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

doi:10.1007/s40484-020-0215-4

Quant. Biol.

2020, Vol. 8

Issue (3) : 238-244 https://doi.org/10.1007/s40484-020-0215-4

RESEARCH ARTICLE

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

Xiaofei Yang^1,², Tun Xu^2,³, Peng Jia^2,³, Han Xia^3,⁴, Li Guo^2,^3,⁵, Lei Zhang^6,^7,^8,⁹, Kai Ye^2,^3,^5,¹⁰(

)

¹. School of Computer Science and Technology, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
². MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
³. School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
⁴. Hugobiotech Co., Ltd., Beijing 102600, China
⁵. The School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
⁶. China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China
⁷. Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC 3004, Australia
⁸. Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC 3004, Australia
⁹. Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
¹⁰. Genome Institute, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China

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Abstract

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.

Keywords dynamic graph model transportation COVID-19 SARS-CoV-2

Corresponding Author(s): Kai Ye

Just Accepted Date: 28 July 2020 Online First Date: 09 September 2020 Issue Date: 25 September 2020

Cite this article:

Xiaofei Yang,Tun Xu,Peng Jia, et al. Transportation, germs, culture: a dynamic graph model of COVID-19 outbreak[J]. Quant. Biol., 2020, 8(3): 238-244.

URL:

https://academic.hep.com.cn/qb/EN/10.1007/s40484-020-0215-4
https://academic.hep.com.cn/qb/EN/Y2020/V8/I3/238

Fig.1 The relations between virus spreading and quarantine strategy.

Fig.2 Impact of transportation and confined space in virus spread.

Fig.3 Imported infections and medical aid affect the spreading and fatality rate.

Fig.4 The dynamic graph model of virus spread.

1	Coronavirus disease. 2019(COVID-19) Situation Report – 29.
2	J. M. Read,, J. R. Bridgen,, D. A. Cummings,, A. Ho, and C. P Jewell,. (2020) Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions. medRxiv, 20018549 https://doi.org/https://doi.org/10.1101/2020.01.23.20018549
3	Q. Li, , X. Guan, , P. Wu, , X. Wang, , L. Zhou, , Y. Tong, , R. Ren, , K. S. M. Leung, , E. H. Y. Lau, , J. Y. Wong, , et al. (2020) Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N. Engl. J. Med., 382, 1199–1207 https://doi.org/10.1056/NEJMoa2001316. pmid: 31995857
4	H. Tian,, Y. Liu,, Y. Li,, M. U. G. Kraemer, , B. Chen, , C.-H. Wu, , J. Cai, , B. Li, , B. Xu, , Q. Yang, , et al. (2020) Early evaluation of transmission control measures in response to the 2019 novel coronavirus outbreak in China. medRxiv, 20019844 https://doi.org/https://doi.org/10.1101/2020.01.30.20019844
5	C. Anastassopoulou,, L. Russo,, A. Tsakris, and C. Siettos, (2020) Data-based analysis, modelling and forecasting of the novel coronavirus (2019-nCoV) outbreak. PLoS ONE 15, e0230405
6	M. Gilbert,, G. Pullano,, F. Pinotti,, E. Valdano, , C. Poletto, , P.-Y. Boëlle, , E. D’Ortenzio, , Y. Yazdanpanah, , S.P. Eholie, , M.A. Pharm, , et al. (2020) Preparedness and vulnerability of African countries against importations of COVID-19: a modelling study. The Lancet. 395, 871–877
7	D. Wang, , B. Hu, , C. Hu, , F. Zhu, , X. Liu, , J. Zhang, , B. Wang, , H. Xiang, , Z. Cheng, , Y. Xiong, , et al. (2020) Clinical Characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA, 323,1061–1069 https://doi.org/10.1001/jama.2020.1585
8	F. Casella, (2020) Can the COVID-19 epidemic be managed on the basis of daily data? arXiv, 2003.06967
9	Z. Tang,, X. Li,, and H. Li, (2020) Prediction of new coronavirus infection based on a modified SEIR model. medRxiv, 20030858 https://doi.org/https://doi.org/10.1101/2020.03.03.20030858
10	D. Guo, (2007) Visual analytics of spatial interaction patterns for pandemic decision support. Int. J. Geogr. Inf. Sci., 21, 859–877 https://doi.org/10.1080/13658810701349037.
11	D. Chang, , M. Lin, , L. Wei, , L. Xie,, G. Zhu,, C. S. Dela Cruz,, and L. Sharma, (2020) Epidemiologic and clinical characteristics of novel coronavirus infections involving 13 patients outside Wuhan, China. JAMA, 323, 1092–1093 https://doi.org/10.1001/jama.2020.1623
12	L. Zou, , F. Ruan, , M. Huang, , L. Liang, , H. Huang, , Z. Hong, , J. Yu, , M. Kang, , Y. Song, , J. Xia, , et al. (2020) SARS-CoV-2 viral load in upper respiratory specimens of infected patients. N. Engl. J. Med., 382, 1177–1179 https://doi.org/10.1056/NEJMc2001737. pmid: 32074444
13	J. F.-W. Chan, , S. Yuan, , K.-H. Kok, , K. K. To, , H. Chu, , J. Yang, , F. Xing, , J. Liu, , C. C. Yip, , R. W. Poon, , et al. (2020) A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet, 395, 514–523 https://doi.org/10.1016/S0140-6736(20)30154-9. pmid: 31986261
14	P. Wenzel, , S. Kopp, , S. Göbel, , T. Jansen, , M. Geyer, , F. Hahn, , K. Kreitner, , F. Escher, , H.P. Schultheiss, , T. and Münzel, (2020) Evidence of SARS-CoV-2 mRNA in endomyocardial biopsies of patients with clinically suspected myocarditis tested negative for COVID-19 in nasopharyngeal swab. Cardiovasc. Res., 116, 1661–1663
15	Epidemiology Working Group for NCIP Epidemic Response, Chinese Center for Disease Control and Prevention. (2020) The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China. Chinese Journal of Epidemiology, 41, 145–151, in Chinese pmid: 32064853.
16	W. J. Guan, , Z. Y. Ni, , Y. Hu, , W. H. Liang, , C. Q. Ou, , J. X. He, , L. Liu, , H. Shan, , C. L. Lei, , D. S. C. Hui, , et al., (2020) Clinical characteristics of coronavirus disease 2019 in China. N. Engl. J. Med., 382, 1708–1720 https://doi.org/10.1056/NEJMoa2002032. pmid: 32109013

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