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

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.