Published on 01.12.17 in Vol 3, No 4 (2017): Oct-Dec
Works citing "Discrepancies Between Classic and Digital Epidemiology in Searching for the Mayaro Virus: Preliminary Qualitative and Quantitative Analysis of Google Trends"
According to Crossref, the following articles are citing this article (DOI 10.2196/publichealth.9136):
(note that this is only a small subset of citations)
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Gianfredi V, Bragazzi NL, 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
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Bragazzi NL, 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
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He Z, Zhang CJP, Huang J, Zhai J, Zhou S, Chiu JW, Sheng J, Tsang W, Akinwunmi BO, 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
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. RETRACTED ARTICLE: Digital Ethnography of Zoophilia — A Multinational Mixed-Methods Study. Journal of Sex & Marital Therapy 2019;45(1):1
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Watad A, Watad S, Mahroum N, Sharif K, Amital H, Bragazzi NL, 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
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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
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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
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Rabiolo A, Alladio E, Morales E, McNaught AI, Bandello F, Afifi AA, 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
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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
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Du M, Qin C, Yan W, Liu Q, Wang Y, Zhu L, Liang W, Liu M, Liu J. Trends in Online Search Activity and the Correlation with Daily New Cases of Monkeypox among 102 Countries or Territories. International Journal of Environmental Research and Public Health 2023;20(4):3395
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Yan W, Du M, Qin C, Liu Q, Wang Y, Liang W, Liu M, Liu J. Association between public attention and monkeypox epidemic: A global lag‐correlation analysis. Journal of Medical Virology 2023;95(1)
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Sarasmita MA, Sudarma IW, Susanty S. Leveraging Google Trends to identify Indonesian tuberculosis trends before and after the implementation of a national mandatory notification system. Indian Journal of Tuberculosis 2023;
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Thakur N, Cui S, Patel KA, Hall I, Duggal YN. A Large-Scale Dataset of Search Interests Related to Disease X Originating from Different Geographic Regions. Data 2023;8(11):163
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Thakur N, Cui S, Patel KA, Azizi N, Knieling V, Han C, Poon A, Shah R. Marburg Virus Outbreak and a New Conspiracy Theory: Findings from a Comprehensive Analysis and Forecasting of Web Behavior. Computation 2023;11(11):234
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