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

Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (662) Infodemiology and Infoveillance (822)

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

Discrepancies Between Classic and Digital Epidemiology in Searching for the Mayaro Virus: Preliminary Qualitative and Quantitative Analysis of Google Trends

Discrepancies Between Classic and Digital Epidemiology in Searching for the Mayaro Virus: Preliminary Qualitative and Quantitative Analysis of Google Trends

Discrepancies Between Classic and Digital Epidemiology in Searching for the Mayaro Virus: Preliminary Qualitative and Quantitative Analysis of Google Trends

Authors of this article:

Mohammad Adawi 1 Author Orcid Image ;   Nicola Luigi Bragazzi 2, 3 Author Orcid Image ;   Abdulla Watad 4, 5 Author Orcid Image ;   Kassem Sharif 4, 5 Author Orcid Image ;   Howard Amital 4, 5 Author Orcid Image ;   Naim Mahroum 4, 5 Author Orcid Image
Article Authors Cited by (10) Tweetations (7) Metrics

Journals

  1. Gianfredi V, Bragazzi N, Nucci D, Martini M, Rosselli R, Minelli L, Moretti M. Harnessing Big Data for Communicable Tropical and Sub-Tropical Disorders: Implications From a Systematic Review of the Literature. Frontiers in Public Health 2018;6 View
  2. Bragazzi N, Mahroum N. Google Trends Predicts Present and Future Plague Cases During the Plague Outbreak in Madagascar: Infodemiological Study. JMIR Public Health and Surveillance 2019;5(1):e13142 View
  3. He Z, Zhang C, Huang J, Zhai J, Zhou S, Chiu J, Sheng J, Tsang W, Akinwunmi B, Ming W. A New Era of Epidemiology: Digital Epidemiology for Investigating the COVID-19 Outbreak in China. Journal of Medical Internet Research 2020;22(9):e21685 View
  4. Sendler D, Lew-Starowicz M. Digital Ethnography of Zoophilia — A Multinational Mixed-Methods Study. Journal of Sex & Marital Therapy 2019;45(1):1 View
  5. Watad A, Watad S, Mahroum N, Sharif K, Amital H, Bragazzi N, Adawi M. Forecasting the West Nile Virus in the United States: An Extensive Novel Data Streams–Based Time Series Analysis and Structural Equation Modeling of Related Digital Searching Behavior. JMIR Public Health and Surveillance 2019;5(1):e9176 View
  6. Sharif K, Watad A, Bridgewood C, Kanduc D, Amital H, Shoenfeld Y. Insights into the autoimmune aspect of premature ovarian insufficiency. Best Practice & Research Clinical Endocrinology & Metabolism 2019;33(6):101323 View
  7. 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
  8. Rabiolo A, Alladio E, Morales E, McNaught A, Bandello F, Afifi A, Marchese A. Forecasting the COVID-19 Epidemic By Integrating Symptom Search Behavior Into Predictive Models: Infoveillance Study. Journal of Medical Internet Research 2021;23(8):e28876 View
  9. Kostkova P, Saigí-Rubió F, Eguia H, Borbolla D, Verschuuren M, Hamilton C, Azzopardi-Muscat N, Novillo-Ortiz D. Data and Digital Solutions to Support Surveillance Strategies in the Context of the COVID-19 Pandemic. Frontiers in Digital Health 2021;3 View