Visualizing COVID-19 pandemic risk through network connectedness

With the domestic and international spread of the coronavirus disease 2019 (COVID-19), much attention has been given to estimating pandemic risk. We propose the novel application of a well-established scientific approach – the network analysis – to provide a direct visualization of the COVID-19 pand...

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Detalles Bibliográficos
Autores principales: So, Mike K.P., Tiwari, Agnes, Chu, Amanda M.Y., Tsang, Jenny T.Y., Chan, Jacky N.L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. 2020
Materias:
Perspective
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7207126/
https://www.ncbi.nlm.nih.gov/pubmed/32437929
http://dx.doi.org/10.1016/j.ijid.2020.05.011
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author So, Mike K.P.
Tiwari, Agnes
Chu, Amanda M.Y.
Tsang, Jenny T.Y.
Chan, Jacky N.L.
author_facet So, Mike K.P.
Tiwari, Agnes
Chu, Amanda M.Y.
Tsang, Jenny T.Y.
Chan, Jacky N.L.
author_sort So, Mike K.P.
collection PubMed
description With the domestic and international spread of the coronavirus disease 2019 (COVID-19), much attention has been given to estimating pandemic risk. We propose the novel application of a well-established scientific approach – the network analysis – to provide a direct visualization of the COVID-19 pandemic risk; infographics are provided in the figures. By showing visually the degree of connectedness between different regions based on reported confirmed cases of COVID-19, we demonstrate that network analysis provides a relatively simple yet powerful way to estimate the pandemic risk.
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spelling pubmed-72071262020-05-11 Visualizing COVID-19 pandemic risk through network connectedness So, Mike K.P. Tiwari, Agnes Chu, Amanda M.Y. Tsang, Jenny T.Y. Chan, Jacky N.L. Int J Infect Dis Perspective With the domestic and international spread of the coronavirus disease 2019 (COVID-19), much attention has been given to estimating pandemic risk. We propose the novel application of a well-established scientific approach – the network analysis – to provide a direct visualization of the COVID-19 pandemic risk; infographics are provided in the figures. By showing visually the degree of connectedness between different regions based on reported confirmed cases of COVID-19, we demonstrate that network analysis provides a relatively simple yet powerful way to estimate the pandemic risk. The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. 2020-07 2020-05-08 /pmc/articles/PMC7207126/ /pubmed/32437929 http://dx.doi.org/10.1016/j.ijid.2020.05.011 Text en © 2020 The Author(s) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Perspective
So, Mike K.P.
Tiwari, Agnes
Chu, Amanda M.Y.
Tsang, Jenny T.Y.
Chan, Jacky N.L.
Visualizing COVID-19 pandemic risk through network connectedness
title Visualizing COVID-19 pandemic risk through network connectedness
title_full Visualizing COVID-19 pandemic risk through network connectedness
title_fullStr Visualizing COVID-19 pandemic risk through network connectedness
title_full_unstemmed Visualizing COVID-19 pandemic risk through network connectedness
title_short Visualizing COVID-19 pandemic risk through network connectedness
title_sort visualizing covid-19 pandemic risk through network connectedness
topic Perspective
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7207126/
https://www.ncbi.nlm.nih.gov/pubmed/32437929
http://dx.doi.org/10.1016/j.ijid.2020.05.011
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