Tweet
Volume 27, Issue 39, 29/Sep/2022
Espinosa Laura, Wijermans Ariana, Orchard Francisco, Höhle Michael, Czernichow Thomas, Coletti Pietro, Hermans Lisa, Faes Christel, Kissling Esther, Mollet Thomas.
Abstract
Background
The European Centre for Disease Prevention and Control (ECDC) systematically collates information from sources to rapidly detect early public health threats. The lack of a freely available, customisable and automated early warning tool using data from Twitter prompted the ECDC to develop epitweetr, which collects, geolocates and aggregates tweets generating signals and email alerts.
Aim
This study aims to compare the performance of epitweetr to manually monitoring tweets for the purpose of early detecting public health threats.
Methods
We calculated the general and specific positive predictive value (PPV) of signals generated by epitweetr between 19 October and 30 November 2020. Sensitivity, specificity, timeliness and accuracy and performance of tweet geolocation and signal detection algorithms obtained from epitweetr and the manual monitoring of 1,200 tweets were compared.
Results
The epitweetr geolocation algorithm had an accuracy of 30.1% at national, and 25.9% at subnational levels. The signal detection algorithm had 3.0% general PPV and 74.6% specific PPV. Compared to manual monitoring, epitweetr had greater sensitivity (47.9% and 78.6%, respectively), and reduced PPV (97.9% and 74.6%, respectively). Median validation time difference between 16 common events detected by epitweetr and manual monitoring was -48.6 hours (IQR: −102.8 to −23.7).
Conclusion
Epitweetr has shown sufficient performance as an early warning tool for public health threats using Twitter data. Since epitweetr is a free, open-source tool with configurable settings and a strong automated component, it is expected to increase in usability and usefulness to public health experts.
Public health impact of this article
What did you want to address in this study?
Twitter data can be used to detect outbreaks and public health threats. Epitweetr is a new open-source, tool for automated early detection of public health threats that uses Twitter data. We wished to compare the performance of epitweetr against the manual monitoring of Twitter for early detection of public health threats.
What have we learnt from this study?
Epitweetr has been shown to have an advantage over manually monitoring Twitter data in terms of being able to detect public health threats early. This type of tool has been shown to be useful to public health experts, and it is highly recommended that epitweetr be used to collect epidemic data in combination with existing tools.
What are the implications of your findings for public health?
Making epitweetr available publicly, and including several customisable settings allows users to adapt this tool to their specific needs and also further develop this tool. Since epitweetr has a strong automated component providing data in a timely manner, it can become a useful tool in the daily detection of infectious diseases, or other health threats, in public health settings.
Espinosa Laura, Wijermans Ariana, Orchard Francisco, Höhle Michael, Czernichow Thomas, Coletti Pietro, Hermans Lisa, Faes Christel, Kissling Esther, Mollet Thomas.
Abstract
Background
The European Centre for Disease Prevention and Control (ECDC) systematically collates information from sources to rapidly detect early public health threats. The lack of a freely available, customisable and automated early warning tool using data from Twitter prompted the ECDC to develop epitweetr, which collects, geolocates and aggregates tweets generating signals and email alerts.
Aim
This study aims to compare the performance of epitweetr to manually monitoring tweets for the purpose of early detecting public health threats.
Methods
We calculated the general and specific positive predictive value (PPV) of signals generated by epitweetr between 19 October and 30 November 2020. Sensitivity, specificity, timeliness and accuracy and performance of tweet geolocation and signal detection algorithms obtained from epitweetr and the manual monitoring of 1,200 tweets were compared.
Results
The epitweetr geolocation algorithm had an accuracy of 30.1% at national, and 25.9% at subnational levels. The signal detection algorithm had 3.0% general PPV and 74.6% specific PPV. Compared to manual monitoring, epitweetr had greater sensitivity (47.9% and 78.6%, respectively), and reduced PPV (97.9% and 74.6%, respectively). Median validation time difference between 16 common events detected by epitweetr and manual monitoring was -48.6 hours (IQR: −102.8 to −23.7).
Conclusion
Epitweetr has shown sufficient performance as an early warning tool for public health threats using Twitter data. Since epitweetr is a free, open-source tool with configurable settings and a strong automated component, it is expected to increase in usability and usefulness to public health experts.
Public health impact of this article
What did you want to address in this study?
Twitter data can be used to detect outbreaks and public health threats. Epitweetr is a new open-source, tool for automated early detection of public health threats that uses Twitter data. We wished to compare the performance of epitweetr against the manual monitoring of Twitter for early detection of public health threats.
What have we learnt from this study?
Epitweetr has been shown to have an advantage over manually monitoring Twitter data in terms of being able to detect public health threats early. This type of tool has been shown to be useful to public health experts, and it is highly recommended that epitweetr be used to collect epidemic data in combination with existing tools.
What are the implications of your findings for public health?
Making epitweetr available publicly, and including several customisable settings allows users to adapt this tool to their specific needs and also further develop this tool. Since epitweetr has a strong automated component providing data in a timely manner, it can become a useful tool in the daily detection of infectious diseases, or other health threats, in public health settings.