Abstract
Social media services such as Twitter are valuable sources of information for surveillance systems. A digital syndromic surveillance system has several advantages including its ability to overcome the problem of time delay in traditional surveillance systems. Despite the progress made with using digital syndromic surveillance systems, the possibility of tracking avian influenza (AI) using online sources has not been fully explored. In this study, a Twitter-based data analysis framework was developed to automatically monitor avian influenza outbreaks in a real-time manner. The framework was implemented to find worrisome posts and alerting news on Twitter, filter irrelevant ones, and detect the onset of outbreaks in several countries. The system collected and analyzed over 209,000 posts discussing avian influenza on Twitter from July 2017 to November 2018. We examined the potential of Twitter data to represent the date, severity and virus type of official reports. Furthermore, we investigated whether filtering irrelevant tweets can positively impact the performance of the system. The proposed approach was empirically evaluated using a real-world outbreak-reporting source. We found that 75% of real-world outbreak notifications of AI were identifiable from Twitter. This shows the capability of the system to serve as a complementary approach to official AI reporting methods. Moreover, we observed that one-third of outbreak notifications were reported on Twitter earlier than official reports. This feature could augment traditional surveillance systems and provide a possibility of early detection of outbreaks. This study could potentially provide a first stepping stone for building digital disease outbreak warning systems to assist epidemiologists and animal health professionals in making relevant decisions.
Highlights
Social media services such as Twitter are valuable sources of information for surveillance systems
Extensive work has examined the value of Internet-based sources such as social media, search engines and news contents for conducting infectious disease surveillance[9,10,11]
We looked for sudden changes in the daily time-series of country-based tweets
Summary
Social media services such as Twitter are valuable sources of information for surveillance systems. This usually results in delayed and labour-intensive actions performed by health authorities[3,4,5] Online services, such as Twitter, are gaining importance as a valuable source of information for decision support systems in healthcare[4,6]. The main goal of digital surveillance systems is to monitor and detect potential epidemic events from informal online sources[8] These systems are used to help health authorities and decision-makers to react promptly to disease emergencies and reduce or eliminate the consequences. The improvement was achieved by constructing location-specific time-series and removing irrelevant content Another reason why further research on digital surveillance systems for animal diseases is required lies in the significant economic and health impacts that surveillance systems can offer to the poultry industry
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