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

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 E D I T O R I A L C O M M E N T A R Y