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Article

Enhancing Influenza Epidemics Forecasting Accuracy in China with Both Official and Unofficial Online News Articles, 2019–2020

by 1,2, 2, 3,4 and 5,*
1
School of Management, Xi’an Jiaotong University, Xi’an 710049, China
2
Department of Information Systems, City University of Hong Kong, Hong Kong 999077, China
3
College of Public Health, University of Georgia, Athens, GA 30602, USA
4
School of Economics, University of Nottingham Ningbo China, Ningbo 315000, China
5
College of Business, Southern University of Science and Technology, Shenzhen 518000, China
*
Author to whom correspondence should be addressed.
Academic Editor: Paul B. Tchounwou
Int. J. Environ. Res. Public Health 2021, 18(12), 6591; https://doi.org/10.3390/ijerph18126591
Received: 3 May 2021 / Revised: 5 June 2021 / Accepted: 15 June 2021 / Published: 18 June 2021
(This article belongs to the Section Global Health)
Real-time online data sources have contributed to timely and accurate forecasting of influenza activities while also suffered from instability and linguistic noise. Few previous studies have focused on unofficial online news articles, which are abundant in their numbers, rich in information, and relatively low in noise. This study examined whether monitoring both official and unofficial online news articles can improve influenza activity forecasting accuracy during influenza outbreaks. Data were retrieved from a Chinese commercial online platform and the website of the Chinese National Influenza Center. We modeled weekly fractions of influenza-related online news articles and compared them against weekly influenza-like illness (ILI) rates using autoregression analyses. We retrieved 153,958,695 and 149,822,871 online news articles focusing on the south and north of mainland China separately from 6 October 2019 to 17 May 2020. Our model based on online news articles could significantly improve the forecasting accuracy, compared to other influenza surveillance models based on historical ILI rates (p = 0.002 in the south; p = 0.000 in the north) or adding microblog data as an exogenous input (p = 0.029 in the south; p = 0.000 in the north). Our finding also showed that influenza forecasting based on online news articles could be 1–2 weeks ahead of official ILI surveillance reports. The results revealed that monitoring online news articles could supplement traditional influenza surveillance systems, improve resource allocation, and offer models for surveillance of other emerging diseases. View Full-Text
Keywords: digital disease detection; seasonal influenza surveillance; online news; autoregressive exogenous model digital disease detection; seasonal influenza surveillance; online news; autoregressive exogenous model
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MDPI and ACS Style

Li, J.; Sia, C.-L.; Chen, Z.; Huang, W. Enhancing Influenza Epidemics Forecasting Accuracy in China with Both Official and Unofficial Online News Articles, 2019–2020. Int. J. Environ. Res. Public Health 2021, 18, 6591. https://doi.org/10.3390/ijerph18126591

AMA Style

Li J, Sia C-L, Chen Z, Huang W. Enhancing Influenza Epidemics Forecasting Accuracy in China with Both Official and Unofficial Online News Articles, 2019–2020. International Journal of Environmental Research and Public Health. 2021; 18(12):6591. https://doi.org/10.3390/ijerph18126591

Chicago/Turabian Style

Li, Jingwei, Choon-Ling Sia, Zhuo Chen, and Wei Huang. 2021. "Enhancing Influenza Epidemics Forecasting Accuracy in China with Both Official and Unofficial Online News Articles, 2019–2020" International Journal of Environmental Research and Public Health 18, no. 12: 6591. https://doi.org/10.3390/ijerph18126591

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