干旱区生态环境知识资源中心(Arid): Retrospective analysis of the accuracy of predicting the alert level of COVID-19 in 202 countries using Google Trends and machine learning

Arid

DOI	10.7189/jogh.10.020511
	Retrospective analysis of the accuracy of predicting the alert level of COVID-19 in 202 countries using Google Trends and machine learning
	Peng, Yuanyuan; Li, Cuilian; Rong, Yibiao; Chen, Xinjian; Chen, Haoyu
通讯作者	Chen, XJ (corresponding author), Soochow Univ, Sch Elect & Informat Engn, 1 Shizi St, Suzhou 215006, Peoples R China. ; Chen, HY (corresponding author), Shantou Univ, Joint Shantou Int Eye Ctr, North Dongxia Rd, Shantou 515041, Peoples R China. ; Chen, HY (corresponding author), Chinese Univ Hong Kong, North Dongxia Rd, Shantou 515041, Peoples R China.
来源期刊	JOURNAL OF GLOBAL HEALTH
ISSN	2047-2978
EISSN	2047-2986
出版年	2020
卷号	10 期号:2
英文摘要	Background Internet search engine data, such as Google Trends, was shown to be correlated with the incidence of COVID-19, but only in several countries. We aim to develop a model from a small number of countries to predict the epidemic alert level in all the countries worldwide. Methods The interest over time and interest by region Google Trends data of Coronavirus, pneumonia, and six COVID symptom-related terms were searched. The daily incidence of COVID-19 from 10 January to 23 April 2020 of 202 countries was retrieved from the World Health Organization. Three alert levels were defined. Ten weeks' data from 20 countries were used for training with machine learning algorithms. 71 he features were selected according to the correlation and importance. The model was then tested on 2830 samples of 202 countries. Results Our model performed well in 154 (76.2%) countries, of which each had no more than four misclassified samples. In these 154 countries, the accuracy was 0.8133, and the kappa coefficient was 0.6828. While in all 202 countries, the accuracy was 0.7527, and the kappa coefficient was 0.5841. The proposed algorithm based on Random Forest Classification and nine features performed better compared to other machine learning methods arid the models with different numbers of features. Conclusions Our result suggested that the model developed from 20 countries with Google Trends data and Random Forest Classification can be applied to predict the epidemic alert levels of most countries worldwide.
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000612476300164
WOS类目	Public, Environmental & Occupational Health
WOS研究方向	Public, Environmental & Occupational Health
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/369197
作者单位	[Peng, Yuanyuan; Chen, Xinjian] Soochow Univ, Sch Elect & Informat Engn, 1 Shizi St, Suzhou 215006, Peoples R China; [Li, Cuilian; Chen, Haoyu] Shantou Univ, Joint Shantou Int Eye Ctr, North Dongxia Rd, Shantou 515041, Peoples R China; [Li, Cuilian; Chen, Haoyu] Chinese Univ Hong Kong, North Dongxia Rd, Shantou 515041, Peoples R China; [Rong, Yibiao] Shantou Univ, Coll Engn, Shantou, Peoples R China
推荐引用方式 GB/T 7714	Peng, Yuanyuan,Li, Cuilian,Rong, Yibiao,et al. Retrospective analysis of the accuracy of predicting the alert level of COVID-19 in 202 countries using Google Trends and machine learning[J],2020,10(2).
APA	Peng, Yuanyuan,Li, Cuilian,Rong, Yibiao,Chen, Xinjian,&Chen, Haoyu.(2020).Retrospective analysis of the accuracy of predicting the alert level of COVID-19 in 202 countries using Google Trends and machine learning.JOURNAL OF GLOBAL HEALTH,10(2).
MLA	Peng, Yuanyuan,et al."Retrospective analysis of the accuracy of predicting the alert level of COVID-19 in 202 countries using Google Trends and machine learning".JOURNAL OF GLOBAL HEALTH 10.2(2020).