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      Impact of US vaccination strategy on COVID-19 wave dynamics

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          Abstract

          We employ the epidemic Renormalization Group (eRG) framework to understand, reproduce and predict the COVID-19 pandemic diffusion across the US. The human mobility across different geographical US divisions is modelled via open source flight data alongside the impact of social distancing for each such division. We analyse the impact of the vaccination strategy on the current pandemic wave dynamics in the US. We observe that the ongoing vaccination campaign will not impact the current pandemic wave and therefore strict social distancing measures must still be enacted. To curb the current and the next waves our results indisputably show that vaccinations alone are not enough and strict social distancing measures are required until sufficient immunity is achieved. Our results are essential for a successful vaccination strategy in the US.

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          A Contribution to the Mathematical Theory of Epidemics

          W O Kermack, A McKendrick (1927)
          Article 0 comments Cited 1469 times – based on 0 reviews     Review now
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            The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study

            Kiesha Prem, Yang Liu, Timothy Russell … (2020)
            Summary Background In December, 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus, emerged in Wuhan, China. Since then, the city of Wuhan has taken unprecedented measures in response to the outbreak, including extended school and workplace closures. We aimed to estimate the effects of physical distancing measures on the progression of the COVID-19 epidemic, hoping to provide some insights for the rest of the world. Methods To examine how changes in population mixing have affected outbreak progression in Wuhan, we used synthetic location-specific contact patterns in Wuhan and adapted these in the presence of school closures, extended workplace closures, and a reduction in mixing in the general community. Using these matrices and the latest estimates of the epidemiological parameters of the Wuhan outbreak, we simulated the ongoing trajectory of an outbreak in Wuhan using an age-structured susceptible-exposed-infected-removed (SEIR) model for several physical distancing measures. We fitted the latest estimates of epidemic parameters from a transmission model to data on local and internationally exported cases from Wuhan in an age-structured epidemic framework and investigated the age distribution of cases. We also simulated lifting of the control measures by allowing people to return to work in a phased-in way and looked at the effects of returning to work at different stages of the underlying outbreak (at the beginning of March or April). Findings Our projections show that physical distancing measures were most effective if the staggered return to work was at the beginning of April; this reduced the median number of infections by more than 92% (IQR 66–97) and 24% (13–90) in mid-2020 and end-2020, respectively. There are benefits to sustaining these measures until April in terms of delaying and reducing the height of the peak, median epidemic size at end-2020, and affording health-care systems more time to expand and respond. However, the modelled effects of physical distancing measures vary by the duration of infectiousness and the role school children have in the epidemic. Interpretation Restrictions on activities in Wuhan, if maintained until April, would probably help to delay the epidemic peak. Our projections suggest that premature and sudden lifting of interventions could lead to an earlier secondary peak, which could be flattened by relaxing the interventions gradually. However, there are limitations to our analysis, including large uncertainties around estimates of R 0 and the duration of infectiousness. Funding Bill & Melinda Gates Foundation, National Institute for Health Research, Wellcome Trust, and Health Data Research UK.
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              Renormalization Group and Critical Phenomena. I. Renormalization Group and the Kadanoff Scaling Picture

              Kenneth G. Wilson (1971)
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                Author and article information

                Contributors
                Corentin Cot: cot@ipnl.in2p3.fr
                Journal
                Journal ID (nlm-ta): Sci Rep
                Journal ID (iso-abbrev): Sci Rep
                Title: Scientific Reports
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2045-2322
                Publication date (Electronic): 26 May 2021
                Publication date PMC-release: 26 May 2021
                Publication date Collection: 2021
                Volume: 11
                Electronic Location Identifier: 10960
                Affiliations
                [1 ]GRID grid.433124.3, ISNI 0000 0001 0664 3574, Institut de Physique des deux Infinis de Lyon (IP2I), UMR5822, CNRS/IN2P3, ; 69622 Villeurbanne, France
                [2 ]GRID grid.25697.3f, ISNI 0000 0001 2172 4233, University of Lyon, Université Claude Bernard Lyon 1, ; 69001 Lyon, France
                [3 ]GRID grid.9580.4, ISNI 0000 0004 0643 5232, Department of Computer Science, , Reykjavík University, ; Menntavegur 1, 102 Reykjavík, Iceland
                [4 ]GRID grid.10825.3e, ISNI 0000 0001 0728 0170, CP3-Origins & the Danish Institute for Advanced Study, University of Southern Denmark, ; Campusvej 55, 5230 Odense, Denmark
                [5 ]GRID grid.470211.1, Dipartimento di Fisica E. Pancini, , Università di Napoli Federico II & INFN sezione di Napoli, Complesso Universitario di Monte S. Angelo Edificio 6, ; via Cintia, 80126 Napoli Italy
                Article
                Publisher ID: 90539
                DOI: 10.1038/s41598-021-90539-2
                PMC ID: 8155037
                PubMed ID: 34040088
                SO-VID: 5b5a5adf-7609-4ed1-aa53-f044b1fd1ebc
                Copyright © © The Author(s) 2021
                License:

                Open AccessThis 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
                Date received : 13 January 2021
                Date accepted : 29 April 2021
                Categories
                Subject: Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2021

                ScienceOpen disciplines: Uncategorized
                Keywords: epidemiology,mathematics and computing
                Data availability:
                ScienceOpen disciplines: Uncategorized
                Keywords: epidemiology, mathematics and computing

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