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

Our study developed model IBE to predict the incidence of mumps in Yunnan province, offering a potential tool for early detection of mumps outbreaks. The performance of model IBE underscores the potential of integrating search engine data and environmental factors to enhance mumps incidence forecasting. This approach offers a promising tool for improving public health surveillance and enabling rapid responses to mumps outbreaks.

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Publication Details

Journal
Journal of Medical Internet Research
Published
2025-02-06
DOI
https://doi.org/10.2196/66072
Citations
2
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
2.74
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article

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

Shuzhen Deng, Xinyin Wu, Xin Xiong, Litao Chang et al.
2 citations
Journal of Medical Internet Research
COVID-19 epidemiological studies
2.74
article

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

Shuzhen Deng, Xinyin Wu, Xin Xiong, Litao Chang, Linghui Xiang
article en
2 citations

Abstract

Our study developed model IBE to predict the incidence of mumps in Yunnan province, offering a potential tool for early detection of mumps outbreaks. The performance of model IBE underscores the potential of integrating search engine data and environmental factors to enhance mumps incidence forecasting. This approach offers a promising tool for improving public health surveillance and enabling rapid responses to mumps outbreaks.

Journal of Medical Internet ResearchVol. 27
Central South University (CN), Yunnan Center for Disease Control And Prevention (CN)
Openalex Percentile: Top 13%
COVID-19 epidemiological studies
2.74
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