Published on in Vol 3, No 4 (2017): Oct-Dec

Health Information–Seeking Patterns of the General Public and Indications for Disease Surveillance: Register-Based Study Using Lyme Disease

Health Information–Seeking Patterns of the General Public and Indications for Disease Surveillance: Register-Based Study Using Lyme Disease

Health Information–Seeking Patterns of the General Public and Indications for Disease Surveillance: Register-Based Study Using Lyme Disease

Journals

  1. 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
  2. Kapitány‐Fövény M, Ferenci T, Sulyok Z, Kegele J, Richter H, Vályi‐Nagy I, Sulyok M. Can Google Trends data improve forecasting of Lyme disease incidence?. Zoonoses and Public Health 2019;66(1):101 View
  3. 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
  4. Pesälä S, Virtanen M, Mukka M, Ylilammi K, Mustonen P, Kaila M, Helve O. Healthcare professionals’ queries on oseltamivir and influenza in Finland 2011‐2016—Can we detect influenza epidemics with specific online searches?. Influenza and Other Respiratory Viruses 2019;13(4):364 View
  5. Basch C, MacLean S, Romero R, Ethan D. Health Information Seeking Behavior Among College Students. Journal of Community Health 2018;43(6):1094 View
  6. 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
  7. Zhou C, Xiu H, Wang Y, Yu X. Characterizing the dissemination of misinformation on social media in health emergencies: An empirical study based on COVID-19. Information Processing & Management 2021;58(4):102554 View
  8. 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
  9. Mukka M, Pesälä S, Hammer C, Mustonen P, Jormanainen V, Pelttari H, Kaila M, Helve O. Analyzing Citizens’ and Health Care Professionals’ Searches for Smell/Taste Disorders and Coronavirus in Finland During the COVID-19 Pandemic: Infodemiological Approach Using Database Logs. JMIR Public Health and Surveillance 2021;7(12):e31961 View
  10. 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
  11. Mukka M, Pesälä S, Juutinen A, Virtanen M, Mustonen P, Kaila M, Helve O, Khanijahani A. Online searches of children’s oseltamivir in public primary and specialized care: Detecting influenza outbreaks in Finland using dedicated databases for health care professionals. PLOS ONE 2022;17(8):e0272040 View
  12. Brinkman J, Christopher Z, Moore M, Pollock J, Haglin J, Bingham J. Patient Interest in Robotic Total Joint Arthroplasty Is Exponential: A 10-Year Google Trends Analysis. Arthroplasty Today 2022;15:13 View
  13. Longanga Diese E, Baker E, Akpan I, Acharya R, Raines-Milenkov A, Felini M, Hussain A, Donlan J. Health information-seeking behavior among Congolese refugees. PLOS ONE 2022;17(9):e0273650 View
  14. Zhang L, Wang X, Wang J, Yang P, Zhou P, Liao G. A study on predicting crisis information dissemination in epidemic-level public health events. Journal of Safety Science and Resilience 2023;4(3):253 View
  15. Zeman P. Tick-Bite “Meteo”-Prevention: An Evaluation of Public Responsiveness to Tick Activity Forecasts Available Online. Life 2023;13(9):1908 View
  16. Maxwell S, Brooks C, Kim D, McNeely C, Cho S, Thomas K. Improving Surveillance of Human Tick-Borne Disease Risks: Spatial Analysis Using Multimodal Databases. JMIR Public Health and Surveillance 2023;9:e43790 View
  17. Boligarla S, Laison E, Li J, Mahadevan R, Ng A, Lin Y, Thioub M, Huang B, Ibrahim M, Nasri B. Leveraging machine learning approaches for predicting potential Lyme disease cases and incidence rates in the United States using Twitter. BMC Medical Informatics and Decision Making 2023;23(1) View
  18. Kopsco H, Krell R, Mather T, Connally N. Identifying Trusted Sources of Lyme Disease Prevention Information Among Internet Users Connected to Academic Public Health Resources: Internet-Based Survey Study. JMIR Formative Research 2023;7:e43516 View
  19. Brinkman J, McQuivey K, Hassebrock J, Moore M, Pollock J, Tokish J. Public Interest in Shoulder Platelet-Rich Plasma Injections Is Increasing: A 10-Year Google Trends Analysis. Arthroscopy, Sports Medicine, and Rehabilitation 2023;5(4):100744 View
  20. Hadley M, Patil U, Colvin K, Sentell T, Massey P, Gallant M, Manganello J. Exploring the impact of digital health literacy on COVID-19 behaviors in New York state college students during the COVID-19 pandemic. Computer Methods and Programs in Biomedicine Update 2023;4:100126 View
  21. Mukka M, Pesälä S, Mustonen P, Kaila M, Helve O, Aslam M. Detecting epinephrine auto-injector shortages in Finland 2016–2022: Log-data analysis of online information seeking. PLOS ONE 2024;19(4):e0299092 View