This paper is in the following e-collection/theme issue:

Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Infodemiology and Infoveillance (1349) Infectious Diseases (non-STD/STI) (1575) Influenza and Influenza-Like-Illness (ILI) Surveillance (217) Surveillance Systems (462)

Published on in Vol 2, No 2 (2016): Jul-Dec

Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis

Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis

Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis

Authors of this article:

J Danielle Sharpe1, 2 Author Orcid Image ;   Richard S Hopkins1 Author Orcid Image ;   Robert L Cook1 Author Orcid Image ;   Catherine W Striley1 Author Orcid Image
Article Authors Cited by (49) Tweetations (43) Metrics

Journals

  1. Borron S, Watts S, Tull J, Baeza S, Diebold S, Barrow A. Intentional Misuse and Abuse of Loperamide: A New Look at a Drug with “Low Abuse Potential”. The Journal of Emergency Medicine 2017;53(1):73 View
  2. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  3. Wakamiya S, Matsune S, Okubo K, Aramaki E. Causal Relationships Among Pollen Counts, Tweet Numbers, and Patient Numbers for Seasonal Allergic Rhinitis Surveillance: Retrospective Analysis. Journal of Medical Internet Research 2019;21(2):e10450 View
  4. 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
  5. 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
  6. Mougin F, Auber D, Bourqui R, Diallo G, Dutour I, Jouhet V, Thiessard F, Thiébaut R, Thébault P. Visualizing omics and clinical data: Which challenges for dealing with their variety?. Methods 2018;132:3 View
  7. Gao J, Zhang Y, Zhou T. Computational socioeconomics. Physics Reports 2019;817:1 View
  8. Edo-Osagie O, De La Iglesia B, Lake I, Edeghere O. A scoping review of the use of Twitter for public health research. Computers in Biology and Medicine 2020;122:103770 View
  9. Kim M, Yune S, Chang S, Jung Y, Sa S, Han H. The Fever Coach Mobile App for Participatory Influenza Surveillance in Children: Usability Study. JMIR mHealth and uHealth 2019;7(10):e14276 View
  10. 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
  11. Kolff C, Scott V, Stockwell M. The use of technology to promote vaccination: A social ecological model based framework. Human Vaccines & Immunotherapeutics 2018;14(7):1636 View
  12. Yang C, Chen R, Chou W, Lee Y, Lo Y. An Integrated Influenza Surveillance Framework Based on National Influenza-Like Illness Incidence and Multiple Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation. Journal of Medical Internet Research 2019;21(2):e12341 View
  13. Ferland R, Froda S. A statistical tool for comparing seasonal ILI surveillance data. Scientific Reports 2019;9(1) View
  14. Sarker A, Gonzalez-Hernandez G, Ruan Y, Perrone J. Machine Learning and Natural Language Processing for Geolocation-Centric Monitoring and Characterization of Opioid-Related Social Media Chatter. JAMA Network Open 2019;2(11):e1914672 View
  15. Moore J, Barnett I, Boland M, Chen Y, Demiris G, Gonzalez-Hernandez G, Herman D, Himes B, Hubbard R, Kim D, Morris J, Mowery D, Ritchie M, Shen L, Urbanowicz R, Holmes J. Ideas for how informaticians can get involved with COVID-19 research. BioData Mining 2020;13(1) View
  16. Dembek Z, Chekol T, Wu A. Best practice assessment of disease modelling for infectious disease outbreaks. Epidemiology and Infection 2018;146(10):1207 View
  17. Riley W, Oh A, Aklin W, Wolff-Hughes D. National Institutes of Health Support of Digital Health Behavior Research. Health Education & Behavior 2019;46(2_suppl):12S View
  18. Sarker A, Chandrashekar P, Magge A, Cai H, Klein A, Gonzalez G. Discovering Cohorts of Pregnant Women From Social Media for Safety Surveillance and Analysis. Journal of Medical Internet Research 2017;19(10):e361 View
  19. Martinho T. Researching Culture through Big Data: Computational Engineering and the Human and Social Sciences. Social Sciences 2018;7(12):264 View
  20. Safarishahrbijari A, Osgood N. Social Media Surveillance for Outbreak Projection via Transmission Models: Longitudinal Observational Study. JMIR Public Health and Surveillance 2019;5(2):e11615 View
  21. Soliman M, Lyubchich V, Gel Y. Complementing the power of deep learning with statistical model fusion: Probabilistic forecasting of influenza in Dallas County, Texas, USA. Epidemics 2019;28:100345 View
  22. Paul M, Dredze M. Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services 2017;9(5):1 View
  23. Zeraatkar K, Ahmadi M. Trends of infodemiology studies: a scoping review. Health Information & Libraries Journal 2018;35(2):91 View
  24. Smith D, Triberti S. Situating Wikipedia as a health information resource in various contexts: A scoping review. PLOS ONE 2020;15(2):e0228786 View
  25. Astill J, Dara R, Fraser E, Sharif S. Detecting and Predicting Emerging Disease in Poultry With the Implementation of New Technologies and Big Data: A Focus on Avian Influenza Virus. Frontiers in Veterinary Science 2018;5 View
  26. Kagashe I, Yan Z, Suheryani I. Enhancing Seasonal Influenza Surveillance: Topic Analysis of Widely Used Medicinal Drugs Using Twitter Data. Journal of Medical Internet Research 2017;19(9):e315 View
  27. 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
  28. O'Leary D, Storey V. A Google–Wikipedia–Twitter Model as a Leading Indicator of the Numbers of Coronavirus Deaths. Intelligent Systems in Accounting, Finance and Management 2020;27(3):151 View
  29. Scheerer C, Rüth M, Tizek L, Köberle M, Biedermann T, Zink A. Googling for Ticks and Borreliosis in Germany: Nationwide Google Search Analysis From 2015 to 2018. Journal of Medical Internet Research 2020;22(10):e18581 View
  30. Ding C, Liu X, Yang S. The value of infectious disease modeling and trend assessment: a public health perspective. Expert Review of Anti-infective Therapy 2021;19(9):1135 View
  31. Gabarron E, Rivera-Romero O, Miron-Shatz T, Grainger R, Denecke K. Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review. Yearbook of Medical Informatics 2021;30(01):200 View
  32. Murayama T, Shimizu N, Fujita S, Wakamiya S, Aramaki E, Wen T. Predicting regional influenza epidemics with uncertainty estimation using commuting data in Japan. PLOS ONE 2021;16(4):e0250417 View
  33. Choi H, Choi W, Han E. Suggestion of a simpler and faster influenza-like illness surveillance system using 2014–2018 claims data in Korea. Scientific Reports 2021;11(1) View
  34. Jabour A, Varghese J, Damad A, Ghailan K, Mehmood A. Examining the Correlation of Google Influenza Trend with Hospital Data: Retrospective Study. Journal of Multidisciplinary Healthcare 2021;Volume 14:3073 View
  35. Said Abasse K, Toulouse-Fournier A, Paquet C, Côté A, Smith P, Bergeron F, Archambault P. Collaborative writing applications in support of knowledge translation and management during pandemics: A scoping review. International Journal of Medical Informatics 2022;165:104814 View
  36. Liu J, Suzuki S. Real-Time Detection of Flu Season Onset: A Novel Approach to Flu Surveillance. International Journal of Environmental Research and Public Health 2022;19(6):3681 View
  37. Stockwell M, Reed C, Vargas C, Wang L, Alba L, Jia H, LaRussa P, Larson E, Saiman L. Five-Year Community Surveillance Study for Acute Respiratory Infections Using Text Messaging: Findings From the MoSAIC Study. Clinical Infectious Diseases 2022;75(6):987 View
  38. Pourkarim M, Nayebzadeh S, Alavian S, Hataminasab S. Digital Marketing: A Unique Multidisciplinary Approach towards the Elimination of Viral Hepatitis. Pathogens 2022;11(6):626 View
  39. Santangelo O, Gianfredi V, Provenzano S. Wikipedia searches and the epidemiology of infectious diseases: A systematic review. Data & Knowledge Engineering 2022;142:102093 View
  40. Samuel G, Lucassen A. The environmental sustainability of data-driven health research: A scoping review. DIGITAL HEALTH 2022;8:205520762211112 View
  41. Déguilhem A, Malaab J, Talmatkadi M, Renner S, Foulquié P, Fagherazzi G, Loussikian P, Marty T, Mebarki A, Texier N, Schuck S. Identifying Profiles and Symptoms of Patients With Long COVID in France: Data Mining Infodemiology Study Based on Social Media. JMIR Infodemiology 2022;2(2):e39849 View
  42. Ganser I, Thiébaut R, Buckeridge D. Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis. JMIR Public Health and Surveillance 2022;8(10):e36211 View
  43. Wang A, Dara R, Yousefinaghani S, Maier E, Sharif S. A Review of Social Media Data Utilization for the Prediction of Disease Outbreaks and Understanding Public Perception. Big Data and Cognitive Computing 2023;7(2):72 View
  44. Deiner M, Deiner N, Hristidis V, McLeod S, Doan T, Lietman T, Porco T. Use of Large Language Models to Assess the Likelihood of Epidemics From the Content of Tweets: Infodemiology Study. Journal of Medical Internet Research 2024;26:e49139 View
  45. Tricco A, Zarin W, Lillie E, Pham B, Straus S. Utility of social media and crowd-sourced data for pharmacovigilance: a scoping review protocol. BMJ Open 2017;7(1):e013474 View

Books/Policy Documents

  1. Samaras L, García-Barriocanal E, Sicilia M. Innovation in Health Informatics. View
  2. da Silva D, Goncalves G, dos Santos S, Pugliese V, Navas J, de Barros Santana R, Queiroz F, Dias L, da Cunha A, Tasinaffo P. Information Technology – New Generations. View
  3. Bax E, Donald J, Gerber M, Giaffo L, Sharma T, Thompson N, Williams K. Proceedings of the Future Technologies Conference (FTC) 2020, Volume 2. View