Published on in Vol 4, No 1 (2018): Jan-Mar

Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis

Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis

Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis

Journals

  1. Viboud C, Santillana M. Fitbit-informed influenza forecasts. The Lancet Digital Health 2020;2(2):e54 View
  2. Koehlmoos T, Janvrin M, Korona-Bailey J, Madsen C, Sturdivant R. COVID-19 Self-Reported Symptom Tracking Programs in the United States: Framework Synthesis. Journal of Medical Internet Research 2020;22(10):e23297 View
  3. Tana J, Kettunen J, Eirola E, Paakkonen H. Diurnal Variations of Depression-Related Health Information Seeking: Case Study in Finland Using Google Trends Data. JMIR Mental Health 2018;5(2):e43 View
  4. Mavragani A, Ochoa G. Infoveillance of infectious diseases in USA: STDs, tuberculosis, and hepatitis. Journal of Big Data 2018;5(1) View
  5. 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
  6. Syamsuddin M, Fakhruddin M, Sahetapy-Engel J, Soewono E. Causality Analysis of Google Trends and Dengue Incidence in Bandung, Indonesia With Linkage of Digital Data Modeling: Longitudinal Observational Study. Journal of Medical Internet Research 2020;22(7):e17633 View
  7. Cheng H, Wu Y, Lin M, Liu Y, Tsai Y, Wu J, Pan K, Ke C, Chen C, Liu D, Lin I, Chuang J. Applying Machine Learning Models with An Ensemble Approach for Accurate Real-Time Influenza Forecasting in Taiwan: Development and Validation Study. Journal of Medical Internet Research 2020;22(8):e15394 View
  8. Hegde A, Masthi R, Krishnappa D. Hyperlocal Postcode Based Crowdsourced Surveillance Systems in the COVID-19 Pandemic Response. Frontiers in Public Health 2020;8 View
  9. Leal Neto O, Cruz O, Albuquerque J, Nacarato de Sousa M, Smolinski M, Pessoa Cesse E, Libel M, Vieira de Souza W. Participatory Surveillance Based on Crowdsourcing During the Rio 2016 Olympic Games Using the Guardians of Health Platform: Descriptive Study. JMIR Public Health and Surveillance 2020;6(2):e16119 View
  10. Mavragani A, Ochoa G, Tsagarakis K. Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review. Journal of Medical Internet Research 2018;20(11):e270 View
  11. Lu F, Hattab M, Clemente C, Biggerstaff M, Santillana M. Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches. Nature Communications 2019;10(1) View
  12. Lutz C, Huynh M, Schroeder M, Anyatonwu S, Dahlgren F, Danyluk G, Fernandez D, Greene S, Kipshidze N, Liu L, Mgbere O, McHugh L, Myers J, Siniscalchi A, Sullivan A, West N, Johansson M, Biggerstaff M. Applying infectious disease forecasting to public health: a path forward using influenza forecasting examples. BMC Public Health 2019;19(1) View
  13. Baltrusaitis K, Vespignani A, Rosenfeld R, Gray J, Raymond D, Santillana M. Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation. JMIR Public Health and Surveillance 2019;5(4):e13403 View
  14. Mavragani A, Ochoa G. Google Trends in Infodemiology and Infoveillance: Methodology Framework. JMIR Public Health and Surveillance 2019;5(2):e13439 View
  15. Talaei-Khoei A, Wilson J, Kazemi S. Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment. JMIR Public Health and Surveillance 2019;5(1):e11357 View
  16. Darwish A, Rahhal Y, Jafar A. A comparative study on predicting influenza outbreaks using different feature spaces: application of influenza-like illness data from Early Warning Alert and Response System in Syria. BMC Research Notes 2020;13(1) View
  17. Roth J, Battegay M, Juchler F, Vogt J, Widmer A. Introduction to Machine Learning in Digital Healthcare Epidemiology. Infection Control & Hospital Epidemiology 2018;39(12):1457 View
  18. Jarynowski A, Wójta-Kempa M, BElik V. Perception of Emergent Epidemic of COVID-2019 / SARS CoV-2 on the Polish Internet. SSRN Electronic Journal 2020 View
  19. 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
  20. Clemente L, Lu F, Santillana M. Improved Real-Time Influenza Surveillance: Using Internet Search Data in Eight Latin American Countries. JMIR Public Health and Surveillance 2019;5(2):e12214 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. Tideman S, Santillana M, Bickel J, Reis B. Internet search query data improve forecasts of daily emergency department volume. Journal of the American Medical Informatics Association 2019;26(12):1574 View
  23. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  24. Rangarajan P, Mody S, Marathe M, Priedhorsky R. Forecasting dengue and influenza incidences using a sparse representation of Google trends, electronic health records, and time series data. PLOS Computational Biology 2019;15(11):e1007518 View
  25. 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
  26. Su K, Xu L, Li G, Ruan X, Li X, Deng P, Li X, Li Q, Chen X, Xiong Y, Lu S, Qi L, Shen C, Tang W, Rong R, Hong B, Ning Y, Long D, Xu J, Shi X, Yang Z, Zhang Q, Zhuang Z, Zhang L, Xiao J, Li Y. Forecasting influenza activity using self-adaptive AI model and multi-source data in Chongqing, China. EBioMedicine 2019;47:284 View
  27. Tseng V, Jia-Ching Ying J, Wong S, Cook D, Liu J. Computational Intelligence Techniques for Combating COVID-19: A Survey. IEEE Computational Intelligence Magazine 2020;15(4):10 View
  28. Masthi R, Jahan A, Bharathi D, Abhilash P, Kaniyarakkal V, TV S, Gowda G, TS R, Goud R, Rao S, Hegde A. Postcode based participatory disease surveillance systems : a comparison with traditional risk-based surveillance and its application in the COVID-19 pandemic (Preprint). JMIR Public Health and Surveillance 2020 View
  29. Kardeş S. Public interest in spa therapy during the COVID-19 pandemic: analysis of Google Trends data among Turkey. International Journal of Biometeorology 2021;65(6):945 View
  30. Vigfusson Y, Karlsson T, Onken D, Song C, Einarsson A, Kishore N, Mitchell R, Brooks-Pollock E, Sigmundsdottir G, Danon . Cell-phone traces reveal infection-associated behavioral change. Proceedings of the National Academy of Sciences 2021;118(6):e2005241118 View
  31. Kardeş S, Kuzu A, Raiker R, Pakhchanian H, Karagülle M. Public interest in rheumatic diseases and rheumatologist in the United States during the COVID-19 pandemic: evidence from Google Trends. Rheumatology International 2021;41(2):329 View
  32. Runkle J, Sugg M, Graham G, Hodge B, March T, Mullendore J, Tove F, Salyers M, Valeika S, Vaughan E. Participatory COVID-19 Surveillance Tool in Rural Appalachia. Public Health Reports 2021;136(3):327 View
  33. Nsoesie E, Oladeji O, Abah A, Ndeffo-Mbah M. Forecasting influenza-like illness trends in Cameroon using Google Search Data. Scientific Reports 2021;11(1) View
  34. 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
  35. Agarwal A, Uniyal D, Toshniwal D, Deb D. Dense Vector Embedding Based Approach to Identify Prominent Disseminators From Twitter Data Amid COVID-19 Outbreak. IEEE Transactions on Emerging Topics in Computational Intelligence 2021;5(3):308 View
  36. 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
  37. Turtle J, Riley P, Ben-Nun M, Riley S, Perkins A. Accurate influenza forecasts using type-specific incidence data for small geographic units. PLOS Computational Biology 2021;17(7):e1009230 View
  38. Kiang M, Chen J, Krieger N, Buckee C, Alexander M, Baker J, Buckner R, Coombs G, Rich-Edwards J, Carlson K, Onnela J. Sociodemographic characteristics of missing data in digital phenotyping. Scientific Reports 2021;11(1) View
  39. Oto O, Kardeş S, Guller N, Safak S, Dirim A, Başhan Y, Demir E, Artan A, Yazıcı H, Turkmen A. Impact of the COVID-19 pandemic on interest in renal diseases. Environmental Science and Pollution Research 2021 View

Books/Policy Documents

  1. Simsek M, Obinikpo A, Kantarci B. Connected Health in Smart Cities. View
  2. Samaras L, García-Barriocanal E, Sicilia M. Innovation in Health Informatics. View