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      Tracking external introductions of HIV using phylodynamics reveals a major source of infections in rural KwaZulu-Natal, South Africa

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          Abstract

          Despite increasing access to antiretrovirals, HIV incidence in rural KwaZulu-Natal remains among the highest ever reported in Africa. While many epidemiological factors have been invoked to explain such high incidence, widespread human mobility and viral movement suggest that transmission between communities may be a major source of new infections. High cross-community transmission rates call into question how effective increasing the coverage of antiretroviral therapy locally will be at preventing new infections, especially if many new cases arise from external introductions. To help address this question, we use a phylodynamic model to reconstruct epidemic dynamics and estimate the relative contribution of local transmission versus external introductions to overall incidence in KwaZulu-Natal from HIV-1 phylogenies. By comparing our results with population-based surveillance data, we show that we can reliably estimate incidence from viral phylogenies once viral movement in and out of the local population is accounted for. Our analysis reveals that early epidemic dynamics were largely driven by external introductions. More recently, we estimate that 35 per cent (95% confidence interval: 20–60%) of new infections arise from external introductions. These results highlight the growing need to consider larger-scale regional transmission dynamics when designing and testing prevention strategies.

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          Most cited references32

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          Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics.

          Kingman's coalescent process opens the door for estimation of population genetics model parameters from molecular sequences. One paramount parameter of interest is the effective population size. Temporal variation of this quantity characterizes the demographic history of a population. Because researchers are rarely able to choose a priori a deterministic model describing effective population size dynamics for data at hand, nonparametric curve-fitting methods based on multiple change-point (MCP) models have been developed. We propose an alternative to change-point modeling that exploits Gaussian Markov random fields to achieve temporal smoothing of the effective population size in a Bayesian framework. The main advantage of our approach is that, in contrast to MCP models, the explicit temporal smoothing does not require strong prior decisions. To approximate the posterior distribution of the population dynamics, we use efficient, fast mixing Markov chain Monte Carlo algorithms designed for highly structured Gaussian models. In a simulation study, we demonstrate that the proposed temporal smoothing method, named Bayesian skyride, successfully recovers "true" population size trajectories in all simulation scenarios and competes well with the MCP approaches without evoking strong prior assumptions. We apply our Bayesian skyride method to 2 real data sets. We analyze sequences of hepatitis C virus contemporaneously sampled in Egypt, reproducing all key known aspects of the viral population dynamics. Next, we estimate the demographic histories of human influenza A hemagglutinin sequences, serially sampled throughout 3 flu seasons.
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            The Confounding Effect of Population Structure on Bayesian Skyline Plot Inferences of Demographic History

            Many coalescent-based methods aiming to infer the demographic history of populations assume a single, isolated and panmictic population (i.e. a Wright-Fisher model). While this assumption may be reasonable under many conditions, several recent studies have shown that the results can be misleading when it is violated. Among the most widely applied demographic inference methods are Bayesian skyline plots (BSPs), which are used across a range of biological fields. Violations of the panmixia assumption are to be expected in many biological systems, but the consequences for skyline plot inferences have so far not been addressed and quantified. We simulated DNA sequence data under a variety of scenarios involving structured populations with variable levels of gene flow and analysed them using BSPs as implemented in the software package BEAST. Results revealed that BSPs can show false signals of population decline under biologically plausible combinations of population structure and sampling strategy, suggesting that the interpretation of several previous studies may need to be re-evaluated. We found that a balanced sampling strategy whereby samples are distributed on several populations provides the best scheme for inferring demographic change over a typical time scale. Analyses of data from a structured African buffalo population demonstrate how BSP results can be strengthened by simulations. We recommend that sample selection should be carefully considered in relation to population structure previous to BSP analyses, and that alternative scenarios should be evaluated when interpreting signals of population size change.
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              Universal test and treat and the HIV epidemic in rural South Africa: a phase 4, open-label, community cluster randomised trial

              Universal antiretroviral therapy (ART), as per the 2015 WHO recommendations, might reduce population HIV incidence. We investigated the effect of universal test and treat on HIV acquisition at population level in a high prevalence rural region of South Africa.
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                Author and article information

                Journal
                Virus Evol
                Virus Evol
                vevolu
                Virus Evolution
                Oxford University Press
                2057-1577
                July 2018
                11 December 2018
                11 December 2018
                : 4
                : 2
                : vey037
                Affiliations
                [1 ]Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC, USA
                [2 ]Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
                [3 ]KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
                [4 ]School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa
                [5 ]Africa Health Research Institute, Durban, South Africa
                [6 ]Research Department of Infection & Population Health, University College London, UK
                [7 ]Division of Infection and Immunity, University College London, UK
                [8 ]Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
                [9 ]Swiss Institute of Bioinformatics, Lausanne, Switzerland
                [10 ]Centre for the AIDS Programme of Research in South Africa (CAPRISA), Durban, South Africa
                [11 ]Department of Global Health, University of Washington, Seattle, USA
                Author notes
                Corresponding author: E-mail: drasmus@ 123456ncsu.edu
                Author information
                http://orcid.org/0000-0001-9457-7561
                Article
                vey037
                10.1093/ve/vey037
                6290119
                30555720
                c99893a2-75ff-46df-9e73-e528312189dd
                © The Author(s) 2018. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Page count
                Pages: 15
                Funding
                Funded by: FP7 Ideas: European Research Council 10.13039/100011199
                Award ID: 335529
                Funded by: Wellcome Trust 10.13039/100004440
                Award ID: Africa Health Research Institute Award
                Funded by: ETH Zurich
                Award ID: Postdoctoral Fellowship Award
                Funded by: European Union 2020 Research and Innovation Programme
                Award ID: 634650
                Funded by: South African Medical Research Council 10.13039/501100001322
                Award ID: MRC-RFA-UFSP-01-2013/UKZN HIVEPI
                Funded by: Royal Society Newton Advanced Fellowship
                Categories
                Research Article

                hiv,molecular epidemiology,phylodynamics,migration
                hiv, molecular epidemiology, phylodynamics, migration

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