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(US...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.展开更多
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).
文摘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.
