Epitweetr: Early warning of public health threats using Twitter data

Abstract

Background: ECDC performs epidemic intelligence activities to systematically collate information from a variety of sources, including Twitter, to rapidly detect public health events. The lack of a freely available, customisable and automated early warning tool using Twitter data, prompted ECDC to develop epitweetr. The specific objectives are to assess the performance of the geolocation and signal detection algorithms used by epitweetr and to assess the performance of epitweetr in comparison with the manual monitoring of Twitter for early detection of public health threats. Methods: Epitweetr collects, geolocates and aggregates tweets to generate signals and email alerts. Firstly, we evaluated manually the tweet geolocation characteristics of 1,200 tweets, and assessed its accuracy in extracting the correct location and its performance in detecting tweets with available information on the tweet geolocation. Secondly, we evaluated signals generated by epitweetr between 19 October and 30 November 2020 and we calculated the positive predictive value (PPV). Then, we evaluated the sensitivity, specificity and timeliness of epitweetr in comparison with Twitter manual monitoring. Findings: The epitweetr geolocation algorithm had an accuracy of 30.1% and 25.9% at national and subnational levels, respectively. General and specific PPV of the signal detection algorithm was 3.0% and 74.6%, respectively. Epitweetr and/or manual monitoring detected 570 signals and 454 events. Epitweetr had a sensitivity of 78.6% [75.2% - 82.0%] and PPV of 74.6% [70.5% - 78.6%]; and the manual monitoring had a sensitivity of 47.9% [43.8% - 52.0%] and PPV of 97.9% [95.8% - 99.9%]. The median validation time difference between sixteen common events detected by epitweetr and manual monitoring was -48.6 hours [(-102.8) - (-23.7) hours]. Interpretation: Epitweetr has shown to have sufficient performance as an early warning tool for public health threats using Twitter data. Having developed epitweetr as a free, open-source tool with several configurable settings and a strong automated component, it is expected to increase its usability and usefulness to public health experts.

A Real-Time Open Public Sources Text Analysis System

“…Epitweetr [11] is a system that detects outbreaks and public health threats using Twitter's data. The system is fully automatic and provides additional support for public health experts in detecting and identifying the geo-location of an outbreak.…”

“…Many existing systems have applications comparable to ours, but they still have a few limitations. Some do the text analysis task on a single Twitter data stream [7], [8], [9], [10], while others only serve on their specific domains [11], [12], [13]. Also, many systems are not real-time as Trend Miner, [10], or not fully automatic as LRA Crisis Tracker, [12].…”

Acute Hepatitis of Unknown Origin in Children; Lessons Learned from the COVID-19 Pandemic

“…A variety of protocols for investigation, diagnosis, and reporting of cases have been developed and different arms of extensive surveillance are conducted. In this approach, all signals about hepatitis cases on media or social media are now monitored (9), and as well as previous experiences, public health authorities are raising public awareness through these platforms (10,11). In addition, wastewater samples collected during the COVID-19 pandemic are re-investigated and compared to recent samples to trace signals of emerging pathogens (12).…”