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. 2020 Jan 27;12(2):145.
doi: 10.3390/v12020145.

Phylodynamic Analysis Complements Partner Services by Identifying Acute and Unreported HIV Transmission

Ellsworth M Campbell  1 , Anne Patala  1   2 , Anupama Shankar  1 , Jin-Fen Li  1 , Jeffrey A Johnson  1 , Emily Westheimer  3 , Cynthia L Gay  4 , Stephanie E Cohen  5 , William M Switzer  1 , Philip J Peters  1
Affiliations

Phylodynamic Analysis Complements Partner Services by Identifying Acute and Unreported HIV Transmission

Ellsworth M Campbell et al. Viruses. .

Abstract

Tailoring public health responses to growing HIV transmission clusters depends on accurately mapping the risk network through which it spreads and identifying acute infections that represent the leading edge of cluster growth. HIV transmission links, especially those involving persons with acute HIV infection (AHI), can be difficult to uncover, or confirm during partner services investigations. We integrated molecular, epidemiologic, serologic and behavioral data to infer and evaluate transmission linkages between participants of a prospective study of AHI conducted in North Carolina, New York City and San Francisco from 2011-2013. Among the 547 participants with newly diagnosed HIV with polymerase sequences, 465 sex partners were reported, of whom only 35 (7.5%) had HIV sequences. Among these 35 contacts, 23 (65.7%) links were genetically supported and 12 (34.3%) were not. Only five links were reported between participants with AHI but none were genetically supported. In contrast, phylodynamic inference identified 102 unreported transmission links, including 12 between persons with AHI. Importantly, all putative transmission links between persons with AHI were found among large clusters with more than five members. Taken together, the presence of putative links between acute participants who did not name each other as contacts that are found only among large clusters underscores the potential for unobserved or undiagnosed intermediaries. Phylodynamics identified many more links than partner services alone and, if routinely and rapidly integrated, can illuminate transmission patterns not readily captured by partner services investigations.

Keywords: Phylodynamics, HIV transmission, sexual network, risk network, contact network, genetic network, acute HIV infection, MicrobeTrace, network visualization.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Contact and close genetic links, categorized by recency of HIV infection and type of reported contact. Links between participants were categorized according to the recency of infection, i.e., Acute-to-Acute (AcuteAcute), Acute-to-Established (AcuteEst) or Established-to-Established (EstEst). Links were also categorized by contact type, indicating whether a link was reported, genetically inferred (distOnly) or both (epi + dist). (A) Represents the counts of each category of link that was observed. (B) Kernel density plots of the pairwise genetic distances (x-axis), broken out by recency classification and colored by contact type (distOnly or epi + dist). Kernel density plots are smoothed and normalized functions designed to capture the density of observations at an arbitrary value, enabling comparison of disparate distributions, similar to a histogram.
Figure 2
Figure 2
Age characteristics of cluster members, stratified by cluster size and study site (NC, SF, NYC). HIV transmission clusters varied with respect to size and the age of cluster members at the three study sites (North Carolina (NC), New York City (NYC), San Francisco (SF)). (A) Box-and-whisker plots of participant age, stratified by cluster size categories (singletons (n = 1), dyads (n = 2), and ≥3 (3+)) and study site. Underplotted behind the box-and-whisker plots are circles representing each participant, where the y-axis position corresponds to their age and circle size corresponds to cluster size. Each circle is colored by the recency of HIV-infection (Acute or Established) and scaled according to the size of their respective transmission clusters. (B) Kernel density plots showing the average age for participants of each cluster on the x-axis, colored by study site. Please note that the NC plot is behind the SF and NYC plots and does not always appear yellow in color in overlapping plot areas.
Figure 3
Figure 3
Superimposed risk networks and genetic clusters in three HIV study sites (North Carolina, San Francisco and New York City, stratified by cluster size. Network of all sexual contacts reported between participants and their partners diagnosed with HIV visualized using MicrobeTrace (http://microbetrace.cdc.gov); uninfected partners are not shown. A force-directed layout algorithm was applied to sort clusters by increasing size. Links are colored by contact type, ‘genetic only’ links in red, ‘epi only’ links in black and ‘both’ genetic and epi links in blue. Individuals are represented as circles, colored by recency of HIV infection. Blue circles represent established HIV-infection and yellow circles represent acute HIV-infection (AHI). Select clusters of interest are highlighted in yellow and labeled: Cluster (a) contains members in San Francisco and New York City. Clusters (b1), (b2) and (b3) consist primarily of members that did not name any high-risk contacts, where the majority of members of clusters (b2) and (b3) were diagnosed with acute HIV infection. Cluster (c) membership consists of two females who named each other as contacts and whose viruses are linked by a close genetic distance (d ≤ 1.5%).
Figure 4
Figure 4
An example decision tree from the random forest model, trained to differentiate participants by cluster size based on their demographic and behavioral risk characteristics. Each colored box represents the number of participants meeting preceding criteria. Each box displays the mean cluster size, number of participants and their overall prevalence in the data. The criteria, statistical significance and confidence intervals of the difference in mean cluster size (∆Size) are displayed below each branching point of the tree.
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