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

Xiaofei Yang; Tun Xu; Peng Jia; Han Xia; Li Guo; Lei Zhang; Kai Ye

doi:10.1007/s40484-020-0215-4

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

Quant Biol. 2020;8(3):238-244. doi: 10.1007/s40484-020-0215-4. Epub 2020 Sep 9.

Authors

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}

Affiliations

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.

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.

Supplementary materials: The supplementary materials can be found online with this article at 10.1007/s40484-020-0215-4.

Keywords: COVID-19; SARS-CoV-2; dynamic graph model; transportation.