Discovering Time-Varying Public Interest for COVID-19 Case Prediction in South Korea Using Search Engine Queries: Infodemiology Study
BackgroundThe number of confirmed COVID-19 cases is a crucial indicator of policies and lifestyles. Previous studies have attempted to forecast cases using machine learning techniques that use a previous number of case counts and search engine queries predetermined by experts...
Saved in:
| Main Authors: | Seong-Ho Ahn, Kwangil Yim, Hyun-Sik Won, Kang-Min Kim, Dong-Hwa Jeong |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2024-12-01
|
| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2024/1/e63476 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Predicting Prefecture-Level Well-Being Indicators in Japan Using Search Volumes in Internet Search Engines: Infodemiology Study
by: Myung Si Yang, et al.
Published: (2024-11-01) -
Trends in Exercise-Related Internet Search Keywords by Sex, Age, and Lifestyle: Infodemiological Study
by: Kosuke Uemura, et al.
Published: (2024-11-01) -
Temporal trends in online searches related to COVID-19 vaccine safety: A digital infodemiology study
by: Akshaya Srikanth Bhagavathula, et al.
Published: (2024-10-01) -
Discovering the allure of forests: Exploring adolescent queries in nature-rich environments
by: A. M. Arroz, et al.
Published: (2025-01-01) -
Discovering the allure of forests: Exploring adolescent queries in nature-rich environments.
by: A M Arroz, et al.
Published: (2025-01-01)