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

Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1042) Outbreak and Pandemic Preparedness and Management (1728) Infodemiology and Infoveillance (1350) JMIR Theme Issue: COVID-19 Special Issue (2453) Behavioural Surveillance for Public Health (229) Early Results from COVID-19 Studies (299) Innovative Methods in Public Health and Surveillance (332) Infectious Diseases (non-STD/STI) (1576) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1518)

Published on in Vol 9 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42446, first published .
Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19

Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19

Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19

Authors of this article:

Amanda MY Chu1 Author Orcid Image ;   Andy C Y Chong2 Author Orcid Image ;   Nick H T Lai3 Author Orcid Image ;   Agnes Tiwari4, 5 Author Orcid Image ;   Mike K P So3 Author Orcid Image
Article Authors Cited by (3) Tweetations (8) Metrics

Journals

  1. Shih D, Wu Y, Wu T, Chang S, Shih M. Infodemiology of Influenza-like Illness: Utilizing Google Trends’ Big Data for Epidemic Surveillance. Journal of Clinical Medicine 2024;13(7):1946 View
  2. Asai M, Chu A, So M. Dynamic Network Poisson Autoregression with Application to COVID-19 Count Data. Journal of Data Science 2024:1 View
  3. So M, Chan T, Chu A. Bayesian Systemic Risk Analysis using Latent Space Network Models. Data Science in Science 2024;3(1) View