Forecasting of COVID-19 Onset Cases: A Data-Driven Analysis in the Early Stage of Delay

发布时间：2020-12-03 作者：浏览次数：

Speaker:	王学丽	DateTime:	2020年12月3日（周四）下午19:30-20:30
Brief Introduction to Speaker: 王学丽，北京工商大学教授。毕业于北京大学数学科学学院，数理统计学专业，理学博士；美国华盛顿大学生物统计系，访问学者。在Statistic Sinica、Journal of Statistic Planning and Inference、Environmental Research and Public Health、Food Control、Environmental Science and Pollution Research等期刊上发表论文四十余篇；主持国家重点研发计划-子课题，国家自然科学基金，全国统计科学研究项目等项目十余项。课题论文获得第九届全国统计科学研究优秀成果一等奖；指导的研究生多人获得“国家奖学金”和“北京市优秀毕业生”。现任中国现场统计研究会高维数据分会理事,计算统计分会理事，人工智能+食品安全专家委员会委员等。		Place:	腾讯会议（会议号请联系左国新老师索取）
Abstract:The outbreak of COVID-19 has become a global public health event. In this article, a total of 5434 cases were collected from National Health Commission and other provincial Health Commission in China, spanning from 1 December 2019 to 23 February 2020. We studied the delayed distribution of patients from onset to be confirmed. The delay is divided into two stages, which takes about 15 days or even longer. Therefore, considering the right truncation of the data, we proposed a “predict-in-advance” method, used the number of "visiting hospital cases" to predict the number of "onset cases". The results not only shows that our prediction shortens the delay of the second stage, but also the predicted value of onset cases is quite close to the real value of onset cases, which can effectively predict the epidemic trend of sudden infectious diseases, and provide an important reference for the government to formulate control measures in advance.

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