LH. Cluster randomised trials. London, UK: Chapman and Hall/CRC; 2017.
41. Grabowski MK, Redd AD. Molecular tools for studying HIV transmission in sexual networks. Curr Opin HIV AIDS 2014; 9:126–133.
42. Volz EM, Frost SDW. Inferring the source of transmission with phylogenetic data. PLoS Comput Biol 2013; 9:e1003397.
43. Grabowski MK, Lessler J, Redd AD, et al. The role of viral introductions in sustaining community-based HIV epidemics in rural Uganda: evidence from spatial clustering, phylogenetics, and egocentric transmission models. PLoS Med 2014; 11:e1001610.
44▪▪. Cohen MS, Chen YQ, McCauley M, et al. Antiretroviral therapy for the prevention of HIV-1 transmission. New Engl J Med 2016; 375:830–839.

Primary findings of the HPTN 052 study that demonstrated categorically that ART prevents transmission of HIV.

45. Hall M, Woolhouse M, Rambaut A. Epidemic reconstruction in a phylogenetics framework: transmission trees as partitions of the node set. PLoS Comput Biol 2015; 11:e1004613.
46. Lewis F, Hughes GJ, Rambaut A, et al. Episodic sexual transmission of HIV revealed by molecular phylodynamics. PLoS Med 2008; 5:e50.
47▪. Romero-Severson EO, Bulla I, Leitner T. Phylogenetically resolving epidemiologic linkage. Proc Natl Acad Sci U S A 2016; 113:2690.

This study demonstrate that the topology of phylogenies can be used to infer directionality of transmission using multiple sequences per infected host.

48▪. Wymant C, Hall M, Ratmann O, et al. STOP-HCV Consortium, The Maela Pneumococcal Collaboration, and The BEEHIVE Collaboration. PHYLOSCANNER: inferring transmission from within- and between-host pathogen genetic diversity. Mol Biol Evol 2018; 35:719–733.

The authors present algorithms and software for analysing NGS data from HIV to infer transmission using within-host diversity.

49. Volz EM, Koopman JS, Ward MJ, et al. Simple epidemiological dynamics explain phylogenetic clustering of HIV from patients with recent infection. PLoS Comput Biol 2012; 8:e1002552.
50. Wawer MJ, Gray RH, Sewankambo NK, et al. Rates of HIV-1 transmission per coital act, by stage of HIV-1 infection, in Rakai, Uganda. J Infect Dis 2005; 191:1403–1409.
51▪. Grabowski MK, Lessler J. Phylogenetic insights into age-disparate partnerships and HIV. Lancet HIV 2017; 4:e8–e9.

The authors explain why the transmission cycle postulated by de Oliveira et al.[11▪] cannot be inferred from the data presented in the study.

52. Gupta RK, Gregson J, Parkin N, et al. HIV-1 drug resistance before initiation or re-initiation of first-line antiretroviral therapy in low-income and middle-income countries: a systematic review and meta-regression analysis. Lancet Infect Dis 2018; 18:346–355.
53. Bansi-Matharu L, Cambiano V, Apollo T, et al. 90-90-90 by 2020? Estimation and projection of the adult HIV epidemic and ART programme in Zimbabwe - 2017 to 2020. J Int AIDS Soc 2018; 21:e25205.
54. Eaton JW, Johnson LF, Salomon JA, et al. HIV treatment as prevention: systematic comparison of mathematical models of the potential impact of antiretroviral therapy on HIV incidence in South Africa. PLoS Med 2012; 9:e1001245.
55. Ratmann O, Hodcroft EB, Pickles M, et al. PANGEA-HIV Consortium. Phylogenetic tools for generalized HIV-1 epidemics: findings from the PANGEA-HIV methods comparison. Mol Biol Evol 2017; 34:185–203.
56. Suchard MA, Baele G, Lemey P, et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol 2018; 4:vey016.
57. Drummond AJ, Suchard MA, Xie DJMBE. Bayesian phylogenetics with BEAUti and the BEAST 17. Mol Biol Evol 2012; 29:1969–1973.
58. Hadfield J, Megill C, Bell SM, et al. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics 2018; 34:4121–4123.
59. Coltart CEM, Hoppe A, Parker M, et al. Ethical considerations in global HIV phylogenetic research. Lancet HIV 2018; 5:e656–e666.
60. Bonsall D, Golubchik T, de Cesare M, et al. A comprehensive genomics solution for HIV surveillance and clinical monitoring in a global health setting. bioRxiv 2018. 397083.
61. Brown LB, Miller WC, Kamanga G, et al. HIV partner notification is effective and feasible in sub-Saharan Africa: opportunities for HIV treatment and prevention. J Acquir Immune Defic Syndr 2011; 56:437–444.
62. Cohen MS, Chen YQ, McCauley M, et al. Prevention of HIV-1 infection with early antiretroviral therapy. New Engl J Med 2011; 365:493–505.
63. Safren SA, Mayer KH, Ou S-S, et al. Adherence to early antiretroviral therapy: results from HPTN 052, a phase iii, multinational randomized trial of ART to prevent HIV-1 sexual transmission in serodiscordant couples. J Acquir Immune Defic Syndr 2015; 69:234–240.
64. Rutherford GW, Woo JM. Contact tracing and the control of human immunodeficiency virus infection. JAMA 1988; 259:3609–3610.
65. Gray RH, Kiwanuka N, Quinn TC, et al. Male circumcision and HIV acquisition and transmission: cohort studies in Rakai, Uganda. AIDS 2000; 14:2371–2381.
66. Gray RH, Wawer MJ, Brookmeyer R, et al. Rakai Project Team. Probability of HIV-1 transmission per coital act in monogamous, heterosexual, HIV-1-discordant couples in Rakai, Uganda. Lancet 2001; 357:1149–1153.
67. Quinn TC, Wawer MJ, Sewankambo N, et al. Viral load and heterosexual transmission of human immunodeficiency virus type 1. New Engl J Med 2000; 342:921–929.
Keywords:

HIV; Phylogenetics And Networks for Generalized Epidemics in Africa; phylogenetics; sub-Saharan Africa; transmission dynamics

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