时 间：2022年10月11日10:00
地 点：腾讯会议ID：322-355-770
报告人：陈松蹊 教授
主持人：周勇 教授
摘 要：
The real-world performance of vaccines against COVID-19 infections is critically important to counter the pandemics. We propose a varying coefficient stochastic epidemic model to estimate the vaccine efficacy based on the publicly available epidemiological and vaccination data. To tackle the challenges posed by the unobserved state variables, we develop a multi-step decentralized estimation procedure that uses different data segments to estimate different parameters. A B-spline structure is used to approximate the underlying infection rates and to facilitate model simulation in obtaining an objective function between the imputed and the simulation-based estimates of the latent state variables, leading to simulation-based estimation of the diagnosis rate using data in the pre-vaccine period and the vaccine effect parameters using data in the post-vaccine periods. And the time-varying infection, recovery and death rates are estimated by kernel regressions. We apply the proposed method to analyze the data in ten countries which collectively used 8 vaccines. The analysis reveals that the average effectiveness of the full vaccination was at least 22\% higher than that of the partial vaccination and was largely above the WHO recognized level of 50\% including the Delta and Omicron variant dominated periods.
报告人简介：
陈松蹊，中国科学院院士，北京大学讲席教授。国际数理统计学会（IMS）会士，美国科学促进会（AAAS）会士，美国统计学会（ASA）会士，国际统计学会（ISI）当选会员，入选斯坦福大学2020全球前2%顶尖科学家榜单。现任国际伯努利学会科学书记，北京大学统计科学中心科学委员会主席，中国统计学会常务理事。曾任国际数理统计学会理事会常务理事，国家统计局咨询委员，北京大学统计科学中心创始主任，光华管理学院商务统计与经济计量系主任。曾任The Annals of Statistics、Journal of the American Statistical Association等期刊编委，并先后在澳大利亚、新加坡、美国等著名高校任职。主要研究方向为超高维大数据统计推断、环境统计、非参数统计等，在超高维假设检验方法和非参数经验似然方法方面取得了丰硕成果，致力于推动统计学的前沿交叉研究，在发展统计学方法应用于国家大气污染防治、新型冠状病毒疫情分析、中国宏观经济计量分析等方面做出了杰出贡献，引领了统计学的关键性发展。