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应急状态下新冠流行曲线预测的方法学研究——基于北京市百度搜索和传统流感样病例监测

engineering 2023年第12期31卷 112-119页

作者： Ting Zhang Liuyang Yang Xuan Han Guohui Fan Jie Qian Xuancheng Hu Shengjie Lai Zhongjie Li Zhimin Liu Luzhao Feng Weizhong Yang School of Population Medicine and Public Health Chinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 100730China Department of management science and information system Faculty of Management and EconomicsKunming University of Science and TechnologyKunming 650504China WorldPop School of Geography and Environmental ScienceUniversity of SouthamptonSouthampton SO171BJUK The Third Affiliated Hospital of Kunming Medical University Kunming 650118China

Surveillance is an essential work on infectious diseases prevention and *** the pandemic occurred,the inadequacy of traditional surveillance was exposed,but it also provided a valuable opportunity to explore new surveillance *** study aimed to estimate the transmission dynamics and epidemic curve of severe acute respiratory syndrome coronavirus 2(SARS-Co V-2)Omicron BF.7 in Beijing under the emergent situation using baidu index and influenza-like illness(ILI)surveillance.A novel hybrid model(multiattention bidirectional gated recurrent unit(MABG)-susceptible-exposed-infected-removed(SEIR))was developed,which leveraged a deep learning algorithm(MABG)to scrutinize the past records of ILI occurrences and the baidu index of diverse symptoms such as fever,pyrexia,cough,sore throat,anti-fever medicine,and runny *** considering the current baidu index and the correlation between ILI cases and coronavirus disease 2019(COVID-19)cases,a transmission dynamics model(SEIR)was formulated to estimate the transmission dynamics and epidemic curve of SARS-Co *** the COVID-19 pandemic,when conventional surveillance measures have been suspended temporarily,cases of ILI can serve as a useful indicator for estimating the epidemiological trends of *** the specific case of Beijing,it has been ascertained that cumulative infection attack rate surpass 80.25%(95%confidence interval(95%CI):77.51%-82.99%)since December 17,2022,with the apex of the outbreak projected to transpire on December *** culmination of existing patients is expected to occur three days subsequent to this *** reproduction number(Rt)represents the average number of secondary infections generated from a single infected individual at a specific point in time during an epidemic,remained below 1 since December 17,*** traditional disease surveillance systems should be complemented with information from modern surveillance data such as online data sources with advanced techn

关键词： COVID-19 Epidemic curve baidu search engine Influenza-like illness Deep learning Transmission dynamics model

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