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      Off-season RSV epidemics in Australia after easing of COVID-19 restrictions

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      1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 7 , 9 , 4 , 3 , 1 , 2 , 3 , 10 , 11 , 12 , 2 , 13 , 14 , 15 , 16 , 7 , 8 , 2 , 17 , 5 , 6 , 1 , 2 , 3 , 18 , 3 , 3 , 10 , 1 , 2 , 2 , 5 , 6 , , 3 , 10 , , 17 , , 7 , 8 , , the Australian RSV study group
      Nature Communications
      Nature Publishing Group UK
      Molecular evolution, Infectious diseases, Viral epidemiology, Epidemiology

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          Abstract

          Human respiratory syncytial virus (RSV) is an important cause of acute respiratory infection with the most severe disease in the young and elderly. Non-pharmaceutical interventions and travel restrictions for controlling COVID-19 have impacted the circulation of most respiratory viruses including RSV globally, particularly in Australia, where during 2020 the normal winter epidemics were notably absent. However, in late 2020, unprecedented widespread RSV outbreaks occurred, beginning in spring, and extending into summer across two widely separated regions of the Australian continent, New South Wales (NSW) and Australian Capital Territory (ACT) in the east, and Western Australia. Through genomic sequencing we reveal a major reduction in RSV genetic diversity following COVID-19 emergence with two genetically distinct RSV-A clades circulating cryptically, likely localised for several months prior to an epidemic surge in cases upon relaxation of COVID-19 control measures. The NSW/ACT clade subsequently spread to the neighbouring state of Victoria and to cause extensive outbreaks and hospitalisations in early 2021. These findings highlight the need for continued surveillance and sequencing of RSV and other respiratory viruses during and after the COVID-19 pandemic, as mitigation measures may disrupt seasonal patterns, causing larger or more severe outbreaks.

          Abstract

          Non-pharmaceutical interventions for COVID-19 also reduced incidence of respiratory pathogens such as respiratory syncytial virus (RSV). Here, the authors report the resurgence of RSV in Australia following lifting of some of the restrictions and describe reduction in genetic diversity in circulating clades.

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          Most cited references45

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          MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

          We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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            RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

            Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
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              FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments

              Background We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. Methodology/Principal Findings Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the “CAT” approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100–1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. Conclusions/Significance FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.
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                Author and article information

                Contributors
                veej@hku.hk
                David.Smith@health.wa.gov.au
                jen.kok@health.nsw.gov.au
                ian.barr@influenzacentre.org
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                24 May 2022
                24 May 2022
                2022
                : 13
                : 2884
                Affiliations
                [1 ]GRID grid.452919.2, ISNI 0000 0001 0436 7430, Centre for Virus Research, , Westmead Institute for Medical Research, ; Westmead, NSW 2145 Australia
                [2 ]GRID grid.1013.3, ISNI 0000 0004 1936 834X, Sydney Institute for Infectious Diseases, Sydney Medical School, , The University of Sydney, ; Sydney, NSW 2006 Australia
                [3 ]GRID grid.2824.c, ISNI 0000 0004 0589 6117, PathWest Laboratory Medicine WA, Department of Microbiology, ; Nedlands, WA 6009 Australia
                [4 ]GRID grid.1012.2, ISNI 0000 0004 1936 7910, School of Biomedical Sciences, , The University of Western Australia, ; Crawley, WA 6009 Australia
                [5 ]GRID grid.194645.b, ISNI 0000000121742757, School of Public Health, LKS Faculty of Medicine, , The University of Hong Kong, ; Hong Kong, China
                [6 ]GRID grid.194645.b, ISNI 0000000121742757, HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, , The University of Hong Kong, ; Hong Kong, China
                [7 ]GRID grid.416153.4, ISNI 0000 0004 0624 1200, WHO Collaborating Centre for Reference and Research on Influenza, , Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, ; Melbourne, VIC 3000 Australia
                [8 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Department of Microbiology and Immunology, , University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, ; Melbourne, VIC 3000 Australia
                [9 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Department of Infectious Diseases, , University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, ; Melbourne, VIC 3000 Australia
                [10 ]GRID grid.1012.2, ISNI 0000 0004 1936 7910, School of Medicine, , The University of Western Australia, ; Crawley, WA 6009 Australia
                [11 ]GRID grid.410667.2, ISNI 0000 0004 0625 8600, Department of Infectious Diseases, , Perth Children’s Hospital, ; Nedlands, WA 6009 Australia
                [12 ]GRID grid.414659.b, ISNI 0000 0000 8828 1230, Wesfarmers Centre of Vaccines and Infectious Diseases, , Telethon Kids Institute, ; Nedlands, WA 6009 Australia
                [13 ]GRID grid.413973.b, ISNI 0000 0000 9690 854X, Departments of Infectious Diseases and Microbiology, , The Children’s Hospital at Westmead, ; Westmead, NSW 2145 Australia
                [14 ]GRID grid.1058.c, ISNI 0000 0000 9442 535X, Murdoch Children’s Research Institute, ; Melbourne, VIC 3000 Australia
                [15 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Department of Paediatrics, , University of Melbourne & Royal Children’s Hospital, ; Melbourne, VIC 3000 Australia
                [16 ]GRID grid.416107.5, ISNI 0000 0004 0614 0346, Immunisation Service, Royal Children’s Hospital, ; Melbourne, VIC 3000 Australia
                [17 ]GRID grid.413252.3, ISNI 0000 0001 0180 6477, NSW Health Pathology - Institute for Clinical Pathology and Medical Research, , Westmead Hospital, ; Westmead, NSW 2145 Australia
                [18 ]GRID grid.413314.0, ISNI 0000 0000 9984 5644, Departments of Clinical Microbiology and Infectious Diseases, , Canberra Hospital, ; Garran, ACT 2605 Australia
                [19 ]GRID grid.419789.a, ISNI 0000 0000 9295 3933, Microbiology Laboratory, Monash Pathology, , Monash Health, ; Melbourne, VIC Australia
                Author information
                http://orcid.org/0000-0003-1374-3551
                http://orcid.org/0000-0002-0856-0294
                http://orcid.org/0000-0002-7163-2844
                http://orcid.org/0000-0003-0944-3118
                http://orcid.org/0000-0002-8622-7809
                http://orcid.org/0000-0001-9596-3552
                http://orcid.org/0000-0003-3293-6279
                http://orcid.org/0000-0002-3107-5182
                http://orcid.org/0000-0002-8796-283X
                http://orcid.org/0000-0002-7351-418X
                Article
                30485
                10.1038/s41467-022-30485-3
                9130497
                35610217
                6db3a8fa-46d1-4da0-99bc-9b1d7ae9d1e4
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 22 July 2021
                : 27 April 2022
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                © The Author(s) 2022

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                molecular evolution,infectious diseases,viral epidemiology,epidemiology
                Uncategorized
                molecular evolution, infectious diseases, viral epidemiology, epidemiology

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