Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Transportation, Germs, Culture: A Dynamic Graph Model of 2019-nCoV Spread

Version 1 : Received: 4 February 2020 / Approved: 5 February 2020 / Online: 5 February 2020 (11:33:39 CET)

How to cite: Yang, X.; Xu, T.; Jia, P.; Xia, H.; Guo, L.; Ye, K. Transportation, Germs, Culture: A Dynamic Graph Model of 2019-nCoV Spread. Preprints 2020, 2020020063 (doi: 10.20944/preprints202002.0063.v1). Yang, X.; Xu, T.; Jia, P.; Xia, H.; Guo, L.; Ye, K. Transportation, Germs, Culture: A Dynamic Graph Model of 2019-nCoV Spread. Preprints 2020, 2020020063 (doi: 10.20944/preprints202002.0063.v1).

Abstract

Since the outbreak of 2019 novel coronavirus (2019-nCoV) at the hardest-hit city of Wuhan, the fast-moving spread has killed over three hundred people and infected more than ten thousands in China1. There are more than one hundred cases outside of China, affecting a dozen of countries globally2. The genome sequence of 2019-nCoV has been reported and fast diagnostic kits, effective treatment as well as preventive vaccines are rapidly being developed3. Initial fast-growing confirmed cases triggered lock-down of Wuhan as well as nearby cities in Hubei Province. Mathematical models have been proposed by scientists around the world to project the numbers of infected cases in the coming days 4,5. However, major factors such as transportation and cultural customs have not been weighed enough. Our model is not set out for precise prediction of the number of infected cases, rather, it is meant for a glance of the dynamics under a public epidemic emergency situation and of different contributing factors. We hope that our model and simulation would provide more insights and perspective information to public health authorities around the globe for better informed prevention and containment solution.

Subject Areas

Transportation; dynamic graph model; 2019-nCoV

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