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Internet search data could Be used as novel indicator for assessing COVID-19 epidemic.
Li, Kang; Liang, Yanling; Li, Jianjun; Liu, Meiliang; Feng, Yi; Shao, Yiming.
  • Li K; Key Laboratory of Molecular Microbiology and Technology, Ministry of Education, College of Life Sciences, Nankai University, Tianjin, China.
  • Liang Y; State Key Laboratory for Infectious Disease Prevention and Control, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
  • Li J; State Key Laboratory for Infectious Disease Prevention and Control, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
  • Liu M; School of Public Health, Guangxi Medical University, Nanning, Guangxi, China.
  • Feng Y; Guangxi Center for Disease Prevention and Control, Nanning, Guangxi, China.
  • Shao Y; School of Public Health, Guangxi Medical University, Nanning, Guangxi, China.
Infect Dis Model ; 5: 848-854, 2020.
Article in English | MEDLINE | ID: covidwho-813617
ABSTRACT
The pandemic of the coronavirus disease (COVID-19) poses a huge challenge all countries, since no one is well prepared for it. To be better prepared for future pandemics, we evaluated association between the internet search data with reported COVID-19 cases to verify whether it could become an early indicator for emerging epidemic. After the keyword filtering and Index composition, we found that there were close correlations between Composite Index and suspected cases for COVID-19 (r = 0.921, P < 0.05). The Search Index was applied for the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model to quantify the relationship. Compared with the model based on surveillance data only, the ARIMAX model had smaller Akaike Information Criterion (AIC = 403.51) and the most accurate predictive values. Overall, the Internet search data could serve as a convenient indicator for predicting the epidemic and to monitor its trends.
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Full text: Available Collection: International databases Database: MEDLINE Document Type: Article Language: English Journal: Infect Dis Model Clinical aspect: Prediction / Prognosis Year: 2020

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Full text: Available Collection: International databases Database: MEDLINE Document Type: Article Language: English Journal: Infect Dis Model Clinical aspect: Prediction / Prognosis Year: 2020
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