Epitweetr: Early warning of public health threats using Twitter data

Author:

Espinosa Laura1ORCID,Wijermans Ariana1,Orchard Francisco2ORCID,Höhle Michael3ORCID,Czernichow Thomas42ORCID,Coletti Pietro5ORCID,Hermans Lisa5ORCID,Faes Christel5ORCID,Kissling Esther2ORCID,Mollet Thomas61ORCID

Affiliation:

1. European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden

2. Epiconcept, Paris, France

3. Stockholm University, Stockholm, Sweden

4. Current affiliation: Aleia, Paris, France

5. Hasselt University, Hasselt, Belgium

6. Current affiliation: International Federation of Red Cross and Red Crescent Societies, Geneva, Switzerland

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.

Publisher

European Centre for Disease Control and Prevention (ECDC)

Subject

Virology,Public Health, Environmental and Occupational Health,Epidemiology

Reference30 articles.

1. Decision No. 1082/2013/EU of the European Parliament and of the Council of 22 October 2013 on serious cross-border threats to health and repealing Decision No 2119/98/EC. Luxembourg: Official Journal of the European Union; 5 Nov 2013. Available from: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32013D1082&from=EN

2. European Centre for Disease Prevention and Control (ECDC). Communicable disease threats to public health in the European Union - Annual epidemiological report for 2019. Stockholm: ECDC; 2020. Available from: https://www.ecdc.europa.eu/en/publications-data/communicable-disease-threats-public-health-european-union-2019

3. European Centre for Disease Prevention and Control (ECDC). Sources - Worldwide data on COVID-19. Stockholm: ECDC; 2020. Available from: https://www.ecdc.europa.eu/en/publications-data/sources-worldwide-data-covid-19

4. Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020.;Li;Euro Surveill,2020

5. Using Big Data to Monitor the Introduction and Spread of Chikungunya, Europe, 2017.;Rocklöv;Emerg Infect Dis,2019

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