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Background <p>Hepatitis B (HB) poses a significant disease burden in Henan due to its high morbidity and prevalence. Traditional surveillance systems often suffer from reporting delays, limiting real-time epidemic monitoring. Baidu Index (BI) has emerged as a valuable tool for gathering disease-related information, potentially enhancing disease surveillance capabilities. This study aimed to estimate HB epidemics by integrating BI into the traditional surveillance systems.</p> Methods <p>Monthly HB incidence data from January 2014 to September 2023 in Henan were collected. The dataset was divided into a training set (from January 2014 to September 2022) and a test set (from October 2022 to September 2023). The training set was utilized to develop Bayesian structural time series (BSTS) and seasonal autoregressive integrated moving average (SARIMA) models, including BI as a covariate (SARIMAX). Model performance was evaluated using mean absolute deviation (MAD), root mean square error (RMSE), mean absolute percentage error (MAPE), and mean error rate (MER). A sensitivity analysis was conducted to ensure robustness.</p> Results <p>A total of 739,386 HB cases were reported. HB showed an increasing trend before 2020, followed by fluctuations influenced by the COVID-19 pandemic, eventually stabilizing at a high level. A distinct seasonal pattern was observed, with a peak in March and a trough in December. The BSTS model incorporating BI demonstrated superior forecasting performance. For the 12-month ahead forecast, the BSTS model with BI achieved a MAPE of 0.112, a MAD of 615.94, an RMSE of 777.99, and a MER of 0.113, outperforming the BSTS model without BI (MAPE: 0.246; MAD: 998.22; RMSE: 1305.37; MER: 0.183). Similarly, the SARIMAX model outperformed the standard SARIMA model. Moreover, the best BSTS, with or without BI, resulted in lower forecasting error rates compared to the best SARIMA and SARIMAX. Sensitivity analyses confirmed the stability of these findings.</p> Conclusions <p>Integrating BI significantly improves HB incidence forecasting accuracy in Henan. The BSTS model demonstrates particular superiority over traditional SARIMA approaches, offering a more reliable tool for real-time epidemic monitoring. These findings support incorporating internet search data into public health early warning systems to enhance HB surveillance and facilitate timely interventions toward elimination goals.</p>

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Early warning of hepatitis B epidemics in Henan Province, China, from 2014 to 2023 based on Baidu Index and Bayesian Structural Time Series model

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  • 发表日期：

    2026年1月19日

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  • Yongbin Wang,
  • Xianxiang Lan,
  • Pan Hu,
  • Fei Lin,
  • Chunjie Xu

摘要

Background

Hepatitis B (HB) poses a significant disease burden in Henan due to its high morbidity and prevalence. Traditional surveillance systems often suffer from reporting delays, limiting real-time epidemic monitoring. Baidu Index (BI) has emerged as a valuable tool for gathering disease-related information, potentially enhancing disease surveillance capabilities. This study aimed to estimate HB epidemics by integrating BI into the traditional surveillance systems.

Methods

Monthly HB incidence data from January 2014 to September 2023 in Henan were collected. The dataset was divided into a training set (from January 2014 to September 2022) and a test set (from October 2022 to September 2023). The training set was utilized to develop Bayesian structural time series (BSTS) and seasonal autoregressive integrated moving average (SARIMA) models, including BI as a covariate (SARIMAX). Model performance was evaluated using mean absolute deviation (MAD), root mean square error (RMSE), mean absolute percentage error (MAPE), and mean error rate (MER). A sensitivity analysis was conducted to ensure robustness.

Results

A total of 739,386 HB cases were reported. HB showed an increasing trend before 2020, followed by fluctuations influenced by the COVID-19 pandemic, eventually stabilizing at a high level. A distinct seasonal pattern was observed, with a peak in March and a trough in December. The BSTS model incorporating BI demonstrated superior forecasting performance. For the 12-month ahead forecast, the BSTS model with BI achieved a MAPE of 0.112, a MAD of 615.94, an RMSE of 777.99, and a MER of 0.113, outperforming the BSTS model without BI (MAPE: 0.246; MAD: 998.22; RMSE: 1305.37; MER: 0.183). Similarly, the SARIMAX model outperformed the standard SARIMA model. Moreover, the best BSTS, with or without BI, resulted in lower forecasting error rates compared to the best SARIMA and SARIMAX. Sensitivity analyses confirmed the stability of these findings.

Conclusions

Integrating BI significantly improves HB incidence forecasting accuracy in Henan. The BSTS model demonstrates particular superiority over traditional SARIMA approaches, offering a more reliable tool for real-time epidemic monitoring. These findings support incorporating internet search data into public health early warning systems to enhance HB surveillance and facilitate timely interventions toward elimination goals.