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 3, No 4 (2017): Oct-Dec

Syndromic Surveillance Models Using Web Data: The Case of Influenza in Greece and Italy Using Google Trends

Syndromic Surveillance Models Using Web Data: The Case of Influenza in Greece and Italy Using Google Trends

Syndromic Surveillance Models Using Web Data: The Case of Influenza in Greece and Italy Using Google Trends

Authors of this article:

Loukas Samaras1 Author Orcid Image ;   Elena García-Barriocanal1 Author Orcid Image ;   Miguel-Angel Sicilia1 Author Orcid Image
Article Authors Cited by (20) Tweetations (4) Metrics

Journals

  1. Chang Y, Chiang W, Wang W, Lin C, Hung L, Tsai Y, Chen Y. Assessing Epidemic Diseases and Public Opinion through Popular Search Behavior Using Non-English Language Google Trends (Preprint). JMIR Public Health and Surveillance 2018 View
  2. Mavragani A, Sampri A, Sypsa K, Tsagarakis K. Integrating Smart Health in the US Health Care System: Infodemiology Study of Asthma Monitoring in the Google Era. JMIR Public Health and Surveillance 2018;4(1):e24 View
  3. Lambrou G, Hatziagapiou K, Toumpaniaris P, Ioannidou P, Koutsouris D. Computational Modelling in Epidemiological Dispersion Using Diffusion and Epidemiological Equations. International Journal of Reliable and Quality E-Healthcare 2019;8(4):1 View
  4. Schneider P, van Gool C, Spreeuwenberg P, Hooiveld M, Donker G, Barnett D, Paget J. Using web search queries to monitor influenza-like illness: an exploratory retrospective analysis, Netherlands, 2017/18 influenza season. Eurosurveillance 2020;25(21) View
  5. Chang Y, Chiang W, Wang W, Lin C, Hung L, Tsai Y, Suen J, Chen Y. Google Trends-based non-English language query data and epidemic diseases: a cross-sectional study of the popular search behaviour in Taiwan. BMJ Open 2020;10(7):e034156 View
  6. 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
  7. Lansdall-Welfare T, Lightman S, Cristianini N. Seasonal variation in antidepressant prescriptions, environmental light and web queries for seasonal affective disorder. British Journal of Psychiatry 2019;215(2):481 View
  8. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  9. Bragazzi N, Gianfredi V, Villarini M, Rosselli R, Nasr A, Hussein A, Martini M, Behzadifar M. Vaccines Meet Big Data: State-of-the-Art and Future Prospects. From the Classical 3Is (“Isolate–Inactivate–Inject”) Vaccinology 1.0 to Vaccinology 3.0, Vaccinomics, and Beyond: A Historical Overview. Frontiers in Public Health 2018;6 View
  10. 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
  11. Johnson A, Bhaumik R, Tabidze I, Mehta S. Nowcasting Sexually Transmitted Infections in Chicago: Predictive Modeling and Evaluation Study Using Google Trends. JMIR Public Health and Surveillance 2020;6(4):e20588 View
  12. Brkic F, Besser G, Schally M, Schmid E, Parzefall T, Riss D, Liu D. Biannual Differences in Interest Peaks for Web Inquiries Into Ear Pain and Ear Drops: Infodemiology Study. Journal of Medical Internet Research 2021;23(6):e28328 View
  13. Satpathy P, Kumar S, Prasad P. Suitability of Google Trends™ for Digital Surveillance During Ongoing COVID-19 Epidemic: A Case Study from India. Disaster Medicine and Public Health Preparedness 2023;17 View
  14. Simonart T, Lam Hoai X, de Maertelaer V. Worldwide Evolution of Vaccinable and Nonvaccinable Viral Skin Infections: Google Trends Analysis. JMIR Dermatology 2022;5(4):e35034 View
  15. Cai O, Sousa-Pinto B. United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study. JMIR Public Health and Surveillance 2022;8(3):e32364 View
  16. Simonart T, Lam Hoai X, De Maertelaer V. Epidemiologic evolution of common cutaneous infestations and arthropod bites: A Google Trends analysis. JAAD International 2021;5:69 View
  17. Ravkin H, Yom-Tov E, Nesher L. The Effect of Nonpharmaceutical Interventions Implemented in Response to the COVID-19 Pandemic on Seasonal Respiratory Syncytial Virus: Analysis of Google Trends Data. Journal of Medical Internet Research 2022;24(12):e42781 View
  18. Feng Y, Cui X, Lv J, Yan B, Meng X, Zhang L, Guo Y, Peng L. Deep learning models for hepatitis E incidence prediction leveraging meteorological factors. PLOS ONE 2023;18(3):e0282928 View
  19. Hsu C, Yang C, Perez A. Google trends as an early indicator of African swine fever outbreaks in Southeast Asia. Frontiers in Veterinary Science 2024;11 View

Books/Policy Documents

  1. Samaras L, García-Barriocanal E, Sicilia M. Innovation in Health Informatics. View