Analyzing the Evolutionary Characteristics of the Cluster of COVID-19 under Anti-contagion Policies

With the rampaging of Coronavirus disease 2019 (COVID-19) across the world, analyzing the dynamic characteristics and understanding the evolutionary patterns of clusters are becoming even more crucial for people and policymakers to make timely responses for avoiding injury caused by COVID-19. To solve the scarcity of the fine-grained spatiotemporal data, we construct a novel dataset about the spread of patients during the resurgent period of the COVID-19 epidemic at the Xinfadi Market in Beijing. Leveraging our self-build dataset, we analyze the evolutionary characteristics of the cluster of COVID-19 under anti-contagion policies and obtained some remarkable evolution patterns. These findings can provide significant insights for policymakers and researchers to understand the evolutionary characteristics regarding the cluster of COVID-19 and deploy effective anti-contagion policies.

[1]  Y Zhao,et al.  [Inference of start time of resurgent COVID-19 epidemic in Beijing with SEIR dynamics model and evaluation of control measure effect]. , 2020, Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi.

[2]  M. Newman,et al.  The structure of scientific collaboration networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Jun Hu,et al.  Analysis of the Terrorist Organization Alliance Network Based on Complex Network Theory , 2019, IEEE Access.

[4]  Xin Jin,et al.  Research on Social Network Structure and Public Opinions Dissemination of Micro-blog Based on Complex Network Analysis , 2013, J. Networks.

[5]  Isabel J. Raabe,et al.  Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world , 2020, Nature Human Behaviour.

[6]  N. Marwan,et al.  Change of influenza pandemics because of climate change: Complex network simulations , 2018, Revue d'Épidémiologie et de Santé Publique.

[7]  Jun Han,et al.  Reemergent Cases of COVID-19 — Xinfadi Wholesales Market, Beijing Municipality, China, June 11, 2020 , 2020, China CDC weekly.

[8]  Abdul Rehman Gilal,et al.  Complex Network of Dengue Epidemic and Link Prediction , 2016 .

[9]  Mohamed Ridza Wahiddin,et al.  Two-mode complex network modeling of dengue epidemic in Selangor, Malaysia , 2014, The 5th International Conference on Information and Communication Technology for The Muslim World (ICT4M).

[10]  Jianhua Gong,et al.  Spatial-temporal characteristics of epidemic spread in-out flow—Using SARS epidemic in Beijing as a case study , 2012, Science China Earth Sciences.

[11]  Luna Yue Huang,et al.  The effect of large-scale anti-contagion policies on the COVID-19 pandemic , 2020, Nature.

[12]  An Zeng,et al.  Identifying important scholars via directed scientific collaboration networks , 2018, Scientometrics.

[13]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .

[14]  Mehmet A. Orgun,et al.  Optimal Social Trust Path Selection in Complex Social Networks , 2010, AAAI.

[15]  Jessica T Davis,et al.  The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak , 2020, Science.

[16]  Jianhua Gong,et al.  Exploring the epidemic transmission network of SARS in-out flow in mainland China , 2012, Chinese science bulletin = Kexue tongbao.