Predicting pulmonary tuberculosis incidence in China using Baidu search index: an ARIMAX model approach

Predicting pulmonary tuberculosis incidence in China using Baidu search index: an ARIMAX model approach

Blog Article

Background: Existing researches have established a correlation between internet search data and the epidemics of numerous infectious diseases.This study aims to develop a prediction model to explore the relationship between the Pulmonary Tuberculosis (PTB) epidemic trend in China and the Baidu search index.Methods: Collect the number of new cases of PTB in China from January 2011 to August 2022.Use Spearman rank correlation and interaction analysis to identify Baidu keywords related to PTB Pyro-Swag and construct a PTB comprehensive search index.Evaluate the predictive performance of autoregressive integrated moving average (ARIMA) and ARIMA with explanatory variable (ARIMAX) models for the number of PTB cases.

Results: Incidence of PTB had shown a fluctuating downward trend.The Spearman rank correlation coefficient between the PTB comprehensive search index and its incidence was 0.834 (P < 0.001).The ARIMA model had an AIC value of 2804.

41, and the MAPE value was 13.19%.The ARIMAX model incorporating the Baidu index demonstrated an AIC value of 2761.58 and a MAPE value of 5.33%.

Conclusions: The ARIMAX model is superior to PEPOGEST ARIMA in terms of fitting and predicting accuracy.Additionally, the use of Baidu Index has proven to be effective in predicting cases of PTB.