Spatio-temporal evaluation of social media as a tool for livestock disease surveillance

Samuel Munaf*, Kevin Swingler, Franz Brülisauer, Anthony O'Hare, George Gunn, Aaron Reeves

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
28 Downloads (Pure)

Abstract

Recent outbreaks of Avian Influenza across Europe have highlighted the potential for syndromic surveillance systems that consider other modes of data, namely social media. This study investigates the feasibility of using social media, primarily Twitter, to monitor illness outbreaks such as avian flu. Using temporal, geographical, and correlation analyses, we investigated the association between avian influenza tweets and officially verified cases in the United Kingdom in 2021 and 2022. Pearson correlation coefficient, bivariate Moran's I analysis and time series analysis, were among the methodologies used. The findings show a weak, statistically insignificant relationship between the number of tweets and confirmed cases in a temporal context, implying that relying simply on social media data for surveillance may be insufficient. The spatial analysis provided insights into the overlaps between confirmed cases and tweet locations, shedding light on regionally targeted interventions during outbreaks. Although social media can be useful for understanding public sentiment and concerns during outbreaks, it must be combined with traditional surveillance methods and official data sources for a more accurate and comprehensive approach. Improved data mining techniques and real-time analysis can improve outbreak detection and response even further. This study underscores the need of having a strong surveillance system in place to properly monitor and manage disease outbreaks and protect public health.

Original languageEnglish
Article number100657
JournalOne Health
Volume17
Early online date26 Nov 2023
DOIs
Publication statusPrint publication - Dec 2023

Bibliographical note

© 2023 The Authors. Published by Elsevier B.V.

Keywords

  • Anomaly detection
  • Avian flu
  • Disease surveillance
  • Infodemiology
  • Infoveillance
  • Social media
  • Spatio-temporal
  • Time series
  • Veterinary epidemiology

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