Open AccessAutomated monitoring of tweets for early detection of the 2014 Ebola epidemic
Author(s) -
Aditya Joshi,
Ross Sparks,
Sarvnaz Karimi,
Sheng-Lun Jason Yan,
Abrar Ahmad Chughtai,
Cécile Paris,
C. Raina MacIntyre
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0230322
Subject(s) - sierra leone , disease surveillance , ebola virus , infectious disease (medical specialty) , social media , computer science , geography , computer security , data science , medicine , disease , world wide web , history , ethnology , pathology
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.