About: Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: an ecological study

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About: Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: an ecological study Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: an ecological study
Creator	Liu, Li Li, Ye Gournis, Effie Khan, Yasmin Johnson, Ian Belanger, Paul Buckeridge, David Leung, Garvin
Source	Medline; PMC
abstract	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.
has issue date	2018-04-20(xsd:dateTime)
bibo:doi	10.17269/s41997-018-0059-0
bibo:pmid	29981081
has license	green-oa
sha1sum (hex)	cabb9259f5598960fcf54a7444d969d628337528
schema:url	https://doi.org/10.17269/s41997-018-0059-0
resource representing a document's title	Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: an ecological study
has PubMed Central identifier	PMC6964588
has PubMed identifier	29981081
schema:publication	Canadian Journal of Public Health
resource representing a document's body	covid:cabb9259f5598960fcf54a7444d969d628337528#body_text
is schema:about of	named entity 'Canada' named entity 'All' named entity 'data' named entity 'comparator' named entity 'corresponded' named entity 'random sample' named entity 'Twitter' named entity 'Twitter' named entity '28P' named entity 'KEYWORD' named entity 'DEMONSTRATED' named entity 'CORRELATIONS' named entity 'TO COMPARE' named entity 'NUMBER OF' named entity 'LOW' named entity 'CORRELATION' named entity 'LABORATORY TESTING' named entity 'SYNDROME' named entity 'NEEDED' named entity 'RELATED' named entity 'STUDY' named entity 'USED' named entity 'CROSS-CORRELATION' named entity 'EMERGENCY DEPARTMENT VISITS' named entity 'METHODS' named entity '0.5' named entity 'UNDERSTAND' named entity 'ILLNESS' named entity 'TWITTER' named entity 'PUBLIC HEALTH' named entity 'MEAN' named entity 'LOCATION' named entity 'JUNE' named entity 'SURVEILLANCE' named entity '2015' named entity 'SIMPLE' named entity 'FREQUENCY' named entity '10%' named entity 'RESPIRATORY' named entity 'USING' named entity 'CATEGORIES' named entity 'CLASSIFICATION' named entity 'AMERICAN' named entity 'CONCLUSION' named entity 'ROUTINE' named entity 'ACQUIRED' named entity 'TELEPHONE' named entity 'TIME SERIES DATA' named entity 'MASS GATHERING' named entity 'AVAILABLE' named entity 'ROLE' named entity 'SYNDROMES' named entity 'SIGNIFICANT' named entity 'DISEASE DATA' named entity 'LOWEST' named entity 'CATEGORIZING' named entity 'DETECTED' named entity 'COMPARATOR' named entity 'POSTS' named entity 'BEING' named entity '0.4' named entity 'DAILY' named entity 'A MASS' named entity 'HEAT' named entity 'FOUND' named entity 'EVENT' named entity 'ACQUIRING' named entity 'TEMPERATURE' named entity 'CREATED' named entity 'CLASSIFIED' named entity 'EXAMINED' named entity 'RANDOM SAMPLE' named entity 'TO EXTRACT' named entity 'ALERTS'

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