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Transportation, germs, culture: a dynamic graph model of COVID-19 outbreak

BACKGROUND: Various models have been applied to predict the trend of the epidemic since the outbreak of COVID-19. METHODS: In this study, we designed a dynamic graph model, not for precisely predicting the number of infected cases, but for a glance of the dynamics under a public epidemic emergency s...

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Autores principales: Yang, Xiaofei, Xu, Tun, Jia, Peng, Xia, Han, Guo, Li, Zhang, Lei, Ye, Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Higher Education Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7479740/
https://www.ncbi.nlm.nih.gov/pubmed/32923014
http://dx.doi.org/10.1007/s40484-020-0215-4
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author Yang, Xiaofei
Xu, Tun
Jia, Peng
Xia, Han
Guo, Li
Zhang, Lei
Ye, Kai
author_facet Yang, Xiaofei
Xu, Tun
Jia, Peng
Xia, Han
Guo, Li
Zhang, Lei
Ye, Kai
author_sort Yang, Xiaofei
collection PubMed
description BACKGROUND: Various models have been applied to predict the trend of the epidemic since the outbreak of COVID-19. METHODS: In this study, we designed a dynamic graph model, not for precisely predicting the number of infected cases, but for a glance of the dynamics under a public epidemic emergency situation and of different contributing factors. RESULTS: We demonstrated the impact of asymptomatic transmission in this outbreak and showed the effectiveness of city lockdown to halt virus spread within a city. We further illustrated that sudden emergence of a large number of cases could overwhelm the city medical system, and external medical aids are critical to not only containing the further spread of the virus but also reducing fatality. CONCLUSION: Our model simulation showed that highly populated modern cities are particularly vulnerable and lessons learned in China could facilitate other countries to plan the proactive and decisive actions. We shall pay close attention to the asymptomatic transmission being suggested by rapidly accumulating evidence as dramatic changes in quarantine protocol are required to contain SARS-CoV-2 from spreading globally. [Image: see text] SUPPLEMENTARY MATERIALS: The supplementary materials can be found online with this article at 10.1007/s40484-020-0215-4.
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spelling pubmed-74797402020-09-09 Transportation, germs, culture: a dynamic graph model of COVID-19 outbreak Yang, Xiaofei Xu, Tun Jia, Peng Xia, Han Guo, Li Zhang, Lei Ye, Kai Quant Biol Research Article BACKGROUND: Various models have been applied to predict the trend of the epidemic since the outbreak of COVID-19. METHODS: In this study, we designed a dynamic graph model, not for precisely predicting the number of infected cases, but for a glance of the dynamics under a public epidemic emergency situation and of different contributing factors. RESULTS: We demonstrated the impact of asymptomatic transmission in this outbreak and showed the effectiveness of city lockdown to halt virus spread within a city. We further illustrated that sudden emergence of a large number of cases could overwhelm the city medical system, and external medical aids are critical to not only containing the further spread of the virus but also reducing fatality. CONCLUSION: Our model simulation showed that highly populated modern cities are particularly vulnerable and lessons learned in China could facilitate other countries to plan the proactive and decisive actions. We shall pay close attention to the asymptomatic transmission being suggested by rapidly accumulating evidence as dramatic changes in quarantine protocol are required to contain SARS-CoV-2 from spreading globally. [Image: see text] SUPPLEMENTARY MATERIALS: The supplementary materials can be found online with this article at 10.1007/s40484-020-0215-4. Higher Education Press 2020-09-09 2020 /pmc/articles/PMC7479740/ /pubmed/32923014 http://dx.doi.org/10.1007/s40484-020-0215-4 Text en © Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Research Article
Yang, Xiaofei
Xu, Tun
Jia, Peng
Xia, Han
Guo, Li
Zhang, Lei
Ye, Kai
Transportation, germs, culture: a dynamic graph model of COVID-19 outbreak
title Transportation, germs, culture: a dynamic graph model of COVID-19 outbreak
title_full Transportation, germs, culture: a dynamic graph model of COVID-19 outbreak
title_fullStr Transportation, germs, culture: a dynamic graph model of COVID-19 outbreak
title_full_unstemmed Transportation, germs, culture: a dynamic graph model of COVID-19 outbreak
title_short Transportation, germs, culture: a dynamic graph model of COVID-19 outbreak
title_sort transportation, germs, culture: a dynamic graph model of covid-19 outbreak
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7479740/
https://www.ncbi.nlm.nih.gov/pubmed/32923014
http://dx.doi.org/10.1007/s40484-020-0215-4
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