
Transportation, germs, culture: a dynamic graph model of COVID‐19 outbreak
Author(s) -
Yang Xiaofei,
Xu Tun,
Jia Peng,
Xia Han,
Guo Li,
Zhang Lei,
Ye Kai
Publication year - 2020
Publication title -
quantitative biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.707
H-Index - 15
eISSN - 2095-4697
pISSN - 2095-4689
DOI - 10.1007/s40484-020-0215-4
Subject(s) - outbreak , pandemic , transmission (telecommunications) , quarantine , covid-19 , case fatality rate , public health , geography , virology , epidemic model , operations research , computer science , environmental health , medicine , infectious disease (medical specialty) , telecommunications , disease , engineering , population , nursing , pathology
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.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom