Published on in Vol 16, No 10 (2014): October

A Case Study of the New York City 2012-2013 Influenza Season With Daily Geocoded Twitter Data From Temporal and Spatiotemporal Perspectives

A Case Study of the New York City 2012-2013 Influenza Season With Daily Geocoded Twitter Data From Temporal and Spatiotemporal Perspectives

A Case Study of the New York City 2012-2013 Influenza Season With Daily Geocoded Twitter Data From Temporal and Spatiotemporal Perspectives

Journals

  1. Tozzi A, Gesualdo F, D’Ambrosio A, Pandolfi E, Agricola E, Lopalco P. Can Digital Tools Be Used for Improving Immunization Programs?. Frontiers in Public Health 2016;4 View
  2. Chen S, Xu Q, Buchenberger J, Bagavathi A, Fair G, Shaikh S, Krishnan S. Dynamics of Health Agency Response and Public Engagement in Public Health Emergency: A Case Study of CDC Tweeting Patterns During the 2016 Zika Epidemic. JMIR Public Health and Surveillance 2018;4(4):e10827 View
  3. Sharpe J, Hopkins R, Cook R, Striley C. Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis. JMIR Public Health and Surveillance 2016;2(2):e161 View
  4. Zeraatkar K, Ahmadi M. Trends of infodemiology studies: a scoping review. Health Information & Libraries Journal 2018;35(2):91 View
  5. Simonsen L, Gog J, Olson D, Viboud C. Infectious Disease Surveillance in the Big Data Era: Towards Faster and Locally Relevant Systems. Journal of Infectious Diseases 2016;214(suppl 4):S380 View
  6. Murayama T, Shimizu N, Fujita S, Wakamiya S, Aramaki E, Wen T. Robust two-stage influenza prediction model considering regular and irregular trends. PLOS ONE 2020;15(5):e0233126 View
  7. Gunn J, Shah S. Big data and opportunities for injury surveillance. Injury Prevention 2016;22(Suppl 1):i3 View
  8. Samuel J, Ali G, Rahman M, Esawi E, Samuel Y. COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification. Information 2020;11(6):314 View
  9. McGough S, Brownstein J, Hawkins J, Santillana M, Althouse B. Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data. PLOS Neglected Tropical Diseases 2017;11(1):e0005295 View
  10. Zhou L, Zhang D, Yang C, Wang Y. Harnessing social media for health information management. Electronic Commerce Research and Applications 2018;27:139 View
  11. Șerban O, Thapen N, Maginnis B, Hankin C, Foot V. Real-time processing of social media with SENTINEL: A syndromic surveillance system incorporating deep learning for health classification. Information Processing & Management 2019;56(3):1166 View
  12. Gruebner O, Lowe S, Sykora M, Shankardass K, Subramanian S, Galea S, Olson D. A novel surveillance approach for disaster mental health. PLOS ONE 2017;12(7):e0181233 View
  13. Brown B, Smeeth L, van Staa T, Buchan I. Better care through better use of data in GP–patient partnerships. British Journal of General Practice 2017;67(655):54 View
  14. Agarwal V, Zhang L, Zhu J, Fang S, Cheng T, Hong C, Shah N. Impact of Predicting Health Care Utilization Via Web Search Behavior: A Data-Driven Analysis. Journal of Medical Internet Research 2016;18(9):e251 View
  15. Meng H, Kath S, Li D, Nguyen Q, Giraud-Carrier C. National substance use patterns on Twitter. PLOS ONE 2017;12(11):e0187691 View
  16. Relia K, Akbari M, Duncan D, Chunara R. Socio-spatial Self-organizing Maps. Proceedings of the ACM on Human-Computer Interaction 2018;2(CSCW):1 View
  17. Hartley D, Giannini C, Wilson S, Frieder O, Margolis P, Kotagal U, White D, Connelly B, Wheeler D, Tadesse D, Macaluso M, Nishiura H. Coughing, sneezing, and aching online: Twitter and the volume of influenza-like illness in a pediatric hospital. PLOS ONE 2017;12(7):e0182008 View
  18. Oldroyd R, Morris M, Birkin M. Identifying Methods for Monitoring Foodborne Illness: Review of Existing Public Health Surveillance Techniques. JMIR Public Health and Surveillance 2018;4(2):e57 View
  19. Kunkle S, Christie G, Yach D, El-Sayed A. The Importance of Computer Science for Public Health Training: An Opportunity and Call to Action. JMIR Public Health and Surveillance 2016;2(1):e10 View
  20. Yin Z, Fabbri D, Rosenbloom S, Malin B. A Scalable Framework to Detect Personal Health Mentions on Twitter. Journal of Medical Internet Research 2015;17(6):e138 View
  21. Househ M. Communicating Ebola through social media and electronic news media outlets: A cross-sectional study. Health Informatics Journal 2016;22(3):470 View
  22. Wakamiya S, Kawai Y, Aramaki E. Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study. JMIR Public Health and Surveillance 2018;4(3):e65 View
  23. Kunneman F, Lambooij M, Wong A, Bosch A, Mollema L. Monitoring stance towards vaccination in twitter messages. BMC Medical Informatics and Decision Making 2020;20(1) View
  24. Paul M, Dredze M. Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services 2017;9(5):1 View
  25. Lu F, Hou S, Baltrusaitis K, Shah M, Leskovec J, Sosic R, Hawkins J, Brownstein J, Conidi G, Gunn J, Gray J, Zink A, Santillana M. Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis. JMIR Public Health and Surveillance 2018;4(1):e4 View
  26. Rabarison K, Croston M, Englar N, Bish C, Flynn S, Johnson C. Measuring Audience Engagement for Public Health Twitter Chats: Insights From #LiveFitNOLA. JMIR Public Health and Surveillance 2017;3(2):e34 View
  27. Santillana M, Nguyen A, Louie T, Zink A, Gray J, Sung I, Brownstein J. Cloud-based Electronic Health Records for Real-time, Region-specific Influenza Surveillance. Scientific Reports 2016;6(1) View
  28. Geofrey A, Kipanyula M, Fue K, Sanga C. Understanding Strategies for Implementing Integrated Information Systems for Rabies Surveillance. International Journal of User-Driven Healthcare 2017;7(1):13 View
  29. Mollema L, Harmsen I, Broekhuizen E, Clijnk R, De Melker H, Paulussen T, Kok G, Ruiter R, Das E. Disease Detection or Public Opinion Reflection? Content Analysis of Tweets, Other Social Media, and Online Newspapers During the Measles Outbreak in the Netherlands in 2013. Journal of Medical Internet Research 2015;17(5):e128 View
  30. Nguyen Q, Meng H, Li D, Kath S, McCullough M, Paul D, Kanokvimankul P, Nguyen T, Li F. Social media indicators of the food environment and state health outcomes. Public Health 2017;148:120 View
  31. Thapen N, Simmie D, Hankin C, Gillard J, Danforth C. DEFENDER: Detecting and Forecasting Epidemics Using Novel Data-Analytics for Enhanced Response. PLOS ONE 2016;11(5):e0155417 View
  32. Santillana M, Nguyen A, Dredze M, Paul M, Nsoesie E, Brownstein J, Salathé M. Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance. PLOS Computational Biology 2015;11(10):e1004513 View
  33. Shin S, Kim T, Seo D, Sohn C, Kim S, Ryoo S, Lee Y, Lee J, Kim W, Lim K, Olson D. Correlation between National Influenza Surveillance Data and Search Queries from Mobile Devices and Desktops in South Korea. PLOS ONE 2016;11(7):e0158539 View
  34. Yang J, Tsou M, Jung C, Allen C, Spitzberg B, Gawron J, Han S. Social media analytics and research testbed (SMART): Exploring spatiotemporal patterns of human dynamics with geo-targeted social media messages. Big Data & Society 2016;3(1):205395171665291 View
  35. Weitzman E, Magane K, Chen P, Amiri H, Naimi T, Wisk L. Online Searching and Social Media to Detect Alcohol Use Risk at Population Scale. American Journal of Preventive Medicine 2020;58(1):79 View
  36. Chan M, Jamieson K, Albarracin D. Prospective associations of regional social media messages with attitudes and actual vaccination: A big data and survey study of the influenza vaccine in the United States. Vaccine 2020;38(40):6236 View
  37. Burke-Garcia A, Stanton C. A tale of two tools: Reliability and feasibility of social media measurement tools examining e-cigarette twitter mentions. Informatics in Medicine Unlocked 2017;8:8 View
  38. Lee M, Jung I. Modified spatial scan statistics using a restricted likelihood ratio for ordinal outcome data. Computational Statistics & Data Analysis 2019;133:28 View
  39. Stock K. Mining location from social media: A systematic review. Computers, Environment and Urban Systems 2018;71:209 View
  40. Ozdikis O, Oğuztüzün H, Karagoz P. A survey on location estimation techniques for events detected in Twitter. Knowledge and Information Systems 2017;52(2):291 View
  41. Adnan M, Gao X, Bai X, Newbern E, Sherwood J, Jones N, Baker M, Wood T, Gao W. Potential Early Identification of a Large Campylobacter Outbreak Using Alternative Surveillance Data Sources: Autoregressive Modelling and Spatiotemporal Clustering. JMIR Public Health and Surveillance 2020;6(3):e18281 View
  42. Hampson G, Towse A, Dreitlein W, Henshall C, Pearson S. Real-world evidence for coverage decisions: opportunities and challenges. Journal of Comparative Effectiveness Research 2018;7(12):1133 View
  43. Puspitasari I, Firdauzy A. Characterizing Consumer Behavior in Leveraging Social Media for E-Patient and Health-Related Activities. International Journal of Environmental Research and Public Health 2019;16(18):3348 View
  44. Cumbraos-Sánchez M, Hermoso R, Iñiguez D, Paño-Pardo J, Allende Bandres M, Latorre Martinez M. Qualitative and quantitative evaluation of the use of Twitter as a tool of antimicrobial stewardship. International Journal of Medical Informatics 2019;131:103955 View
  45. Barros J, Duggan J, Rebholz-Schuhmann D. Disease mentions in airport and hospital geolocations expose dominance of news events for disease concerns. Journal of Biomedical Semantics 2018;9(1) View
  46. Hu Y. Geo‐text data and data‐driven geospatial semantics. Geography Compass 2018;12(11) View
  47. Brownstein J, Chu S, Marathe A, Marathe M, Nguyen A, Paolotti D, Perra N, Perrotta D, Santillana M, Swarup S, Tizzoni M, Vespignani A, Vullikanti A, Wilson M, Zhang Q. Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches. JMIR Public Health and Surveillance 2017;3(4):e83 View
  48. Musa I, Park H, Munkhdalai L, Ryu K. Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization. Sustainability 2018;10(10):3414 View
  49. Bowen D, Wang J, Holland K, Bartholow B, Sumner S. Conversational topics of social media messages associated with state-level mental distress rates. Journal of Mental Health 2020;29(2):234 View
  50. Tulloch J, Vivancos R, Christley R, Radford A, Warner J. Mapping tweets to a known disease epidemiology; a case study of Lyme disease in the United Kingdom and Republic of Ireland. Journal of Biomedical Informatics 2019;100:100060 View
  51. Katsuki T, Mackey T, Cuomo R. Establishing a Link Between Prescription Drug Abuse and Illicit Online Pharmacies: Analysis of Twitter Data. Journal of Medical Internet Research 2015;17(12):e280 View
  52. Gruebner O, Sykora M, Lowe S, Shankardass K, Trinquart L, Jackson T, Subramanian S, Galea S. Mental health surveillance after the terrorist attacks in Paris. The Lancet 2016;387(10034):2195 View
  53. Hu H, Wang H, Wang F, Langley D, Avram A, Liu M. Prediction of influenza-like illness based on the improved artificial tree algorithm and artificial neural network. Scientific Reports 2018;8(1) View
  54. Masri S, Jia J, Li C, Zhou G, Lee M, Yan G, Wu J. Use of Twitter data to improve Zika virus surveillance in the United States during the 2016 epidemic. BMC Public Health 2019;19(1) View
  55. Domnich A, Panatto D, Signori A, Lai P, Gasparini R, Amicizia D, Olson D. Age-Related Differences in the Accuracy of Web Query-Based Predictions of Influenza-Like Illness. PLOS ONE 2015;10(5):e0127754 View
  56. Little R, West B, Boonstra P, Hu J. Measures of the Degree of Departure from Ignorable Sample Selection. Journal of Survey Statistics and Methodology 2020;8(5):932 View
  57. Barros J, Duggan J, Rebholz-Schuhmann D. The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review. Journal of Medical Internet Research 2020;22(3):e13680 View
  58. Rahman M, Ali G, Li X, Samuel J, Paul K, Chong P, Yakubov M. Socioeconomic factors analysis for COVID-19 US reopening sentiment with Twitter and census data. Heliyon 2021;7(2):e06200 View
  59. Park S, Han S, Kim J, Molaie M, Vu H, Singh K, Han J, Lee W, Cha M. COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication. Journal of Medical Internet Research 2021;23(3):e23272 View
  60. Jia Q, Guo Y, Wang G, Barnes S. Big Data Analytics in the Fight against Major Public Health Incidents (Including COVID-19): A Conceptual Framework. International Journal of Environmental Research and Public Health 2020;17(17):6161 View
  61. Ramirez A, Aguilar R, Merck A, Despres C, Sukumaran P, Cantu-Pawlik S, Chalela P. Use of #SaludTues Tweetchats for the Dissemination of Culturally Relevant Information on Latino Health Equity: Exploratory Case Study. JMIR Public Health and Surveillance 2021;7(3):e21266 View
  62. Kogan N, Clemente L, Liautaud P, Kaashoek J, Link N, Nguyen A, Lu F, Huybers P, Resch B, Havas C, Petutschnig A, Davis J, Chinazzi M, Mustafa B, Hanage W, Vespignani A, Santillana M. An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time. Science Advances 2021;7(10):eabd6989 View
  63. Kwok S, Vadde S, Wang G. Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis. Journal of Medical Internet Research 2021;23(5):e26953 View
  64. Ramya B, Shetty S, Amaresh A, Rakshitha R. Smart Simon Bot with Public Sentiment Analysis for Novel Covid-19 Tweets Stratification. SN Computer Science 2021;2(3) View
  65. Wang R, Wu H, Wu Y, Zheng J, Li Y. Improving influenza surveillance based on multi-granularity deep spatiotemporal neural network. Computers in Biology and Medicine 2021;134:104482 View
  66. Kabir M, Madria S. EMOCOV: Machine learning for emotion detection, analysis and visualization using COVID-19 tweets. Online Social Networks and Media 2021;23:100135 View
  67. Poirier C, Hswen Y, Bouzillé G, Cuggia M, Lavenu A, Brownstein J, Brewer T, Santillana M, Chong K. Influenza forecasting for French regions combining EHR, web and climatic data sources with a machine learning ensemble approach. PLOS ONE 2021;16(5):e0250890 View
  68. Zhang Y, Bambrick H, Mengersen K, Tong S, Hu W. Using internet-based query and climate data to predict climate-sensitive infectious disease risks: a systematic review of epidemiological evidence. International Journal of Biometeorology 2021 View
  69. Thorpe Huerta D, Hawkins J, Brownstein J, Hswen Y. Exploring discussions of health and risk and public sentiment in Massachusetts during COVID-19 pandemic mandate implementation: A Twitter analysis. SSM - Population Health 2021;15:100851 View
  70. Miliou I, Xiong X, Rinzivillo S, Zhang Q, Rossetti G, Giannotti F, Pedreschi D, Vespignani A, Viboud C. Predicting seasonal influenza using supermarket retail records. PLOS Computational Biology 2021;17(7):e1009087 View

Books/Policy Documents

  1. Tonkin E. Working with Text. View
  2. Samaras L, García-Barriocanal E, Sicilia M. Innovation in Health Informatics. View
  3. Geofrey A, Kipanyula M, Fue K, Sanga C. Veterinary Science. View
  4. Abdelguiom G, Iahad N. Innovative Systems for Intelligent Health Informatics. View
  5. Hu Y, Adams B. Handbook of Big Geospatial Data. View