Improving Google Flu Trends for COVID-19 estimates using Weibo posts  

作　　者：Shuhui Guo Fan Fang Tao Zhou Wei Zhang Qiang Guo Rui Zeng Xiaohong Chen Jianguo Liu Xin Lu 

机构地区：[1]College of Systems Engineering,National University of Defense Technology,Changsha,410073,China [2]Big Data Research Center,University of Electronic Science and Technology of China,Chengdu,611713,China [3]West China Biomedical Big Data Center,West China Hospital,Sichuan University,Chengdu,610041,China [4]Research Center of Complex Systems Science,University of Shanghai for Science and Technology,Shanghai,200093,China [5]MD Department of Cardiology,West China Hospital,Sichuan University,Chengdu,610041,China [6]School of Business,Central South University,Changsha,410083,China [7]Institute of Big Data and Internet Innovations,Hunan University of Technology and Business,Changsha,410205,China [8]Institute of Accounting and Finance,Shanghai University of Finance and Economics,Shanghai,200433,China

出　　处：《Data Science and Management》2021年第3期13-21,共9页数据科学与管理（英文）

基　　金：National Natural Science Foundation of China(Project No.:91846301,72025405,82041020,11975071,61773248,71771152);Sichuan Science and Technology Plan Project(Project No.:2020YFS0007);Hunan Science and Technology Plan Project(Project No.:2019GK2131,2020TP1013,2020JJ4673);Major Program of National Fund of Philosophy and Social Science of China(Project No.:18ZDA088,20ZDA060);Scientific Research Project of Shanghai Science and Technology Committee(Project No.:19511102202).

摘　　要：While incomplete non-medical data has been integrated into prediction models for epidemics,the accuracy and the generalizability of the data are difficult to guarantee.To comprehensively evaluate the ability and applicability of using social media data to predict the development of COVID-19,a new confirmed case prediction algorithm improving the Google Flu Trends algorithm is established,called Weibo COVID-19 Trends(WCT),based on the post dataset generated by all users in Wuhan on Sina Weibo.A genetic algorithm is designed to select the keyword set for filtering COVID-19 related posts.WCT can constantly outperform the highest average test score in the training set between daily new confirmed case counts and the prediction results.It remains to produce the best prediction results among other algorithms when the number of forecast days increases from one to eight days with the highest correlation score from 0.98(P<0.01)to 0.86(P<0.01)during all analysis period.Additionally,WCT effectively improves the Google Flu Trends algorithm's shortcoming of overestimating the epidemic peak value.This study offers a highly adaptive approach for feature engineering of third-party data in epidemic prediction,providing useful insights for the prediction of newly emerging infectious diseases at an early stage.

关 键 词：COVID-19 Epidemic estimates Weibo Google Flu Trends Genetic algorithm 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]