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

Infodemiology and Infoveillance (1598) Influenza and Influenza-Like-Illness (ILI) Surveillance (239) Vaccination and Immunization in the Digital Age (461) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1159) Machine Learning (2234) Information Retrieval (363) Infectious Diseases (non-STD/STI) (1758)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66072, first published .
Forecasting the Incidence of Mumps Based on the Baidu Index and Environmental Data in Yunnan, China: Deep Learning Model Study

Forecasting the Incidence of Mumps Based on the Baidu Index and Environmental Data in Yunnan, China: Deep Learning Model Study

Forecasting the Incidence of Mumps Based on the Baidu Index and Environmental Data in Yunnan, China: Deep Learning Model Study

Authors of this article:

Xin Xiong1 Author Orcid Image ;   Linghui Xiang1 Author Orcid Image ;   Litao Chang2 Author Orcid Image ;   Irene XY Wu1, 3 Author Orcid Image ;   Shuzhen Deng2 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Hu K, Li C, Yang X, Ou S, Zhang X, Xiao D, Yu M. Modeling mumps incidence in China: spatiotemporal clusters and evolving risk factors (2005–2020). BMC Public Health 2025;25(1) View
  2. Ye Z, Zhu X, Fan H, Yao W, Wu Y, He T. Epidemiological characteristics and LSTM-based simulation of mumps incidence in Ningbo, China from 2005 to 2024. BMC Public Health 2026 View