Quantitative analysis of the impact of various urban socioeconomic indicators on search-engine-based estimation of COVID-19 prevalence  

作　　者：Ligui Wang Mengxuan Lin Jiaojiao Wang Hui Chen Mingjuan Yang Shaofu Qiu Tao Zheng Zhenjun Li Hongbin Song 

机构地区：[1]Department of Infectious Disease Prevention and Control,Center for Disease Control and Prevention of Chinese People's Liberation Army,Beijing,China [2]Academy of Military Medical Sciences,Academy of Military Science of Chinese PLA,Beijing,China [3]The State Key Laboratory of Management and Control for Complex Systems,Institute of Automation,Chinese Academy of Sciences,Beijing,China [4]State Key Laboratory for Infectious Disease Prevention and Control,National Institute for Communicable Disease Control and Prevention,Chinese Center for Disease Control and Prevention,Beijing,China

出　　处：《Infectious Disease Modelling》2022年第2期117-126,共10页传染病建模（英文）

基　　金：supported by the National Key R&D Program of China(2021YFC2302004);the National Natural Science Foundation of China(Grant Nos.72074209,72042018,71621002).

摘　　要：Numerous studies have proposed search engine-based estimation of COVID-19 prevalence during the COVID-19 pandemic;however,their estimation models do not consider the impact of various urban socioeconomic indicators(USIs).This study quantitatively analysed the impact of various USIs on search engine-based estimation of COVID-19 prevalence using 15 USIs(including total population,gross regional product(GRP),and population density)from 369 cities in China.The results suggested that 13 USIs affected either the correlation(SC-corr)or time lag(SC-lag)between search engine query volume and new COVID-19 cases(p<0.05).Total population and GRP impacted SC-corr considerably,with their correlation coefficients r for SC-corr being 0.65 and 0.59,respectively.Total population,GRP per capita,and proportion of the population with a high school diploma or higher had simultaneous positive impacts on SC-corr and SC-lag(p<0.05);these three indicators explained 37e50%of the total variation in SC-corr and SC-lag.Estimations for different urban agglomerations revealed that the goodness of fit,R2,for search engine-based estimation was more than 0.6 only when total urban population,GRP per capita,and proportion of the population with a high school diploma or higher exceeded 11.08 million,120,700,and 38.13%,respectively.A greater urban size indicated higher accuracy of search engine-based estimation of COVID-19 prevalence.Therefore,the accuracy and time lag for search engine-based estimation of infectious disease prevalence can be improved only when the total urban population,GRP per capita,and proportion of the population with a high school diploma or higher are greater than the aforementioned thresholds.

关 键 词：Urban socioeconomic indicators Search engine-based estimation Effectiveness evaluation 

分 类 号：R563.1[医药卫生—呼吸系统]