Quantifying the Aftermath: Recent Outbreaks Among People Who Inject Drugs and the Utility of Phylodynamics

Quantifying the Aftermath: Recent Outbreaks Among People Who Inject Drugs and the Utility of... HIV-1 transmission, people who inject drugs, molecular epidemiology, genetic clustering, phylodynamics, public health surveillance (See the Major Article by Ragonnet-Cronin et al, on pages 1875–82.) Despite increasing access to effective combination antiretroviral therapies, outbreaks of human immunodeficiency virus-1 (HIV-1) transmission continue to arise in vulnerable key populations in the setting of concentrated epidemics. In the context of an ongoing opioid crisis, socioeconomic inequalities, and limited access to care or harm reduction measures, there have been several prominent outbreaks among people who inject drugs (PWID). For example, 135 new HIV diagnoses were recorded from January to April 2015 within a network of PWID in a rural community of about 4200 persons in Indiana, United States [1]. In Canada, a disproportionate number of new HIV-1 diagnoses are accumulating in Aboriginal communities in Saskatchewan, driven by a long history of social and economic disparities, injection drug use, and a critical lack of access to HIV services [2]. Some severe HIV-1 outbreaks have become foci of detailed retrospective case studies with the objective of understanding the main drivers of transmission rates. Recent studies in this area have combined conventional methods, such as contact tracing, with relatively newer approaches based on the analysis of pathogen sequences, such as phylogenetic reconstruction [3] or genetic clustering [4]. A genetic cluster is a group of sequences that are more similar to each other than to the remaining sequences in the sample population. Genetic clustering methods have been used in virology research for several decades to classify subtypes [5], and increasingly more often to identify potential transmission outbreaks, particularly in the field of HIV-1 research [6]. These retrospective studies generally aim to identify cases in clusters defined by location and/or time that are also closely linked epidemiologically, that is, in terms of the number of transmission events separating them. This growing popularity of applying genetic clustering to potential outbreaks has been accompanied by increased scrutiny of the underlying methods [7]. For instance, multiple studies have observed that clusters of genetic similarity may result from oversampling acute and early infections [8, 9] instead of elevated transmission rates. This implies that clusters based on genetic similarity tend to group together individuals diagnosed early after infection and already engaged with HIV prevention and care services [9]. Phylodynamics is a term coined by Grenfell and colleagues [10] to refer to the emerging science of reconstructing how virus phylogenies are shaped by evolution and the underlying immunological and epidemiological processes in the host population. Clustering is a rudimentary form of phylodynamic inference, because clusters are often derived from features of phylogenetic tree shapes (eg, compact and robust subtrees [11]) and interpreted in an epidemiological context. Extracting clusters of infections can also yield smaller and reproducible targets for a more detailed phylodynamic analysis [12]. Because most phylodynamic analyses require computationally demanding methods (eg, numerical solution of differential equations, Monte Carlo sampling), targeting a defined cluster can significantly reduce the processing time required to estimate parameters that have a fundamental and time-dependent role in understanding the unfolding disease dynamics, such as the basic reproduction number (R0) [13–16]. However, downsizing for computational tractability is not necessarily straightforward, as poorly identified clusters (eg, due to nonepidemiological effects such as over-sampling) can falsely masquerade as growing epidemics [17]. In this issue of The Journal of Infectious Diseases, Ragonnet-Cronin and colleagues present a detailed analysis of the epidemic dynamics in a genetic cluster of related HIV-1 infections among PWID in Scotland. In the context of a resurgence of HIV diagnoses among PWID in 2015, the authors analyzed 228 HIV-1 pol sequences that were collected for routine baseline resistance testing, together with large numbers of related sequences from protected (UK HIV Drug Resistance Database, UKRDB) and public (Los Alamos National Laboratory, LANL) databases, for a total of 2572 sequences. Pairwise distance clustering identified a single large cluster of 104 HIV-1 subtype C sequences from PWIDs in Scotland. More than two-thirds of cases in the cluster were diagnosed after 2014. A lack of contemporaneous data may have contributed to the absence of background sequences in this cluster, because neither the UKRDB nor the LANL database contained samples more recent than 2014 when they were accessed for this study. All sequences in the cluster carried a combination of minor drug resistance mutations (E138A and V179E), which are more common among subtype C infections [18] and seldom observed in the United Kingdom. Next, the authors employed a Bayesian sampling method to fit a “birth-death skyline” model [19] to this cluster to reconstruct the effective reproductive number (Re), the expected number of secondary infections per case over the course of an epidemic; Re values greater than 1 suggest a growing epidemic. The model was adjusted to accommodate a shift in sampling rates over time. Overall, the mean Re was estimated at 1.5 cases with periods of elevated Re mapped to 2008–2010 and 2014–2016. It was unclear whether similar estimates of Re might have been obtained from subtrees relating nonclustered HIV-1 sequences from the same site—such an analysis would not have to be limited to subtype C sequences from the clinic and could establish the cluster as representing an outbreak rather than a sampling artifact. This is timely work that compellingly represents the utility of current phylodynamic methods in HIV-1 research. While retrospective studies are useful for understanding the historical context of an outbreak, they are often deployed after large numbers of cases have already been diagnosed. Thus, the authors raise an important question of whether a more timely application of clustering and/or phylodynamic analysis might better support HIV-1 prevention efforts by alerting public health personnel at an earlier stage of the outbreak. This particular outbreak was flagged early because of a rare drug-resistance profile. By the time the reported analysis was completed, however, an estimated two-thirds of the outbreak had been sampled through a research-driven initiative, rather than active surveillance. How early could the cluster have been detected by phylodynamic methods or by case counting [20] had the resistance profile not been so notable? We believe there was a missed opportunity to address this question by comparing estimates of Re or case counts against the background for time-censored databases. How many cases could have been averted through timely intervention, triggered into action by assessing changing cluster dynamics? We highlight these points to call for a profound shift in use and further development of phylodynamic methods. Specifically, they need to be able to provide timely and actionable information for public health, to differentiate emerging or active subepidemics from those that are “latent” instead of being applied retrospectively. Phylodynamic methods offer the opportunity for a more automated approach to identifying developing outbreaks in the population at large, by comparing the changing cluster profile in a database over time. Previously, near-real-time monitoring was demonstrated as feasible in British Columbia, Canada, finding a single cluster that expanded by 11 cases in 3 months, and subsequently initiating follow up strategies where identifying information was securely handled within the preexisting clinical and public health frameworks [21]. New technologies, both in sequencing and computational tools, bring this goal within reach, as evidenced during the recent Zika outbreaks [22, 23]. However, real-time sampling requires us to re-evaluate our assumptions and standardized practices in phylodynamics. Genetic clusters do not capture the full extent of an outbreak, because these constructs cannot account for unsampled infected individuals. Reaching these undiagnosed individuals is imperative to support public health efforts. Moreover, because the objective is to detect outbreaks while the proportion of sampled infections is low, birth-death models such as employed in this study no longer hold high sampling fractions as an advantage over coalescent models. We also need more-flexible models to account for changes in sampling strategy over time, both in terms of the proportion sampled, and where from. Karcher and colleagues recently showed that preferential sampling can bias phylodynamic estimates [24], whereas Hall and colleagues have illustrated issues with sampling of structured populations for the coalescent skyline family of models [25]. Technical improvements are not the only challenge for contemporaneous outbreak studies; there are also ethical considerations that these novel methods bring to the fore, especially regarding the identifiability of sequence information. This, of course, is true of all studies handling HIV data, but the importance of how changing technologies can clash with current definitions of patient privacy and informed consent bears reiterating. In the context of evaluating subepidemics, phylodynamic studies are primarily interested in what is happening at the population, rather than the individual, level. Carrying out phylodynamic analyses within the ethical framework of public health does not necessarily require informed consent, but the stoichiometry of that framework requires a significant public health benefit to outweigh the individual-level risk. As a research field, we need to demonstrate this benefit, working together with a range of knowledge users and stakeholders—public health officials, physicians, ethicists, persons living with or at risk of acquiring HIV, and community advisory board representatives—to optimize the advantages that novel phylodynamics approaches can bring to public health, while not jeopardizing our ethical and legal responsibilities to people living with HIV [26, 27]. Until we can develop more timely applications of phylodynamics, our contribution to the ethical equation would remain as being prospectors sifting the aftermath of an epidemic for the largest human toll. Notes Disclaimer. The views expressed are those of the authors and should not be construed to represent the positions of the US Army or the Department of Defense. Financial support. This work was supported by the Canadian Institutes of Health Research (grant numbers PJT-153391, BOP-149562, FRN-130609); by the Government of Canada through Genome Canada and the Ontario Genomics Institute (grant number OGI-131); and by a cooperative agreement (W81XWH-11-2-0174) between the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., and the US Department of Defense. Potential conflicts of interest. All authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed. References 1. Conrad C , Bradley HM , Broz D , et al. Community outbreak of HIV infection linked to injection drug use of oxymorphone–Indiana, 2015 . MMWR Morb Mortal Wkly Rep 2015 ; 64 : 443 – 44 . Google Scholar PubMed 2. Vogel L . HIV in Saskatchewan merits urgent response . Can Med Assoc J 2015 ; 187 : 793 – 4 . Google Scholar CrossRef Search ADS 3. Peters PJ , Pontones P , Hoover KW , et al. HIV infection linked to injection use of oxymorphone in Indiana, 2014–2015 . N Engl J Med 2016 ; 375 : 229 – 39 . Google Scholar CrossRef Search ADS PubMed 4. Campbell EM , Jia H , Shankar A , et al. Detailed transmission network analysis of a large opiate-driven outbreak of HIV infection in the United States . J Infect Dis 2017 ; 216 : 1053 – 62 . Google Scholar CrossRef Search ADS PubMed 5. Okamoto H , Tsuda F , Sakugawa H , et al. Typing hepatitis B virus by homology in nucleotide sequence: comparison of surface antigen subtypes . J Gen Virol 1988 ; 69 : 2575 – 83 . Google Scholar CrossRef Search ADS PubMed 6. Hassan AS , Pybus OG , Sanders EJ , Albert J , Esbjörnsson J . Defining HIV-1 transmission clusters based on sequence data . AIDS 2017 ; 31 : 1211 – 22 . Google Scholar CrossRef Search ADS PubMed 7. Villandre L , Stephens DA , Labbe A , Günthard HF , Kouyos R , Stadler T ; Swiss HIV Cohort Study . Assessment of overlap of phylogenetic transmission clusters and communities in simple sexual contact networks: applications to HIV-1 . PLoS One 2016 ; 11 : e0148459 . Google Scholar CrossRef Search ADS PubMed 8. Volz EM , Koopman JS , Ward MJ , Brown AL , Frost SD . Simple epidemiological dynamics explain phylogenetic clustering of HIV from patients with recent infection . PLoS Comput Biol 2012 ; 8 : e1002552 . Google Scholar CrossRef Search ADS PubMed 9. Poon AFY . Impacts and shortcomings of genetic clustering methods for infectious disease outbreaks . Virus Evol 2016 ; 2 : vew031 . Google Scholar CrossRef Search ADS PubMed 10. Grenfell BT , Pybus OG , Gog JR , et al. Unifying the epidemiological and evolutionary dynamics of pathogens . Science 2004 ; 303 : 327 – 32 . Google Scholar CrossRef Search ADS PubMed 11. Hué S , Clewley JP , Cane PA , Pillay D . HIV-1 pol gene variation is sufficient for reconstruction of transmissions in the era of antiretroviral therapy . AIDS 2004 ; 18 : 719 – 28 . Google Scholar CrossRef Search ADS PubMed 12. Lewis F , Hughes GJ , Rambaut A , Pozniak A , Leigh Brown AJ . Episodic sexual transmission of HIV revealed by molecular phylodynamics . PLoS Med 2008 ; 5 : e50 . Google Scholar CrossRef Search ADS PubMed 13. Stadler T , Kouyos R , von Wyl V , et al. ; Swiss HIV Cohort Study . Estimating the basic reproductive number from viral sequence data . Mol Biol Evol 2012 ; 29 : 347 – 57 . Google Scholar CrossRef Search ADS PubMed 14. Dearlove B , Wilson DJ . Coalescent inference for infectious disease: meta-analysis of hepatitis C . Philos Trans R Soc Lond B Biol Sci 2013 ; 368 : 20120314 . Google Scholar CrossRef Search ADS PubMed 15. Rasmussen DA , Volz EM , Koelle K . Phylodynamic inference for structured epidemiological models . PLoS Comput Biol 2014 ; 10 : e1003570 . Google Scholar CrossRef Search ADS PubMed 16. Popinga A , Vaughan T , Stadler T , Drummond AJ . Inferring epidemiological dynamics with Bayesian coalescent inference: the merits of deterministic and stochastic models . Genetics 2015 ; 199 : 595 – 607 . Google Scholar CrossRef Search ADS PubMed 17. Dearlove BL , Xiang F , Frost SDW . Biased phylodynamic inferences from analysing clusters of viral sequences . Virus Evol 2017 ; 3 : vex020 . Google Scholar CrossRef Search ADS PubMed 18. Sluis-Cremer N , Jordan MR , Huber K , et al. E138A in HIV-1 reverse transcriptase is more common in subtype C than B: implications for rilpivirine use in resource-limited settings . Antiviral Res 2014 ; 107 : 31 – 4 . Google Scholar CrossRef Search ADS PubMed 19. Stadler T , Kühnert D , Bonhoeffer S , Drummond AJ . Birth-death skyline plot reveals temporal changes of epidemic spread in HIV and hepatitis C virus (HCV) . Proc Natl Acad Sci U S A 2013 ; 110 : 228 – 33 . Google Scholar CrossRef Search ADS PubMed 20. McVea D , Liang R , Joy J , Harrigan PR , Poon A . A framework for predicting phylogenetic clusters of HIV at high risk for growth . [Conference on Retroviruses and Opportunistic Infections, Abstract 848]. Top Antivir Med 2017 ; 25(Suppl 1) : 360s . 21. Poon AF , 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 – 8 . Google Scholar CrossRef Search ADS PubMed 22. Hadfield J , Megill C , Bell SM , et al. Nextstrain: real-time tracking of pathogen evolution . bioRxiv 2017 . https://www.biorxiv.org/content/early/2017/11/22/224048. Accessed 19 March 2018 . 23. Faria NR , Quick J , Claro IM , et al. Establishment and cryptic transmission of Zika virus in Brazil and the Americas . Nature 2017 ; 546 : 406 – 10 . Google Scholar CrossRef Search ADS PubMed 24. Karcher MD , Palacios JA , Bedford T , Suchard MA , Minin VN . Quantifying and mitigating the effect of preferential sampling on phylodynamic inference . PLoS Comput Biol 2016 ; 12 : e1004789 . Google Scholar CrossRef Search ADS PubMed 25. Hall MD , Woolhouse MEJ , Rambaut A . The effects of sampling strategy on the quality of reconstruction of viral population dynamics using Bayesian skyline family coalescent methods: a simulation study . Virus Evol 2016 ; 2 : vew003 . Google Scholar CrossRef Search ADS PubMed 26. Gilbert M , Swenson L , Unger D , Scheim A , Grace D . Need for robust and inclusive public health ethics review of the monitoring of HIV phylogenetic clusters for HIV prevention . Lancet HIV 2016 ; 3 : e461 . Google Scholar CrossRef Search ADS PubMed 27. Schairer C , Mehta S , Vinterbo S , Hoenigl M , Kalichman M , Little S . Perceptions of molecular epidemiology studies of HIV among stakeholders . J Public Health Res 2017 ; 6 : 127 – 34 . Google Scholar CrossRef Search ADS © The Author(s) 2018. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journal of Infectious Diseases Oxford University Press

Quantifying the Aftermath: Recent Outbreaks Among People Who Inject Drugs and the Utility of Phylodynamics

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Abstract

HIV-1 transmission, people who inject drugs, molecular epidemiology, genetic clustering, phylodynamics, public health surveillance (See the Major Article by Ragonnet-Cronin et al, on pages 1875–82.) Despite increasing access to effective combination antiretroviral therapies, outbreaks of human immunodeficiency virus-1 (HIV-1) transmission continue to arise in vulnerable key populations in the setting of concentrated epidemics. In the context of an ongoing opioid crisis, socioeconomic inequalities, and limited access to care or harm reduction measures, there have been several prominent outbreaks among people who inject drugs (PWID). For example, 135 new HIV diagnoses were recorded from January to April 2015 within a network of PWID in a rural community of about 4200 persons in Indiana, United States [1]. In Canada, a disproportionate number of new HIV-1 diagnoses are accumulating in Aboriginal communities in Saskatchewan, driven by a long history of social and economic disparities, injection drug use, and a critical lack of access to HIV services [2]. Some severe HIV-1 outbreaks have become foci of detailed retrospective case studies with the objective of understanding the main drivers of transmission rates. Recent studies in this area have combined conventional methods, such as contact tracing, with relatively newer approaches based on the analysis of pathogen sequences, such as phylogenetic reconstruction [3] or genetic clustering [4]. A genetic cluster is a group of sequences that are more similar to each other than to the remaining sequences in the sample population. Genetic clustering methods have been used in virology research for several decades to classify subtypes [5], and increasingly more often to identify potential transmission outbreaks, particularly in the field of HIV-1 research [6]. These retrospective studies generally aim to identify cases in clusters defined by location and/or time that are also closely linked epidemiologically, that is, in terms of the number of transmission events separating them. This growing popularity of applying genetic clustering to potential outbreaks has been accompanied by increased scrutiny of the underlying methods [7]. For instance, multiple studies have observed that clusters of genetic similarity may result from oversampling acute and early infections [8, 9] instead of elevated transmission rates. This implies that clusters based on genetic similarity tend to group together individuals diagnosed early after infection and already engaged with HIV prevention and care services [9]. Phylodynamics is a term coined by Grenfell and colleagues [10] to refer to the emerging science of reconstructing how virus phylogenies are shaped by evolution and the underlying immunological and epidemiological processes in the host population. Clustering is a rudimentary form of phylodynamic inference, because clusters are often derived from features of phylogenetic tree shapes (eg, compact and robust subtrees [11]) and interpreted in an epidemiological context. Extracting clusters of infections can also yield smaller and reproducible targets for a more detailed phylodynamic analysis [12]. Because most phylodynamic analyses require computationally demanding methods (eg, numerical solution of differential equations, Monte Carlo sampling), targeting a defined cluster can significantly reduce the processing time required to estimate parameters that have a fundamental and time-dependent role in understanding the unfolding disease dynamics, such as the basic reproduction number (R0) [13–16]. However, downsizing for computational tractability is not necessarily straightforward, as poorly identified clusters (eg, due to nonepidemiological effects such as over-sampling) can falsely masquerade as growing epidemics [17]. In this issue of The Journal of Infectious Diseases, Ragonnet-Cronin and colleagues present a detailed analysis of the epidemic dynamics in a genetic cluster of related HIV-1 infections among PWID in Scotland. In the context of a resurgence of HIV diagnoses among PWID in 2015, the authors analyzed 228 HIV-1 pol sequences that were collected for routine baseline resistance testing, together with large numbers of related sequences from protected (UK HIV Drug Resistance Database, UKRDB) and public (Los Alamos National Laboratory, LANL) databases, for a total of 2572 sequences. Pairwise distance clustering identified a single large cluster of 104 HIV-1 subtype C sequences from PWIDs in Scotland. More than two-thirds of cases in the cluster were diagnosed after 2014. A lack of contemporaneous data may have contributed to the absence of background sequences in this cluster, because neither the UKRDB nor the LANL database contained samples more recent than 2014 when they were accessed for this study. All sequences in the cluster carried a combination of minor drug resistance mutations (E138A and V179E), which are more common among subtype C infections [18] and seldom observed in the United Kingdom. Next, the authors employed a Bayesian sampling method to fit a “birth-death skyline” model [19] to this cluster to reconstruct the effective reproductive number (Re), the expected number of secondary infections per case over the course of an epidemic; Re values greater than 1 suggest a growing epidemic. The model was adjusted to accommodate a shift in sampling rates over time. Overall, the mean Re was estimated at 1.5 cases with periods of elevated Re mapped to 2008–2010 and 2014–2016. It was unclear whether similar estimates of Re might have been obtained from subtrees relating nonclustered HIV-1 sequences from the same site—such an analysis would not have to be limited to subtype C sequences from the clinic and could establish the cluster as representing an outbreak rather than a sampling artifact. This is timely work that compellingly represents the utility of current phylodynamic methods in HIV-1 research. While retrospective studies are useful for understanding the historical context of an outbreak, they are often deployed after large numbers of cases have already been diagnosed. Thus, the authors raise an important question of whether a more timely application of clustering and/or phylodynamic analysis might better support HIV-1 prevention efforts by alerting public health personnel at an earlier stage of the outbreak. This particular outbreak was flagged early because of a rare drug-resistance profile. By the time the reported analysis was completed, however, an estimated two-thirds of the outbreak had been sampled through a research-driven initiative, rather than active surveillance. How early could the cluster have been detected by phylodynamic methods or by case counting [20] had the resistance profile not been so notable? We believe there was a missed opportunity to address this question by comparing estimates of Re or case counts against the background for time-censored databases. How many cases could have been averted through timely intervention, triggered into action by assessing changing cluster dynamics? We highlight these points to call for a profound shift in use and further development of phylodynamic methods. Specifically, they need to be able to provide timely and actionable information for public health, to differentiate emerging or active subepidemics from those that are “latent” instead of being applied retrospectively. Phylodynamic methods offer the opportunity for a more automated approach to identifying developing outbreaks in the population at large, by comparing the changing cluster profile in a database over time. Previously, near-real-time monitoring was demonstrated as feasible in British Columbia, Canada, finding a single cluster that expanded by 11 cases in 3 months, and subsequently initiating follow up strategies where identifying information was securely handled within the preexisting clinical and public health frameworks [21]. New technologies, both in sequencing and computational tools, bring this goal within reach, as evidenced during the recent Zika outbreaks [22, 23]. However, real-time sampling requires us to re-evaluate our assumptions and standardized practices in phylodynamics. Genetic clusters do not capture the full extent of an outbreak, because these constructs cannot account for unsampled infected individuals. Reaching these undiagnosed individuals is imperative to support public health efforts. Moreover, because the objective is to detect outbreaks while the proportion of sampled infections is low, birth-death models such as employed in this study no longer hold high sampling fractions as an advantage over coalescent models. We also need more-flexible models to account for changes in sampling strategy over time, both in terms of the proportion sampled, and where from. Karcher and colleagues recently showed that preferential sampling can bias phylodynamic estimates [24], whereas Hall and colleagues have illustrated issues with sampling of structured populations for the coalescent skyline family of models [25]. Technical improvements are not the only challenge for contemporaneous outbreak studies; there are also ethical considerations that these novel methods bring to the fore, especially regarding the identifiability of sequence information. This, of course, is true of all studies handling HIV data, but the importance of how changing technologies can clash with current definitions of patient privacy and informed consent bears reiterating. In the context of evaluating subepidemics, phylodynamic studies are primarily interested in what is happening at the population, rather than the individual, level. Carrying out phylodynamic analyses within the ethical framework of public health does not necessarily require informed consent, but the stoichiometry of that framework requires a significant public health benefit to outweigh the individual-level risk. As a research field, we need to demonstrate this benefit, working together with a range of knowledge users and stakeholders—public health officials, physicians, ethicists, persons living with or at risk of acquiring HIV, and community advisory board representatives—to optimize the advantages that novel phylodynamics approaches can bring to public health, while not jeopardizing our ethical and legal responsibilities to people living with HIV [26, 27]. Until we can develop more timely applications of phylodynamics, our contribution to the ethical equation would remain as being prospectors sifting the aftermath of an epidemic for the largest human toll. Notes Disclaimer. The views expressed are those of the authors and should not be construed to represent the positions of the US Army or the Department of Defense. Financial support. This work was supported by the Canadian Institutes of Health Research (grant numbers PJT-153391, BOP-149562, FRN-130609); by the Government of Canada through Genome Canada and the Ontario Genomics Institute (grant number OGI-131); and by a cooperative agreement (W81XWH-11-2-0174) between the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., and the US Department of Defense. Potential conflicts of interest. All authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed. References 1. Conrad C , Bradley HM , Broz D , et al. Community outbreak of HIV infection linked to injection drug use of oxymorphone–Indiana, 2015 . MMWR Morb Mortal Wkly Rep 2015 ; 64 : 443 – 44 . Google Scholar PubMed 2. Vogel L . HIV in Saskatchewan merits urgent response . Can Med Assoc J 2015 ; 187 : 793 – 4 . Google Scholar CrossRef Search ADS 3. Peters PJ , Pontones P , Hoover KW , et al. HIV infection linked to injection use of oxymorphone in Indiana, 2014–2015 . N Engl J Med 2016 ; 375 : 229 – 39 . Google Scholar CrossRef Search ADS PubMed 4. Campbell EM , Jia H , Shankar A , et al. Detailed transmission network analysis of a large opiate-driven outbreak of HIV infection in the United States . J Infect Dis 2017 ; 216 : 1053 – 62 . Google Scholar CrossRef Search ADS PubMed 5. Okamoto H , Tsuda F , Sakugawa H , et al. Typing hepatitis B virus by homology in nucleotide sequence: comparison of surface antigen subtypes . J Gen Virol 1988 ; 69 : 2575 – 83 . Google Scholar CrossRef Search ADS PubMed 6. Hassan AS , Pybus OG , Sanders EJ , Albert J , Esbjörnsson J . Defining HIV-1 transmission clusters based on sequence data . AIDS 2017 ; 31 : 1211 – 22 . Google Scholar CrossRef Search ADS PubMed 7. Villandre L , Stephens DA , Labbe A , Günthard HF , Kouyos R , Stadler T ; Swiss HIV Cohort Study . Assessment of overlap of phylogenetic transmission clusters and communities in simple sexual contact networks: applications to HIV-1 . PLoS One 2016 ; 11 : e0148459 . Google Scholar CrossRef Search ADS PubMed 8. Volz EM , Koopman JS , Ward MJ , Brown AL , Frost SD . Simple epidemiological dynamics explain phylogenetic clustering of HIV from patients with recent infection . PLoS Comput Biol 2012 ; 8 : e1002552 . Google Scholar CrossRef Search ADS PubMed 9. Poon AFY . Impacts and shortcomings of genetic clustering methods for infectious disease outbreaks . Virus Evol 2016 ; 2 : vew031 . Google Scholar CrossRef Search ADS PubMed 10. Grenfell BT , Pybus OG , Gog JR , et al. Unifying the epidemiological and evolutionary dynamics of pathogens . Science 2004 ; 303 : 327 – 32 . Google Scholar CrossRef Search ADS PubMed 11. Hué S , Clewley JP , Cane PA , Pillay D . HIV-1 pol gene variation is sufficient for reconstruction of transmissions in the era of antiretroviral therapy . AIDS 2004 ; 18 : 719 – 28 . Google Scholar CrossRef Search ADS PubMed 12. Lewis F , Hughes GJ , Rambaut A , Pozniak A , Leigh Brown AJ . Episodic sexual transmission of HIV revealed by molecular phylodynamics . PLoS Med 2008 ; 5 : e50 . Google Scholar CrossRef Search ADS PubMed 13. Stadler T , Kouyos R , von Wyl V , et al. ; Swiss HIV Cohort Study . Estimating the basic reproductive number from viral sequence data . Mol Biol Evol 2012 ; 29 : 347 – 57 . Google Scholar CrossRef Search ADS PubMed 14. Dearlove B , Wilson DJ . Coalescent inference for infectious disease: meta-analysis of hepatitis C . Philos Trans R Soc Lond B Biol Sci 2013 ; 368 : 20120314 . Google Scholar CrossRef Search ADS PubMed 15. Rasmussen DA , Volz EM , Koelle K . Phylodynamic inference for structured epidemiological models . PLoS Comput Biol 2014 ; 10 : e1003570 . Google Scholar CrossRef Search ADS PubMed 16. Popinga A , Vaughan T , Stadler T , Drummond AJ . Inferring epidemiological dynamics with Bayesian coalescent inference: the merits of deterministic and stochastic models . Genetics 2015 ; 199 : 595 – 607 . Google Scholar CrossRef Search ADS PubMed 17. Dearlove BL , Xiang F , Frost SDW . Biased phylodynamic inferences from analysing clusters of viral sequences . Virus Evol 2017 ; 3 : vex020 . Google Scholar CrossRef Search ADS PubMed 18. Sluis-Cremer N , Jordan MR , Huber K , et al. E138A in HIV-1 reverse transcriptase is more common in subtype C than B: implications for rilpivirine use in resource-limited settings . Antiviral Res 2014 ; 107 : 31 – 4 . Google Scholar CrossRef Search ADS PubMed 19. Stadler T , Kühnert D , Bonhoeffer S , Drummond AJ . Birth-death skyline plot reveals temporal changes of epidemic spread in HIV and hepatitis C virus (HCV) . Proc Natl Acad Sci U S A 2013 ; 110 : 228 – 33 . Google Scholar CrossRef Search ADS PubMed 20. McVea D , Liang R , Joy J , Harrigan PR , Poon A . A framework for predicting phylogenetic clusters of HIV at high risk for growth . [Conference on Retroviruses and Opportunistic Infections, Abstract 848]. Top Antivir Med 2017 ; 25(Suppl 1) : 360s . 21. Poon AF , 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 – 8 . Google Scholar CrossRef Search ADS PubMed 22. Hadfield J , Megill C , Bell SM , et al. Nextstrain: real-time tracking of pathogen evolution . bioRxiv 2017 . https://www.biorxiv.org/content/early/2017/11/22/224048. Accessed 19 March 2018 . 23. Faria NR , Quick J , Claro IM , et al. Establishment and cryptic transmission of Zika virus in Brazil and the Americas . Nature 2017 ; 546 : 406 – 10 . Google Scholar CrossRef Search ADS PubMed 24. Karcher MD , Palacios JA , Bedford T , Suchard MA , Minin VN . Quantifying and mitigating the effect of preferential sampling on phylodynamic inference . PLoS Comput Biol 2016 ; 12 : e1004789 . Google Scholar CrossRef Search ADS PubMed 25. Hall MD , Woolhouse MEJ , Rambaut A . The effects of sampling strategy on the quality of reconstruction of viral population dynamics using Bayesian skyline family coalescent methods: a simulation study . Virus Evol 2016 ; 2 : vew003 . Google Scholar CrossRef Search ADS PubMed 26. Gilbert M , Swenson L , Unger D , Scheim A , Grace D . Need for robust and inclusive public health ethics review of the monitoring of HIV phylogenetic clusters for HIV prevention . Lancet HIV 2016 ; 3 : e461 . Google Scholar CrossRef Search ADS PubMed 27. Schairer C , Mehta S , Vinterbo S , Hoenigl M , Kalichman M , Little S . Perceptions of molecular epidemiology studies of HIV among stakeholders . J Public Health Res 2017 ; 6 : 127 – 34 . Google Scholar CrossRef Search ADS © The Author(s) 2018. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

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The Journal of Infectious DiseasesOxford University Press

Published: Apr 10, 2018

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