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Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: an ecological study
OBJECTIVES: This study examined Twitter for public health surveillance during a mass gathering in Canada with two objectives: to explore the feasibility of acquiring, categorizing and using geolocated Twitter data and to compare Twitter data against other data sources used for Pan Parapan American G...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6964588/ https://www.ncbi.nlm.nih.gov/pubmed/29981081 http://dx.doi.org/10.17269/s41997-018-0059-0 |
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author | Khan, Yasmin Leung, Garvin J. Belanger, Paul Gournis, Effie Buckeridge, David L. Liu, Li Li, Ye Johnson, Ian L. |
author_facet | Khan, Yasmin Leung, Garvin J. Belanger, Paul Gournis, Effie Buckeridge, David L. Liu, Li Li, Ye Johnson, Ian L. |
author_sort | Khan, Yasmin |
collection | PubMed |
description | OBJECTIVES: This study examined Twitter for public health surveillance during a mass gathering in Canada with two objectives: to explore the feasibility of acquiring, categorizing and using geolocated Twitter data and to compare Twitter data against other data sources used for Pan Parapan American Games (P/PAG) surveillance. METHODS: Syndrome definitions were created using keyword categorization to extract posts from Twitter. Categories were developed iteratively for four relevant syndromes: respiratory, gastrointestinal, heat-related illness, and influenza-like illness (ILI). All data sources corresponded to the location of Toronto, Canada. Twitter data were acquired from a publicly available stream representing a 1% random sample of tweets from June 26 to September 10, 2015. Cross-correlation analyses of time series data were conducted between Twitter and comparator surveillance data sources: emergency department visits, telephone helpline calls, laboratory testing positivity rate, reportable disease data, and temperature. RESULTS: The frequency of daily tweets that were classified into syndromes was low, with the highest mean number of daily tweets being for ILI and respiratory syndromes (22.0 and 21.6, respectively) and the lowest, for the heat syndrome (4.1). Cross-correlation analyses of Twitter data demonstrated significant correlations for heat syndrome with two data sources: telephone helpline calls (r = 0.4) and temperature data (r = 0.5). CONCLUSION: Using simple syndromes based on keyword classification of geolocated tweets, we found a correlation between tweets and two routine data sources for heat alerts, the only public health event detected during P/PAG. Further research is needed to understand the role for Twitter in surveillance. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.17269/s41997-018-0059-0) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6964588 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-69645882020-02-04 Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: an ecological study Khan, Yasmin Leung, Garvin J. Belanger, Paul Gournis, Effie Buckeridge, David L. Liu, Li Li, Ye Johnson, Ian L. Can J Public Health Quantitative Research OBJECTIVES: This study examined Twitter for public health surveillance during a mass gathering in Canada with two objectives: to explore the feasibility of acquiring, categorizing and using geolocated Twitter data and to compare Twitter data against other data sources used for Pan Parapan American Games (P/PAG) surveillance. METHODS: Syndrome definitions were created using keyword categorization to extract posts from Twitter. Categories were developed iteratively for four relevant syndromes: respiratory, gastrointestinal, heat-related illness, and influenza-like illness (ILI). All data sources corresponded to the location of Toronto, Canada. Twitter data were acquired from a publicly available stream representing a 1% random sample of tweets from June 26 to September 10, 2015. Cross-correlation analyses of time series data were conducted between Twitter and comparator surveillance data sources: emergency department visits, telephone helpline calls, laboratory testing positivity rate, reportable disease data, and temperature. RESULTS: The frequency of daily tweets that were classified into syndromes was low, with the highest mean number of daily tweets being for ILI and respiratory syndromes (22.0 and 21.6, respectively) and the lowest, for the heat syndrome (4.1). Cross-correlation analyses of Twitter data demonstrated significant correlations for heat syndrome with two data sources: telephone helpline calls (r = 0.4) and temperature data (r = 0.5). CONCLUSION: Using simple syndromes based on keyword classification of geolocated tweets, we found a correlation between tweets and two routine data sources for heat alerts, the only public health event detected during P/PAG. Further research is needed to understand the role for Twitter in surveillance. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.17269/s41997-018-0059-0) contains supplementary material, which is available to authorized users. Springer International Publishing 2018-04-20 /pmc/articles/PMC6964588/ /pubmed/29981081 http://dx.doi.org/10.17269/s41997-018-0059-0 Text en © The Canadian Public Health Association 2018 |
spellingShingle | Quantitative Research Khan, Yasmin Leung, Garvin J. Belanger, Paul Gournis, Effie Buckeridge, David L. Liu, Li Li, Ye Johnson, Ian L. Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: an ecological study |
title | Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: an ecological study |
title_full | Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: an ecological study |
title_fullStr | Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: an ecological study |
title_full_unstemmed | Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: an ecological study |
title_short | Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: an ecological study |
title_sort | comparing twitter data to routine data sources in public health surveillance for the 2015 pan/parapan american games: an ecological study |
topic | Quantitative Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6964588/ https://www.ncbi.nlm.nih.gov/pubmed/29981081 http://dx.doi.org/10.17269/s41997-018-0059-0 |
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