Published on in Vol 6, No 2 (2020): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18828, first published .
Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study

Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study

Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study

Journals

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  10. Sousa-Pinto B, Anto A, Czarlewski W, Anto J, Fonseca J, Bousquet J. Assessment of the Impact of Media Coverage on COVID-19–Related Google Trends Data: Infodemiology Study. Journal of Medical Internet Research 2020;22(8):e19611 View
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  29. Syeda H, Syed M, Sexton K, Syed S, Begum S, Syed F, Prior F, Yu Jr F. Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review. JMIR Medical Informatics 2021;9(1):e23811 View
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  31. Peng Y, Li C, Rong Y, Chen X, Chen H. Retrospective analysis of the accuracy of predicting the alert level of COVID-19 in 202 countries using Google Trends and machine learning. Journal of Global Health 2020;10(2) View
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  35. Izquierdo J, Ancochea J, Soriano J. Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing. Journal of Medical Internet Research 2020;22(10):e21801 View
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  38. Momynaliev K, Khoperskay L, Pshenichnaya N, Abuova G, Akimkin V. Infodemiological study of coronavirus epidemic using Google Trends in Central Asian Republics of Kazakhstan, Kyrgyzstan, Uzbekistan, Tajikistan. Medical alphabet 2021;(34):47 View
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  40. Anand Shankar Raja M, Kallarakal T. “COVID-19 and students perception about MOOCs” a case of Indian higher educational institutions. Interactive Technology and Smart Education 2020;ahead-of-print(ahead-of-print) View
  41. Rezaeipour M. COVID-19-Related Weight Gain in School-Aged Children. International Journal of Endocrinology and Metabolism 2020;19(1) View
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  43. Rokhmah D, Ali K, Putri S, Khoiron K. Increase in public interest concerning alternative medicine during the COVID-19 pandemic in Indonesia: a Google Trends study. F1000Research 2020;9:1201 View
  44. Lu T, Reis B. Internet search patterns reveal clinical course of COVID-19 disease progression and pandemic spread across 32 countries. npj Digital Medicine 2021;4(1) View
  45. Nayak J, Naik B, Dinesh P, Vakula K, Dash P, Pelusi D. Significance of deep learning for Covid-19: state-of-the-art review. Research on Biomedical Engineering 2021 View
  46. Asgari Mehrabadi M, Dutt N, Rahmani A. The Causality Inference of Public Interest in Restaurants and Bars on Daily COVID-19 Cases in the United States: Google Trends Analysis. JMIR Public Health and Surveillance 2021;7(4):e22880 View
  47. Heckman C, Lin Y, Riley M, Wang Y, Bhurosy T, Mitarotondo A, Xu B, Stapleton J. Association Between State Indoor Tanning Legislation and Google Search Trends Data in the United States From 2006 to 2019: Time-Series Analysis. JMIR Dermatology 2021;4(1):e26707 View
  48. Yousefinaghani S, Dara R, Mubareka S, Sharif S. Prediction of COVID-19 Waves Using Social Media and Google Search: A Case Study of the US and Canada. Frontiers in Public Health 2021;9 View
  49. Gil-Jardiné C, Chenais G, Pradeau C, Tentillier E, Revel P, Combes X, Galinski M, Tellier E, Lagarde E. Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using artificial neural network for natural language classification. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine 2021;29(1) View
  50. Rokhmah D, Ali K, Putri S, Khoiron K. Increase in public interest concerning alternative medicine during the COVID-19 pandemic in Indonesia: a Google Trends study. F1000Research 2021;9:1201 View
  51. El-Morshedy M, Altun E, Eliwa M. A new statistical approach to model the counts of novel coronavirus cases. Mathematical Sciences 2021 View
  52. Li W, Sun K, Zhu Y, Song J, Yang J, Qian L, Wang S. Analyzing the Research Evolution in Response to COVID-19. ISPRS International Journal of Geo-Information 2021;10(4):237 View
  53. Kafieh R, Arian R, Saeedizadeh N, Amini Z, Serej N, Minaee S, Yadav S, Vaezi A, Rezaei N, Haghjooy Javanmard S, Blyuss K. COVID-19 in Iran: Forecasting Pandemic Using Deep Learning. Computational and Mathematical Methods in Medicine 2021;2021:1 View
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  59. Shahid O, Nasajpour M, Pouriyeh S, Parizi R, Han M, Valero M, Li F, Aledhari M, Sheng Q. Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance. Journal of Biomedical Informatics 2021;117:103751 View
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  61. Yu C, Chang S, Chang T, Wu J, Lin Y, Chien H, Chen R. A COVID-19 Pandemic Artificial Intelligence–Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study. Journal of Medical Internet Research 2021;23(5):e27806 View
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  63. Desai P. News Sentiment Informed Time-series Analyzing AI (SITALA) to curb the spread of COVID-19 in Houston. Expert Systems with Applications 2021;180:115104 View
  64. Kansal A, Gautam J, Chintalapudi N, Jain S, Battineni G. Google Trend Analysis and Paradigm Shift of Online Education Platforms during the COVID-19 Pandemic. Infectious Disease Reports 2021;13(2):418 View
  65. Barcelos T, Muniz L, Dantas D, Cotrim Junior D, Cavalcante J, Faerstein E. Análise de fake news veiculadas durante a pandemia de COVID-19 no Brasil. Revista Panamericana de Salud Pública 2021;45:1 View
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  67. Rovetta A. Reliability of Google Trends: Analysis of the Limits and Potential of Web Infoveillance During COVID-19 Pandemic and for Future Research. Frontiers in Research Metrics and Analytics 2021;6 View
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Books/Policy Documents

  1. Soliman M, Darwish A, Hassanien A. Digital Transformation and Emerging Technologies for Fighting COVID-19 Pandemic: Innovative Approaches. View
  2. Rekha H, Behera H, Nayak J, Naik B. Intelligent Computing in Control and Communication. View
  3. Saire J, Cruz J. Information Management and Big Data. View
  4. Folorunso S, Awotunde J, Adeboye N, Matiluko O. Advances in Data Science and Intelligent Data Communication Technologies for COVID-19. View
  5. Orojo O, Tepper J, McGinnity T, Mahmud M. Applied Intelligence and Informatics. View