Automated monitoring of tweets for early detection of the 2014 Ebola epidemic

Aditya Joshi; Ross Sparks; Sarvnaz Karimi; Sheng-Lun Jason Yan; Abrar Ahmad Chughtai; Cecile Paris; C Raina MacIntyre

doi:10.1371/journal.pone.0230322

Automated monitoring of tweets for early detection of the 2014 Ebola epidemic

PLoS One. 2020 Mar 17;15(3):e0230322. doi: 10.1371/journal.pone.0230322. eCollection 2020.

Authors

Aditya Joshi¹, Ross Sparks¹, Sarvnaz Karimi¹, Sheng-Lun Jason Yan², Abrar Ahmad Chughtai², Cecile Paris¹, C Raina MacIntyre^{3

4}

Affiliations

¹ Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sydney, NSW, Australia.
² School of Public Health and Community Medicine, University of New South Wales (UNSW), Sydney, NSW, Australia.
³ Kirby Institute, University of New South Wales (UNSW), Sydney, NSW, Australia.
⁴ College of Public Service & Community Solutions, Arizona State University, Phoenix, AZ, United States of America.

Abstract

First reported in March 2014, an Ebola epidemic impacted West Africa, most notably Liberia, Guinea and Sierra Leone. We demonstrate the value of social media for automated surveillance of infectious diseases such as the West Africa Ebola epidemic. We experiment with two variations of an existing surveillance architecture: the first aggregates tweets related to different symptoms together, while the second considers tweets about each symptom separately and then aggregates the set of alerts generated by the architecture. Using a dataset of tweets posted from the affected region from 2011 to 2014, we obtain alerts in December 2013, which is three months prior to the official announcement of the epidemic. Among the two variations, the second, which produces a restricted but useful set of alerts, can potentially be applied to other infectious disease surveillance and alert systems.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Data Mining / methods*
Datasets as Topic
Ebolavirus
Epidemics / prevention & control*
Epidemics / statistics & numerical data
Epidemiological Monitoring*
Guinea / epidemiology
Hemorrhagic Fever, Ebola / diagnosis
Hemorrhagic Fever, Ebola / epidemiology*
Hemorrhagic Fever, Ebola / virology
Humans
Liberia / epidemiology
Sierra Leone / epidemiology
Social Media / statistics & numerical data*

Grants and funding

Raina was supported by a NHMRC Principal Research Fellowship, grant number 1137582. Abrar was employed at and Sheng-Lun was a student at University of New South Wales at the time of the research. Aditya, Cecile, Sarvnaz and Ross were employed by the Commonwealth Scientific and Industrial Research Organisation (CSIRO). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.