题 目: Chasing the invisible: can epidemic spreading models help?
报告人: Lei-Han Tang
Professor, COVID-19 Modelling Group，Hong Kong Baptist University
时 间: 5月11日（周一）13:00-14:00
地 点: Online (腾讯会议)
会议ID：785 791 503
主持人: 宋晨/李志远 研究员
摘 要: Globally, one of the biggest challenges in determining the appropriate policy response to the 2019 Coronavirus Disease (COVID-19) has been the uncertainty of pre-symptomatic transmission and the lack of tools to quantify and predict its impact. A number of studies 1,2 suggested that this group of viral carriers could contribute significantly to the spread of the SARS-CoV-2 virus. In east Asian countries, mask-wearing, contact-tracing and testing combined have been effective in containing early outbreaks, while several western countries taking a lax approach have witnessed a prolonged exponential growth of the pandemic in the past two months. Singapore, the city-state that adopted a mixed policy, withheld the first wave of imported cases but failed to prevent local outbreaks during the second wave that was 20 times stronger in the number of imported cases.
In this talk, I will first introduce a variant of the stochastic SEIR model with parameters calibrated against COVID-19 disease progression and transmission characteristics 3. Due to its linear nature, the model, when applied to a large population, affords analytical solutions in terms of Laplace transforms. The efficacy of various prevention measures can be evaluated quantitatively. The seeding and initial growth of outbreaks, on the other hand, are dominated by chance events that require a different approach. Large social gatherings, for example, could set off an outbreak in an otherwise subcritical community. We quantify risks associated with rare-event dominated transmission through numerical explorations of a discrete-time stochastic model, and discuss preventative measures that need to be in place as economic and social activities resume.
1. https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30147-X/fulltext [Huang, Rui, Juan Xia, Yuxin Chen, Chun Shan, and Chao Wu. "A family cluster of SARS-CoV-2 infection involving 11 patients in Nanjing, China." The Lancet Infectious Diseases (2020).]
2. https://link.springer.com/article/10.1007/s11427-020-1661-4 [Hu, Zhiliang, Ci Song, Chuanjun Xu, Guangfu Jin, Yaling Chen, Xin Xu, Hongxia Ma et al. "Clinical characteristics of 24 asymptomatic infections with COVID-19 screened among close contacts in Nanjing, China." Science China Life Sciences (2020): 1-6.]
3. https://arxiv.org/abs/2003.07353 [Liang Tian, Xuefei Li, Fei Qi, Qian-Yuan Tang, Viola Tang, Jiang Liu, Zhiyuan Li, Xingye Cheng, Xuanxuan Li, Yingchen Shi, Haiguang Liu, Lei-Han Tang, “Calibrated Intervention and Containment of the COVID-19 Pandemic”, March 16, 2020.]
Dr. Tang completed his PhD in statistical physics at the Carnegie Mellon University in 1987. He did postdoctoral work on quasicrystals, growing surfaces, and other nonequilibrium and disordered systems at various US and German institutions including Texas A&M University, the Institute for Solid State Science at KFA Jülich and the Institute for Theoretical Physics at the University of Cologne. He was appointed Lecturer at the Imperial College of Science, Technology and Medicine in 1996 and subsequently joined the Physics Department at the Hong Kong Baptist University as Associate Professor and promoted to Professor in 2005. He was also affiliated with the Beijing Computational Science Research Center since 2010 as the head of the Complex Systems Laboratory.
Dr. Tang’s research combines analytical and computational approaches to explore the effect of equilibrium and nonequilibrium fluctuations on the stability of ordered structures in various physical and biophysical contexts, in particular the energetics and dynamics of defects that disrupt ordering. In recent years, he has collaborated with experimentalists on the development of quantitative tools to analyze and integrate biological data and information.