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Актуальні інфекційні захворювання
день перший день другий

Актуальні інфекційні захворювання
день перший день другий

Журнал «Актуальная инфектология» Том 8, №5, 2020

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Phylodynamics as a tool to assess effectiveness of HIV prevention interventions

Авторы: Tetyana Vasylyeva(1), Alexander Zarebski(1), Pavlo Smyrnov(2), Ania Korobchuk(2), Mariia Liulchuk(3), Victoriia Zadorozhna(3), Oliver G. Pybus(1), Samuel R. Friedman(4)
(1) — Department of Zoology, University of Oxford, Oxford, UK
(2) — Alliance for Public Health, Kyiv, Ukraine
(3) — L.V. Gromashevskyi Institute of Epidemiology and Infectious Diseases, Kyiv, Ukraine
(4) — New York University, New York, USA

Рубрики: Инфекционные заболевания

Разделы: Медицинские форумы

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One of the challenges in HIV epidemiology is the lack of reliable and comparable measures to assess population-level effects of various HIV prevention interventions on a long-term scale. Molecular epidemiology is becoming an essential part of epidemic investigations and outbreak response. Here we applied phylodynamic methods to estimate changes in HIV transmission dynamics following a Transmission Reduction Intervention Project (TRIP) implemented in 2013–2016 in Odessa, Ukraine, and to compare it to the estimated HIV transmission dynamics in the capital of Ukraine, Kyiv. Kyiv and Odessa share similar HIV epidemiological profiles and have experienced a similar number of HIV prevention interventions, other than TRIP, which makes Kyiv an appropriate control location.
Methods. First, we created Kyiv and Odessa datasets from sequences available publicly and collected through the TRIP intervention. Odessa dataset comprised of N = 275 HIV pol genetic sequences sampled in 2001–2019, while Kyiv dataset comprised of N = 93 sequences sampled in 2000–2019. We estimated maximum likelihood (ML) phylogenetic trees for both datasets using RAxML. In all of the ML analyses we used an HKY nucleotide substitution model with gamma-distributed rate variation among sites and ran a bootstrap analysis (100 replicates). We identified potential transmission clusters in the Odessa phylogenetic tree with ClusterPicker. Any clade with two or more sequences, within-cluster genetic distance < 1.5 %, and bootstrap statistical support > 90 % was defined as a possible transmission cluster. We then applied birth-death skyline (BDSKY) model to both datasets to estimate the changes in the effective reproductive number (Re) and the becoming uninfectious (getting diagnosed and treated) rate (δ). 
Results. We identified 12 transmission clusters in Odessa; 10 pairs and 2 clusters of three sequences. Clustering was correlated with younger age and higher average viral load at sampling, but was not correlated with transmission risk groups or a recent HIV infection status. The BDSKY analysis showed that the effective reproductive number was similar in Odessa and Kyiv for 10 years before the initiation of TRIP (Re ≈ 1–2, 95% HPD between 0.03–5.7). From 2013 (TRIP initiation) there was a decline in Re and it is now below the epidemiological threshold of 1 in Odessa (Re = 0.4, 95% HPD 0.1–0.8), but not in Kyiv (Re = 2.3, 95% HPD 0.2–5.4). Similarly, the becoming uninfectious rate increased in Odessa since the initiation of TRIP from δ = 0.18 (≈ 5.5 years infectious period), 95% HPD 0.05–0.4, to δ = 0.94 (≈ 1 year infectious period), 95% HPD 0.3–1.9, compared to constant rate ≈ 0.3 (≈ 3 years infectious period), 95% HPD 0.05–0.1, in Kyiv. 
Conclusions. As the amount of gene sequence data grows rapidly, new applications need to be explored to ensure the most efficient use of this resource. Here we applied molecular epidemiology to show that the HIV effective reproductive number has likely declined in Odessa, but not in Kyiv, in the past 7 years. Given that Odessa and Kyiv shared the same HIV prevention programs in 2013–2019 apart from TRIP, we suggest that this reduction is attributed to the intervention in question, though more evidence is needed to determine whether the changes are truly attributable to TRIP and how long in the future can we expect to see the effect of the intervention. We conclude that molecular epidemiology can and should be used as a post-intervention effectiveness assessment tool.


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