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

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

Aditya Joshi  1 , Ross Sparks  1 , Sarvnaz Karimi  1 , Sheng-Lun Jason Yan  2 , Abrar Ahmad Chughtai  2 , Cecile Paris  1 , C Raina MacIntyre  3   4
Affiliations

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

Aditya Joshi et al. PLoS One. .

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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Adapted architecture using Data Aggregation.
Fig 2
Fig 2. Adapted architecture for Alert Aggregation.
Fig 3
Fig 3. Daily counts in the aggregated dataset for both the symptoms.
See this image and copyright information in PMC

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