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Citing this Article

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Published on 20.10.16 in Vol 2, No 2 (2016): Jul-Dec

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

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

J Danielle Sharpe, Richard S Hopkins, Robert L Cook, Catherine W Striley

JMIR Public Health Surveill 2016 (Oct 20); 2(2):e161 

According to Crossref, the following articles are citing this article (DOI 10.2196/publichealth.5901):

(note that this is only a small subset of citations)

  1. Borron SW, Watts SH, 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
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  2. . Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206
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  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
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  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
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  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
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  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
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  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
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  9. Kim M, Yune S, Chang S, Jung Y, Sa SO, Han HW. The Fever Coach Mobile App for Participatory Influenza Surveillance in Children: Usability Study. JMIR mHealth and uHealth 2019;7(10):e14276
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  10. Brownstein JS, Chu S, Marathe A, Marathe MV, Nguyen AT, Paolotti D, Perra N, Perrotta D, Santillana M, Swarup S, Tizzoni M, Vespignani A, Vullikanti AKS, Wilson ML, Zhang Q. Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches. JMIR Public Health and Surveillance 2017;3(4):e83
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  11. Kolff CA, Scott VP, Stockwell MS. The use of technology to promote vaccination: A social ecological model based framework. Human Vaccines & Immunotherapeutics 2018;14(7):1636
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  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
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  13. Ferland R, Froda S. A statistical tool for comparing seasonal ILI surveillance data. Scientific Reports 2019;9(1)
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  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
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  15. Moore JH, Barnett I, Boland MR, Chen Y, Demiris G, Gonzalez-Hernandez G, Herman DS, Himes BE, Hubbard RA, Kim D, Morris JS, Mowery DL, Ritchie MD, Shen L, Urbanowicz R, Holmes JH. Ideas for how informaticians can get involved with COVID-19 research. BioData Mining 2020;13(1)
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  16. Dembek ZF, Chekol T, Wu A. Best practice assessment of disease modelling for infectious disease outbreaks. Epidemiology and Infection 2018;146(10):1207
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  17. Riley WT, Oh A, Aklin WM, Wolff-Hughes DL. National Institutes of Health Support of Digital Health Behavior Research. Health Education & Behavior 2019;46(2_suppl):12S
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  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
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  19. . Researching Culture through Big Data: Computational Engineering and the Human and Social Sciences. Social Sciences 2018;7(12):264
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  20. Safarishahrbijari A, Osgood ND. Social Media Surveillance for Outbreak Projection via Transmission Models: Longitudinal Observational Study. JMIR Public Health and Surveillance 2019;5(2):e11615
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  21. Soliman M, Lyubchich V, Gel YR. Complementing the power of deep learning with statistical model fusion: Probabilistic forecasting of influenza in Dallas County, Texas, USA. Epidemics 2019;28:100345
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  22. Paul MJ, Dredze M. Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services 2017;9(5):1
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  24. Smith DA, Triberti S. Situating Wikipedia as a health information resource in various contexts: A scoping review. PLOS ONE 2020;15(2):e0228786
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  25. Astill J, Dara RA, Fraser EDG, 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
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  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
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  27. Barros JM, 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
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  28. O'Leary DE, Storey VC. 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
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  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
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  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
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  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
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  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
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  33. Choi H, Choi WS, 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)
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  34. Jabour AM, Varghese J, Damad AH, Ghailan KY, Mehmood AM. Examining the Correlation of Google Influenza Trend with Hospital Data: Retrospective Study. Journal of Multidisciplinary Healthcare 2021;Volume 14:3073
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  35. Said Abasse K, Toulouse-Fournier A, Paquet C, Côté A, Smith PY, 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
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  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
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  37. Stockwell MS, Reed C, Vargas CY, Wang L, Alba LR, Jia H, LaRussa P, Larson EL, 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
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  38. Pourkarim M, Nayebzadeh S, Alavian SM, Hataminasab SH. Digital Marketing: A Unique Multidisciplinary Approach towards the Elimination of Viral Hepatitis. Pathogens 2022;11(6):626
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  39. Santangelo OE, Gianfredi V, Provenzano S. Wikipedia searches and the epidemiology of infectious diseases: A systematic review. Data & Knowledge Engineering 2022;142:102093
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  40. Samuel G, Lucassen A. The environmental sustainability of data-driven health research: A scoping review. DIGITAL HEALTH 2022;8:205520762211112
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  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
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  42. Ganser I, Thiébaut R, Buckeridge DL. Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis. JMIR Public Health and Surveillance 2022;8(10):e36211
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  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
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  44. Deiner MS, Deiner NA, Hristidis V, McLeod SD, Doan T, Lietman TM, Porco TC. 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
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According to Crossref, the following books are citing this article (DOI 10.2196/publichealth.5901):

  1. Samaras L, García-Barriocanal E, Sicilia M. Innovation in Health Informatics. 2020. :39
    CrossRef
  2. da Silva DA, Goncalves GS, dos Santos SC, Pugliese VU, Navas J, de Barros Santana RM, Queiroz FS, Dias LAV, da Cunha AM, Tasinaffo PM. Information Technology – New Generations. 2018. Chapter 34:233
    CrossRef
  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. 2021. Chapter 31:489
    CrossRef