Published on in Vol 16, No 1 (2014): January

Guess Who’s Not Coming to Dinner? Evaluating Online Restaurant Reservations for Disease Surveillance

Guess Who’s Not Coming to Dinner? Evaluating Online Restaurant Reservations for Disease Surveillance

Guess Who’s Not Coming to Dinner? Evaluating Online Restaurant Reservations for Disease Surveillance

Journals

  1. Wang Z, Bauch C, Bhattacharyya S, d'Onofrio A, Manfredi P, Perc M, Perra N, Salathé M, Zhao D. Statistical physics of vaccination. Physics Reports 2016;664:1 View
  2. Paul M, Dredze M. Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services 2017;9(5):1 View
  3. Yan S, Chughtai A, Macintyre C. Utility and potential of rapid epidemic intelligence from internet-based sources. International Journal of Infectious Diseases 2017;63:77 View
  4. Santillana M, Zhang D, Althouse B, Ayers J. What Can Digital Disease Detection Learn from (an External Revision to) Google Flu Trends?. American Journal of Preventive Medicine 2014;47(3):341 View
  5. Cesare N, Dwivedi P, Nguyen Q, Nsoesie E. Use of social media, search queries, and demographic data to assess obesity prevalence in the United States. Palgrave Communications 2019;5(1) View
  6. Aiello A, Renson A, Zivich P. Social Media– and Internet-Based Disease Surveillance for Public Health. Annual Review of Public Health 2020;41(1):101 View
  7. Wang H, Chen D, Yu H, Chen Y. Forecasting the Incidence of Dementia and Dementia-Related Outpatient Visits With Google Trends: Evidence From Taiwan. Journal of Medical Internet Research 2015;17(11):e264 View
  8. Nsoesie E, Butler P, Ramakrishnan N, Mekaru S, Brownstein J. Monitoring Disease Trends using Hospital Traffic Data from High Resolution Satellite Imagery: A Feasibility Study. Scientific Reports 2015;5(1) View
  9. Ramakrishnan N, Lu C, Marathe M, Marathe A, Vullikanti A, Eubank S, Leman S, Roan M, Brownstein J, Summers K, Getoor L, Srinivasan A, Choudhury T, Gupta D, Mares D. Model-Based Forecasting of Significant Societal Events. IEEE Intelligent Systems 2015;30(5):86 View
  10. Zhang Y, Arab A, Cowling B, Stoto M. Characterizing Influenza surveillance systems performance: application of a Bayesian hierarchical statistical model to Hong Kong surveillance data. BMC Public Health 2014;14(1) View
  11. Mergel I. The Long Way From Government Open Data to Mobile Health Apps: Overcoming Institutional Barriers in the US Federal Government. JMIR mHealth and uHealth 2014;2(4):e58 View
  12. McIver D, Hawkins J, Chunara R, Chatterjee A, Bhandari A, Fitzgerald T, Jain S, Brownstein J. Characterizing Sleep Issues Using Twitter. Journal of Medical Internet Research 2015;17(6):e140 View
  13. Park H, Jung H, On J, Park S, Kang H. Digital Epidemiology: Use of Digital Data Collected for Non-epidemiological Purposes in Epidemiological Studies. Healthcare Informatics Research 2018;24(4):253 View
  14. Althouse B, Scarpino S, Meyers L, Ayers J, Bargsten M, Baumbach J, Brownstein J, Castro L, Clapham H, Cummings D, Del Valle S, Eubank S, Fairchild G, Finelli L, Generous N, George D, Harper D, Hébert-Dufresne L, Johansson M, Konty K, Lipsitch M, Milinovich G, Miller J, Nsoesie E, Olson D, Paul M, Polgreen P, Priedhorsky R, Read J, Rodríguez-Barraquer I, Smith D, Stefansen C, Swerdlow D, Thompson D, Vespignani A, Wesolowski A. Enhancing disease surveillance with novel data streams: challenges and opportunities. EPJ Data Science 2015;4(1) View
  15. Volkova S, Ayton E, Porterfield K, Corley C, Chowell G. Forecasting influenza-like illness dynamics for military populations using neural networks and social media. PLOS ONE 2017;12(12):e0188941 View
  16. Wenham C, Gray E, Keane C, Donati M, Paolotti D, Pebody R, Fragaszy E, McKendry R, Edmunds W. Self-Swabbing for Virological Confirmation of Influenza-Like Illness Among an Internet-Based Cohort in the UK During the 2014-2015 Flu Season: Pilot Study. Journal of Medical Internet Research 2018;20(3):e71 View
  17. Schwab-Reese L, Hovdestad W, Tonmyr L, Fluke J. The potential use of social media and other internet-related data and communications for child maltreatment surveillance and epidemiological research: Scoping review and recommendations. Child Abuse & Neglect 2018;85:187 View
  18. Lee E, Asher J, Goldlust S, Kraemer J, Lawson A, Bansal S. Mind the Scales: Harnessing Spatial Big Data for Infectious Disease Surveillance and Inference. Journal of Infectious Diseases 2016;214(suppl 4):S409 View
  19. Wirtz B, Müller W, Weyerer J. Digital Pandemic Response Systems: A Strategic Management Framework Against Covid-19. International Journal of Public Administration 2021;44(11-12):896 View
  20. Wojcik S, Bijral A, Johnston R, Lavista Ferres J, King G, Kennedy R, Vespignani A, Lazer D. Survey data and human computation for improved flu tracking. Nature Communications 2021;12(1) View
  21. 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
  22. Groseclose S, Buckeridge D. Public Health Surveillance Systems: Recent Advances in Their Use and Evaluation. Annual Review of Public Health 2017;38(1):57 View
  23. Hammond A, Kim J, Sadler H, Vandemaele K. Influenza surveillance systems using traditional and alternative sources of data: A scoping review. Influenza and Other Respiratory Viruses 2022;16(6):965 View
  24. Fulk A, Romero-Alvarez D, Abu-Saymeh Q, Saint Onge J, Peterson A, Agusto F, Aboelhadid S. Using Google Health Trends to investigate COVID-19 incidence in Africa. PLOS ONE 2022;17(6):e0269573 View
  25. Yuhan B, Yasuda M, Joshi R, Charous S, Hurtuk A. No-Show Rates in an Academic Otolaryngology Practice Before and During the COVID-19 Pandemic. Cureus 2024 View

Books/Policy Documents

  1. Perra N, Gonçalves B. Social Phenomena. View