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Transportation, Germs, Culture: A Dynamic Graph Model of COVID-19 Outbreak

15 Pages Posted: 5 Mar 2020

See all articles by Xiaofei Yang

Xiaofei Yang

Xi'an Jiaotong University (XJTU) - Department of Computer Science and Technology

Tun Xu

Xi'an Jiaotong University (XJTU) - MOE Key Lab for Intelligent Networks & Networks Security

Peng Jia

Xi'an Jiaotong University (XJTU) - MOE Key Lab for Intelligent Networks & Networks Security

Han Xia

Xi'an Jiaotong University (XJTU) - School of Automation Science and Engineering

Li Guo

Xi'an Jiaotong University (XJTU) - MOE Key Lab for Intelligent Networks & Networks Security

Lei Zhang

Xi'an Jiaotong University (XJTU) - China-Australia Joint Research Center for Infectious Diseases

Kai Ye

Xi'an Jiaotong University (XJTU) - MOE Key Lab for Intelligent Networks & Networks Security

More...

Abstract

Background: Various forms of model have been applied to predict the trend of the epidemic since the outbreak of COVID-19 at the hardest-hit city of Wuhan.

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.

Findings: 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.

Interpretation: 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.

Funding Statement: This study was supported by the National Science Foundation of China (grand NO. 61702406 31671372, 31701739, and 8191101420), the National Key R&D Program of China (grand NO. 2018YFC0910400, 2017YFC0907500), the National Science and Technology Major Project of China (grand NO. 2018ZX10302205), the “World-Class Universities and the Characteristic Development Guidance Funds for the Central Universities”, Thousand Talents Plan Professorship for Young Scholars (3111500001), Xi'an Jiaotong University Young Talent Support Program and Xi’an Jiaotong University Basic Research and Profession Grant (xtr022019003).

Declaration of Interests: None.

Keywords: COVID-19; SARS-CoV-2; city lockdown; dynamic graph model; asymptomatic transmission

Suggested Citation

Yang, Xiaofei and Xu, Tun and Jia, Peng and Xia, Han and Guo, Li and Zhang, Lei and Ye, Kai, Transportation, Germs, Culture: A Dynamic Graph Model of COVID-19 Outbreak (2/21/2020). Available at SSRN: https://ssrn.com/abstract=3544816 or http://dx.doi.org/10.2139/ssrn.3544816

Xiaofei Yang

Xi'an Jiaotong University (XJTU) - Department of Computer Science and Technology

China

Tun Xu

Xi'an Jiaotong University (XJTU) - MOE Key Lab for Intelligent Networks & Networks Security

Xi’an
China

Peng Jia

Xi'an Jiaotong University (XJTU) - MOE Key Lab for Intelligent Networks & Networks Security

Xi’an
China

Han Xia

Xi'an Jiaotong University (XJTU) - School of Automation Science and Engineering

Xi’an
710049
China

Li Guo

Xi'an Jiaotong University (XJTU) - MOE Key Lab for Intelligent Networks & Networks Security

Xi’an
China

Lei Zhang

Xi'an Jiaotong University (XJTU) - China-Australia Joint Research Center for Infectious Diseases

Xi’an
China

Kai Ye (Contact Author)

Xi'an Jiaotong University (XJTU) - MOE Key Lab for Intelligent Networks & Networks Security ( email )

Xi’an
China

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