Published on in Vol 22, No 12 (2020): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23518, first published .
COVID-19 Symptom-Related Google Searches and Local COVID-19 Incidence in Spain: Correlational Study

COVID-19 Symptom-Related Google Searches and Local COVID-19 Incidence in Spain: Correlational Study

COVID-19 Symptom-Related Google Searches and Local COVID-19 Incidence in Spain: Correlational Study

Journals

  1. Szilagyi I, Ullrich T, Lang-Illievich K, Klivinyi C, Schittek G, Simonis H, Bornemann-Cimenti H. Google Trends for Pain Search Terms in the World’s Most Populated Regions Before and After the First Recorded COVID-19 Case: Infodemiological Study. Journal of Medical Internet Research 2021;23(4):e27214 View
  2. 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
  3. Riswantini D, Nugraheni E, Arisal A, Khotimah P, Munandar D, Suwarningsih W. Big Data Research in Fighting COVID-19: Contributions and Techniques. Big Data and Cognitive Computing 2021;5(3):30 View
  4. Rovetta A. The Impact of COVID-19 on Conspiracy Hypotheses and Risk Perception in Italy: Infodemiological Survey Study Using Google Trends. JMIR Infodemiology 2021;1(1):e29929 View
  5. Saegner T, Austys D. Forecasting and Surveillance of COVID-19 Spread Using Google Trends: Literature Review. International Journal of Environmental Research and Public Health 2022;19(19):12394 View
  6. An L, Russell D, Mihalcea R, Bacon E, Huffman S, Resnicow K. Online Search Behavior Related to COVID-19 Vaccines: Infodemiology Study. JMIR Infodemiology 2021;1(1):e32127 View
  7. Turvy A. State-Level COVID-19 Symptom Searches and Case Data: Quantitative Analysis of Political Affiliation as a Predictor for Lag Time Using Google Trends and Centers for Disease Control and Prevention Data. JMIR Formative Research 2022;6(12):e40825 View
  8. Nikolić V, Subotić N, Subotić J, Marković-Denić L. Google trends as an aid in predicting the course of the COVID-19 epidemic in Serbia. Medicinski casopis 2021;55(2):59 View
  9. Ma S, Yang S. COVID-19 forecasts using Internet search information in the United States. Scientific Reports 2022;12(1) View
  10. Rovetta A, Castaldo L. Authors’ Response to Peer Reviews of “Influence of Mass Media on Italian Web Users During the COVID-19 Pandemic: Infodemiological Analysis”. JMIRx Med 2021;2(4):e34138 View
  11. Wang X, Dong Y, Thompson W, Nair H, Li Y. Short-term local predictions of COVID-19 in the United Kingdom using dynamic supervised machine learning algorithms. Communications Medicine 2022;2(1) View
  12. Wen C, Liu W, He Z, Liu C. Research on emergency management of global public health emergencies driven by digital technology: A bibliometric analysis. Frontiers in Public Health 2023;10 View
  13. Syifa' N, Purborini N. Trends of Influenza’s Symptoms Drug Search Terms in Indonesian-Language using Google Trends in the Covid-19 Pandemic. Borneo Journal of Pharmacy 2022;5(2):179 View
  14. Ward T, Johnsen A, Ng S, Chollet F. Forecasting SARS-CoV-2 transmission and clinical risk at small spatial scales by the application of machine learning architectures to syndromic surveillance data. Nature Machine Intelligence 2022;4(10):814 View
  15. Samadbeik M, Garavand A, Aslani N, Ebrahimzadeh F, Fatehi F, Kardeş S. Assessing the online search behavior for COVID-19 outbreak: Evidence from Iran. PLOS ONE 2022;17(7):e0267818 View
  16. Alshareef M, Alotiby A. Prevalence and Perception Among Saudi Arabian Population About Resharing of Information on Social Media Regarding Natural Remedies as Protective Measures Against COVID-19. International Journal of General Medicine 2021;Volume 14:5127 View
  17. Robinson E, Jones A. Hangover-Related Internet Searches Before and During the COVID-19 Pandemic in England: Observational Study. JMIR Formative Research 2023;7:e40518 View
  18. Wang L, Lin M, Wang J, Chen H, Yang M, Qiu S, Zheng T, Li Z, Song H. Quantitative analysis of the impact of various urban socioeconomic indicators on search-engine-based estimation of COVID-19 prevalence. Infectious Disease Modelling 2022;7(2):117 View
  19. Zayed B, Talaia A, Gaaboobah M, Amer S, Mansour F. Google Trends as a predictive tool in the era of COVID-19: a scoping review. Postgraduate Medical Journal 2023;99(1175):962 View
  20. Kohlmann S, Stielow L, Löwe B. Did online information seeking for depression increase during COVID-19 lockdown times? A google trend analysis on data from Germany and the UK. Journal of Affective Disorders Reports 2023;13:100587 View
  21. Porcu G, Chen Y, Bonaugurio A, Villa S, Riva L, Messina V, Bagarella G, Maistrello M, Leoni O, Cereda D, Matone F, Gori A, Corrao G. Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends. Frontiers in Public Health 2023;11 View
  22. Ruan Y, Huang T, Zhou W, Zhu J, Liang Q, Zhong L, Tang X, Liu L, Chen S, Xie Y. The lead time and geographical variations of Baidu Search Index in the early warning of COVID-19. Scientific Reports 2023;13(1) View
  23. Marty R, Ramos-Maqueda M, Khan N, Reichert A. The evolution of the COVID-19 pandemic through the lens of google searches. Scientific Reports 2023;13(1) View
  24. Yeung A, Parvanov E, Horbańczuk J, Kletecka-Pulker M, Kimberger O, Willschke H, Atanasov A. Public interest in different types of masks and its relationship with pandemic and policy measures during the COVID-19 pandemic: a study using Google Trends data. Frontiers in Public Health 2023;11 View
  25. Hudde A, Wessel J. More afraid of the virus than of bad weather? Exploring the link between weather conditions and cycling volume in German cities before and during the COVID-19 pandemic. Transportation Research Part F: Traffic Psychology and Behaviour 2024;101:267 View
  26. Aldosery A, Carruthers R, Kay K, Cave C, Reynolds P, Kostkova P. Enhancing public health response: a framework for topics and sentiment analysis of COVID-19 in the UK using Twitter and the embedded topic model. Frontiers in Public Health 2024;12 View

Books/Policy Documents

  1. Sharma P, Joshi T, Mathpal S, Tamta S, Chandra S. Omics approaches and technologies in COVID-19. View
  2. Galido A, Ecleo J. Novel & Intelligent Digital Systems: Proceedings of the 2nd International Conference (NiDS 2022). View
  3. Butt Z. Accelerating Strategic Changes for Digital Transformation in the Healthcare Industry. View
  4. Butt Z. Artificial Intelligence, Big Data, Blockchain and 5G for the Digital Transformation of the Healthcare Industry. View
  5. Fu H. Wisdom, Well-Being, Win-Win. View