A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives.
Identifieur interne : 000449 ( PubMed/Corpus ); précédent : 000448; suivant : 000450A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives.
Auteurs : Ruchit Nagar ; Qingyu Yuan ; Clark C. Freifeld ; Mauricio Santillana ; Aaron Nojima ; Rumi Chunara ; John S. BrownsteinSource :
- Journal of medical Internet research [ 1438-8871 ] ; 2014.
English descriptors
- KwdEn :
- MESH :
- epidemiology : Influenza, Human, New York City.
- statistics & numerical data : Blogging.
- Disease Outbreaks, Geographic Mapping, Humans, Internet, Prospective Studies, Retrospective Studies, Spatio-Temporal Analysis.
Abstract
Twitter has shown some usefulness in predicting influenza cases on a weekly basis in multiple countries and on different geographic scales. Recently, Broniatowski and colleagues suggested Twitter's relevance at the city-level for New York City. Here, we look to dive deeper into the case of New York City by analyzing daily Twitter data from temporal and spatiotemporal perspectives. Also, through manual coding of all tweets, we look to gain qualitative insights that can help direct future automated searches.
DOI: 10.2196/jmir.3416
PubMed: 25331122
Links to Exploration step
pubmed:25331122Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives.</title><author><name sortKey="Nagar, Ruchit" sort="Nagar, Ruchit" uniqKey="Nagar R" first="Ruchit" last="Nagar">Ruchit Nagar</name><affiliation><nlm:affiliation>Children's Hospital Informatics Program, Boston Children's Hospital, Boston, MA, United States. ruchit.nagar@yale.edu.</nlm:affiliation></affiliation></author><author><name sortKey="Yuan, Qingyu" sort="Yuan, Qingyu" uniqKey="Yuan Q" first="Qingyu" last="Yuan">Qingyu Yuan</name></author><author><name sortKey="Freifeld, Clark C" sort="Freifeld, Clark C" uniqKey="Freifeld C" first="Clark C" last="Freifeld">Clark C. Freifeld</name></author><author><name sortKey="Santillana, Mauricio" sort="Santillana, Mauricio" uniqKey="Santillana M" first="Mauricio" last="Santillana">Mauricio Santillana</name></author><author><name sortKey="Nojima, Aaron" sort="Nojima, Aaron" uniqKey="Nojima A" first="Aaron" last="Nojima">Aaron Nojima</name></author><author><name sortKey="Chunara, Rumi" sort="Chunara, Rumi" uniqKey="Chunara R" first="Rumi" last="Chunara">Rumi Chunara</name></author><author><name sortKey="Brownstein, John S" sort="Brownstein, John S" uniqKey="Brownstein J" first="John S" last="Brownstein">John S. Brownstein</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2014">2014</date><idno type="RBID">pubmed:25331122</idno><idno type="pmid">25331122</idno><idno type="doi">10.2196/jmir.3416</idno><idno type="wicri:Area/PubMed/Corpus">000449</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000449</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives.</title><author><name sortKey="Nagar, Ruchit" sort="Nagar, Ruchit" uniqKey="Nagar R" first="Ruchit" last="Nagar">Ruchit Nagar</name><affiliation><nlm:affiliation>Children's Hospital Informatics Program, Boston Children's Hospital, Boston, MA, United States. ruchit.nagar@yale.edu.</nlm:affiliation></affiliation></author><author><name sortKey="Yuan, Qingyu" sort="Yuan, Qingyu" uniqKey="Yuan Q" first="Qingyu" last="Yuan">Qingyu Yuan</name></author><author><name sortKey="Freifeld, Clark C" sort="Freifeld, Clark C" uniqKey="Freifeld C" first="Clark C" last="Freifeld">Clark C. Freifeld</name></author><author><name sortKey="Santillana, Mauricio" sort="Santillana, Mauricio" uniqKey="Santillana M" first="Mauricio" last="Santillana">Mauricio Santillana</name></author><author><name sortKey="Nojima, Aaron" sort="Nojima, Aaron" uniqKey="Nojima A" first="Aaron" last="Nojima">Aaron Nojima</name></author><author><name sortKey="Chunara, Rumi" sort="Chunara, Rumi" uniqKey="Chunara R" first="Rumi" last="Chunara">Rumi Chunara</name></author><author><name sortKey="Brownstein, John S" sort="Brownstein, John S" uniqKey="Brownstein J" first="John S" last="Brownstein">John S. Brownstein</name></author></analytic><series><title level="j">Journal of medical Internet research</title><idno type="eISSN">1438-8871</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Blogging (statistics & numerical data)</term><term>Disease Outbreaks</term><term>Geographic Mapping</term><term>Humans</term><term>Influenza, Human (epidemiology)</term><term>Internet</term><term>New York City (epidemiology)</term><term>Prospective Studies</term><term>Retrospective Studies</term><term>Spatio-Temporal Analysis</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term><term>New York City</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Blogging</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Disease Outbreaks</term><term>Geographic Mapping</term><term>Humans</term><term>Internet</term><term>Prospective Studies</term><term>Retrospective Studies</term><term>Spatio-Temporal Analysis</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Twitter has shown some usefulness in predicting influenza cases on a weekly basis in multiple countries and on different geographic scales. Recently, Broniatowski and colleagues suggested Twitter's relevance at the city-level for New York City. Here, we look to dive deeper into the case of New York City by analyzing daily Twitter data from temporal and spatiotemporal perspectives. Also, through manual coding of all tweets, we look to gain qualitative insights that can help direct future automated searches.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">25331122</PMID><DateCompleted><Year>2015</Year><Month>06</Month><Day>18</Day></DateCompleted><DateRevised><Year>2018</Year><Month>12</Month><Day>02</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1438-8871</ISSN><JournalIssue CitedMedium="Internet"><Volume>16</Volume><Issue>10</Issue><PubDate><Year>2014</Year><Month>Oct</Month><Day>20</Day></PubDate></JournalIssue><Title>Journal of medical Internet research</Title><ISOAbbreviation>J. Med. Internet Res.</ISOAbbreviation></Journal><ArticleTitle>A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives.</ArticleTitle><Pagination><MedlinePgn>e236</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.2196/jmir.3416</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Twitter has shown some usefulness in predicting influenza cases on a weekly basis in multiple countries and on different geographic scales. Recently, Broniatowski and colleagues suggested Twitter's relevance at the city-level for New York City. Here, we look to dive deeper into the case of New York City by analyzing daily Twitter data from temporal and spatiotemporal perspectives. Also, through manual coding of all tweets, we look to gain qualitative insights that can help direct future automated searches.</AbstractText><AbstractText Label="OBJECTIVE" NlmCategory="OBJECTIVE">The intent of the study was first to validate the temporal predictive strength of daily Twitter data for influenza-like illness emergency department (ILI-ED) visits during the New York City 2012-2013 influenza season against other available and established datasets (Google search query, or GSQ), and second, to examine the spatial distribution and the spread of geocoded tweets as proxies for potential cases.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">From the Twitter Streaming API, 2972 tweets were collected in the New York City region matching the keywords "flu", "influenza", "gripe", and "high fever". The tweets were categorized according to the scheme developed by Lamb et al. A new fourth category was added as an evaluator guess for the probability of the subject(s) being sick to account for strength of confidence in the validity of the statement. Temporal correlations were made for tweets against daily ILI-ED visits and daily GSQ volume. The best models were used for linear regression for forecasting ILI visits. A weighted, retrospective Poisson model with SaTScan software (n=1484), and vector map were used for spatiotemporal analysis.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">Infection-related tweets (R=.763) correlated better than GSQ time series (R=.683) for the same keywords and had a lower mean average percent error (8.4 vs 11.8) for ILI-ED visit prediction in January, the most volatile month of flu. SaTScan identified primary outbreak cluster of high-probability infection tweets with a 2.74 relative risk ratio compared to medium-probability infection tweets at P=.001 in Northern Brooklyn, in a radius that includes Barclay's Center and the Atlantic Avenue Terminal.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">While others have looked at weekly regional tweets, this study is the first to stress test Twitter for daily city-level data for New York City. Extraction of personal testimonies of infection-related tweets suggests Twitter's strength both qualitatively and quantitatively for ILI-ED prediction compared to alternative daily datasets mixed with awareness-based data such as GSQ. Additionally, granular Twitter data provide important spatiotemporal insights. A tweet vector-map may be useful for visualization of city-level spread when local gold standard data are otherwise unavailable.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Nagar</LastName><ForeName>Ruchit</ForeName><Initials>R</Initials><Identifier Source="ORCID">http://orcid.org/0000-0002-9461-8121</Identifier><AffiliationInfo><Affiliation>Children's Hospital Informatics Program, Boston Children's Hospital, Boston, MA, United States. ruchit.nagar@yale.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yuan</LastName><ForeName>Qingyu</ForeName><Initials>Q</Initials><Identifier Source="ORCID">http://orcid.org/0000-0002-7542-8402</Identifier></Author><Author ValidYN="Y"><LastName>Freifeld</LastName><ForeName>Clark C</ForeName><Initials>CC</Initials><Identifier Source="ORCID">http://orcid.org/0000-0002-6281-219X</Identifier></Author><Author ValidYN="Y"><LastName>Santillana</LastName><ForeName>Mauricio</ForeName><Initials>M</Initials><Identifier Source="ORCID">http://orcid.org/0000-0002-4206-418X</Identifier></Author><Author ValidYN="Y"><LastName>Nojima</LastName><ForeName>Aaron</ForeName><Initials>A</Initials><Identifier Source="ORCID">http://orcid.org/0000-0001-9787-4465</Identifier></Author><Author ValidYN="Y"><LastName>Chunara</LastName><ForeName>Rumi</ForeName><Initials>R</Initials><Identifier Source="ORCID">http://orcid.org/0000-0002-5346-7259</Identifier></Author><Author ValidYN="Y"><LastName>Brownstein</LastName><ForeName>John S</ForeName><Initials>JS</Initials><Identifier Source="ORCID">http://orcid.org/0000-0001-8568-5317</Identifier></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>R01 LM010812</GrantID><Acronym>LM</Acronym><Agency>NLM NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>5 R01 LM010812-05</GrantID><Acronym>LM</Acronym><Agency>NLM NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>10</Month><Day>20</Day></ArticleDate></Article><MedlineJournalInfo><Country>Canada</Country><MedlineTA>J Med Internet Res</MedlineTA><NlmUniqueID>100959882</NlmUniqueID><ISSNLinking>1438-8871</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D057218" MajorTopicYN="N">Blogging</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D004196" MajorTopicYN="Y">Disease Outbreaks</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D062305" MajorTopicYN="N">Geographic Mapping</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007251" MajorTopicYN="N">Influenza, Human</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D020407" MajorTopicYN="Y">Internet</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009519" MajorTopicYN="N">New York City</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011446" MajorTopicYN="N">Prospective Studies</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012189" MajorTopicYN="N">Retrospective Studies</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D062211" MajorTopicYN="N">Spatio-Temporal Analysis</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Google Flu Trends</Keyword><Keyword MajorTopicYN="N">New York City</Keyword><Keyword MajorTopicYN="N">Twitter</Keyword><Keyword MajorTopicYN="N">influenza</Keyword><Keyword MajorTopicYN="N">infodemiology</Keyword><Keyword MajorTopicYN="N">mHealth</Keyword><Keyword MajorTopicYN="N">medical informatics</Keyword><Keyword MajorTopicYN="N">social media, natural language processing</Keyword><Keyword MajorTopicYN="N">spatiotemporal</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2014</Year><Month>03</Month><Day>22</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2014</Year><Month>08</Month><Day>30</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2014</Year><Month>08</Month><Day>08</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>10</Month><Day>22</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2014</Year><Month>10</Month><Day>22</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2015</Year><Month>6</Month><Day>19</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">25331122</ArticleId><ArticleId IdType="pii">v16i10e236</ArticleId><ArticleId IdType="doi">10.2196/jmir.3416</ArticleId><ArticleId IdType="pmc">PMC4259880</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>PLoS One. 2010;5(11):e14118</Citation><ArticleIdList><ArticleId IdType="pubmed">21124761</ArticleId></ArticleIdList></Reference><Reference><Citation>Am J Prev Med. 2011 May;40(5 Suppl 2):S154-8</Citation><ArticleIdList><ArticleId IdType="pubmed">21521589</ArticleId></ArticleIdList></Reference><Reference><Citation>PLoS One. 2011;6(5):e19467</Citation><ArticleIdList><ArticleId IdType="pubmed">21573238</ArticleId></ArticleIdList></Reference><Reference><Citation>Am J Trop Med Hyg. 2012 Jan;86(1):39-45</Citation><ArticleIdList><ArticleId IdType="pubmed">22232449</ArticleId></ArticleIdList></Reference><Reference><Citation>J Med Internet Res. 2012;14(6):e156</Citation><ArticleIdList><ArticleId IdType="pubmed">23154246</ArticleId></ArticleIdList></Reference><Reference><Citation>Nature. 2013 Feb 14;494(7436):155-6</Citation><ArticleIdList><ArticleId IdType="pubmed">23407515</ArticleId></ArticleIdList></Reference><Reference><Citation>PLoS One. 2013;8(7):e69305</Citation><ArticleIdList><ArticleId IdType="pubmed">23894447</ArticleId></ArticleIdList></Reference><Reference><Citation>J Med Internet Res. 2013;15(7):e147</Citation><ArticleIdList><ArticleId IdType="pubmed">23896182</ArticleId></ArticleIdList></Reference><Reference><Citation>PLoS Comput Biol. 2013;9(10):e1003256</Citation><ArticleIdList><ArticleId IdType="pubmed">24146603</ArticleId></ArticleIdList></Reference><Reference><Citation>J Med Internet Res. 2013;15(10):e237</Citation><ArticleIdList><ArticleId IdType="pubmed">24158773</ArticleId></ArticleIdList></Reference><Reference><Citation>PLoS One. 2013;8(12):e83672</Citation><ArticleIdList><ArticleId IdType="pubmed">24349542</ArticleId></ArticleIdList></Reference><Reference><Citation>Science. 2014 Mar 14;343(6176):1203-5</Citation><ArticleIdList><ArticleId IdType="pubmed">24626916</ArticleId></ArticleIdList></Reference><Reference><Citation>J Med Internet Res. 2014;16(1):e20</Citation><ArticleIdList><ArticleId IdType="pubmed">24440770</ArticleId></ArticleIdList></Reference><Reference><Citation>PLoS Med. 2005 Mar;2(3):e59</Citation><ArticleIdList><ArticleId IdType="pubmed">15719066</ArticleId></ArticleIdList></Reference><Reference><Citation>AMIA Annu Symp Proc. 2006;:244-8</Citation><ArticleIdList><ArticleId IdType="pubmed">17238340</ArticleId></ArticleIdList></Reference><Reference><Citation>Stat Med. 2007 Mar 30;26(7):1594-607</Citation><ArticleIdList><ArticleId IdType="pubmed">16795130</ArticleId></ArticleIdList></Reference><Reference><Citation>Nature. 2009 Feb 19;457(7232):1012-4</Citation><ArticleIdList><ArticleId IdType="pubmed">19020500</ArticleId></ArticleIdList></Reference><Reference><Citation>J Med Internet Res. 2009;11(1):e11</Citation><ArticleIdList><ArticleId IdType="pubmed">19329408</ArticleId></ArticleIdList></Reference><Reference><Citation>Clin Infect Dis. 2009 Nov 15;49(10):1557-64</Citation><ArticleIdList><ArticleId IdType="pubmed">19845471</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Sante/explor/StressCovidV1/Data/PubMed/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000449 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/PubMed/Corpus/biblio.hfd -nk 000449 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Sante
|area= StressCovidV1
|flux= PubMed
|étape= Corpus
|type= RBID
|clé= pubmed:25331122
|texte= A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives.
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/PubMed/Corpus/RBID.i -Sk "pubmed:25331122" \
| HfdSelect -Kh $EXPLOR_AREA/Data/PubMed/Corpus/biblio.hfd \
| NlmPubMed2Wicri -a StressCovidV1
| This area was generated with Dilib version V0.6.33. |  |