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      Early detection of variants of concern via funnel plots of regional reproduction numbers

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Early detection of the emergence of a new variant of concern (VoC) is essential to develop strategies that contain epidemic outbreaks. For example, knowing in which region a VoC starts spreading enables prompt actions to circumscribe the geographical area where the new variant can spread, by containing it locally. This paper presents ‘funnel plots’ as a statistical process control method that, unlike tools whose purpose is to identify rises of the reproduction number ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt}

          \begin{document}$${R}_{t}$$\end{document}
          ), detects when a regional \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt}
          \begin{document}$${R}_{t}$$\end{document}
          departs from the national average and thus represents an anomaly. The name of the method refers to the funnel-like shape of the scatter plot that the data take on. Control limits with prescribed false alarm rate are derived from the observation that regional \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt}
          \begin{document}$${R}_{t}$$\end{document}
          's are normally distributed with variance inversely proportional to the number of infectious cases. The method is validated on public COVID-19 data demonstrating its efficacy in the early detection of SARS-CoV-2 variants in India, South Africa, England, and Italy, as well as of a malfunctioning episode of the diagnostic infrastructure in England, during which the Immensa lab in Wolverhampton gave 43,000 incorrect negative tests relative to South West and West Midlands territories.

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          Most cited references30

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          SARS-CoV-2 variants, spike mutations and immune escape

          Although most mutations in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome are expected to be either deleterious and swiftly purged or relatively neutral, a small proportion will affect functional properties and may alter infectivity, disease severity or interactions with host immunity. The emergence of SARS-CoV-2 in late 2019 was followed by a period of relative evolutionary stasis lasting about 11 months. Since late 2020, however, SARS-CoV-2 evolution has been characterized by the emergence of sets of mutations, in the context of ‘variants of concern’, that impact virus characteristics, including transmissibility and antigenicity, probably in response to the changing immune profile of the human population. There is emerging evidence of reduced neutralization of some SARS-CoV-2 variants by postvaccination serum; however, a greater understanding of correlates of protection is required to evaluate how this may impact vaccine effectiveness. Nonetheless, manufacturers are preparing platforms for a possible update of vaccine sequences, and it is crucial that surveillance of genetic and antigenic changes in the global virus population is done alongside experiments to elucidate the phenotypic impacts of mutations. In this Review, we summarize the literature on mutations of the SARS-CoV-2 spike protein, the primary antigen, focusing on their impacts on antigenicity and contextualizing them in the protein structure, and discuss them in the context of observed mutation frequencies in global sequence datasets. The evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been characterized by the emergence of mutations and so-called variants of concern that impact virus characteristics, including transmissibility and antigenicity. In this Review, members of the COVID-19 Genomics UK (COG-UK) Consortium and colleagues summarize mutations of the SARS-CoV-2 spike protein, focusing on their impacts on antigenicity and contextualizing them in the protein structure, and discuss them in the context of observed mutation frequencies in global sequence datasets.
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            A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics

            Abstract The quantification of transmissibility during epidemics is essential to designing and adjusting public health responses. Transmissibility can be measured by the reproduction number R, the average number of secondary cases caused by an infected individual. Several methods have been proposed to estimate R over the course of an epidemic; however, they are usually difficult to implement for people without a strong background in statistical modeling. Here, we present a ready-to-use tool for estimating R from incidence time series, which is implemented in popular software including Microsoft Excel (Microsoft Corporation, Redmond, Washington). This tool produces novel, statistically robust analytical estimates of R and incorporates uncertainty in the distribution of the serial interval (the time between the onset of symptoms in a primary case and the onset of symptoms in secondary cases). We applied the method to 5 historical outbreaks; the resulting estimates of R are consistent with those presented in the literature. This tool should help epidemiologists quantify temporal changes in the transmission intensity of future epidemics by using surveillance data.
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              SARS-CoV-2 variants of concern are emerging in India

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                Author and article information

                Contributors
                giuseppe.denicolao@unipv.it
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                19 January 2023
                19 January 2023
                2023
                : 13
                : 1052
                Affiliations
                [1 ]GRID grid.8982.b, ISNI 0000 0004 1762 5736, Department of Mathematics, , University of Pavia, ; Pavia, Italy
                [2 ]GRID grid.5390.f, ISNI 0000 0001 2113 062X, Department of Mathematics, Computer Science and Physics, , University of Udine, ; Udine, Italy
                [3 ]GRID grid.419425.f, ISNI 0000 0004 1760 3027, Division of Infectious Diseases I, , Fondazione IRCCS Policlinico San Matteo, ; Pavia, Italy
                [4 ]GRID grid.11696.39, ISNI 0000 0004 1937 0351, Department of Industrial Engineering, , University of Trento, ; Trento, Italy
                [5 ]GRID grid.5133.4, ISNI 0000 0001 1941 4308, Department of Surgical Medical and Health Sciences, , University of Trieste, ; Trieste, Italy
                [6 ]GRID grid.4643.5, ISNI 0000 0004 1937 0327, Department of Electronics, Information and Bioengineering, , Politecnico di Milano, ; Milan, Italy
                [7 ]GRID grid.5326.2, ISNI 0000 0001 1940 4177, Institute of Electronics, Information Engineering and Telecommunication (IEIIT), , Italian National Research Council (CNR), ; Turin, Italy
                [8 ]GRID grid.8982.b, ISNI 0000 0004 1762 5736, Department of Electrical, Computer and Biomedical Engineering, , University of Pavia, ; Pavia, Italy
                [9 ]GRID grid.8982.b, ISNI 0000 0004 1762 5736, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, , University of Pavia, ; Pavia, Italy
                Author information
                http://orcid.org/0000-0002-6314-1965
                http://orcid.org/0000-0002-1202-2712
                http://orcid.org/0000-0002-8600-1738
                http://orcid.org/0000-0002-3712-9911
                Article
                27116
                10.1038/s41598-022-27116-8
                9852294
                36658143
                8ebe886c-0ef9-4776-9274-7b9184ae5cea
                © The Author(s) 2023

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 19 July 2022
                : 26 December 2022
                Funding
                Funded by: European Union - NextGenerationEU
                Award ID: Grant Uniud-DM737
                Award Recipient :
                Funded by: PERISCOPE
                Award ID: 101016233, H2020-SC1-PHE-CORONAVIRUS-2020-2-RTD
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2023

                Uncategorized
                epidemiology,scientific data,statistics
                Uncategorized
                epidemiology, scientific data, statistics

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