Spatio-temporal evaluation of social media as a tool for livestock disease surveillance
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...
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ftdoajarticles:oai:doaj.org/article:d7830f5ad0ef41edabc92b92dd88eae3 2024-01-14T10:05:33+01:00 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 2023-12-01T00:00:00Z https://doi.org/10.1016/j.onehlt.2023.100657 https://doaj.org/article/d7830f5ad0ef41edabc92b92dd88eae3 EN eng Elsevier http://www.sciencedirect.com/science/article/pii/S2352771423001775 https://doaj.org/toc/2352-7714 2352-7714 doi:10.1016/j.onehlt.2023.100657 https://doaj.org/article/d7830f5ad0ef41edabc92b92dd88eae3 One Health, Vol 17, Iss , Pp 100657- (2023) Veterinary epidemiology Disease surveillance Infodemiology Infoveillance Time series Social media Medicine (General) R5-920 article 2023 ftdoajarticles https://doi.org/10.1016/j.onehlt.2023.100657 2023-12-17T01:44:53Z 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. Article in Journal/Newspaper Avian flu Directory of Open Access Journals: DOAJ Articles One Health 17 100657 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Veterinary epidemiology Disease surveillance Infodemiology Infoveillance Time series Social media Medicine (General) R5-920 |
spellingShingle |
Veterinary epidemiology Disease surveillance Infodemiology Infoveillance Time series Social media Medicine (General) R5-920 Samuel Munaf Kevin Swingler Franz Brülisauer Anthony O'Hare George Gunn Aaron Reeves Spatio-temporal evaluation of social media as a tool for livestock disease surveillance |
topic_facet |
Veterinary epidemiology Disease surveillance Infodemiology Infoveillance Time series Social media Medicine (General) R5-920 |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Samuel Munaf Kevin Swingler Franz Brülisauer Anthony O'Hare George Gunn Aaron Reeves |
author_facet |
Samuel Munaf Kevin Swingler Franz Brülisauer Anthony O'Hare George Gunn Aaron Reeves |
author_sort |
Samuel Munaf |
title |
Spatio-temporal evaluation of social media as a tool for livestock disease surveillance |
title_short |
Spatio-temporal evaluation of social media as a tool for livestock disease surveillance |
title_full |
Spatio-temporal evaluation of social media as a tool for livestock disease surveillance |
title_fullStr |
Spatio-temporal evaluation of social media as a tool for livestock disease surveillance |
title_full_unstemmed |
Spatio-temporal evaluation of social media as a tool for livestock disease surveillance |
title_sort |
spatio-temporal evaluation of social media as a tool for livestock disease surveillance |
publisher |
Elsevier |
publishDate |
2023 |
url |
https://doi.org/10.1016/j.onehlt.2023.100657 https://doaj.org/article/d7830f5ad0ef41edabc92b92dd88eae3 |
genre |
Avian flu |
genre_facet |
Avian flu |
op_source |
One Health, Vol 17, Iss , Pp 100657- (2023) |
op_relation |
http://www.sciencedirect.com/science/article/pii/S2352771423001775 https://doaj.org/toc/2352-7714 2352-7714 doi:10.1016/j.onehlt.2023.100657 https://doaj.org/article/d7830f5ad0ef41edabc92b92dd88eae3 |
op_doi |
https://doi.org/10.1016/j.onehlt.2023.100657 |
container_title |
One Health |
container_volume |
17 |
container_start_page |
100657 |
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1788059908873125888 |