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      Reconstructing the recent West Nile virus lineage 2 epidemic in Europe and Italy using discrete and continuous phylogeography

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          Abstract

          West Nile virus lineage 2 (WNV-2) was mainly confined to sub-Saharan Africa until the early 2000s, when it was identified for the first time in Central Europe causing outbreaks of human and animal infection. The aim of this study was to reconstruct the origin and dispersion of WNV-2 in Central Europe and Italy on a phylodynamic and phylogeographical basis. To this aim, discrete and continuous space phylogeographical models were applied to a total of 33 newly characterised full-length viral genomes obtained from mosquitoes, birds and humans in Northern Italy in the years 2013–2015 aligned with 64 complete sequences isolated mainly in Europe. The European isolates segregated into two highly significant clades: a small one including three sequences and a large clade including the majority of isolates obtained in Central Europe since 2004. Discrete phylogeographical analysis showed that the most probable location of the root of the largest European clade was in Hungary a mean 12.78 years ago. The European clade bifurcated into two highly supported subclades: one including most of the Central/East European isolates and the other encompassing all of the isolates obtained in Greece. The continuous space phylogeographical analysis of the Italian clade showed that WNV-2 entered Italy in about 2008, probably by crossing the Adriatic sea and reaching a central area of the Po Valley. The epidemic then spread simultaneously eastward, to reach the region of the Po delta in 2013, and westward to the border area between Lombardy and Piedmont in 2014; later, the western strain changed direction southward, and reached the central area of the Po valley once again in 2015. Over a period of about seven years, the virus spread all over an area of northern Italy by following the Po river and its main tributaries.

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          Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty.

          Recent developments in marginal likelihood estimation for model selection in the field of Bayesian phylogenetics and molecular evolution have emphasized the poor performance of the harmonic mean estimator (HME). Although these studies have shown the merits of new approaches applied to standard normally distributed examples and small real-world data sets, not much is currently known concerning the performance and computational issues of these methods when fitting complex evolutionary and population genetic models to empirical real-world data sets. Further, these approaches have not yet seen widespread application in the field due to the lack of implementations of these computationally demanding techniques in commonly used phylogenetic packages. We here investigate the performance of some of these new marginal likelihood estimators, specifically, path sampling (PS) and stepping-stone (SS) sampling for comparing models of demographic change and relaxed molecular clocks, using synthetic data and real-world examples for which unexpected inferences were made using the HME. Given the drastically increased computational demands of PS and SS sampling, we also investigate a posterior simulation-based analogue of Akaike's information criterion (AIC) through Markov chain Monte Carlo (MCMC), a model comparison approach that shares with the HME the appealing feature of having a low computational overhead over the original MCMC analysis. We confirm that the HME systematically overestimates the marginal likelihood and fails to yield reliable model classification and show that the AICM performs better and may be a useful initial evaluation of model choice but that it is also, to a lesser degree, unreliable. We show that PS and SS sampling substantially outperform these estimators and adjust the conclusions made concerning previous analyses for the three real-world data sets that we reanalyzed. The methods used in this article are now available in BEAST, a powerful user-friendly software package to perform Bayesian evolutionary analyses.
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            The outbreak of West Nile virus infection in the New York City area in 1999.

            In late August 1999, an unusual cluster of cases of meningoencephalitis associated with muscle weakness was reported to the New York City Department of Health. The initial epidemiologic and environmental investigations suggested an arboviral cause. Active surveillance was implemented to identify patients hospitalized with viral encephalitis and meningitis. Cerebrospinal fluid, serum, and tissue specimens from patients with suspected cases underwent serologic and viral testing for evidence of arboviral infection. Outbreak surveillance identified 59 patients who were hospitalized with West Nile virus infection in the New York City area during August and September of 1999. The median age of these patients was 71 years (range, 5 to 95). The overall attack rate of clinical West Nile virus infection was at least 6.5 cases per million population, and it increased sharply with age. Most of the patients (63 percent) had clinical signs of encephalitis; seven patients died (12 percent). Muscle weakness was documented in 27 percent of the patients and flaccid paralysis in 10 percent; in all of the latter, nerve conduction studies indicated an axonal polyneuropathy in 14 percent. An age of 75 years or older was an independent risk factor for death (relative risk adjusted for the presence or absence of diabetes mellitus, 8.5; 95 percent confidence interval, 1.2 to 59.1), as was the presence of diabetes mellitus (age-adjusted relative risk, 5.1; 95 percent confidence interval, 1.5 to 17.3). This outbreak of West Nile meningoencephalitis in the New York City metropolitan area represents the first time this virus has been detected in the Western Hemisphere. Given the subsequent rapid spread of the virus, physicians along the eastern seaboard of the United States should consider West Nile virus infection in the differential diagnosis of encephalitis and viral meningitis during the summer months, especially in older patients and in those with muscle weakness.
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              SpreaD3: Interactive Visualization of Spatiotemporal History and Trait Evolutionary Processes.

              Model-based phylogenetic reconstructions increasingly consider spatial or phenotypic traits in conjunction with sequence data to study evolutionary processes. Alongside parameter estimation, visualization of ancestral reconstructions represents an integral part of these analyses. Here, we present a complete overhaul of the spatial phylogenetic reconstruction of evolutionary dynamics software, now called SpreaD3 to emphasize the use of data-driven documents, as an analysis and visualization package that primarily complements Bayesian inference in BEAST (http://beast.bio.ed.ac.uk, last accessed 9 May 2016). The integration of JavaScript D3 libraries (www.d3.org, last accessed 9 May 2016) offers novel interactive web-based visualization capacities that are not restricted to spatial traits and extend to any discrete or continuously valued trait for any organism of interest.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: ResourcesRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysis
                Role: Formal analysis
                Role: InvestigationRole: Methodology
                Role: InvestigationRole: Methodology
                Role: MethodologyRole: Writing – review & editing
                Role: Data curation
                Role: Methodology
                Role: Supervision
                Role: MethodologyRole: Writing – review & editing
                Role: Methodology
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: Resources
                Role: Resources
                Role: Supervision
                Role: Supervision
                Role: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Supervision
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                5 July 2017
                2017
                : 12
                : 7
                : e0179679
                Affiliations
                [1 ]Department of Biomedical and Clinical Sciences "L.Sacco", University of Milan, Milano, Italy
                [2 ]CRC-Coordinated Research Center “EpiSoMI”, University of Milan, Milano, Italy
                [3 ]Molecular Virology Unit, Microbiology and Virology Department, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
                [4 ]Experimental Zooprophylactic Institute of Lombardy and Emilia-Romagna (IZSLER), Brescia, Italy
                [5 ]Experimental Zooprophylactic Institute of Lombardy and Emilia-Romagna (IZSLER), Reggio Emilia, Italy
                [6 ]Experimental Zooprophylactic Institute of Lombardy and Emilia-Romagna (IZSLER), Parma, Italy
                [7 ]Experimental Zooprophylactic Institute of Venice, Legnaro, Padua, Italy
                [8 ]Department of Molecular Medicine, University of Padova, Padova, Italy
                National and Kapodistrian University of Athens, GREECE
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-1886-2915
                Article
                PONE-D-17-08367
                10.1371/journal.pone.0179679
                5497961
                28678837
                3586eddc-a3fe-48cd-9e92-04d24ceda3ff
                © 2017 Zehender et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 5 March 2017
                : 4 June 2017
                Page count
                Figures: 4, Tables: 2, Pages: 16
                Funding
                This work was partially financed by the “NANOMAX” Bandiera project (2013–2015) funded by Italian Ministry for Education, University and Research (grant number G42I12000180005).
                Categories
                Research Article
                Biology and life sciences
                Organisms
                Viruses
                RNA viruses
                Flaviviruses
                West Nile virus
                Biology and life sciences
                Microbiology
                Medical microbiology
                Microbial pathogens
                Viral pathogens
                Flaviviruses
                West Nile virus
                Medicine and health sciences
                Pathology and laboratory medicine
                Pathogens
                Microbial pathogens
                Viral pathogens
                Flaviviruses
                West Nile virus
                Biology and life sciences
                Organisms
                Viruses
                Viral pathogens
                Flaviviruses
                West Nile virus
                Biology and Life Sciences
                Biogeography
                Phylogeography
                Ecology and Environmental Sciences
                Biogeography
                Phylogeography
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                Evolutionary Biology
                Population Genetics
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                Genetics
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                Population Biology
                Population Genetics
                Phylogeography
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                Evolutionary Biology
                Evolutionary Systematics
                Phylogenetics
                Phylogenetic Analysis
                Biology and Life Sciences
                Taxonomy
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                Computer and Information Sciences
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                Evolutionary Systematics
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                Custom metadata
                All relevant data are within the paper, its Supporting Information files, and available within the NCBI at the accession numbers listed in S1 Table.

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