A dynamic microsimulation model for epidemics


Posted: 2021-10-30 19:00:00
Soc Sci Med . 2021 Oct 18;291:114461. doi: 10.1016/j.socscimed.2021.114461. Online ahead of print. Fiona Spooner 1 , Jesse F Abrams 2 , Karyn Morrissey 3 , Gavin Shaddick 4 , Michael Batty 5 , Richard Milton 5 , Adam Dennett 5 , Nik Lomax 6 , Nick Malleson 6 , Natalie Nelissen 6 , Alex Coleman 7 , Jamil Nur 8 , Ying Jin 8 , Rory Greig 9 , Charlie Shenton 9 , Mark Birkin 10 Affiliations Expand Affiliations 1 Our World in Data at the Global Change Lab, London, UK. 2 Institute for Data Science and Artificial Intelligence & Global Systems Institute, University of Exeter, UK; Joint Centre for Excellence in Environmental Intelligence, Exeter, UK. 3 Department of Technology, Management and Economics, Technical University of Denmark, Lyngby, Denmark. 4 Joint Centre for Excellence in Environmental Intelligence, Exeter, UK; Alan Turing Institute, London, UK. 5 Bartlett Centre for Advanced Spatial Analysis, University College London, London, UK. 6 School of Geography and Leeds Institute for Data Analytics, University of Leeds, Leeds, UK; Alan Turing Institute, London, UK. 7 Research Computing, University of Leeds, Leeds, UK. 8 Martin Centre for Architectural and Urban Studies, University of Cambridge, 1 Scroope Terrace, Cambridge, UK. 9 Improbable, London, UK. 10 School of Geography and Leeds Institute for Data Analytics, University of Leeds, Leeds, UK; Alan Turing Institute, London, UK. Electronic address: m.h.birkin@leeds.ac.uk. Item in Clipboard Fiona Spooner et al. Soc Sci Med. 2021. Show details Display options Display options Format Soc Sci Med . 2021 Oct 18;291:114461. doi: 10.1016/j.socscimed.2021.114461. Online ahead of print. Authors Fiona Spooner 1 , Jesse F Abrams 2 , Karyn Morrissey 3 , Gavin Shaddick 4 , Michael Batty 5 , Richard Milton 5 , Adam Dennett 5 , Nik Lomax 6 , Nick Malleson 6 , Natalie Nelissen 6 , Alex Coleman 7 , Jamil Nur 8 , Ying Jin 8 , Rory Greig 9 , Charlie Shenton 9 , Mark Birkin 10 Affiliations 1 Our World in Data at the Global Change Lab, London, UK. 2 Institute for Data Science and Artificial Intelligence & Global Systems Institute, University of Exeter, UK; Joint Centre for Excellence in Environmental Intelligence, Exeter, UK. 3 Department of Technology, Management and Economics, Technical University of Denmark, Lyngby, Denmark. 4 Joint Centre for Excellence in Environmental Intelligence, Exeter, UK; Alan Turing Institute, London, UK. 5 Bartlett Centre for Advanced Spatial Analysis, University College London, London, UK. 6 School of Geography and Leeds Institute for Data Analytics, University of Leeds, Leeds, UK; Alan Turing Institute, London, UK. 7 Research Computing, University of Leeds, Leeds, UK. 8 Martin Centre for Architectural and Urban Studies, University of Cambridge, 1 Scroope Terrace, Cambridge, UK. 9 Improbable, London, UK. 10 School of Geography and Leeds Institute for Data Analytics, University of Leeds, Leeds, UK; Alan Turing Institute, London, UK. Electronic address: m.h.birkin@leeds.ac.uk. Item in Clipboard CiteDisplay options Display options Format Abstract A large evidence base demonstrates that the outcomes of COVID-19 and national and local interventions are not distributed equally across different communities. The need to inform policies and mitigation measures aimed at reducing the spread of COVID-19 highlights the need to understand the complex links between our daily activities and COVID-19 transmission that reflect the characteristics of British society. As a result of a partnership between academic and private sector researchers, we introduce a novel data driven modelling framework together with a computationally efficient approach to running complex simulation models of this type. We demonstrate the power and spatial flexibility of the framework to assess the effects of different interventions in a case study where the effects of the first UK national lockdown are estimated for the county of Devon. Here we find that an earlier lockdown is estimated to result in a lower peak in COVID-19 cases and 47% fewer infections overall during the initial COVID-19 outbreak. The framework we outline here will be crucial in gaining a greater understanding of the effects of policy interventions in different areas and within different populations. Keywords: COVID-19; Coronavirus; Dynamics; Microsimulation; SEIR; Spatial-interaction. Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved. LinkOut - more resources Research Materials [x] Cite Copy Format: Send To [x]

参考サイト PubMed: covid-19



バイオクイックニュース日本語版:COVID-19特集

バイオクイックニュース日本語版
9月 18, 2020 バイオアソシエイツ

COVID-19の病原性は、宿主MicroRNAの枯渇によるものという仮説が発表された

なぜ COVID-19 ウイルスは致命的であるのに、他の多くのコロナウイルスは無害で風邪をひくだけなのか? ポーランドとアメリカのアラバマ大学バーミンガム校(UAB)の研究チームがその答えを提案した。COVID-19ウイルスはマイクロRNAの「スポンジ」として機能するという。ポーランドのグダニスク医科大学の Rafal Bartoszewski 博士らによるこの仮説は、American Journal of Physiology-Lung Cellular and Molecular…

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