Correlation between reported human infection with avian influenza A H7N9 virus and cyber user awareness: what can we learn from digital epidemiology?

Xie, Tiansheng; Yang, Zongxing; Yang, Shigui; Wu, Nanping; Li, Lanjuan

doi:10.1016/j.ijid.2013.11.013

Cited by 26 publications

(24 citation statements)

References 9 publications

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“…Contrary to our expectation, no existing statistical correlation was established between case-related influencing indicators and BDI/SMI, which was discrepant with our previous findings [18] and other studies [7,37]. This might be attributable to the fact that Ebola epidemics did not occur in China.…”

Section: Discussioncontrasting

confidence: 99%

Chinese Public Attention to the Outbreak of Ebola in West Africa: Evidence from the Online Big Data Platform

Liu

Jiang

et al. 2016

IJERPH

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Objective: The outbreak of the Ebola epidemic in West Africa in 2014 exerted enormous global public reaction via the Internet and social media. This study aimed to investigate and evaluate the public reaction to Ebola in China and identify the primitive correlation between possible influence factors caused by the outbreak of Ebola in West Africa and Chinese public attention via Internet surveillance. Methods: Baidu Index (BDI) and Sina Micro Index (SMI) were collected from their official websites, and the disease-related data were recorded from the websites of the World Health Organization (WHO), U.S. Centers for Disease Control and Prevention (CDC), and U.S. National Ministries of Health. The average BDI of Internet users in different regions were calculated to identify the public reaction to the Ebola outbreak. Spearman’s rank correlation was used to check the relationship of epidemic trends with BDI and SMI. Additionally, spatio-temporal analysis and autocorrelation analysis were performed to detect the clustered areas with the high attention to the topic of “Ebola”. The related news reports were collected from authoritative websites to identify potential patterns. Results: The BDI and the SMI for “Ebola” showed a similar fluctuating trend with a correlation coefficient = 0.9 (p < 0.05). The average BDI in Beijing, Tibet, and Shanghai was higher than other cities. However, the disease-related indicators did not identify potential correlation with both indices above. A hotspot area was detected in Tibet by local autocorrelation analysis. The most likely cluster identified by spatiotemporal cluster analysis was in the northeast regions of China with the relative risk (RR) of 2.26 (p ≤ 0.01) from 30 July to 14 August in 2014. Qualitative analysis indicated that negative news could lead to a continuous increase of the public’s attention until the appearance of a positive news report. Conclusions: Confronted with the risk of cross-border transmission of the infectious disease, online surveillance might be used as an innovative approach to perform public communication and health education through examining the public’s reaction and attitude.

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Section: Discussioncontrasting

confidence: 99%

Chinese Public Attention to the Outbreak of Ebola in West Africa: Evidence from the Online Big Data Platform

Liu

Jiang

et al. 2016

IJERPH

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“…To date, the Baidu Index has been used to track influenza [6] and H7N9 [8] trends in China. However, previous studies have primarily focused on a single spatial scale [1,[11][12][13]. In this study, however, to better understand the relationship between HFMD cases and search volume levels, the search index of 11 keywords (Table 1) that are most highly correlated with HFMD are collected at the provincial and municipal scales.…”

Section: Search Engine Datamentioning

confidence: 99%

“…In monitoring disease trends in a particular geographic area, previous studies have typically assessed the search volume (e.g., via Google Trends) of several keywords [5,[11][12][13]. Only a few studies have accounted for the internal spatial structure of an area, and most studies have not attempted to reduce search engine data biases levels.…”

Section: Introductionmentioning

confidence: 99%

Towards Identifying and Reducing the Bias of Disease Information Extracted from Search Engine Data

et al. 2016

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The estimation of disease prevalence in online search engine data (e.g., Google Flu Trends (GFT)) has received a considerable amount of scholarly and public attention in recent years. While the utility of search engine data for disease surveillance has been demonstrated, the scientific community still seeks ways to identify and reduce biases that are embedded in search engine data. The primary goal of this study is to explore new ways of improving the accuracy of disease prevalence estimations by combining traditional disease data with search engine data. A novel method, Biased Sentinel Hospital-based Area Disease Estimation (B-SHADE), is introduced to reduce search engine data bias from a geographical perspective. To monitor search trends on Hand, Foot and Mouth Disease (HFMD) in Guangdong Province, China, we tested our approach by selecting 11 keywords from the Baidu index platform, a Chinese big data analyst similar to GFT. The correlation between the number of real cases and the composite index was 0.8. After decomposing the composite index at the city level, we found that only 10 cities presented a correlation of close to 0.8 or higher. These cities were found to be more stable with respect to search volume, and they were selected as sample cities in order to estimate the search volume of the entire province. After the estimation, the correlation improved from 0.8 to 0.864. After fitting the revised search volume with historical cases, the mean absolute error was 11.19% lower than it was when the original search volume and historical cases were combined. To our knowledge, this is the first study to reduce search engine data bias levels through the use of rigorous spatial sampling strategies.

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“…To get a fuller picture of media and public reactions to the crisis, our Internet surveillance started from December 5, 2013, a week before the crisis, and ended on January 10, 2014, a week after the crisis. The term "hepatitis B vaccine" was used as the keyword to collect three types of data from Baidu and Sina Weibo: the daily number of online news reports, the daily number of Weibo posts, and the daily Baidu search index (daily weighted volume of relevant Baidu searches) [11,[23][24][25][26]. Using the same keyword 'hepatitis B vaccine', original microblog (Weibo) posts were searched and collected retrospectively from Sina Weibo platform between December 12, 2013 and January 3, 2014.…”

Section: Study Period and Data Collectionmentioning

confidence: 99%

Media and public reactions toward vaccination during the ‘hepatitis B vaccine crisis’ in China

Chen

Zhang

Jiang

et al. 2015

Vaccine

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Correlation between reported human infection with avian influenza A H7N9 virus and cyber user awareness: what can we learn from digital epidemiology?

Cited by 26 publications

References 9 publications

Chinese Public Attention to the Outbreak of Ebola in West Africa: Evidence from the Online Big Data Platform

Chinese Public Attention to the Outbreak of Ebola in West Africa: Evidence from the Online Big Data Platform

Towards Identifying and Reducing the Bias of Disease Information Extracted from Search Engine Data

Media and public reactions toward vaccination during the ‘hepatitis B vaccine crisis’ in China

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