National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic

Broniatowski, David A.; Paul, Michael J.; Dredze, Mark

doi:10.1371/journal.pone.0083672

Cited by 411 publications

(340 citation statements)

References 11 publications

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“…Many studies have assessed the use of internet-user activity data because they can produce real-time indicators [10][11][12][13][14][15][16][17][18]. Several data sources have been explored, including Wikipedia, Twitter or Google search-engine data.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Leveraging hospital big data to monitor flu epidemics

Bouzillé

Poirier

Campillo‐Gimenez

et al. 2018

Computer Methods and Programs in Biomedicine

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Background and ObjectiveInfluenza epidemics are a major public health concern and require a costly and time-consuming surveillance system at different geographical scales. The main challenge is being able to predict epidemics. Besides traditional surveillance systems, such as the French Sentinel network, several studies proposed prediction models based on internet-user activity. Here, we assessed the potential of hospital big data to monitor influenza epidemics. MethodsWe used the clinical data warehouse of the Academic Hospital of Rennes (France) and then built different queries to retrieve relevant information from electronic health records to gather weekly influenza-like illness activity. ACCEPTED MANUSCRIPT A C C E P T E D M A N U S C R I P T ResultsWe found that the query most highly correlated with Sentinel network estimates was based on emergency reports concerning discharged patients with a final diagnosis of influenza (Pearson's correlation coefficient (PCC) of 0.931). The other tested queries were based on structured data (ICD-10 codes of influenza in Diagnosis-related Groups, and influenza PCR tests) and performed best (PCC of 0.981 and 0.953, respectively) during the flu season 2014-15. This suggests that both ICD-10 codes and PCR results are associated with severe epidemics. Finally, our approach allowed us to obtain additional patients' characteristics, such as the sex ratio or age groups, comparable with those from the Sentinel network. ConclusionsHospital big data seem to have a great potential for monitoring influenza epidemics in near realtime. Such a method could constitute a complementary tool to standard surveillance systems by providing additional characteristics on the concerned population or by providing information earlier. This system could also be easily extended to other diseases with possible activity changes.Additional work is needed to assess the real efficacy of predictive models based on hospital big data to predict flu epidemics.

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Section: Accepted Manuscriptmentioning

confidence: 99%

Leveraging hospital big data to monitor flu epidemics

Bouzillé

Poirier

Campillo‐Gimenez

et al. 2018

Computer Methods and Programs in Biomedicine

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“…These activity traces are embedded in search queries [36,, social media messages [77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92], and web server access logs [34,72,93]. At a basic level, traces are extracted by counting query strings, words or phrases, or web page URLs that are related to the metric of interest, forming a time series of occurrences for each item.…”

Section: Author Summarymentioning

confidence: 99%

“…When appropriately trained, these methods can be quite accurate; for example, many of the cited models can produce near real-time estimates of case counts with correlations upwards of r = 0.95. The collection of disease surveillance work cited above has estimated incidence for a wide variety of infectious and noninfectious conditions: avian influenza [52], cancer [55], chicken pox [67], cholera [81], dengue [50,53,84], dysentery [76], gastroenteritis [56,61,67], gonorrhea [64], hand foot and mouth disease (HFMD) [72], HIV/AIDS [75,76], influenza [34,36,54,57,59,62,63,65,67,68,71,74,[77][78][79][80]82,83,[85][86][87][88][89][90][91][92][93], kidney stones [51], listeriosis [70], malaria [66], methicillin-resistant Staphylococcus aureus (MRSA) [58]<...>…”

Section: Author Summarymentioning

confidence: 99%

Global Disease Monitoring and Forecasting with Wikipedia

Generous

Fairchild²,

Deshpande³

et al. 2016

OJPHI

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Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with r 2 up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without retraining is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

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“…This geographic information is often referred to as a geotag, and is a form of volunteered geographic information (VGI) (Goodchild, 2007). Current estimates suggest that 1-3% of tweets include a geotag (Morstatter et al, 2013;Broniatowski et al, 2013;Hecht & Stephens, 2014).…”

Section: Twittermentioning

confidence: 99%

“…Aside from the text of the tweet (TweetText), the dataset also included additional metadata about each tweet: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 • The location from which the tweet originated was provided by the geotag fields in the dataset; longitude and latitude. Every tweet in the dataset included this geocoded information, which represents approximately 1-3% of all tweets (Morstatter et al, 2013;Broniatowski et al, 2013;Hecht & Stephens, 2014).…”

Section: Twitter Datamentioning

confidence: 99%