The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: an ecological study
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Digital disease detection - harnessing the Web for public health surveillance
Brownstein, J. S., Freifeld, C. C., & Madoff, L. C. (2009) · New England Journal of Medicine · doi:10.1056/nejmp0900702 · Cited by 819 · cited 1×
“The ubiquitous use of mobile phones and the Internet has further led to consideration of novel data sources for syndromic surveillance (Keller et al. 2009; Brownstein et al. 2009; Charles-Smith et al. 2015; Jia and Mohamed 2015).”
BackgroundBroniatowski, D. A., Paul, M., & Dredze, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0083672 · Cited by 474 · cited 2×
“2013; Broniatowski et al.”
BackgroundWhat is Syndromic Surveillance?
Henning, K. J. (2004) · PsycEXTRA Dataset · doi:10.1037/e307182005-001 · Cited by 323
Charles, L., Reynolds, T. L., Cameron, M. A., Conway, M., Lau, E. H. Y., Olsen, J. M., Pavlin, J. A., Shigematsu, M., Streichert, L. C., Suda, K. J., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0139701 · Cited by 322 · cited 6×
“2009; Charles-Smith et al.”
BackgroundMethodInfluentialUse of Unstructured Event-Based Reports for Global Infectious Disease Surveillance
Mikaela Keller, A., Blench, M., Tolentino, H., Freifeld, C. C., Mandl, K. D., Mawudeku, A., Eysenbach, G., & Brownstein, J. S. (2009) · Emerging infectious diseases · doi:10.3201/eid1505.081114 · Cited by 213 · cited 2×
“By extracting relevant tweets based on keywords related to a health syndrome or condition of interest, social media has been used as a data source during emergencies such as Hurricane Isaac and the Boston Marathon explosions (Keller et al. 2009; Cassa et al. 2013; Bennett et al. 2013).”
BackgroundMethodMollema, L., Harmsen, I. A., Broekhuizen, E., Clijnk, R., de Melker, H. E., Paulussen, T. G., Kok, G., Ruiter, R. A. C., & Das, E. H. H. J. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.3863 · Cited by 156 · cited 1×
“Further, additional techniques could be considered to analyze the content of tweets beyond the keywords, to better understand context or sentiment (Mollema et al. 2015).”
BackgroundTwitter as a Sentinel in Emergency Situations: Lessons from the Boston Marathon Explosions
Cassa, C. A., Chunara, R., Mandl, K. D., & Brownstein, J. S. (2013) · PLoS Currents · doi:10.1371/currents.dis.ad70cd1c8bc585e9470046cde334ee4b · Cited by 129 · cited 2×
“2009; Cassa et al.”
MethodBurton, S. H., Tanner, K., Giraud-Carrier, C. G., West, J. H., & Barnes, M. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.2121 · Cited by 121 · cited 1×
“The proportion of Twitter users that are GPSenabled or have location identified in their profile is estimated to be smaller than the actual number of individuals tweeting (Burton et al. 2012).”
BackgroundLondon 2012 Olympic and Paralympic Games: public health surveillance and epidemiology
McCloskey, B., Endericks, T., Catchpole, M., Zambon, M. C. F. A., McLauchlin, J., Shetty, N., Manuel, R. J., Turbitt, D., Smith, G., Crook, P., et al. (2014) · The Lancet · doi:10.1016/s0140-6736(13)62342-9 · Cited by 110 · cited 6×
“…for communicable diseases also supports the finding in a previous study that social media data sources can be used for reassurance during high profile events when no signals have been detected from multiple data sources and, thus, support ongoing activities for the event (McCloskey et al. 2014).”
BackgroundResultInfluentialThe Reliability of Tweets as a Supplementary Method of Seasonal Influenza Surveillance
Aslam, A., Tsou, M., Spitzberg, B. H., An, L., Gawron, J. M., Gupta, D. K., Peddecord, K. M., Nagel, A. C., Allen, C., Yang, J., et al. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3532 · Cited by 99 · cited 2×
“First, an initial list of keywords was generated for the syndromes based on a review of recent publications that used Twitter in public health surveillance and monitoring (Aslam et al.”
BackgroundMethodHow to Exploit Twitter for Public Health Monitoring?
Krieck, M., Otrusina, L., Smrž, P., Dolog, P., Nejdl, W., Velasco, E., & Denecke, K. (2013) · Methods of Information in Medicine · doi:10.3414/me12-02-0010 · Cited by 70 · cited 6×
“First, an initial list of keywords was generated for the syndromes based on a review of recent publications that used Twitter in public health surveillance and monitoring (Aslam et al. 2014; Denecke et al. 2013; Broniatowski et al. 2013).”
BackgroundMethodInfluentialJia, K., & Mohamed, K. (2015) · African Health Sciences · doi:10.4314/ahs.v15i3.13 · Cited by 34 · cited 2×
“2015; Jia and Mohamed 2015) .”
BackgroundBennett, K. J., Olsen, J. M., Harris, S., Mekaru, S. R., Livinski, A. A., & Brownstein, J. S. (2013) · PLoS Currents · doi:10.1371/currents.dis.d2800aa4e536b9d6849e966e91488003 · Cited by 17
Public health surveillance for the Toronto 2015 Pan/Parapan American Games
Chan, E., Hohenadel, K., Lee, B., Helferty, M., Harris, J., Macdonald, L., & Badiani, T. (2017) · Canada Communicable Disease Report · doi:10.14745/ccdr.v43i78a04 · Cited by 3 · cited 2×
“There were no communicable disease outbreaks during the P/PAG time period, and the only public health incidents that occurred during this period were several heat alerts (Chan et al. 2017).”
Background
Flagged first, then by citations
Research works printed but not linked: 7
- Citron, C., & Khan, Y. (2013). Hazard identification and risk assessment for the 2015 Pan and Para Pan American Games: communicable and infectious diseases. Queen's Printer for Ontario: Toronto.
- Elasticsearch. (2017). Elasticsearch. Mountain View: Elasticsearch BV Available from: https://www.elastic.co/products/elasticsearch. Accessed August 29, 2017.
- Government of Ontario. (2014). Get medical advice: Telehealth Ontario. Toronto: Queen's Printer for Ontario Available from: https://www.ontario.ca/page/get-medical-advice-telehealth-ontario. Accessed August 29, 2017.
- Morstatter, F., Pfeffer, J., Liu, H., & Carley, K. M. (2013). Is the sample good enough? Comparing data from Twitter’s streaming API with Twitter’s firehose. arXiv:1306.5204 preprint. · arXiv
- Ontario Agency for Health Protection and Promotion (Public Health Ontario). (2015). 2015 Pan Am/Parapan Am Games (P/PAG): PHO surveillance report—final summary. Toronto: Queen's Printer for Ontario.
- The Toronto Organizing Committee for the 2015 Pan American and Parapan American Games. (2015). Toronto 2015 Pan Am/Parapan Am Games. Toronto: The Toronto Organizing Committee for the 2015 Pan American and Parapan American Games Available from: http://www.toronto2015.org/. Accessed December 22, 2016.
- World Health Organization. (2015). Public health for mass gatherings: key considerations. Geneva: World Health Organization.
Cited by
Sorted by citations
Social Media Role and Its Impact on Public Health: A Narrative Review
Kanchan, S., & Gaidhane, A. (2023) · Cureus · doi:10.7759/cureus.33737 · Cited by 221
“His study stated that Syndromic surveillance uses pre-diagnostic data, and the inclusion of syndromic data sources in public health surveillance for mass gatherings has been shown is useful [16].”
BackgroundSocial media based surveillance systems for healthcare using machine learning: A systematic review
Gupta, A., & Katarya, R. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103500 · Cited by 203
“Health problems such as respiratory, gastro-intestinal, heat-related illness, and ILI symptoms circulating among the population during mass gathering were also detected using Twitter [67].”
A scoping review of the use of Twitter for public health research
Edo-Osagie, O., de la Iglesia, B., LAKE, I. R., & Edeghere, O. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103770 · Cited by 142
“Illnesses Correlation Analysis [25] Government of ontario, Kingston, Frontenac and Lennox & Addington”
InfluentialKalimeri, K., Delfino, M., Cattuto, C., Perrotta, D., Colizza, V., Guerrisi, C., Turbelin, C., Duggan, J., Edmunds, J., Obi, C., et al. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006173 · Cited by 40
Artificial Intelligence for Surveillance in Public Health
Thiébaut, R., Cossin, S., & Informatics, S. E. F. T. I. Y. S. O. P. H. A. E. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677939 · Cited by 28
“…a source of information for the surveillance of various public health outcomes on a real-time basis such as influenza [9], but also foodborne illnesses [10], or heat alerts [19], which may allow investigation or action in case of alerts, for instance in the context of mass gathering setting [19].”
BackgroundInvestigate the impact of media on public understanding of health and medical science in China
Yang, J. (2023) · SHS Web of Conferences · doi:10.1051/shsconf/202318001008 · Cited by 1
Arjona, J. O. (2022) · GeoFocus Revista Internacional de Ciencia y Tecnología de la Información Geográfica · doi:10.21138/gf.789 · Cited by 1
Twitter Mining for Syndromic Surveillance
Edo-Osagie, O. (2019) · UEA Digital Repository (University of East Anglia) · Cited by 1
Environmental Safety Considerations in Cricket
Orchard, J. W., Cort, M., & Jay, O. (2025) · Springer eBooks · doi:10.1007/978-981-96-6321-7_23
Ramos-Varela, J. M., Cuevas-Tello, J. C., & Noyola, D. E. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03390-8
Metadata
- Authors
- Khan, Y., Leung, G. J., Belanger, P., Gournis, E., Buckeridge, D. L., Liu, L., Li, Y., & Johnson, I. L.
- Affiliations
- University Health Network (CA)
- University of Toronto (CA)
- Toronto Public Health (CA)
- McGill University (CA)
- KFL&A Public Health (CA)
- Corresponding author
- Khan, Yasmin
- Venue
- Canadian Journal of Public Health
- Published
- 2018-04-20
- Volume
- 109
- Issue
- 3
- Pages
- 419-426
- ISSN
- 0008-4263 (ISSN-L)
- 1920-7476
- PMC
- PMC6964588
- OpenAlex
- W2802776178
- Cited by
- 11
- References
- 14
- Citations by year
- 2026: 1 · 2025: 1 · 2023: 2 · 2022: 1 · 2020: 3 · 2019: 3
- FWCI
- 1.17
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Canadian Journal of Public Health (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Social Sciences › Health › Social Media in Health Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Travel-related health issues
- Keywords
- Twitter, social media surveillance, disease surveillance, mass gatherings, heat-related illness, heat alerts, influenza-like illness, respiratory syndrome, gastrointestinal syndrome, geo-tagged tweets, emergency department visits
- MeSH terms
- Canada, Crowding, Feasibility Studies, Humans, Sports, Social Media, Public Health Surveillance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
