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

Jingwei Li, Choon-Ling Sia, Zhuo Chen and Wei Huang
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Jingwei Li: School of Management, Xi’an Jiaotong University, Xi’an 710049, China
Choon-Ling Sia: Department of Information Systems, City University of Hong Kong, Hong Kong 999077, China
Wei Huang: College of Business, Southern University of Science and Technology, Shenzhen 518000, China

IJERPH, 2021, vol. 18, issue 12, 1-13

Abstract: 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.

Keywords: digital disease detection; seasonal influenza surveillance; online news; autoregressive exogenous model (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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