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      Emergence and potential for spread of Chikungunya virus in Brazil

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          Abstract

          Background

          In December 2013, an outbreak of Chikungunya virus (CHIKV) caused by the Asian genotype was notified in the Caribbean. The outbreak has since spread to 38 regions in the Americas. By September 2014, the first autochthonous CHIKV infections were confirmed in Oiapoque, North Brazil, and in Feira de Santana, Northeast Brazil.

          Methods

          We compiled epidemiological and clinical data on suspected CHIKV cases in Brazil and polymerase-chain-reaction-based diagnostic was conducted on 68 serum samples from patients with symptom onset between April and September 2014. Two imported and four autochthonous cases were selected for virus propagation, RNA isolation, full-length genome sequencing, and phylogenetic analysis. We then followed CDC/PAHO guidelines to estimate the risk of establishment of CHIKV in Brazilian municipalities.

          Results

          We detected 41 CHIKV importations and 27 autochthonous cases in Brazil. Epidemiological and phylogenetic analyses indicated local transmission of the Asian CHIKV genotype in Oiapoque. Unexpectedly, we also discovered that the ECSA genotype is circulating in Feira de Santana. The presumed index case of the ECSA genotype was an individual who had recently returned from Angola and developed symptoms in Feira de Santana. We estimate that, if CHIKV becomes established in Brazil, transmission could occur in 94% of municipalities in the country and provide maps of the risk of importation of each strain of CHIKV in Brazil.

          Conclusions

          The etiological strains associated with the early-phase CHIKV outbreaks in Brazil belong to the Asian and ECSA genotypes. Continued surveillance and vector mitigation strategies are needed to reduce the future public health impact of CHIKV in the Americas.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12916-015-0348-x) contains supplementary material, which is available to authorized users.

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          Most cited references 28

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          An integrated semiconductor device enabling non-optical genome sequencing.

          The seminal importance of DNA sequencing to the life sciences, biotechnology and medicine has driven the search for more scalable and lower-cost solutions. Here we describe a DNA sequencing technology in which scalable, low-cost semiconductor manufacturing techniques are used to make an integrated circuit able to directly perform non-optical DNA sequencing of genomes. Sequence data are obtained by directly sensing the ions produced by template-directed DNA polymerase synthesis using all-natural nucleotides on this massively parallel semiconductor-sensing device or ion chip. The ion chip contains ion-sensitive, field-effect transistor-based sensors in perfect register with 1.2 million wells, which provide confinement and allow parallel, simultaneous detection of independent sequencing reactions. Use of the most widely used technology for constructing integrated circuits, the complementary metal-oxide semiconductor (CMOS) process, allows for low-cost, large-scale production and scaling of the device to higher densities and larger array sizes. We show the performance of the system by sequencing three bacterial genomes, its robustness and scalability by producing ion chips with up to 10 times as many sensors and sequencing a human genome.
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            Chikungunya in the Americas.

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              Improving Bayesian population dynamics inference: a coalescent-based model for multiple loci.

              Effective population size is fundamental in population genetics and characterizes genetic diversity. To infer past population dynamics from molecular sequence data, coalescent-based models have been developed for Bayesian nonparametric estimation of effective population size over time. Among the most successful is a Gaussian Markov random field (GMRF) model for a single gene locus. Here, we present a generalization of the GMRF model that allows for the analysis of multilocus sequence data. Using simulated data, we demonstrate the improved performance of our method to recover true population trajectories and the time to the most recent common ancestor (TMRCA). We analyze a multilocus alignment of HIV-1 CRF02_AG gene sequences sampled from Cameroon. Our results are consistent with HIV prevalence data and uncover some aspects of the population history that go undetected in Bayesian parametric estimation. Finally, we recover an older and more reconcilable TMRCA for a classic ancient DNA data set.
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                Author and article information

                Contributors
                marcionunesbrasil@yahoo.com.br
                nuno.faria@zoo.ox.ac.uk
                janaina.mvasconcelos@yahoo.com.br
                nick.golding@zoo.ox.ac.uk
                moritz.kraemer@zoo.ox.ac.uk
                layannafoliveira@gmail.com
                raimundaazevedo@iec.pa.gov.br
                daisydasilva@gmail.com
                elianapinto@iec.pa.gov.br
                spatroca@gmail.com
                valeriacarvalho@iec.pa.gov.br
                giovanini.coelho@saude.gov.br
                anacecilia@iec.pa.gov.br
                suelirodrigues@iec.pa.gov.br
                joao.vianezjr@gmail.com
                brunonunes@iec.pa.gov.br
                jedson.cardoso@iec.pa.gov.br
                rtesh@utmb.edu
                simon.i.hay@gmail.com
                oliver.pybus@zoo.ox.ac.uk
                pedro.vasconcelos@globo.com
                Journal
                BMC Med
                BMC Med
                BMC Medicine
                BioMed Central (London )
                1741-7015
                30 April 2015
                30 April 2015
                2015
                : 13
                Affiliations
                [ ]Center for Technological Innovation, Evandro Chagas Institute, Ministry of Health, Ananindeua, PA 67030-000 Brazil
                [ ]Department of Zoology, University of Oxford, South Parks Road, Oxford, OX1 3PS UK
                [ ]Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, OX1 3PS UK
                [ ]Department of Arbovirology and Hemorrhagic Fevers, Evandro Chagas Institute, Ministry of Health, Ananindeua, PA 67030-000 Brazil
                [ ]National Dengue Control Program, Brazilian Ministry of Health, Brasilia, DF 70058-900 Brazil
                [ ]Department of Pathology, University of Texas Medical Branch, Galveston, Texas TX 77555-0609 USA
                [ ]Fogarty International Center, National Institutes of Health, Bethesda, MD 20892 USA
                [ ]Department of Pathology, Para State University, Belem, PA 66087-670 Brazil
                Article
                348
                10.1186/s12916-015-0348-x
                4433093
                25976325
                © Nunes et al.; licensee BioMed Central. 2015

                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 use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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                © The Author(s) 2015

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