J. Med. Internet Res. - A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives.

Tópicos

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Resumo

CKGROUND: 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.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.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.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.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.

Resumo Limpo

ckground twitter shown use predict influenza case week basi multipl countri differ geograph scale recent broniatowski colleagu suggest twitter relev citylevel new york citi look dive deeper case new york citi analyz daili twitter data tempor spatiotempor perspect also manual code tweet look gain qualit insight can help direct futur autom searchesobject intent studi first valid tempor predict strength daili twitter data influenzalik ill emerg depart ili visit new york citi influenza season avail establish dataset googl search queri gsq second examin spatial distribut spread geocod tweet proxi potenti casesmethod twitter stream api tweet collect new york citi region match keyword flu influenza gripe high fever tweet categor accord scheme develop lamb et al new fourth categori ad evalu guess probabl subject sick account strength confid valid statement tempor correl made tweet daili ili visit daili gsq volum best model use linear regress forecast ili visit weight retrospect poisson model satscan softwar n vector map use spatiotempor analysisresult infectionrel tweet r correl better gsq time seri r keyword lower mean averag percent error vs ili visit predict januari volatil month flu satscan identifi primari outbreak cluster highprob infect tweet relat risk ratio compar mediumprob infect tweet p northern brooklyn radius includ barclay center atlant avenu terminalconclus other look week region tweet studi first stress test twitter daili citylevel data new york citi extract person testimoni infectionrel tweet suggest twitter strength qualit quantit ili predict compar altern daili dataset mix awarenessbas data gsq addit granular twitter data provid import spatiotempor insight tweet vectormap may use visual citylevel spread local gold standard data otherwis unavail

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